Research ArticleComparative Analysis of Classifiers for Developing an AdaptiveComputer-Assisted EEG Analysis System for Diagnosing Epilepsy
Malik Anas Ahmad1 Yasar Ayaz1 Mohsin Jamil1 Syed Omer Gillani1
Muhammad Babar Rasheed2 Muhammad Imran3 Nadeem Ahmed Khan4
Waqas Majeed4 and Nadeem Javaid25
983089 SMME National University of Sciences amp Technology Islamabad 983092983092983088983088983088 Pakistan983090
Department of Electrical Engineering COMSATS Institute of IT Islamabad 983092983092983088983088983088 Pakistan983091King Saud University PO Box 983097983090983089983092983092 Riyadh 983089983089983093983092983091 Saudi Arabia983092Lahore University of Management Sciences Lahore 983093983092983088983088983088 Pakistan983093Department of Computer Science COMSATS Institute of IT Islamabad 983092983092983088983088983088 Pakistan
Correspondence should be addressed to Nadeem Javaid nadeemjavaidcomsatsedupk
Received 983089983093 September 983090983088983089983092 Revised 983090983089 December 983090983088983089983092 Accepted 983089983096 January 983090983088983089983093
Academic Editor obias Loddenkemper
Copyright copy 983090983088983089983093 Malik Anas Ahmad et al Tis is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited
Computer-assisted analysis o electroencephalogram (EEG) has a tremendous potential to assist clinicians during the diagnosis o epilepsy Tese systems are trained to classiy the EEG based on the ground truth provided by the neurologists So there shouldbe a mechanism in these systems using which a systemrsquos incorrect markings can be mentioned and the system should improve itsclassi1047297cation by learning rom them We have developed a simple mechanism or neurologists to improve classi1047297cation rate whileencountering any alse classi1047297cation Tis system is based on taking discrete wavelet transorm (DW) o the signals epochs whichare then reduced using principal component analysis and then they are ed into a classi1047297er Afer discussing our approach we haveshown the classi1047297cation perormance o three types o classi1047297ers support vector machine (SVM) quadratic discriminant analysisand arti1047297cial neural network We ound SVM to be the best working classi1047297er Our work exhibits the importance and viability o a sel-improving and user adapting computer-assisted EEG analysis system or diagnosing epilepsy which processes each channelexclusive to each other along with the perormance comparison o different machine learning techniques in the suggested system
1 Introduction
Epilepsy is a chronic neurological disease Te hallmark o this disease is recurring seizures It has been cited thatone out o hundred people suffers rom this disorder [983089]Electroencephalography is the most widely used techniqueor diagnosis o epilepsy EEG signal is the representationo voltage 1047298uctuations which are caused by the 1047298ow o neurons ionic current Billions o neurons maintain brainselectric charge Membrane transport proteins pump ionsacross their membranes Neurons are electrically charged by these membranes Due to volume conduction wave o ionsreaches the electrodes on the scalp that pushes and pulls theelectron on the electrode metal Te voltage difference due to
pull andpush o the electronsis measured by voltmeterwhose
readings aredisplayedas the EEG potentialNeuron generatestoo small o a charge to be measured by an EEG and it is thesummation o synchronous activity o thousands o neuronsthat have similar spatial orientation which is measured by anEEG Unique patterns are generated in the EEG during anepileptic seizure Tese unique patterns help the cliniciansduring diagnosis and treatment o this neurological disorderTat is why EEG is widely used to detect and locate theepileptic seizure and zone Localization o the abnormalepileptic brain activity is very signi1047297cant or diagnosis o epileptic disorder
Usually the duration o a typical EEG varies rom ew minutes to ew hours but in case o prolonged EEG it can even
Hindawi Publishing CorporationBioMed Research InternationalVolume 2015 Article ID 638036 14 pageshttpdxdoiorg1011552015638036
last as long as 983095983090 hours Tis generates an immense amounto data to be inspected by the clinician which could prove tobe a daunting task
Advancement in signal processing and machine learningtechniques is making it possible to automatically analyse EEGdata to detect epochs with epileptic patterns A system based
on these techniques can aid a neurologist by highlighting theepileptic patterns in the EEG up to a signi1047297cant level O course the task o diagnosis should be lef to the neurologistHowever the task o the neurologist becomes efficient as itreduces the data to be analysed and lessens up the atigueAlong with classi1047297cation these analysis sofware programscan also provide simultaneous visualization o multiple chan-nels which helps the clinician in differentiating betweengeneralized epilepsy and ocal epilepsy
It is well known that an epileptic seizure brings changesin certain requency bands Tat is why usually the spectralcontent o the EEG is used or diagnosis [983090] Tese areidenti1047297ed as (983088983092ndash983092 Hz) 1038389 (983092ndash983096 Hz) 1103925 (983096ndash983089983090Hz) and 907317(983089983090ndash983091983088 Hz) Noachtar and R emi mention almost ten types o epileptic patterns However most o the existing work only ocuses on one o the epilepticpatterns thatis 983091 Hz spike andwave which is a trademark or absence seizure Other typeso the patterns are rarely addressed [983091]
Computer-assisted EEG classi1047297cation involves severalstages including eature extraction eature reduction andeature classi1047297cation Wavelet transorm has becomethe mostpopular eature extraction technique or EEG analysis dueto its capability to capture transient eatures as well asinormation about time-requency dynamics o the signal[983092] Other previously used eature extraction approaches orepilepsy diagnosis include empirical mode decomposition(EMD) multilevel Fourier transorm (F) and orthogonalmatching pursuit [983093ndash983097] Feature extraction is ollowed by eature reduction to reduce computational complexity andavoid curse o dimensionality Most commonly the reducedeature vector consists o statistical summary measures (suchas mean energy standard deviation kurtosis andentropy) o different sets o original (unreduced) eatures although othermethods such as principal component analysis discriminantanalysis and independent component analysis havealso beenused or eature reduction [983092 983095 983089983088 983089983089] Feature extrac-tionreduction is ollowed by classi1047297cation using a machinelearning algorithm such as arti1047297cial neural networks (ANN)support vector machines (SVM) hidden Markov models andquadratic discriminant analysis [983096 983089983089ndash983089983092]
A very important and novel phase o our system is useradaptation mechanism or retraining mechanism Tere aremultiple reasons according to which introduction o thisphase has lots o advantages During this phase system willtry to adapt its classi1047297cation as per users desire It has beencited that sometimes even the expert neurologists have somedisagreement over a certain observation o an EEG dataTere is also a threat o over1047297tting by the classi1047297er In orderto keep the classi1047297er improving its perormance with theencounter o more and more examples we have introducedthis user adaptive mechanism in our system We consider theexisting systems as dead because they cannot improve theirclassi1047297cation rate afer initial training Tey do not have any
mechanism o learning or improvement rom neurologistscorrective marking [983089983093ndash983089983095] Te agreement between differentEEG readers is low to moderate our adaption mechanismhelps the user in catering this issue as our system tries toadapt the detection accordingto the userscorrective markingTe new corrective markings generate new examples with
improved labels Hence it populates the training exampleswith newly labelled ones So afer retraining machine learningalgorithms in the system users adapt to set o choices
In the next section we will explain our proposed methodwhich will be ollowed by the results In the results section wewill explain how SVM perorms better than QDA and ANNin our proposed method We will also show that exclusiveprocessing o each channel results in a signi1047297cant improve-ment in the classi1047297cation rate Here ldquoepileptic patternrdquo andldquoepileptic spikesrdquo will be used as an alternative to each other
2 Proposed Method
Computer-aided EEG analysis systems use the neurologistsmarking and labelling o the EEG data as a benchmark totrain themselves during initial training phase But afer initialtraining phase these systems have no simple mechanismor these neurologists to improve systems classi1047297cation aferencountering any alse classi1047297cation So we have proposed amethod by which systems classi1047297cation can be improved by the user in a relatively simpler way Tis analysis system only tries to detect the epileptic spikes as mentioned by Noachtarand R emi Later it adapts its detection o epileptic spikesexclusively or every user (Figure 983092)
In this proposed system we are processing each channelor each epileptic pattern exclusive to each other Tis
exclusive processing o each channel notonly helps theuser indiagnosing localized epilepsy but also eases up the classi1047297ers job We have considered that different epileptic patterns areindependent to each other and their separate handling willhelp us in avoiding error propagation rom one epilepticpattern type detection to the other Our systems workinghas two major phases (A) initial training phase and (B)adaptation phase Tese two major phases have urther threeparts which are (983089) eature extraction (983090) eature reductionand (983091) classi1047297cation Next we will brie1047298y explain all o thesesteps
983090983089 Initial Training
983090983089983089 Feature Extraction o decide which parts o the signalare epileptic and which are not we 1047297rst divided whole o thesignal in small chunks known as epochs Ten DW wasapplied on those epochs so that visibility o epileptic activity can be enhanced which is distinguished by some spectralcharacteristics Tese eatures are then processed to makethem more suitable or the classi1047297cation technique
(a) Epoch Size Te 1047297rst important part o the eatureextraction is epoch selection Epoch is a small chunk o thesignal which is processed at a time Te size o the epochis very important Te larger it is the less accurate it willbe Te smaller it is the higher the processing time will be
F983145983143983157983154983141 983089 Epoch size is 983089 sec
Afer testing different epoch sizes we ound epoch size o nonoverlapping 983089 sec window to be best yielding in terms o accuracy It also reestablished the work o Seng et al [ 983089983096](Figure 983089)
(b) DWT As discussed in Introduction spectral analysisis very inormative while examining the epilepsy suspectedpatients EEG Tere are proound advantages o waveletdecomposition which is a multiresolution analysis techniqueA multiresolution analysis technique allows us to analysea signal or multiple requency resolutions while maintain-
ing time resolution unlike a normal requency transormWavelet decomposition allows us to increase requency res-olution in the spectral band o our interest while maintainingthe time resolution in short we can decimate these valuessimultaneously in time and requency domain
During wavelet transorm the original epoch is splitinto different subbands the lower requency inormationis called approximate coefficients and the higher requency inormation is called detailed coefficients Te requency subdivision in these subbands helps us in analysing differentrequency ranges o an EEG epoch while maintaining its timeresolution [983092 983096 983089983091] Te choice o coefficients level is very important as the epileptic activity only resides in the range
o 983088ndash983091983088 Hz Coefficients levels o the DW are determinedwith respect to sampling requency So the detailed levels o interest are adjusted on the run according to the samplingrequency such that we may get at least one exact value o the closest separate (983088983092ndash983092 Hz) 1038389 (983092ndash983096 Hz) 1103925 (983096ndash983089983090 Hz)and 907317 (983089983090ndash983091983088 Hz) components o the signal We discarded allthe detailed coefficient levels which were beyond the 983088ndash983091983088Hzrange
TenDW wasappliedon each epoch with Daubechies-983092(db983092) as mother wavelet Te detailed coefficient levels o theDW were determined with respect to sampling requency
(c) Statistical Features Afer the selection o detailed coe-1047297cients which represent the requency band o our interest
we calculated the statistical eatures by calculating the meanstandard deviation and power o these selected waveletcoefficients Tese statistical eatures are inspired rom Subasiand Gursoy work [983089983091]
(d) Standardization Tese statistical eatures were then stan-dardized During training stage -score standardization wasapplied on these eatures [983089983097] Tis standardization is justlike usual -score normalization but as we do not know the exact mean and standard deviation o the data (to beclassi1047297ed) during classi1047297cationtest stage we used the mean
and standard deviation o the training examples duringtraining stage or standardizing (normalizing) the eaturesduring classi1047297cation stage We normalized the eatures by subtracting and dividing them by training examples meanand standard deviation respectively
983090983090 Feature Reduction In order to avoid overinterpretationby redundant data and misinterpretation by noisy data weapplied eature reduction method Inclusion o this partincreases the processing time thus exacerbating the latency
Dimensionality reduction using principal componentanalysis (PCA) is based on a very important trait that is
variance o the data PCA develops the nonlinear mapping insuch a way that it maximizes the variance o the data whichhelps us in discarding that part o the data which is markedby lesser variances Tis reshaping and omission not only removes the redundant data but also lessens up the noise
During training stage PCA was applied on these eaturesin order to reduce the redundant andor noisy data We keptthe components which projected the approximate 983097983093 o thetotal variance We were able to reduce the 983090983089 eatures into 983097Ten we ed these reduced eatures to classi1047297ers trainer Here
as per our observation we again assumed that the EEG datais stationary or a small length So during the testing stagewe took the PCA coefficients matrix rom training stage and
multiplied it with the standardized statistical eatures o theblind test data and then ed the top 983097 eatures to classi1047297er
983090983091 Classi1047297cation Classi1047297cation is a machine learning tech-nique in which new observations belonging to a category are identi1047297ed Tis identi1047297cation is based on the training set
which contains the observations with known labelling o theircategory Tese observations are also termed as eatures Wetried three types o classi1047297cation methods (983089) SVM (983090) QDAand (983091) ANN (Figure 983091)
Te reduced eatures were ed to these classi1047297ers Herethe reduced eatures mean that those statistical eatures o the selected wavelet coefficients are reduced using PCA asdescribed in previous section All o the three processingparts were exclusive or each channel and each epilepticpattern So like previous parts the classi1047297ers were also trainedand tested exclusively or each channel
Our system requires individual labelling o channelsTere is a separate classi1047297er or each channel and or each
epileptic pattern type So the total number o classi1047297ers isequalto the product o totalnumbero channels by ten whereten represents the number o epileptic pattern described by Noachtar and R emi [983091]
(e) Support Vector Machine Support vector machine (SVM)is a supervised learning models machine learning techniqueSVM tries to represent the examples as points in space whichare mapped in a way that points o different categories can bedivided by a clear gap that is as wide as possible Aferwardsthat division is used to categorise the new test examples basedon which side they all on
analysis (QDA) is a widely used machine learning methodamong statistics pattern recognition and signal process-ing to 1047297nd a quadratic combination o eatures which areresponsible or characterizing an example into two or morecategories QDAs combination o discriminating quadraticmultiplication actors is used or both classi1047297cation anddimensionality reduction
(g) Arti1047297cial Neural Network Arti1047297cial neural network (ANN) is a computational model which is inspired rom ani-mals central nervous system Tat is why ANN is representedby a system o interconnected neurons which are capable o computing values as per their inputs In ANN training the
weights associated with the neurons are iteratively adjustedaccording to the inputs and the difference between theoutputs with expected outputs Te iteration gets stoppedwhen either the combination o neurons starts generating theexpected results within an error o a tolerable error range orthe iteration limit 1047297nishes up
983090983092 Adaption Phase (RetrainingUser Adaptation Mecha-nism) In order to keep the classi1047297er improving its peror-mance with the encounter o more and more examples wehave introduced a user adaptive mechanism in our systemOur system allows the user to interactively select epochso his choice by simply clicking on the correction button
While using our system when a user thinks that a certainepoch is alsely labelledcategorised oursystem allows him tointeractively mark mark that label as a mistake Tese detailswill be saved in a log in the background and they will be usedto retrain the classi1047297er to improve its classi1047297cation rate andadapt itsel according to the user with the passage o time
When the user is going to select the retraining option in oursystem then classi1047297ers willretrain themselves on thepreviousand the newly logged training examples As every user has tolog in with his personal ID every corrective marking detailwill only be saved in that userrsquos older and only classi1047297er willupdate itsel or that user Hence the systems classi1047297er tries toadapt itsel according to that user without damaging anyoneelse classi1047297cation
Te concept behind the inclusion o the retraining is thati there is more than one example with same attributes butdifferent labels the classi1047297er is going to get trained to theone with most population Te userrsquos corrective marking willincrease theexampleso hischoice thus making that classi1047297eradapt itsel to the userrsquos choice in a trivial way Every userwill have exclusive classi1047297ers trained or him and his markingwill not affect other usersrsquo classi1047297er As we know the userssometimes do not agree on the choice o the epileptic patternor its type Te exclusive processing or each user will help thesame sofware keep the system trained or every user and itwill also let different users compare their markings with eachother
We do not have any standard right now to measure whichneurologist is the most righteous among a disagreeing groupo neurologist users So we kept the corrective markings o each user to his account so that it may not interere withthe one who may not agree on his choice So the developedsystem is used to acilitate the neurologistrsquos selection to theuser according to his own choice and afer initial training onevery retraining it tries to adopt more users Tis system doesnot want to dictate to the neurologist but rather learn romhim to adapt him to save his time
We want the classi1047297er to think like the user and supple-menthim by highlighting theepochso his choice so the goldstandard afer ew retraining mechanisms will be the userhimsel Already tested examples with new labels inclusion inthe training examplesor the retraining willbias the classi1047297erschoice in avour o user
3 Experimentation
In this section we will discuss the results in detail At 1047297rstwe will describe the datasets which we used to train test and
validate our method Ten we will discuss their versatility (Figure 983095)
983091983089 Dataset wo labelled datasets o epilepsy suspectedsurace EEG data were available to us Both o these datasetshave lots o versatility in between them in terms o ethnicityage gender and equipment Te datasets available to us wereabout generalised absence seizure which is characterized by the 983091 Hz spike and wave epileptic pattern in almost eachchannel Tat is why we have classi1047297cation results availableonly or one type o epilepsy which is absence seizure
983137983138983148983141 983089 Tis table describes the affiliation o detailed coefficientswith epileptic requency band o interest or 983090983093983094 Hz sampled CHB-MI dataset
Epileptic requency range Detailed coefficientsrsquo level
Beta (907317) CD983091 (983091983090 Hz to 983089983094 Hz)
Alpha (1103925) CD983092 (983089983094 Hz to 983096 Hz)
Teta (1038389) CD983093 (983096 Hz to 983092 Hz)
Delta () CD983094 (983092 Hz to 983090 Hz)
Delta () CD983095 (983090 Hz to 983089 Hz)
983137983138983148983141 983090 Tis table describes the affiliation o detailed coefficientswith epileptic requency band o interest or 983093983089983090 Hz sampled PIMHdataset
Epileptic requency range Detailed coefficientsrsquo level
Beta (907317) CD983092 (983091983089983090 Hz to 983089983093983094 Hz)
Alpha (1103925) CD983093 (983089983093983094 Hz to 983095983096 Hz)
Teta (1038389) CD983094 (983095983096 Hz to 983091983097 Hz)
Delta () CD983095 (983091983097 Hz to 983090 Hz)
Delta () CD983096 (983090 Hz to 983089 Hz)
983091983089983089 CHBMIT Tis database is the online available suraceEEG dataset [983090983088] which is provided by Children HospitalBoston and Massachusetts Institute o echnology and it isavailable at physioNet website [983089983088] It contains 983097983089983094 hours o 983090983091 channels scalp EEG recording rom 983090983092 epilepsy suspectedpatients Tis ECG recording is sampledat 983090983093983094Hz with 983089983094-bitresolution Te 983090983091rd channel is same as 983089983093th channel (able 983089)
983091983089983090 PIMH Te second database o EEG datasets is pro- vided by our collaborator at Punjab Institute o Mental Health(PIMH) Lahore Its sampling requency is 983093983088983088 Hz and it wasrecorded on 983092983091 channels (among which 983091983091 channels are orEEG) Tis dataset consists o 983090983089 patients EEG recording
983091983090 Features
983091983090983089 Feature Extraction Data which interests us lies inbetween the requency range o 983088983091 Hz to 983091983088 Hz So aferapplying DW with db983092 mother wavelet we have to selectdetailed coefficients with this requency range So in case o 983090983093983094 Hz sampled CHBMI dataset we have to go to at least983091 levels o decomposition and discard the earlier two as it isdemonstrated in Figure 983090 In order to get the discriminating
inormation between different types o epileptic patterns andidentiying them correctly without mistaking them with eachother decomposition o this detailed coefficient urther inBeta Alpha Teta and Delta is hugely helpul So we urtherdecomposed them until the 983095th level Hence we used theDWs detailed coefficients o levels 983091 983092 983093 983094 and 983095 or 983090983093983094Hzsampled CHB-MI dataset (able 983090)
Afer the selection o the wavelet coefficients we calcu-lated the statistical eature out o them Te statistical eatureswere the mean power and standard deviation o all o theselected coefficients
In case o 983093983089983090 Hz sampled PIMH dataset we used theDWs detailed coefficients o levels 983092 983093 983094 983095 and 983096
Afer the selection o detailed coefficients we calculatedthe statistical eature out o them Te statistical eatureswere the mean power and standard deviation o all o theshortlisted detailed coefficients
983091983090983090 Standardization During training stage we 1047297rst used
simple -score normalization to standardize the eatures [983089983097]beore applying eature reduction But the real issue arosewhen we tried to normalize them during testing stage Oneway o doing this is that we keep all o the examples andapply -score on them along with the new test data Insteado this time taking process we made an assumption onour observation that mean and standard deviation does notdeviate a lot It is analysed in this study that the EEG timeseries are assumed to be stationary over a small length o thesegments So we used the mean and standard deviation o thetraining examples rom the training stage to normalize thetest examples Figures 983093 and 983094 illustrate our observation inwhich you can see that there is not much deviation in trainand train + test examples mean and standard deviation
983091983091 Classi1047297er Classi1047297cation is used in machine learningto reer to the problem o identiying a discrete category to which a new observation belongs Observations withknown labels are used to train a classi1047297cation algorithm orclassi1047297er using eatures associated with the observation ForCHBMI database we had to train 983090983090983088 classi1047297ers in initialtraining stage Te calculation behind 983090983090983088 is the 983090983090 channelsmultiplied by 983089983088 types o epileptic pattern Te 983090983091rd channelwas same as 983089983093th channel For PIMH dataset 983091983091983088 classi1047297erswere trained where 983091983091 channels o EEG were utilized Wetried three different classi1047297ers and ound SVM to be the mostaccurate
We have used blind validation mechanism or the tendifferent eature data distributions to estimate the classi1047297-cation perormance Tese 983089983088 different and separate blinddata distributions were taken rom a huge set o EEGdataset Tese 983089983088 data distributions we randomly dividedinto two groups We trained our classi1047297er on one hal o thedistribution and tested it on the other hal We repeated thaton all ten distributions Ten we calculated the average o theclassi1047297cation rate or the all ten distributions
983091983090983090983097 out o 983091983090983097983095983094983088983088 epochs were randomly taken or tentimes rom CHBMI dataset Each time hal o them wereused to train and hal o them were used to test the initialclassi1047297cation Te average o the sensitivity speci1047297city and
accuracy or these ten distributionsis considered as the initialtraining phase perormance
Same approach wasapplied on PIMH datasetswhere 983091983090983090983097out o 983090983092983088983097983095 epochs were randomly taken rom PIMH datasetor the six times instead o ten times
Due to unavailability o the non-983091 Hz spike and waveepileptic EEG data currently we have only classi1047297cation ratesor generalized absence seizure
983091983091983089 Exclusive Processing In this study we have analysedthat even in the case o absence seizure epileptic patterns donot appear in the exact same way in each channel Handlingo each channel exclusive to each other was also another very
F983145983143983157983154983141 983090 Selection o DW detailed coefficients or a 983090983093983094 Hz sampled 983089 sec wide epochs EEG signal
important decision We tested the classi1047297cation in both waysthat is one classi1047297er or all o the channels at once versus oneseparate classi1047297er or each channel (Figure 983096)
Tis processing o each channel exclusive to each otherimproved over average accuracy rom approximately 983097983089 toapproximately 983097983093 in case o SVM So or SVM there is
a signi1047297cant improvement o 983092 by this change In case o QDA accuracy rose rom 983097983089 to 983097983092 with an improvemento 983091 and in case o ANN it rose rom 983097983089983096 to 983097983090983097 withan improvement o 983089983089
Results show that SVMsuites ourmethodin the most effi-cient way ANN has a lesser classi1047297cation time and LDA has
a lesser training time as compared to SVM but consideringthe sensitivity and classi1047297cation improvement through cor-rective marking we think that SVM is the better choice thanLDA and ANN In upcoming sections we have shown theresults or all three types o classi1047297er
0 5 10 15 20 250
24
6
8
10
12
14
16times10
4
Mean o the training examplesMean o the training + test examples
F983145983143983157983154983141 983093 Relationship between channelrsquos number and mean value(test + training examples)
(a) Adaptation Mechanism o test the adaptation mechanism983096983088983095corrective epochs were markedby the user ora CHBMI
F983145983143983157983154983141 983094 Relationship between channelrsquos number and standard deviation value (test + training examples)
EEG
Channel 1 DWTD1
A1 D2
A2
Mean
Standard deviation
Power
Mean
Standard deviation
Power
Mean
Standard deviation
Power
SVM or pattern 1
SVM or pattern 2
SVM or pattern 3
SVM or pattern 10
OPor
channel1
Channel 2 DWTD1
A1 D2
A2
Mean
Standard deviation
Power
Mean
Standard deviation
Power
MeanStandard deviation
Power
SVM or pattern 1
SVM or pattern 2
SVM or pattern 3
SVM or pattern 10
OPor
channel2
DWTD1
A1 D2
A2
Mean
Standard deviation
Power
Mean
Standard deviation
Power
Mean
Standard deviation
Power
SVM or pattern 1
SVM or pattern 2
SVM or pattern 3
SVM or pattern 10
Features
Features
PCA
Reduction
Standardization
PCA
Standardization
Reduction
FeaturesPCA
Reduction
Standardization
Surgeonrsquos marking
Surgeonrsquos marking
Surgeonrsquos marking
OPor
channeln
Al
Dl
Al
Al
Dl
Dl
Z-score
Z-score
Z-scoreChannel n
F983145983143983157983154983141 983095 Flowchart
dataset 1047297le and he marked the same amount o epochs oreach channel Tese corrective markings were saved in hislog as training examples Tese corrective markings as thenew examples along with the 983091983090983090983097983088 epochs o initial trainingstage were used to retrain the classi1047297er Te number 983091983090983090983097983088 hascome rom the 983091983090983090983097 randomly selected epochs rom whole o the CHBMI dataset or the ten separate times during initial
training phase Ten later the perormance o the classi1047297erafer retraining was judged again on another random 983091983088983088983088epochs (Figure 983089983092)
In case o PIMH dataset 983093983095 corrective epochs wereselected or PIMH dataset Tis time 983089983097983091983095983092 epochs o thePIMH dataset were used along with the 983093983095 corrective mark-ings as orthe PIMH we randomly selected the 983091983090983090983097 numbers
Processing all channels simultaneously with single classi1047297er
Processing all channels separately with separate classi1047297er oreach channel
F983145983143983157983154983141 983096 Accuracy relationship o different classi1047297ers and theirclassi1047297cation rate
84
86
88
90
92
94
96
98
100
Accuracy afer initial training ()
Accuracy afer retraining ()
ldquo F P 1 F 7 rdquo
ldquo F 7 T 7 rdquo
ldquo T 7 P 7 rdquo
ldquo P 7 O 1 rdquo
ldquo F P 1 F 3 rdquo
ldquo F 3 C 3 rdquo
ldquo C 3 P 3 rdquo
ldquo P 3 O 1 rdquo
ldquo F P 2 F 4 rdquo
ldquo F 4 C 4 rdquo
ldquo C 4 P 4 rdquo
ldquo P 4 O 2 rdquo
ldquo F P 2 F 8 rdquo
ldquo F 8 T 8 rdquo
ldquo T 8 P 8 rdquo
ldquo P 8 O 2 rdquo
ldquo F Z C Z rdquo
ldquo C Z P Z rdquo
ldquo P 7 T 7 rdquo
ldquo T 7 F T 9 rdquo
ldquo F T 9 F T 1 0 rdquo
ldquo F T 1 0 T 8 rdquo
F983145983143983157983154983141 983097 Relation between average classi1047297cation rate and accuracy
o the channel afer initial training and retaining
o epochs or the six times Te retrained classi1047297er was testedon the 983090983091983094983089 remaining epochs
(b) Support Vector Machine We used the support vectormachine classi1047297er package available in MALAB Bioinor-matics oolbox We ound linear kernel to be the mostaccurate SVM kernel with 983093983088 as the box constraint
(c) CHBMIT For CHBMI dataset initial training o theclassi1047297er resulted in 983097983094983091 average accuracy 983097983095983092 average
0
20
40
60
80
100
120
Accuracy afer initial training ()
Accuracy afer retraining ()
ldquo F p 1 rdquo
ldquo F p 2 rdquo
ldquo F 3 rdquo
ldquo F 4 rdquo
ldquo C 3 rdquo
ldquo C 4 rdquo
ldquo P 3 rdquo
ldquo P 4 rdquo
ldquo O 1 rdquo
ldquo O 2 rdquo
ldquo F 7 rdquo
ldquo F 8 rdquo
ldquo T 3 rdquo
ldquo T 4 rdquo
ldquo T 5 rdquo
ldquo T 6 rdquo
ldquo F z rdquo
ldquo C z rdquo
ldquo P z rdquo
ldquo E rdquo
ldquo P G 2 rdquo
ldquo A 1 rdquo
ldquo A 2 rdquo
ldquo T 1 rdquo
ldquo T 2 rdquo
ldquo X 1 rdquo
ldquo X 2 rdquo
ldquo X 3 rdquo
ldquo X 4 rdquo
ldquo X 5 rdquo
ldquo X 6 rdquo
ldquo X 7 rdquo
ldquo P G 1 rdquo
F983145983143983157983154983141 983089983088 Relation between average classi1047297cation rate and accuracy o the channel afer initial training and retaining
82
84
86
88
90
92
94
96
98
Accuracy afer initial training ()Accuracy afer retraining ()
ldquo F P 1 F 7 rdquo
ldquo F 7 T 7 rdquo
ldquo T 7 P 7 rdquo
ldquo P 7 O 1 rdquo
ldquo F P 1 F 3 rdquo
ldquo F 3 C 3 rdquo
ldquo C 3 P 3 rdquo
ldquo P 3 O 1 rdquo
ldquo F P 2 F 4 rdquo
ldquo F 4 C 4 rdquo
ldquo C 4 P 4 rdquo
ldquo P 4 O 2 rdquo
ldquo F P 2 F 8 rdquo
ldquo F 8 T 8 rdquo
ldquo T 8 P 8 rdquo
ldquo P 8 O 2 rdquo
ldquo F Z C Z rdquo
ldquo C Z P Z rdquo
ldquo P 7 T 7 rdquo
ldquo T 7 F T 9 rdquo
ldquo F T 9 F T 1 0 rdquo
ldquo F T 1 0 T 8 rdquo
F983145983143983157983154983141 983089983089 Relation between average classi1047297cation rate and accuracy o the channel afer initial training and retaining
speci1047297city and 983097983091983093 average sensitivity or 983091 Hz spike andwave which is a characteristic o absence seizure Aferinitial training our speci1047297city is better than that o Shoeb[983089983088] and Nasehi and Pourghassem [983090983089] who used the samedataset to validate their technique with different eaturesand application technique Tis shows that our technique
is providing better results even at the initial training phase(Figure 983089983089)
In able 983091 we have shown the average initial classi1047297cationand retrained classi1047297cation results o our system or eachchannel In this system we have shown that afer correctiono ew epochs there is a visible improvement in the systemsclassi1047297cation Te average accuracy o the system rose rom983097983093983093 to 983097983094983091
(d) PIMH For PIMH dataset initial training o the classi1047297erresulted in 983097983088 average accuracy 983097983092 average speci1047297city and983096983088 average sensitivity or 983091 Hz spike and wave which is acharacteristic o absence seizure (Figure 983089983090)
F983145983143983157983154983141 983089983090 Relation between average classi1047297cation rateand accuracy o the channel afer initial training and retaining
80
82
84
86
88
90
92
94
96
98
Accuracy afer initial training ()
Accuracy afer retraining ()
ldquo F P 1 F 7 rdquo
ldquo F 7 T 7 rdquo
ldquo T 7 P 7 rdquo
ldquo P 7 O 1 rdquo
ldquo F P 1 F 3 rdquo
ldquo F 3 C 3 rdquo
ldquo C 3 P 3 rdquo
ldquo P 3 O 1 rdquo
ldquo F P 2 F 4 rdquo
ldquo F 4 C 4 rdquo
ldquo C 4 P 4 rdquo
ldquo P 4 O 2 rdquo
ldquo F P 2 F 8 rdquo
ldquo F 8 T 8 rdquo
ldquo T 8 P 8 rdquo
ldquo P 8 O 2 rdquo
ldquo F Z C Z rdquo
ldquo C Z P Z rdquo
ldquo P 7 T 7 rdquo
ldquo T 7 F T 9 rdquo
ldquo F T 9 F T 1 0 rdquo
ldquo F T 1 0 T 8 rdquo
F983145983143983157983154983141 983089983091 Relation between average classi1047297cation rate and accuracy o the channel afer initial training and retaining
In able 983092 we have shown the average initial classi1047297cationand retrained classi1047297cation results o our system or eachchannel able 983092 shows that our technique is robust and itworks also on a different dataset Te average accuracy o thesystem rose rom approximately 983096983097 to 983097983088
983091983091983090 Discriminate Analysis We used the discriminant anal-
ysis package available in MALAB Statistics oolbox Weound pseudoquadratic to be the best perorming discrimi-nate type with uniorm probability
(a) CHBMIT For CHBMI dataset initial training o theclassi1047297er resulted in 983097983092 average accuracy 983097983094 averagespeci1047297city and 983097983088 average sensitivity or 983091 Hz spike andwave which is a characteristic o absence seizure Afer initialtraining our speci1047297city is better than that o Shoeb [983089983088] andNasehi and Pourghassem [983090983089] (Figure 983089983091)
In able 983093 we have shown the average initial classi1047297cationand retrained classi1047297cation results o our system or eachchannel In this system we have shown that afer correction
010
2030405060708090
100
Accuracy afer initial training ()
Accuracy afer retraining ()
ldquo F 3 rdquo
ldquo F 4 rdquo
ldquo C 3 rdquo
ldquo C 4 rdquo
ldquo P 3 rdquo
ldquo P 4 rdquo
ldquo O 1 rdquo
ldquo O 2 rdquo
ldquo F 7 rdquo
ldquo F 8 rdquo
ldquo T 3 rdquo
ldquo T 4 rdquo
ldquo T 5 rdquo
ldquo T 6 rdquo
ldquo F z rdquo
ldquo C z rdquo
ldquo P z rdquo
ldquo E rdquo
ldquo P G 2 rdquo
ldquo T 1 rdquo
ldquo T 2 rdquo
ldquo X 1 rdquo
ldquo X 2 rdquo
ldquo X 3 rdquo
ldquo X 4 rdquo
ldquo X 5 rdquo
ldquo X 6 rdquo
ldquo X 7 rdquo
ldquo P G 1 rdquo
ldquo A 1 rdquo
ldquo A 2 rdquo
ldquo F p 1 rdquo
ldquo F p 2 rdquo
F983145983143983157983154983141 983089983092 Relation between average classi1047297cation rate and accuracy o the channel afer initial training and retaining
983137983138983148983141 983091 First column shows the channel label the second columnshows the initial training accuracy and third one shows the markedcorrection by a neurologistand thelast one shows the1047297nal accuracy
o ew epochs there is visible improvement in the systemsclassi1047297cation Te average accuracy o the system rose rom983097983092 to 983097983093
(b) PIMH For PIMH dataset initial training o the classi1047297erresulted in 983097983088 average accuracy 983097983093 average speci1047297city and983095983091 average sensitivity or 983091 Hz spike and wave which is acharacteristic o absence seizure
In able 983094 we have shown the average initialclassi1047297cationand retrained classi1047297cation results o our system or each
983137983138983148983141 983092 First column shows the channel label the second columnshows the initial training accuracy and third one shows the markedcorrection by a neurologistand thelast one shows the1047297nal accuracy
channel able 983094 shows that our technique is robust and itworks also on a different dataset Te average accuracy o thesystem rose rom approximately 983096983097 to 983097983088
983091983091983091 Arti1047297cial Neural Network We used eedorward back-propagation package available in MALAB Neural Network oolbox and ound Levenberg-Marquardt to be the bestmethod with 983088983088983093 learning rate
(a) CHBMIT For CHBMI dataset initial training o theclassi1047297er resulted in 983097983090983096983096 average accuracy 983097983096983094983094 averagespeci1047297city and 983095983093983095983093 average sensitivity or 983091 Hz spike andwave which is a characteristic o absence seizure (Figure 983097)
983137983138983148983141 983093 First column shows the channel label the second columnshows the initial training accuracy and third one shows the markedcorrection by a neurologistand thelast one shows the1047297nal accuracy
In able 983095 we have shown the average initial classi1047297cationand retrained classi1047297cation results o our system or eachchannel In this system we have shown that afer correctiono ew epochs there is visible improvement in the systemsclassi1047297cation Te average accuracy o the system rose rom983097983090983096983096 to 983097983091983097983094
(b) PIMH For PIMH dataset initial training o the classi1047297erresulted in 983096983092 average accuracy 983097983092983096 average speci1047297cityand 983093983094983093 average sensitivity or 983091 Hz spike and wave whichis a characteristic o absence seizure (Figure 983089983088)
In able 983096 we have shown the average initial classi1047297cationand retrained classi1047297cation results o our system or each
channel able 983096 shows that our technique is robust and itworks also on a different dataset Te average accuracy o thesystem rose rom approximately 983096983092 to 983096983093983092983091
4 Discussion and Future Work
Computer-assisted analysis o EEG has tremendous potentialor assisting the clinicians in diagnosis A very importantand novel phase o our system is user adaptation mechanismor retraining mechanism Introduction o this phase hasimportance in many aspects In this phase system tries toadapt its classi1047297cation according to users desire Moreoverthis technique personalizes the classi1047297ers classi1047297cation It has
983137983138983148983141 983094 First column shows the channel label the second columnshows the initial training accuracy and third one shows the markedcorrection by a neurologistand thelast one shows the1047297nal accuracy
been cited that sometimes even the expert neurologists havesome disagreementovera certain observation o an EEGdataTis system will be useul or disagreeing users and it will alsohelp them in comparing their results with each other
Tere is also a threat o over1047297tting by the classi1047297erIn order to keep the classi1047297er improving its perormancewith the encounter o more and more examples we haveintroduced this user adaptive mechanism in our system Weconsider the existing systems as dead because these cannotimprove their classi1047297cation rate afer initial training (duringsofware development) Te sel-improving mechanism aferdeployment makes our tool alive Tis system can be madepart o the whole epileptic diagnosis process It will highlight
983137983138983148983141 983095 First column shows the channel label the second columnshows the initial training accuracy and third one shows the markedcorrection by a neurologistand thelast one shows the1047297nal accuracy
the epileptic spikes among the whole EEG thus leading toreduced atigue and time consumption o a user We obtainedhigh classi1047297cation accuracy on datasets obtained rom twodifferent sites which indicates reproducibility o our resultsand robustness o our approach
In the uture we are planning to make this a web basedapplication neurologists can log in and consult each otherrsquosreviews about a particular subject Tis will make our systemexperience a whole versatility o examples and learn rom allo them Integration o the video and its automatic analysis(video EEG) can help a neurologist in diagnosing epilepsy ina better way whereas this can also help him in distinguishingbetween psychogenic and epileptic seizuresWewouldalso be
investigating how much over1047297tting is an issue in the reportedperormances which are now even touching 983089983088983088 based onsome claims Tere is a need or methodcriteria which couldlimit these algorithms improving their detection on a limitednumber o available examples
Tissystem is made keeping in mind thatwe haveto acil-itate the neurologist by supplementing him in the analysis o the EEG We do not want to enorce the classi1047297cation o theEEG data on a user
In the uture we will also include a slider in the systemwhich will allow the user to adjust the sensitivity and speci-1047297city beore retraining Tis assisting system is more like adetection tool which is continuously learning with encounter
983137983138983148983141 983096 Classi1047297cation rate improvement caused by retraining Firstcolumn shows thechannellabel thesecond columnshows theinitialtraining accuracy and third one shows the marked correction by aneurologist and the last one shows the 1047297nal accuracy
o better examples More and better examples will certainly improve its perormance Te agreement between differentneurologists over the EEG readings is low to moderate I we could 1047297nd the agreement on at least ew o the epilepticpatterns correspondence with epileptic disease then we cantake this tool urther ahead and use it or diagnosis instead o
just assistanceOne o the biggest limitations to this study is the unavail-
ability o non-983091 Hz spike and wave data Even though we haveincluded the data eatures o the entire epileptic requency ranges exclusive to each other proo testing on the data will
certainly prove worthy orthe progresso these assisting toolstoward a diagnostic tool
Conflict of Interests
Te authors declare that there is no con1047298ict o interests
regarding the publication o this paper
Acknowledgment
Te authors would like to extend their sincere appreciation tothe Deanship o Scienti1047297c Research at King Saud University or unding this research through Research Group Project(RG no 983089983092983091983093-983088983093983089)
References
[983089] H Adelia Z Zhoub and N Dadmehrc ldquoAnalysis o EEGrecords in an epileptic patient using wavelet transormrdquo Journal of Neuroscience Methods vol 983089983090983091 no 983089 pp 983094983097ndash983096983095 983090983088983088983091
[983090] M A Ahmad W Majeed and N A Khan ldquoAdvancements incomputer aided methods or EEG-based epileptic detectionrdquo inProceedings of the 983095th International Conference on Bio-Inspired Systems and Signal Processing (BIOSIGNALS 983089983092) AngersFrance 983090983088983089983092
[983091] S Noachtar and J R emi ldquoTe role o EEG in epilepsy a criticalreviewrdquo Epilepsy and Behavior vol 983089983093 no 983089 pp 983090983090ndash983091983091 983090983088983088983097
[983092] E B Petersen J Duun-Henriksen A Mazzaretto W Kjar CE Tomsen and H B D Sorensen ldquoGeneric single-channeldetection o absence seizuresrdquo in Proceedings of the Annual International Conference of the IEEE Engineering in Medicineand Biology Society (EMBS rsquo983089983089) pp 983092983096983090983088ndash983092983096983090983091 Boston MassUSA September 983090983088983089983089
[983093] M Kaleem A Guergachi and S Krishnan ldquoEEG seizure
detection and epilepsy diagnosis using a novel variation o Empirical Mode Decompositionrdquo in Proceedings of the 983091983093th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC rsquo983089983091) pp 983092983091983089983092ndash983092983091983089983095 OsakaJapan July 983090983088983089983091
[983094] S M S Alam and M I H Bhuiyan ldquoDetection o seizure andepilepsy usinghigher order statistics in the EMD domainrdquo IEEE Journal of Biomedical and Health Informatics vol 983089983095 no 983090 pp983091983089983090ndash983091983089983096 983090983088983089983091
[983095] H Ocbagabir K A I Aboalayon and M FaezipourldquoEfficientEEG analysis or seizure monitoring in epileptic patientsrdquo inProceedings of the Long Island Systems Applications and Tech-nology Conference (LISAT rsquo983089983091) pp 983089ndash983094 IEEE Farmingdale NYUSA May 983090983088983089983091
[983096] A A Abdullah S A Rahim and A Ibrahim ldquoDevelopment o EEG-based epileptic detection using arti1047297cial neural networkrdquoin Proceedings of the International Conference on Biomedical Engineering (ICoBE rsquo983089983090) pp 983094983088983093ndash983094983089983088 Penang Malaysia 983090983088983089983090
[983097] P Guo J Wang X Z Gao and J M A anskanen ldquoEpilepticEEG signal classi1047297cation with marching pursuit based on har-mony search methodrdquo in Proceedings of the IEEE International Conference on Systems Man and Cybernetics (SMC rsquo983089983090) pp983090983096983091ndash983090983096983096 Seoul Republic o Korea October 983090983088983089983090
[983089983088] A Shoeb ldquoCHB-MI scalp EEG databaserdquo 983090983088983088983088 httpphysionetorgpn983094chbmit
[983089983089] A S M Murugavel S Ramakrishnan U Maheswari and BS Sabetha ldquoCombined Seizure Index with Adaptive Multi-Class SVM or epileptic EEG classi1047297cationrdquo in Proceedings
of the International Conference on Emerging Trends in VLSIEmbedded System Nano Electronics and TelecommunicationSystem (ICEVENT rsquo983089983091) pp 983089ndash983093 Tiruvannammalai IndiaJanuary 983090983088983089983091
[983089983090] M H Abdullah J M Abdullah and M Z Abdullah ldquoSeizuredetection by means o hidden Markov model and station-ary wavelet transorm o electroencephalograph signalsrdquo inProceedings of the IEEE-EMBS International Conference onBiomedical and Health Informatics (BHI rsquo983089983090) pp 983094983090ndash983094983093 HongKong January 983090983088983089983090
[983089983091] A Subasi and M I Gursoy ldquoEEG signal classi1047297cation usingPCA ICA LDA and support vector machinesrdquo Expert Systemswith Applications vol 983091983095 no 983089983090 pp 983096983094983093983097ndash983096983094983094983094 983090983088983089983088
[983089983092] E Sezer H Isik and E Saracoglu ldquoEmployment and compari-son o different arti1047297cial neural networks or epilepsy diagnosisrom EEG signalsrdquo Journal of Medical Systems vol 983091983094 no 983089 pp983091983092983095ndash983091983094983090 983090983088983089983090
[983089983093] Brain Products GmbHProducts and ApplicationsAnalyzer 983090httpwwwbrainproductscomproductdetailsphpid=983089983095
[983089983094] NeuroExplorer Home httpwwwneuroexplorercom
[983089983096] C H Seng R Demirli L Khuon and D Bolger ldquoSeizuredetection in EEG signals using support vector machinesrdquo inProceedings of the 983091983096th Annual Northeast Bioengineering Con- ference (NEBEC rsquo983089983090) pp 983090983091983089ndash983090983091983090 Philadelphia Pa USA March983090983088983089983090
[983089983097] Z A Khan S B Mansoor M A Ahmad and M M MalikldquoInput devices or virtual surgical simulations a comparativestudyrdquo in Proceedings of the 983089983094th International Multi Topic Con- ference (INMIC rsquo983089983091) pp 983089983096983097ndash983089983097983092 Lahore Pakistan December983090983088983089983091
[983090983088] A L Goldberger L A Amaral L Glass et al ldquoPhysioBankPhysiooolkit and PhysioNet components o a new research
[983090983089] S Nasehi and H Pourghassem ldquoEpileptic seizure onset detec-tion algorithm using dynamic cascade eed-orward neural net-worksrdquo in Proceedings of the International Conference on Intelli- gent Computation and Bio-Medical Instrumentation (ICBMI rsquo983089983089)pp 983089983097983094ndash983089983097983097 December 983090983088983089983089
last as long as 983095983090 hours Tis generates an immense amounto data to be inspected by the clinician which could prove tobe a daunting task
Advancement in signal processing and machine learningtechniques is making it possible to automatically analyse EEGdata to detect epochs with epileptic patterns A system based
on these techniques can aid a neurologist by highlighting theepileptic patterns in the EEG up to a signi1047297cant level O course the task o diagnosis should be lef to the neurologistHowever the task o the neurologist becomes efficient as itreduces the data to be analysed and lessens up the atigueAlong with classi1047297cation these analysis sofware programscan also provide simultaneous visualization o multiple chan-nels which helps the clinician in differentiating betweengeneralized epilepsy and ocal epilepsy
It is well known that an epileptic seizure brings changesin certain requency bands Tat is why usually the spectralcontent o the EEG is used or diagnosis [983090] Tese areidenti1047297ed as (983088983092ndash983092 Hz) 1038389 (983092ndash983096 Hz) 1103925 (983096ndash983089983090Hz) and 907317(983089983090ndash983091983088 Hz) Noachtar and R emi mention almost ten types o epileptic patterns However most o the existing work only ocuses on one o the epilepticpatterns thatis 983091 Hz spike andwave which is a trademark or absence seizure Other typeso the patterns are rarely addressed [983091]
Computer-assisted EEG classi1047297cation involves severalstages including eature extraction eature reduction andeature classi1047297cation Wavelet transorm has becomethe mostpopular eature extraction technique or EEG analysis dueto its capability to capture transient eatures as well asinormation about time-requency dynamics o the signal[983092] Other previously used eature extraction approaches orepilepsy diagnosis include empirical mode decomposition(EMD) multilevel Fourier transorm (F) and orthogonalmatching pursuit [983093ndash983097] Feature extraction is ollowed by eature reduction to reduce computational complexity andavoid curse o dimensionality Most commonly the reducedeature vector consists o statistical summary measures (suchas mean energy standard deviation kurtosis andentropy) o different sets o original (unreduced) eatures although othermethods such as principal component analysis discriminantanalysis and independent component analysis havealso beenused or eature reduction [983092 983095 983089983088 983089983089] Feature extrac-tionreduction is ollowed by classi1047297cation using a machinelearning algorithm such as arti1047297cial neural networks (ANN)support vector machines (SVM) hidden Markov models andquadratic discriminant analysis [983096 983089983089ndash983089983092]
A very important and novel phase o our system is useradaptation mechanism or retraining mechanism Tere aremultiple reasons according to which introduction o thisphase has lots o advantages During this phase system willtry to adapt its classi1047297cation as per users desire It has beencited that sometimes even the expert neurologists have somedisagreement over a certain observation o an EEG dataTere is also a threat o over1047297tting by the classi1047297er In orderto keep the classi1047297er improving its perormance with theencounter o more and more examples we have introducedthis user adaptive mechanism in our system We consider theexisting systems as dead because they cannot improve theirclassi1047297cation rate afer initial training Tey do not have any
mechanism o learning or improvement rom neurologistscorrective marking [983089983093ndash983089983095] Te agreement between differentEEG readers is low to moderate our adaption mechanismhelps the user in catering this issue as our system tries toadapt the detection accordingto the userscorrective markingTe new corrective markings generate new examples with
improved labels Hence it populates the training exampleswith newly labelled ones So afer retraining machine learningalgorithms in the system users adapt to set o choices
In the next section we will explain our proposed methodwhich will be ollowed by the results In the results section wewill explain how SVM perorms better than QDA and ANNin our proposed method We will also show that exclusiveprocessing o each channel results in a signi1047297cant improve-ment in the classi1047297cation rate Here ldquoepileptic patternrdquo andldquoepileptic spikesrdquo will be used as an alternative to each other
2 Proposed Method
Computer-aided EEG analysis systems use the neurologistsmarking and labelling o the EEG data as a benchmark totrain themselves during initial training phase But afer initialtraining phase these systems have no simple mechanismor these neurologists to improve systems classi1047297cation aferencountering any alse classi1047297cation So we have proposed amethod by which systems classi1047297cation can be improved by the user in a relatively simpler way Tis analysis system only tries to detect the epileptic spikes as mentioned by Noachtarand R emi Later it adapts its detection o epileptic spikesexclusively or every user (Figure 983092)
In this proposed system we are processing each channelor each epileptic pattern exclusive to each other Tis
exclusive processing o each channel notonly helps theuser indiagnosing localized epilepsy but also eases up the classi1047297ers job We have considered that different epileptic patterns areindependent to each other and their separate handling willhelp us in avoiding error propagation rom one epilepticpattern type detection to the other Our systems workinghas two major phases (A) initial training phase and (B)adaptation phase Tese two major phases have urther threeparts which are (983089) eature extraction (983090) eature reductionand (983091) classi1047297cation Next we will brie1047298y explain all o thesesteps
983090983089 Initial Training
983090983089983089 Feature Extraction o decide which parts o the signalare epileptic and which are not we 1047297rst divided whole o thesignal in small chunks known as epochs Ten DW wasapplied on those epochs so that visibility o epileptic activity can be enhanced which is distinguished by some spectralcharacteristics Tese eatures are then processed to makethem more suitable or the classi1047297cation technique
(a) Epoch Size Te 1047297rst important part o the eatureextraction is epoch selection Epoch is a small chunk o thesignal which is processed at a time Te size o the epochis very important Te larger it is the less accurate it willbe Te smaller it is the higher the processing time will be
F983145983143983157983154983141 983089 Epoch size is 983089 sec
Afer testing different epoch sizes we ound epoch size o nonoverlapping 983089 sec window to be best yielding in terms o accuracy It also reestablished the work o Seng et al [ 983089983096](Figure 983089)
(b) DWT As discussed in Introduction spectral analysisis very inormative while examining the epilepsy suspectedpatients EEG Tere are proound advantages o waveletdecomposition which is a multiresolution analysis techniqueA multiresolution analysis technique allows us to analysea signal or multiple requency resolutions while maintain-
ing time resolution unlike a normal requency transormWavelet decomposition allows us to increase requency res-olution in the spectral band o our interest while maintainingthe time resolution in short we can decimate these valuessimultaneously in time and requency domain
During wavelet transorm the original epoch is splitinto different subbands the lower requency inormationis called approximate coefficients and the higher requency inormation is called detailed coefficients Te requency subdivision in these subbands helps us in analysing differentrequency ranges o an EEG epoch while maintaining its timeresolution [983092 983096 983089983091] Te choice o coefficients level is very important as the epileptic activity only resides in the range
o 983088ndash983091983088 Hz Coefficients levels o the DW are determinedwith respect to sampling requency So the detailed levels o interest are adjusted on the run according to the samplingrequency such that we may get at least one exact value o the closest separate (983088983092ndash983092 Hz) 1038389 (983092ndash983096 Hz) 1103925 (983096ndash983089983090 Hz)and 907317 (983089983090ndash983091983088 Hz) components o the signal We discarded allthe detailed coefficient levels which were beyond the 983088ndash983091983088Hzrange
TenDW wasappliedon each epoch with Daubechies-983092(db983092) as mother wavelet Te detailed coefficient levels o theDW were determined with respect to sampling requency
(c) Statistical Features Afer the selection o detailed coe-1047297cients which represent the requency band o our interest
we calculated the statistical eatures by calculating the meanstandard deviation and power o these selected waveletcoefficients Tese statistical eatures are inspired rom Subasiand Gursoy work [983089983091]
(d) Standardization Tese statistical eatures were then stan-dardized During training stage -score standardization wasapplied on these eatures [983089983097] Tis standardization is justlike usual -score normalization but as we do not know the exact mean and standard deviation o the data (to beclassi1047297ed) during classi1047297cationtest stage we used the mean
and standard deviation o the training examples duringtraining stage or standardizing (normalizing) the eaturesduring classi1047297cation stage We normalized the eatures by subtracting and dividing them by training examples meanand standard deviation respectively
983090983090 Feature Reduction In order to avoid overinterpretationby redundant data and misinterpretation by noisy data weapplied eature reduction method Inclusion o this partincreases the processing time thus exacerbating the latency
Dimensionality reduction using principal componentanalysis (PCA) is based on a very important trait that is
variance o the data PCA develops the nonlinear mapping insuch a way that it maximizes the variance o the data whichhelps us in discarding that part o the data which is markedby lesser variances Tis reshaping and omission not only removes the redundant data but also lessens up the noise
During training stage PCA was applied on these eaturesin order to reduce the redundant andor noisy data We keptthe components which projected the approximate 983097983093 o thetotal variance We were able to reduce the 983090983089 eatures into 983097Ten we ed these reduced eatures to classi1047297ers trainer Here
as per our observation we again assumed that the EEG datais stationary or a small length So during the testing stagewe took the PCA coefficients matrix rom training stage and
multiplied it with the standardized statistical eatures o theblind test data and then ed the top 983097 eatures to classi1047297er
983090983091 Classi1047297cation Classi1047297cation is a machine learning tech-nique in which new observations belonging to a category are identi1047297ed Tis identi1047297cation is based on the training set
which contains the observations with known labelling o theircategory Tese observations are also termed as eatures Wetried three types o classi1047297cation methods (983089) SVM (983090) QDAand (983091) ANN (Figure 983091)
Te reduced eatures were ed to these classi1047297ers Herethe reduced eatures mean that those statistical eatures o the selected wavelet coefficients are reduced using PCA asdescribed in previous section All o the three processingparts were exclusive or each channel and each epilepticpattern So like previous parts the classi1047297ers were also trainedand tested exclusively or each channel
Our system requires individual labelling o channelsTere is a separate classi1047297er or each channel and or each
epileptic pattern type So the total number o classi1047297ers isequalto the product o totalnumbero channels by ten whereten represents the number o epileptic pattern described by Noachtar and R emi [983091]
(e) Support Vector Machine Support vector machine (SVM)is a supervised learning models machine learning techniqueSVM tries to represent the examples as points in space whichare mapped in a way that points o different categories can bedivided by a clear gap that is as wide as possible Aferwardsthat division is used to categorise the new test examples basedon which side they all on
analysis (QDA) is a widely used machine learning methodamong statistics pattern recognition and signal process-ing to 1047297nd a quadratic combination o eatures which areresponsible or characterizing an example into two or morecategories QDAs combination o discriminating quadraticmultiplication actors is used or both classi1047297cation anddimensionality reduction
(g) Arti1047297cial Neural Network Arti1047297cial neural network (ANN) is a computational model which is inspired rom ani-mals central nervous system Tat is why ANN is representedby a system o interconnected neurons which are capable o computing values as per their inputs In ANN training the
weights associated with the neurons are iteratively adjustedaccording to the inputs and the difference between theoutputs with expected outputs Te iteration gets stoppedwhen either the combination o neurons starts generating theexpected results within an error o a tolerable error range orthe iteration limit 1047297nishes up
983090983092 Adaption Phase (RetrainingUser Adaptation Mecha-nism) In order to keep the classi1047297er improving its peror-mance with the encounter o more and more examples wehave introduced a user adaptive mechanism in our systemOur system allows the user to interactively select epochso his choice by simply clicking on the correction button
While using our system when a user thinks that a certainepoch is alsely labelledcategorised oursystem allows him tointeractively mark mark that label as a mistake Tese detailswill be saved in a log in the background and they will be usedto retrain the classi1047297er to improve its classi1047297cation rate andadapt itsel according to the user with the passage o time
When the user is going to select the retraining option in oursystem then classi1047297ers willretrain themselves on thepreviousand the newly logged training examples As every user has tolog in with his personal ID every corrective marking detailwill only be saved in that userrsquos older and only classi1047297er willupdate itsel or that user Hence the systems classi1047297er tries toadapt itsel according to that user without damaging anyoneelse classi1047297cation
Te concept behind the inclusion o the retraining is thati there is more than one example with same attributes butdifferent labels the classi1047297er is going to get trained to theone with most population Te userrsquos corrective marking willincrease theexampleso hischoice thus making that classi1047297eradapt itsel to the userrsquos choice in a trivial way Every userwill have exclusive classi1047297ers trained or him and his markingwill not affect other usersrsquo classi1047297er As we know the userssometimes do not agree on the choice o the epileptic patternor its type Te exclusive processing or each user will help thesame sofware keep the system trained or every user and itwill also let different users compare their markings with eachother
We do not have any standard right now to measure whichneurologist is the most righteous among a disagreeing groupo neurologist users So we kept the corrective markings o each user to his account so that it may not interere withthe one who may not agree on his choice So the developedsystem is used to acilitate the neurologistrsquos selection to theuser according to his own choice and afer initial training onevery retraining it tries to adopt more users Tis system doesnot want to dictate to the neurologist but rather learn romhim to adapt him to save his time
We want the classi1047297er to think like the user and supple-menthim by highlighting theepochso his choice so the goldstandard afer ew retraining mechanisms will be the userhimsel Already tested examples with new labels inclusion inthe training examplesor the retraining willbias the classi1047297erschoice in avour o user
3 Experimentation
In this section we will discuss the results in detail At 1047297rstwe will describe the datasets which we used to train test and
validate our method Ten we will discuss their versatility (Figure 983095)
983091983089 Dataset wo labelled datasets o epilepsy suspectedsurace EEG data were available to us Both o these datasetshave lots o versatility in between them in terms o ethnicityage gender and equipment Te datasets available to us wereabout generalised absence seizure which is characterized by the 983091 Hz spike and wave epileptic pattern in almost eachchannel Tat is why we have classi1047297cation results availableonly or one type o epilepsy which is absence seizure
983137983138983148983141 983089 Tis table describes the affiliation o detailed coefficientswith epileptic requency band o interest or 983090983093983094 Hz sampled CHB-MI dataset
Epileptic requency range Detailed coefficientsrsquo level
Beta (907317) CD983091 (983091983090 Hz to 983089983094 Hz)
Alpha (1103925) CD983092 (983089983094 Hz to 983096 Hz)
Teta (1038389) CD983093 (983096 Hz to 983092 Hz)
Delta () CD983094 (983092 Hz to 983090 Hz)
Delta () CD983095 (983090 Hz to 983089 Hz)
983137983138983148983141 983090 Tis table describes the affiliation o detailed coefficientswith epileptic requency band o interest or 983093983089983090 Hz sampled PIMHdataset
Epileptic requency range Detailed coefficientsrsquo level
Beta (907317) CD983092 (983091983089983090 Hz to 983089983093983094 Hz)
Alpha (1103925) CD983093 (983089983093983094 Hz to 983095983096 Hz)
Teta (1038389) CD983094 (983095983096 Hz to 983091983097 Hz)
Delta () CD983095 (983091983097 Hz to 983090 Hz)
Delta () CD983096 (983090 Hz to 983089 Hz)
983091983089983089 CHBMIT Tis database is the online available suraceEEG dataset [983090983088] which is provided by Children HospitalBoston and Massachusetts Institute o echnology and it isavailable at physioNet website [983089983088] It contains 983097983089983094 hours o 983090983091 channels scalp EEG recording rom 983090983092 epilepsy suspectedpatients Tis ECG recording is sampledat 983090983093983094Hz with 983089983094-bitresolution Te 983090983091rd channel is same as 983089983093th channel (able 983089)
983091983089983090 PIMH Te second database o EEG datasets is pro- vided by our collaborator at Punjab Institute o Mental Health(PIMH) Lahore Its sampling requency is 983093983088983088 Hz and it wasrecorded on 983092983091 channels (among which 983091983091 channels are orEEG) Tis dataset consists o 983090983089 patients EEG recording
983091983090 Features
983091983090983089 Feature Extraction Data which interests us lies inbetween the requency range o 983088983091 Hz to 983091983088 Hz So aferapplying DW with db983092 mother wavelet we have to selectdetailed coefficients with this requency range So in case o 983090983093983094 Hz sampled CHBMI dataset we have to go to at least983091 levels o decomposition and discard the earlier two as it isdemonstrated in Figure 983090 In order to get the discriminating
inormation between different types o epileptic patterns andidentiying them correctly without mistaking them with eachother decomposition o this detailed coefficient urther inBeta Alpha Teta and Delta is hugely helpul So we urtherdecomposed them until the 983095th level Hence we used theDWs detailed coefficients o levels 983091 983092 983093 983094 and 983095 or 983090983093983094Hzsampled CHB-MI dataset (able 983090)
Afer the selection o the wavelet coefficients we calcu-lated the statistical eature out o them Te statistical eatureswere the mean power and standard deviation o all o theselected coefficients
In case o 983093983089983090 Hz sampled PIMH dataset we used theDWs detailed coefficients o levels 983092 983093 983094 983095 and 983096
Afer the selection o detailed coefficients we calculatedthe statistical eature out o them Te statistical eatureswere the mean power and standard deviation o all o theshortlisted detailed coefficients
983091983090983090 Standardization During training stage we 1047297rst used
simple -score normalization to standardize the eatures [983089983097]beore applying eature reduction But the real issue arosewhen we tried to normalize them during testing stage Oneway o doing this is that we keep all o the examples andapply -score on them along with the new test data Insteado this time taking process we made an assumption onour observation that mean and standard deviation does notdeviate a lot It is analysed in this study that the EEG timeseries are assumed to be stationary over a small length o thesegments So we used the mean and standard deviation o thetraining examples rom the training stage to normalize thetest examples Figures 983093 and 983094 illustrate our observation inwhich you can see that there is not much deviation in trainand train + test examples mean and standard deviation
983091983091 Classi1047297er Classi1047297cation is used in machine learningto reer to the problem o identiying a discrete category to which a new observation belongs Observations withknown labels are used to train a classi1047297cation algorithm orclassi1047297er using eatures associated with the observation ForCHBMI database we had to train 983090983090983088 classi1047297ers in initialtraining stage Te calculation behind 983090983090983088 is the 983090983090 channelsmultiplied by 983089983088 types o epileptic pattern Te 983090983091rd channelwas same as 983089983093th channel For PIMH dataset 983091983091983088 classi1047297erswere trained where 983091983091 channels o EEG were utilized Wetried three different classi1047297ers and ound SVM to be the mostaccurate
We have used blind validation mechanism or the tendifferent eature data distributions to estimate the classi1047297-cation perormance Tese 983089983088 different and separate blinddata distributions were taken rom a huge set o EEGdataset Tese 983089983088 data distributions we randomly dividedinto two groups We trained our classi1047297er on one hal o thedistribution and tested it on the other hal We repeated thaton all ten distributions Ten we calculated the average o theclassi1047297cation rate or the all ten distributions
983091983090983090983097 out o 983091983090983097983095983094983088983088 epochs were randomly taken or tentimes rom CHBMI dataset Each time hal o them wereused to train and hal o them were used to test the initialclassi1047297cation Te average o the sensitivity speci1047297city and
accuracy or these ten distributionsis considered as the initialtraining phase perormance
Same approach wasapplied on PIMH datasetswhere 983091983090983090983097out o 983090983092983088983097983095 epochs were randomly taken rom PIMH datasetor the six times instead o ten times
Due to unavailability o the non-983091 Hz spike and waveepileptic EEG data currently we have only classi1047297cation ratesor generalized absence seizure
983091983091983089 Exclusive Processing In this study we have analysedthat even in the case o absence seizure epileptic patterns donot appear in the exact same way in each channel Handlingo each channel exclusive to each other was also another very
F983145983143983157983154983141 983090 Selection o DW detailed coefficients or a 983090983093983094 Hz sampled 983089 sec wide epochs EEG signal
important decision We tested the classi1047297cation in both waysthat is one classi1047297er or all o the channels at once versus oneseparate classi1047297er or each channel (Figure 983096)
Tis processing o each channel exclusive to each otherimproved over average accuracy rom approximately 983097983089 toapproximately 983097983093 in case o SVM So or SVM there is
a signi1047297cant improvement o 983092 by this change In case o QDA accuracy rose rom 983097983089 to 983097983092 with an improvemento 983091 and in case o ANN it rose rom 983097983089983096 to 983097983090983097 withan improvement o 983089983089
Results show that SVMsuites ourmethodin the most effi-cient way ANN has a lesser classi1047297cation time and LDA has
a lesser training time as compared to SVM but consideringthe sensitivity and classi1047297cation improvement through cor-rective marking we think that SVM is the better choice thanLDA and ANN In upcoming sections we have shown theresults or all three types o classi1047297er
0 5 10 15 20 250
24
6
8
10
12
14
16times10
4
Mean o the training examplesMean o the training + test examples
F983145983143983157983154983141 983093 Relationship between channelrsquos number and mean value(test + training examples)
(a) Adaptation Mechanism o test the adaptation mechanism983096983088983095corrective epochs were markedby the user ora CHBMI
F983145983143983157983154983141 983094 Relationship between channelrsquos number and standard deviation value (test + training examples)
EEG
Channel 1 DWTD1
A1 D2
A2
Mean
Standard deviation
Power
Mean
Standard deviation
Power
Mean
Standard deviation
Power
SVM or pattern 1
SVM or pattern 2
SVM or pattern 3
SVM or pattern 10
OPor
channel1
Channel 2 DWTD1
A1 D2
A2
Mean
Standard deviation
Power
Mean
Standard deviation
Power
MeanStandard deviation
Power
SVM or pattern 1
SVM or pattern 2
SVM or pattern 3
SVM or pattern 10
OPor
channel2
DWTD1
A1 D2
A2
Mean
Standard deviation
Power
Mean
Standard deviation
Power
Mean
Standard deviation
Power
SVM or pattern 1
SVM or pattern 2
SVM or pattern 3
SVM or pattern 10
Features
Features
PCA
Reduction
Standardization
PCA
Standardization
Reduction
FeaturesPCA
Reduction
Standardization
Surgeonrsquos marking
Surgeonrsquos marking
Surgeonrsquos marking
OPor
channeln
Al
Dl
Al
Al
Dl
Dl
Z-score
Z-score
Z-scoreChannel n
F983145983143983157983154983141 983095 Flowchart
dataset 1047297le and he marked the same amount o epochs oreach channel Tese corrective markings were saved in hislog as training examples Tese corrective markings as thenew examples along with the 983091983090983090983097983088 epochs o initial trainingstage were used to retrain the classi1047297er Te number 983091983090983090983097983088 hascome rom the 983091983090983090983097 randomly selected epochs rom whole o the CHBMI dataset or the ten separate times during initial
training phase Ten later the perormance o the classi1047297erafer retraining was judged again on another random 983091983088983088983088epochs (Figure 983089983092)
In case o PIMH dataset 983093983095 corrective epochs wereselected or PIMH dataset Tis time 983089983097983091983095983092 epochs o thePIMH dataset were used along with the 983093983095 corrective mark-ings as orthe PIMH we randomly selected the 983091983090983090983097 numbers
Processing all channels simultaneously with single classi1047297er
Processing all channels separately with separate classi1047297er oreach channel
F983145983143983157983154983141 983096 Accuracy relationship o different classi1047297ers and theirclassi1047297cation rate
84
86
88
90
92
94
96
98
100
Accuracy afer initial training ()
Accuracy afer retraining ()
ldquo F P 1 F 7 rdquo
ldquo F 7 T 7 rdquo
ldquo T 7 P 7 rdquo
ldquo P 7 O 1 rdquo
ldquo F P 1 F 3 rdquo
ldquo F 3 C 3 rdquo
ldquo C 3 P 3 rdquo
ldquo P 3 O 1 rdquo
ldquo F P 2 F 4 rdquo
ldquo F 4 C 4 rdquo
ldquo C 4 P 4 rdquo
ldquo P 4 O 2 rdquo
ldquo F P 2 F 8 rdquo
ldquo F 8 T 8 rdquo
ldquo T 8 P 8 rdquo
ldquo P 8 O 2 rdquo
ldquo F Z C Z rdquo
ldquo C Z P Z rdquo
ldquo P 7 T 7 rdquo
ldquo T 7 F T 9 rdquo
ldquo F T 9 F T 1 0 rdquo
ldquo F T 1 0 T 8 rdquo
F983145983143983157983154983141 983097 Relation between average classi1047297cation rate and accuracy
o the channel afer initial training and retaining
o epochs or the six times Te retrained classi1047297er was testedon the 983090983091983094983089 remaining epochs
(b) Support Vector Machine We used the support vectormachine classi1047297er package available in MALAB Bioinor-matics oolbox We ound linear kernel to be the mostaccurate SVM kernel with 983093983088 as the box constraint
(c) CHBMIT For CHBMI dataset initial training o theclassi1047297er resulted in 983097983094983091 average accuracy 983097983095983092 average
0
20
40
60
80
100
120
Accuracy afer initial training ()
Accuracy afer retraining ()
ldquo F p 1 rdquo
ldquo F p 2 rdquo
ldquo F 3 rdquo
ldquo F 4 rdquo
ldquo C 3 rdquo
ldquo C 4 rdquo
ldquo P 3 rdquo
ldquo P 4 rdquo
ldquo O 1 rdquo
ldquo O 2 rdquo
ldquo F 7 rdquo
ldquo F 8 rdquo
ldquo T 3 rdquo
ldquo T 4 rdquo
ldquo T 5 rdquo
ldquo T 6 rdquo
ldquo F z rdquo
ldquo C z rdquo
ldquo P z rdquo
ldquo E rdquo
ldquo P G 2 rdquo
ldquo A 1 rdquo
ldquo A 2 rdquo
ldquo T 1 rdquo
ldquo T 2 rdquo
ldquo X 1 rdquo
ldquo X 2 rdquo
ldquo X 3 rdquo
ldquo X 4 rdquo
ldquo X 5 rdquo
ldquo X 6 rdquo
ldquo X 7 rdquo
ldquo P G 1 rdquo
F983145983143983157983154983141 983089983088 Relation between average classi1047297cation rate and accuracy o the channel afer initial training and retaining
82
84
86
88
90
92
94
96
98
Accuracy afer initial training ()Accuracy afer retraining ()
ldquo F P 1 F 7 rdquo
ldquo F 7 T 7 rdquo
ldquo T 7 P 7 rdquo
ldquo P 7 O 1 rdquo
ldquo F P 1 F 3 rdquo
ldquo F 3 C 3 rdquo
ldquo C 3 P 3 rdquo
ldquo P 3 O 1 rdquo
ldquo F P 2 F 4 rdquo
ldquo F 4 C 4 rdquo
ldquo C 4 P 4 rdquo
ldquo P 4 O 2 rdquo
ldquo F P 2 F 8 rdquo
ldquo F 8 T 8 rdquo
ldquo T 8 P 8 rdquo
ldquo P 8 O 2 rdquo
ldquo F Z C Z rdquo
ldquo C Z P Z rdquo
ldquo P 7 T 7 rdquo
ldquo T 7 F T 9 rdquo
ldquo F T 9 F T 1 0 rdquo
ldquo F T 1 0 T 8 rdquo
F983145983143983157983154983141 983089983089 Relation between average classi1047297cation rate and accuracy o the channel afer initial training and retaining
speci1047297city and 983097983091983093 average sensitivity or 983091 Hz spike andwave which is a characteristic o absence seizure Aferinitial training our speci1047297city is better than that o Shoeb[983089983088] and Nasehi and Pourghassem [983090983089] who used the samedataset to validate their technique with different eaturesand application technique Tis shows that our technique
is providing better results even at the initial training phase(Figure 983089983089)
In able 983091 we have shown the average initial classi1047297cationand retrained classi1047297cation results o our system or eachchannel In this system we have shown that afer correctiono ew epochs there is a visible improvement in the systemsclassi1047297cation Te average accuracy o the system rose rom983097983093983093 to 983097983094983091
(d) PIMH For PIMH dataset initial training o the classi1047297erresulted in 983097983088 average accuracy 983097983092 average speci1047297city and983096983088 average sensitivity or 983091 Hz spike and wave which is acharacteristic o absence seizure (Figure 983089983090)
F983145983143983157983154983141 983089983090 Relation between average classi1047297cation rateand accuracy o the channel afer initial training and retaining
80
82
84
86
88
90
92
94
96
98
Accuracy afer initial training ()
Accuracy afer retraining ()
ldquo F P 1 F 7 rdquo
ldquo F 7 T 7 rdquo
ldquo T 7 P 7 rdquo
ldquo P 7 O 1 rdquo
ldquo F P 1 F 3 rdquo
ldquo F 3 C 3 rdquo
ldquo C 3 P 3 rdquo
ldquo P 3 O 1 rdquo
ldquo F P 2 F 4 rdquo
ldquo F 4 C 4 rdquo
ldquo C 4 P 4 rdquo
ldquo P 4 O 2 rdquo
ldquo F P 2 F 8 rdquo
ldquo F 8 T 8 rdquo
ldquo T 8 P 8 rdquo
ldquo P 8 O 2 rdquo
ldquo F Z C Z rdquo
ldquo C Z P Z rdquo
ldquo P 7 T 7 rdquo
ldquo T 7 F T 9 rdquo
ldquo F T 9 F T 1 0 rdquo
ldquo F T 1 0 T 8 rdquo
F983145983143983157983154983141 983089983091 Relation between average classi1047297cation rate and accuracy o the channel afer initial training and retaining
In able 983092 we have shown the average initial classi1047297cationand retrained classi1047297cation results o our system or eachchannel able 983092 shows that our technique is robust and itworks also on a different dataset Te average accuracy o thesystem rose rom approximately 983096983097 to 983097983088
983091983091983090 Discriminate Analysis We used the discriminant anal-
ysis package available in MALAB Statistics oolbox Weound pseudoquadratic to be the best perorming discrimi-nate type with uniorm probability
(a) CHBMIT For CHBMI dataset initial training o theclassi1047297er resulted in 983097983092 average accuracy 983097983094 averagespeci1047297city and 983097983088 average sensitivity or 983091 Hz spike andwave which is a characteristic o absence seizure Afer initialtraining our speci1047297city is better than that o Shoeb [983089983088] andNasehi and Pourghassem [983090983089] (Figure 983089983091)
In able 983093 we have shown the average initial classi1047297cationand retrained classi1047297cation results o our system or eachchannel In this system we have shown that afer correction
010
2030405060708090
100
Accuracy afer initial training ()
Accuracy afer retraining ()
ldquo F 3 rdquo
ldquo F 4 rdquo
ldquo C 3 rdquo
ldquo C 4 rdquo
ldquo P 3 rdquo
ldquo P 4 rdquo
ldquo O 1 rdquo
ldquo O 2 rdquo
ldquo F 7 rdquo
ldquo F 8 rdquo
ldquo T 3 rdquo
ldquo T 4 rdquo
ldquo T 5 rdquo
ldquo T 6 rdquo
ldquo F z rdquo
ldquo C z rdquo
ldquo P z rdquo
ldquo E rdquo
ldquo P G 2 rdquo
ldquo T 1 rdquo
ldquo T 2 rdquo
ldquo X 1 rdquo
ldquo X 2 rdquo
ldquo X 3 rdquo
ldquo X 4 rdquo
ldquo X 5 rdquo
ldquo X 6 rdquo
ldquo X 7 rdquo
ldquo P G 1 rdquo
ldquo A 1 rdquo
ldquo A 2 rdquo
ldquo F p 1 rdquo
ldquo F p 2 rdquo
F983145983143983157983154983141 983089983092 Relation between average classi1047297cation rate and accuracy o the channel afer initial training and retaining
983137983138983148983141 983091 First column shows the channel label the second columnshows the initial training accuracy and third one shows the markedcorrection by a neurologistand thelast one shows the1047297nal accuracy
o ew epochs there is visible improvement in the systemsclassi1047297cation Te average accuracy o the system rose rom983097983092 to 983097983093
(b) PIMH For PIMH dataset initial training o the classi1047297erresulted in 983097983088 average accuracy 983097983093 average speci1047297city and983095983091 average sensitivity or 983091 Hz spike and wave which is acharacteristic o absence seizure
In able 983094 we have shown the average initialclassi1047297cationand retrained classi1047297cation results o our system or each
983137983138983148983141 983092 First column shows the channel label the second columnshows the initial training accuracy and third one shows the markedcorrection by a neurologistand thelast one shows the1047297nal accuracy
channel able 983094 shows that our technique is robust and itworks also on a different dataset Te average accuracy o thesystem rose rom approximately 983096983097 to 983097983088
983091983091983091 Arti1047297cial Neural Network We used eedorward back-propagation package available in MALAB Neural Network oolbox and ound Levenberg-Marquardt to be the bestmethod with 983088983088983093 learning rate
(a) CHBMIT For CHBMI dataset initial training o theclassi1047297er resulted in 983097983090983096983096 average accuracy 983097983096983094983094 averagespeci1047297city and 983095983093983095983093 average sensitivity or 983091 Hz spike andwave which is a characteristic o absence seizure (Figure 983097)
983137983138983148983141 983093 First column shows the channel label the second columnshows the initial training accuracy and third one shows the markedcorrection by a neurologistand thelast one shows the1047297nal accuracy
In able 983095 we have shown the average initial classi1047297cationand retrained classi1047297cation results o our system or eachchannel In this system we have shown that afer correctiono ew epochs there is visible improvement in the systemsclassi1047297cation Te average accuracy o the system rose rom983097983090983096983096 to 983097983091983097983094
(b) PIMH For PIMH dataset initial training o the classi1047297erresulted in 983096983092 average accuracy 983097983092983096 average speci1047297cityand 983093983094983093 average sensitivity or 983091 Hz spike and wave whichis a characteristic o absence seizure (Figure 983089983088)
In able 983096 we have shown the average initial classi1047297cationand retrained classi1047297cation results o our system or each
channel able 983096 shows that our technique is robust and itworks also on a different dataset Te average accuracy o thesystem rose rom approximately 983096983092 to 983096983093983092983091
4 Discussion and Future Work
Computer-assisted analysis o EEG has tremendous potentialor assisting the clinicians in diagnosis A very importantand novel phase o our system is user adaptation mechanismor retraining mechanism Introduction o this phase hasimportance in many aspects In this phase system tries toadapt its classi1047297cation according to users desire Moreoverthis technique personalizes the classi1047297ers classi1047297cation It has
983137983138983148983141 983094 First column shows the channel label the second columnshows the initial training accuracy and third one shows the markedcorrection by a neurologistand thelast one shows the1047297nal accuracy
been cited that sometimes even the expert neurologists havesome disagreementovera certain observation o an EEGdataTis system will be useul or disagreeing users and it will alsohelp them in comparing their results with each other
Tere is also a threat o over1047297tting by the classi1047297erIn order to keep the classi1047297er improving its perormancewith the encounter o more and more examples we haveintroduced this user adaptive mechanism in our system Weconsider the existing systems as dead because these cannotimprove their classi1047297cation rate afer initial training (duringsofware development) Te sel-improving mechanism aferdeployment makes our tool alive Tis system can be madepart o the whole epileptic diagnosis process It will highlight
983137983138983148983141 983095 First column shows the channel label the second columnshows the initial training accuracy and third one shows the markedcorrection by a neurologistand thelast one shows the1047297nal accuracy
the epileptic spikes among the whole EEG thus leading toreduced atigue and time consumption o a user We obtainedhigh classi1047297cation accuracy on datasets obtained rom twodifferent sites which indicates reproducibility o our resultsand robustness o our approach
In the uture we are planning to make this a web basedapplication neurologists can log in and consult each otherrsquosreviews about a particular subject Tis will make our systemexperience a whole versatility o examples and learn rom allo them Integration o the video and its automatic analysis(video EEG) can help a neurologist in diagnosing epilepsy ina better way whereas this can also help him in distinguishingbetween psychogenic and epileptic seizuresWewouldalso be
investigating how much over1047297tting is an issue in the reportedperormances which are now even touching 983089983088983088 based onsome claims Tere is a need or methodcriteria which couldlimit these algorithms improving their detection on a limitednumber o available examples
Tissystem is made keeping in mind thatwe haveto acil-itate the neurologist by supplementing him in the analysis o the EEG We do not want to enorce the classi1047297cation o theEEG data on a user
In the uture we will also include a slider in the systemwhich will allow the user to adjust the sensitivity and speci-1047297city beore retraining Tis assisting system is more like adetection tool which is continuously learning with encounter
983137983138983148983141 983096 Classi1047297cation rate improvement caused by retraining Firstcolumn shows thechannellabel thesecond columnshows theinitialtraining accuracy and third one shows the marked correction by aneurologist and the last one shows the 1047297nal accuracy
o better examples More and better examples will certainly improve its perormance Te agreement between differentneurologists over the EEG readings is low to moderate I we could 1047297nd the agreement on at least ew o the epilepticpatterns correspondence with epileptic disease then we cantake this tool urther ahead and use it or diagnosis instead o
just assistanceOne o the biggest limitations to this study is the unavail-
ability o non-983091 Hz spike and wave data Even though we haveincluded the data eatures o the entire epileptic requency ranges exclusive to each other proo testing on the data will
certainly prove worthy orthe progresso these assisting toolstoward a diagnostic tool
Conflict of Interests
Te authors declare that there is no con1047298ict o interests
regarding the publication o this paper
Acknowledgment
Te authors would like to extend their sincere appreciation tothe Deanship o Scienti1047297c Research at King Saud University or unding this research through Research Group Project(RG no 983089983092983091983093-983088983093983089)
References
[983089] H Adelia Z Zhoub and N Dadmehrc ldquoAnalysis o EEGrecords in an epileptic patient using wavelet transormrdquo Journal of Neuroscience Methods vol 983089983090983091 no 983089 pp 983094983097ndash983096983095 983090983088983088983091
[983090] M A Ahmad W Majeed and N A Khan ldquoAdvancements incomputer aided methods or EEG-based epileptic detectionrdquo inProceedings of the 983095th International Conference on Bio-Inspired Systems and Signal Processing (BIOSIGNALS 983089983092) AngersFrance 983090983088983089983092
[983091] S Noachtar and J R emi ldquoTe role o EEG in epilepsy a criticalreviewrdquo Epilepsy and Behavior vol 983089983093 no 983089 pp 983090983090ndash983091983091 983090983088983088983097
[983092] E B Petersen J Duun-Henriksen A Mazzaretto W Kjar CE Tomsen and H B D Sorensen ldquoGeneric single-channeldetection o absence seizuresrdquo in Proceedings of the Annual International Conference of the IEEE Engineering in Medicineand Biology Society (EMBS rsquo983089983089) pp 983092983096983090983088ndash983092983096983090983091 Boston MassUSA September 983090983088983089983089
[983093] M Kaleem A Guergachi and S Krishnan ldquoEEG seizure
detection and epilepsy diagnosis using a novel variation o Empirical Mode Decompositionrdquo in Proceedings of the 983091983093th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC rsquo983089983091) pp 983092983091983089983092ndash983092983091983089983095 OsakaJapan July 983090983088983089983091
[983094] S M S Alam and M I H Bhuiyan ldquoDetection o seizure andepilepsy usinghigher order statistics in the EMD domainrdquo IEEE Journal of Biomedical and Health Informatics vol 983089983095 no 983090 pp983091983089983090ndash983091983089983096 983090983088983089983091
[983095] H Ocbagabir K A I Aboalayon and M FaezipourldquoEfficientEEG analysis or seizure monitoring in epileptic patientsrdquo inProceedings of the Long Island Systems Applications and Tech-nology Conference (LISAT rsquo983089983091) pp 983089ndash983094 IEEE Farmingdale NYUSA May 983090983088983089983091
[983096] A A Abdullah S A Rahim and A Ibrahim ldquoDevelopment o EEG-based epileptic detection using arti1047297cial neural networkrdquoin Proceedings of the International Conference on Biomedical Engineering (ICoBE rsquo983089983090) pp 983094983088983093ndash983094983089983088 Penang Malaysia 983090983088983089983090
[983097] P Guo J Wang X Z Gao and J M A anskanen ldquoEpilepticEEG signal classi1047297cation with marching pursuit based on har-mony search methodrdquo in Proceedings of the IEEE International Conference on Systems Man and Cybernetics (SMC rsquo983089983090) pp983090983096983091ndash983090983096983096 Seoul Republic o Korea October 983090983088983089983090
[983089983088] A Shoeb ldquoCHB-MI scalp EEG databaserdquo 983090983088983088983088 httpphysionetorgpn983094chbmit
[983089983089] A S M Murugavel S Ramakrishnan U Maheswari and BS Sabetha ldquoCombined Seizure Index with Adaptive Multi-Class SVM or epileptic EEG classi1047297cationrdquo in Proceedings
of the International Conference on Emerging Trends in VLSIEmbedded System Nano Electronics and TelecommunicationSystem (ICEVENT rsquo983089983091) pp 983089ndash983093 Tiruvannammalai IndiaJanuary 983090983088983089983091
[983089983090] M H Abdullah J M Abdullah and M Z Abdullah ldquoSeizuredetection by means o hidden Markov model and station-ary wavelet transorm o electroencephalograph signalsrdquo inProceedings of the IEEE-EMBS International Conference onBiomedical and Health Informatics (BHI rsquo983089983090) pp 983094983090ndash983094983093 HongKong January 983090983088983089983090
[983089983091] A Subasi and M I Gursoy ldquoEEG signal classi1047297cation usingPCA ICA LDA and support vector machinesrdquo Expert Systemswith Applications vol 983091983095 no 983089983090 pp 983096983094983093983097ndash983096983094983094983094 983090983088983089983088
[983089983092] E Sezer H Isik and E Saracoglu ldquoEmployment and compari-son o different arti1047297cial neural networks or epilepsy diagnosisrom EEG signalsrdquo Journal of Medical Systems vol 983091983094 no 983089 pp983091983092983095ndash983091983094983090 983090983088983089983090
[983089983093] Brain Products GmbHProducts and ApplicationsAnalyzer 983090httpwwwbrainproductscomproductdetailsphpid=983089983095
[983089983094] NeuroExplorer Home httpwwwneuroexplorercom
[983089983096] C H Seng R Demirli L Khuon and D Bolger ldquoSeizuredetection in EEG signals using support vector machinesrdquo inProceedings of the 983091983096th Annual Northeast Bioengineering Con- ference (NEBEC rsquo983089983090) pp 983090983091983089ndash983090983091983090 Philadelphia Pa USA March983090983088983089983090
[983089983097] Z A Khan S B Mansoor M A Ahmad and M M MalikldquoInput devices or virtual surgical simulations a comparativestudyrdquo in Proceedings of the 983089983094th International Multi Topic Con- ference (INMIC rsquo983089983091) pp 983089983096983097ndash983089983097983092 Lahore Pakistan December983090983088983089983091
[983090983088] A L Goldberger L A Amaral L Glass et al ldquoPhysioBankPhysiooolkit and PhysioNet components o a new research
[983090983089] S Nasehi and H Pourghassem ldquoEpileptic seizure onset detec-tion algorithm using dynamic cascade eed-orward neural net-worksrdquo in Proceedings of the International Conference on Intelli- gent Computation and Bio-Medical Instrumentation (ICBMI rsquo983089983089)pp 983089983097983094ndash983089983097983097 December 983090983088983089983089
F983145983143983157983154983141 983089 Epoch size is 983089 sec
Afer testing different epoch sizes we ound epoch size o nonoverlapping 983089 sec window to be best yielding in terms o accuracy It also reestablished the work o Seng et al [ 983089983096](Figure 983089)
(b) DWT As discussed in Introduction spectral analysisis very inormative while examining the epilepsy suspectedpatients EEG Tere are proound advantages o waveletdecomposition which is a multiresolution analysis techniqueA multiresolution analysis technique allows us to analysea signal or multiple requency resolutions while maintain-
ing time resolution unlike a normal requency transormWavelet decomposition allows us to increase requency res-olution in the spectral band o our interest while maintainingthe time resolution in short we can decimate these valuessimultaneously in time and requency domain
During wavelet transorm the original epoch is splitinto different subbands the lower requency inormationis called approximate coefficients and the higher requency inormation is called detailed coefficients Te requency subdivision in these subbands helps us in analysing differentrequency ranges o an EEG epoch while maintaining its timeresolution [983092 983096 983089983091] Te choice o coefficients level is very important as the epileptic activity only resides in the range
o 983088ndash983091983088 Hz Coefficients levels o the DW are determinedwith respect to sampling requency So the detailed levels o interest are adjusted on the run according to the samplingrequency such that we may get at least one exact value o the closest separate (983088983092ndash983092 Hz) 1038389 (983092ndash983096 Hz) 1103925 (983096ndash983089983090 Hz)and 907317 (983089983090ndash983091983088 Hz) components o the signal We discarded allthe detailed coefficient levels which were beyond the 983088ndash983091983088Hzrange
TenDW wasappliedon each epoch with Daubechies-983092(db983092) as mother wavelet Te detailed coefficient levels o theDW were determined with respect to sampling requency
(c) Statistical Features Afer the selection o detailed coe-1047297cients which represent the requency band o our interest
we calculated the statistical eatures by calculating the meanstandard deviation and power o these selected waveletcoefficients Tese statistical eatures are inspired rom Subasiand Gursoy work [983089983091]
(d) Standardization Tese statistical eatures were then stan-dardized During training stage -score standardization wasapplied on these eatures [983089983097] Tis standardization is justlike usual -score normalization but as we do not know the exact mean and standard deviation o the data (to beclassi1047297ed) during classi1047297cationtest stage we used the mean
and standard deviation o the training examples duringtraining stage or standardizing (normalizing) the eaturesduring classi1047297cation stage We normalized the eatures by subtracting and dividing them by training examples meanand standard deviation respectively
983090983090 Feature Reduction In order to avoid overinterpretationby redundant data and misinterpretation by noisy data weapplied eature reduction method Inclusion o this partincreases the processing time thus exacerbating the latency
Dimensionality reduction using principal componentanalysis (PCA) is based on a very important trait that is
variance o the data PCA develops the nonlinear mapping insuch a way that it maximizes the variance o the data whichhelps us in discarding that part o the data which is markedby lesser variances Tis reshaping and omission not only removes the redundant data but also lessens up the noise
During training stage PCA was applied on these eaturesin order to reduce the redundant andor noisy data We keptthe components which projected the approximate 983097983093 o thetotal variance We were able to reduce the 983090983089 eatures into 983097Ten we ed these reduced eatures to classi1047297ers trainer Here
as per our observation we again assumed that the EEG datais stationary or a small length So during the testing stagewe took the PCA coefficients matrix rom training stage and
multiplied it with the standardized statistical eatures o theblind test data and then ed the top 983097 eatures to classi1047297er
983090983091 Classi1047297cation Classi1047297cation is a machine learning tech-nique in which new observations belonging to a category are identi1047297ed Tis identi1047297cation is based on the training set
which contains the observations with known labelling o theircategory Tese observations are also termed as eatures Wetried three types o classi1047297cation methods (983089) SVM (983090) QDAand (983091) ANN (Figure 983091)
Te reduced eatures were ed to these classi1047297ers Herethe reduced eatures mean that those statistical eatures o the selected wavelet coefficients are reduced using PCA asdescribed in previous section All o the three processingparts were exclusive or each channel and each epilepticpattern So like previous parts the classi1047297ers were also trainedand tested exclusively or each channel
Our system requires individual labelling o channelsTere is a separate classi1047297er or each channel and or each
epileptic pattern type So the total number o classi1047297ers isequalto the product o totalnumbero channels by ten whereten represents the number o epileptic pattern described by Noachtar and R emi [983091]
(e) Support Vector Machine Support vector machine (SVM)is a supervised learning models machine learning techniqueSVM tries to represent the examples as points in space whichare mapped in a way that points o different categories can bedivided by a clear gap that is as wide as possible Aferwardsthat division is used to categorise the new test examples basedon which side they all on
analysis (QDA) is a widely used machine learning methodamong statistics pattern recognition and signal process-ing to 1047297nd a quadratic combination o eatures which areresponsible or characterizing an example into two or morecategories QDAs combination o discriminating quadraticmultiplication actors is used or both classi1047297cation anddimensionality reduction
(g) Arti1047297cial Neural Network Arti1047297cial neural network (ANN) is a computational model which is inspired rom ani-mals central nervous system Tat is why ANN is representedby a system o interconnected neurons which are capable o computing values as per their inputs In ANN training the
weights associated with the neurons are iteratively adjustedaccording to the inputs and the difference between theoutputs with expected outputs Te iteration gets stoppedwhen either the combination o neurons starts generating theexpected results within an error o a tolerable error range orthe iteration limit 1047297nishes up
983090983092 Adaption Phase (RetrainingUser Adaptation Mecha-nism) In order to keep the classi1047297er improving its peror-mance with the encounter o more and more examples wehave introduced a user adaptive mechanism in our systemOur system allows the user to interactively select epochso his choice by simply clicking on the correction button
While using our system when a user thinks that a certainepoch is alsely labelledcategorised oursystem allows him tointeractively mark mark that label as a mistake Tese detailswill be saved in a log in the background and they will be usedto retrain the classi1047297er to improve its classi1047297cation rate andadapt itsel according to the user with the passage o time
When the user is going to select the retraining option in oursystem then classi1047297ers willretrain themselves on thepreviousand the newly logged training examples As every user has tolog in with his personal ID every corrective marking detailwill only be saved in that userrsquos older and only classi1047297er willupdate itsel or that user Hence the systems classi1047297er tries toadapt itsel according to that user without damaging anyoneelse classi1047297cation
Te concept behind the inclusion o the retraining is thati there is more than one example with same attributes butdifferent labels the classi1047297er is going to get trained to theone with most population Te userrsquos corrective marking willincrease theexampleso hischoice thus making that classi1047297eradapt itsel to the userrsquos choice in a trivial way Every userwill have exclusive classi1047297ers trained or him and his markingwill not affect other usersrsquo classi1047297er As we know the userssometimes do not agree on the choice o the epileptic patternor its type Te exclusive processing or each user will help thesame sofware keep the system trained or every user and itwill also let different users compare their markings with eachother
We do not have any standard right now to measure whichneurologist is the most righteous among a disagreeing groupo neurologist users So we kept the corrective markings o each user to his account so that it may not interere withthe one who may not agree on his choice So the developedsystem is used to acilitate the neurologistrsquos selection to theuser according to his own choice and afer initial training onevery retraining it tries to adopt more users Tis system doesnot want to dictate to the neurologist but rather learn romhim to adapt him to save his time
We want the classi1047297er to think like the user and supple-menthim by highlighting theepochso his choice so the goldstandard afer ew retraining mechanisms will be the userhimsel Already tested examples with new labels inclusion inthe training examplesor the retraining willbias the classi1047297erschoice in avour o user
3 Experimentation
In this section we will discuss the results in detail At 1047297rstwe will describe the datasets which we used to train test and
validate our method Ten we will discuss their versatility (Figure 983095)
983091983089 Dataset wo labelled datasets o epilepsy suspectedsurace EEG data were available to us Both o these datasetshave lots o versatility in between them in terms o ethnicityage gender and equipment Te datasets available to us wereabout generalised absence seizure which is characterized by the 983091 Hz spike and wave epileptic pattern in almost eachchannel Tat is why we have classi1047297cation results availableonly or one type o epilepsy which is absence seizure
983137983138983148983141 983089 Tis table describes the affiliation o detailed coefficientswith epileptic requency band o interest or 983090983093983094 Hz sampled CHB-MI dataset
Epileptic requency range Detailed coefficientsrsquo level
Beta (907317) CD983091 (983091983090 Hz to 983089983094 Hz)
Alpha (1103925) CD983092 (983089983094 Hz to 983096 Hz)
Teta (1038389) CD983093 (983096 Hz to 983092 Hz)
Delta () CD983094 (983092 Hz to 983090 Hz)
Delta () CD983095 (983090 Hz to 983089 Hz)
983137983138983148983141 983090 Tis table describes the affiliation o detailed coefficientswith epileptic requency band o interest or 983093983089983090 Hz sampled PIMHdataset
Epileptic requency range Detailed coefficientsrsquo level
Beta (907317) CD983092 (983091983089983090 Hz to 983089983093983094 Hz)
Alpha (1103925) CD983093 (983089983093983094 Hz to 983095983096 Hz)
Teta (1038389) CD983094 (983095983096 Hz to 983091983097 Hz)
Delta () CD983095 (983091983097 Hz to 983090 Hz)
Delta () CD983096 (983090 Hz to 983089 Hz)
983091983089983089 CHBMIT Tis database is the online available suraceEEG dataset [983090983088] which is provided by Children HospitalBoston and Massachusetts Institute o echnology and it isavailable at physioNet website [983089983088] It contains 983097983089983094 hours o 983090983091 channels scalp EEG recording rom 983090983092 epilepsy suspectedpatients Tis ECG recording is sampledat 983090983093983094Hz with 983089983094-bitresolution Te 983090983091rd channel is same as 983089983093th channel (able 983089)
983091983089983090 PIMH Te second database o EEG datasets is pro- vided by our collaborator at Punjab Institute o Mental Health(PIMH) Lahore Its sampling requency is 983093983088983088 Hz and it wasrecorded on 983092983091 channels (among which 983091983091 channels are orEEG) Tis dataset consists o 983090983089 patients EEG recording
983091983090 Features
983091983090983089 Feature Extraction Data which interests us lies inbetween the requency range o 983088983091 Hz to 983091983088 Hz So aferapplying DW with db983092 mother wavelet we have to selectdetailed coefficients with this requency range So in case o 983090983093983094 Hz sampled CHBMI dataset we have to go to at least983091 levels o decomposition and discard the earlier two as it isdemonstrated in Figure 983090 In order to get the discriminating
inormation between different types o epileptic patterns andidentiying them correctly without mistaking them with eachother decomposition o this detailed coefficient urther inBeta Alpha Teta and Delta is hugely helpul So we urtherdecomposed them until the 983095th level Hence we used theDWs detailed coefficients o levels 983091 983092 983093 983094 and 983095 or 983090983093983094Hzsampled CHB-MI dataset (able 983090)
Afer the selection o the wavelet coefficients we calcu-lated the statistical eature out o them Te statistical eatureswere the mean power and standard deviation o all o theselected coefficients
In case o 983093983089983090 Hz sampled PIMH dataset we used theDWs detailed coefficients o levels 983092 983093 983094 983095 and 983096
Afer the selection o detailed coefficients we calculatedthe statistical eature out o them Te statistical eatureswere the mean power and standard deviation o all o theshortlisted detailed coefficients
983091983090983090 Standardization During training stage we 1047297rst used
simple -score normalization to standardize the eatures [983089983097]beore applying eature reduction But the real issue arosewhen we tried to normalize them during testing stage Oneway o doing this is that we keep all o the examples andapply -score on them along with the new test data Insteado this time taking process we made an assumption onour observation that mean and standard deviation does notdeviate a lot It is analysed in this study that the EEG timeseries are assumed to be stationary over a small length o thesegments So we used the mean and standard deviation o thetraining examples rom the training stage to normalize thetest examples Figures 983093 and 983094 illustrate our observation inwhich you can see that there is not much deviation in trainand train + test examples mean and standard deviation
983091983091 Classi1047297er Classi1047297cation is used in machine learningto reer to the problem o identiying a discrete category to which a new observation belongs Observations withknown labels are used to train a classi1047297cation algorithm orclassi1047297er using eatures associated with the observation ForCHBMI database we had to train 983090983090983088 classi1047297ers in initialtraining stage Te calculation behind 983090983090983088 is the 983090983090 channelsmultiplied by 983089983088 types o epileptic pattern Te 983090983091rd channelwas same as 983089983093th channel For PIMH dataset 983091983091983088 classi1047297erswere trained where 983091983091 channels o EEG were utilized Wetried three different classi1047297ers and ound SVM to be the mostaccurate
We have used blind validation mechanism or the tendifferent eature data distributions to estimate the classi1047297-cation perormance Tese 983089983088 different and separate blinddata distributions were taken rom a huge set o EEGdataset Tese 983089983088 data distributions we randomly dividedinto two groups We trained our classi1047297er on one hal o thedistribution and tested it on the other hal We repeated thaton all ten distributions Ten we calculated the average o theclassi1047297cation rate or the all ten distributions
983091983090983090983097 out o 983091983090983097983095983094983088983088 epochs were randomly taken or tentimes rom CHBMI dataset Each time hal o them wereused to train and hal o them were used to test the initialclassi1047297cation Te average o the sensitivity speci1047297city and
accuracy or these ten distributionsis considered as the initialtraining phase perormance
Same approach wasapplied on PIMH datasetswhere 983091983090983090983097out o 983090983092983088983097983095 epochs were randomly taken rom PIMH datasetor the six times instead o ten times
Due to unavailability o the non-983091 Hz spike and waveepileptic EEG data currently we have only classi1047297cation ratesor generalized absence seizure
983091983091983089 Exclusive Processing In this study we have analysedthat even in the case o absence seizure epileptic patterns donot appear in the exact same way in each channel Handlingo each channel exclusive to each other was also another very
F983145983143983157983154983141 983090 Selection o DW detailed coefficients or a 983090983093983094 Hz sampled 983089 sec wide epochs EEG signal
important decision We tested the classi1047297cation in both waysthat is one classi1047297er or all o the channels at once versus oneseparate classi1047297er or each channel (Figure 983096)
Tis processing o each channel exclusive to each otherimproved over average accuracy rom approximately 983097983089 toapproximately 983097983093 in case o SVM So or SVM there is
a signi1047297cant improvement o 983092 by this change In case o QDA accuracy rose rom 983097983089 to 983097983092 with an improvemento 983091 and in case o ANN it rose rom 983097983089983096 to 983097983090983097 withan improvement o 983089983089
Results show that SVMsuites ourmethodin the most effi-cient way ANN has a lesser classi1047297cation time and LDA has
a lesser training time as compared to SVM but consideringthe sensitivity and classi1047297cation improvement through cor-rective marking we think that SVM is the better choice thanLDA and ANN In upcoming sections we have shown theresults or all three types o classi1047297er
0 5 10 15 20 250
24
6
8
10
12
14
16times10
4
Mean o the training examplesMean o the training + test examples
F983145983143983157983154983141 983093 Relationship between channelrsquos number and mean value(test + training examples)
(a) Adaptation Mechanism o test the adaptation mechanism983096983088983095corrective epochs were markedby the user ora CHBMI
F983145983143983157983154983141 983094 Relationship between channelrsquos number and standard deviation value (test + training examples)
EEG
Channel 1 DWTD1
A1 D2
A2
Mean
Standard deviation
Power
Mean
Standard deviation
Power
Mean
Standard deviation
Power
SVM or pattern 1
SVM or pattern 2
SVM or pattern 3
SVM or pattern 10
OPor
channel1
Channel 2 DWTD1
A1 D2
A2
Mean
Standard deviation
Power
Mean
Standard deviation
Power
MeanStandard deviation
Power
SVM or pattern 1
SVM or pattern 2
SVM or pattern 3
SVM or pattern 10
OPor
channel2
DWTD1
A1 D2
A2
Mean
Standard deviation
Power
Mean
Standard deviation
Power
Mean
Standard deviation
Power
SVM or pattern 1
SVM or pattern 2
SVM or pattern 3
SVM or pattern 10
Features
Features
PCA
Reduction
Standardization
PCA
Standardization
Reduction
FeaturesPCA
Reduction
Standardization
Surgeonrsquos marking
Surgeonrsquos marking
Surgeonrsquos marking
OPor
channeln
Al
Dl
Al
Al
Dl
Dl
Z-score
Z-score
Z-scoreChannel n
F983145983143983157983154983141 983095 Flowchart
dataset 1047297le and he marked the same amount o epochs oreach channel Tese corrective markings were saved in hislog as training examples Tese corrective markings as thenew examples along with the 983091983090983090983097983088 epochs o initial trainingstage were used to retrain the classi1047297er Te number 983091983090983090983097983088 hascome rom the 983091983090983090983097 randomly selected epochs rom whole o the CHBMI dataset or the ten separate times during initial
training phase Ten later the perormance o the classi1047297erafer retraining was judged again on another random 983091983088983088983088epochs (Figure 983089983092)
In case o PIMH dataset 983093983095 corrective epochs wereselected or PIMH dataset Tis time 983089983097983091983095983092 epochs o thePIMH dataset were used along with the 983093983095 corrective mark-ings as orthe PIMH we randomly selected the 983091983090983090983097 numbers
Processing all channels simultaneously with single classi1047297er
Processing all channels separately with separate classi1047297er oreach channel
F983145983143983157983154983141 983096 Accuracy relationship o different classi1047297ers and theirclassi1047297cation rate
84
86
88
90
92
94
96
98
100
Accuracy afer initial training ()
Accuracy afer retraining ()
ldquo F P 1 F 7 rdquo
ldquo F 7 T 7 rdquo
ldquo T 7 P 7 rdquo
ldquo P 7 O 1 rdquo
ldquo F P 1 F 3 rdquo
ldquo F 3 C 3 rdquo
ldquo C 3 P 3 rdquo
ldquo P 3 O 1 rdquo
ldquo F P 2 F 4 rdquo
ldquo F 4 C 4 rdquo
ldquo C 4 P 4 rdquo
ldquo P 4 O 2 rdquo
ldquo F P 2 F 8 rdquo
ldquo F 8 T 8 rdquo
ldquo T 8 P 8 rdquo
ldquo P 8 O 2 rdquo
ldquo F Z C Z rdquo
ldquo C Z P Z rdquo
ldquo P 7 T 7 rdquo
ldquo T 7 F T 9 rdquo
ldquo F T 9 F T 1 0 rdquo
ldquo F T 1 0 T 8 rdquo
F983145983143983157983154983141 983097 Relation between average classi1047297cation rate and accuracy
o the channel afer initial training and retaining
o epochs or the six times Te retrained classi1047297er was testedon the 983090983091983094983089 remaining epochs
(b) Support Vector Machine We used the support vectormachine classi1047297er package available in MALAB Bioinor-matics oolbox We ound linear kernel to be the mostaccurate SVM kernel with 983093983088 as the box constraint
(c) CHBMIT For CHBMI dataset initial training o theclassi1047297er resulted in 983097983094983091 average accuracy 983097983095983092 average
0
20
40
60
80
100
120
Accuracy afer initial training ()
Accuracy afer retraining ()
ldquo F p 1 rdquo
ldquo F p 2 rdquo
ldquo F 3 rdquo
ldquo F 4 rdquo
ldquo C 3 rdquo
ldquo C 4 rdquo
ldquo P 3 rdquo
ldquo P 4 rdquo
ldquo O 1 rdquo
ldquo O 2 rdquo
ldquo F 7 rdquo
ldquo F 8 rdquo
ldquo T 3 rdquo
ldquo T 4 rdquo
ldquo T 5 rdquo
ldquo T 6 rdquo
ldquo F z rdquo
ldquo C z rdquo
ldquo P z rdquo
ldquo E rdquo
ldquo P G 2 rdquo
ldquo A 1 rdquo
ldquo A 2 rdquo
ldquo T 1 rdquo
ldquo T 2 rdquo
ldquo X 1 rdquo
ldquo X 2 rdquo
ldquo X 3 rdquo
ldquo X 4 rdquo
ldquo X 5 rdquo
ldquo X 6 rdquo
ldquo X 7 rdquo
ldquo P G 1 rdquo
F983145983143983157983154983141 983089983088 Relation between average classi1047297cation rate and accuracy o the channel afer initial training and retaining
82
84
86
88
90
92
94
96
98
Accuracy afer initial training ()Accuracy afer retraining ()
ldquo F P 1 F 7 rdquo
ldquo F 7 T 7 rdquo
ldquo T 7 P 7 rdquo
ldquo P 7 O 1 rdquo
ldquo F P 1 F 3 rdquo
ldquo F 3 C 3 rdquo
ldquo C 3 P 3 rdquo
ldquo P 3 O 1 rdquo
ldquo F P 2 F 4 rdquo
ldquo F 4 C 4 rdquo
ldquo C 4 P 4 rdquo
ldquo P 4 O 2 rdquo
ldquo F P 2 F 8 rdquo
ldquo F 8 T 8 rdquo
ldquo T 8 P 8 rdquo
ldquo P 8 O 2 rdquo
ldquo F Z C Z rdquo
ldquo C Z P Z rdquo
ldquo P 7 T 7 rdquo
ldquo T 7 F T 9 rdquo
ldquo F T 9 F T 1 0 rdquo
ldquo F T 1 0 T 8 rdquo
F983145983143983157983154983141 983089983089 Relation between average classi1047297cation rate and accuracy o the channel afer initial training and retaining
speci1047297city and 983097983091983093 average sensitivity or 983091 Hz spike andwave which is a characteristic o absence seizure Aferinitial training our speci1047297city is better than that o Shoeb[983089983088] and Nasehi and Pourghassem [983090983089] who used the samedataset to validate their technique with different eaturesand application technique Tis shows that our technique
is providing better results even at the initial training phase(Figure 983089983089)
In able 983091 we have shown the average initial classi1047297cationand retrained classi1047297cation results o our system or eachchannel In this system we have shown that afer correctiono ew epochs there is a visible improvement in the systemsclassi1047297cation Te average accuracy o the system rose rom983097983093983093 to 983097983094983091
(d) PIMH For PIMH dataset initial training o the classi1047297erresulted in 983097983088 average accuracy 983097983092 average speci1047297city and983096983088 average sensitivity or 983091 Hz spike and wave which is acharacteristic o absence seizure (Figure 983089983090)
F983145983143983157983154983141 983089983090 Relation between average classi1047297cation rateand accuracy o the channel afer initial training and retaining
80
82
84
86
88
90
92
94
96
98
Accuracy afer initial training ()
Accuracy afer retraining ()
ldquo F P 1 F 7 rdquo
ldquo F 7 T 7 rdquo
ldquo T 7 P 7 rdquo
ldquo P 7 O 1 rdquo
ldquo F P 1 F 3 rdquo
ldquo F 3 C 3 rdquo
ldquo C 3 P 3 rdquo
ldquo P 3 O 1 rdquo
ldquo F P 2 F 4 rdquo
ldquo F 4 C 4 rdquo
ldquo C 4 P 4 rdquo
ldquo P 4 O 2 rdquo
ldquo F P 2 F 8 rdquo
ldquo F 8 T 8 rdquo
ldquo T 8 P 8 rdquo
ldquo P 8 O 2 rdquo
ldquo F Z C Z rdquo
ldquo C Z P Z rdquo
ldquo P 7 T 7 rdquo
ldquo T 7 F T 9 rdquo
ldquo F T 9 F T 1 0 rdquo
ldquo F T 1 0 T 8 rdquo
F983145983143983157983154983141 983089983091 Relation between average classi1047297cation rate and accuracy o the channel afer initial training and retaining
In able 983092 we have shown the average initial classi1047297cationand retrained classi1047297cation results o our system or eachchannel able 983092 shows that our technique is robust and itworks also on a different dataset Te average accuracy o thesystem rose rom approximately 983096983097 to 983097983088
983091983091983090 Discriminate Analysis We used the discriminant anal-
ysis package available in MALAB Statistics oolbox Weound pseudoquadratic to be the best perorming discrimi-nate type with uniorm probability
(a) CHBMIT For CHBMI dataset initial training o theclassi1047297er resulted in 983097983092 average accuracy 983097983094 averagespeci1047297city and 983097983088 average sensitivity or 983091 Hz spike andwave which is a characteristic o absence seizure Afer initialtraining our speci1047297city is better than that o Shoeb [983089983088] andNasehi and Pourghassem [983090983089] (Figure 983089983091)
In able 983093 we have shown the average initial classi1047297cationand retrained classi1047297cation results o our system or eachchannel In this system we have shown that afer correction
010
2030405060708090
100
Accuracy afer initial training ()
Accuracy afer retraining ()
ldquo F 3 rdquo
ldquo F 4 rdquo
ldquo C 3 rdquo
ldquo C 4 rdquo
ldquo P 3 rdquo
ldquo P 4 rdquo
ldquo O 1 rdquo
ldquo O 2 rdquo
ldquo F 7 rdquo
ldquo F 8 rdquo
ldquo T 3 rdquo
ldquo T 4 rdquo
ldquo T 5 rdquo
ldquo T 6 rdquo
ldquo F z rdquo
ldquo C z rdquo
ldquo P z rdquo
ldquo E rdquo
ldquo P G 2 rdquo
ldquo T 1 rdquo
ldquo T 2 rdquo
ldquo X 1 rdquo
ldquo X 2 rdquo
ldquo X 3 rdquo
ldquo X 4 rdquo
ldquo X 5 rdquo
ldquo X 6 rdquo
ldquo X 7 rdquo
ldquo P G 1 rdquo
ldquo A 1 rdquo
ldquo A 2 rdquo
ldquo F p 1 rdquo
ldquo F p 2 rdquo
F983145983143983157983154983141 983089983092 Relation between average classi1047297cation rate and accuracy o the channel afer initial training and retaining
983137983138983148983141 983091 First column shows the channel label the second columnshows the initial training accuracy and third one shows the markedcorrection by a neurologistand thelast one shows the1047297nal accuracy
o ew epochs there is visible improvement in the systemsclassi1047297cation Te average accuracy o the system rose rom983097983092 to 983097983093
(b) PIMH For PIMH dataset initial training o the classi1047297erresulted in 983097983088 average accuracy 983097983093 average speci1047297city and983095983091 average sensitivity or 983091 Hz spike and wave which is acharacteristic o absence seizure
In able 983094 we have shown the average initialclassi1047297cationand retrained classi1047297cation results o our system or each
983137983138983148983141 983092 First column shows the channel label the second columnshows the initial training accuracy and third one shows the markedcorrection by a neurologistand thelast one shows the1047297nal accuracy
channel able 983094 shows that our technique is robust and itworks also on a different dataset Te average accuracy o thesystem rose rom approximately 983096983097 to 983097983088
983091983091983091 Arti1047297cial Neural Network We used eedorward back-propagation package available in MALAB Neural Network oolbox and ound Levenberg-Marquardt to be the bestmethod with 983088983088983093 learning rate
(a) CHBMIT For CHBMI dataset initial training o theclassi1047297er resulted in 983097983090983096983096 average accuracy 983097983096983094983094 averagespeci1047297city and 983095983093983095983093 average sensitivity or 983091 Hz spike andwave which is a characteristic o absence seizure (Figure 983097)
983137983138983148983141 983093 First column shows the channel label the second columnshows the initial training accuracy and third one shows the markedcorrection by a neurologistand thelast one shows the1047297nal accuracy
In able 983095 we have shown the average initial classi1047297cationand retrained classi1047297cation results o our system or eachchannel In this system we have shown that afer correctiono ew epochs there is visible improvement in the systemsclassi1047297cation Te average accuracy o the system rose rom983097983090983096983096 to 983097983091983097983094
(b) PIMH For PIMH dataset initial training o the classi1047297erresulted in 983096983092 average accuracy 983097983092983096 average speci1047297cityand 983093983094983093 average sensitivity or 983091 Hz spike and wave whichis a characteristic o absence seizure (Figure 983089983088)
In able 983096 we have shown the average initial classi1047297cationand retrained classi1047297cation results o our system or each
channel able 983096 shows that our technique is robust and itworks also on a different dataset Te average accuracy o thesystem rose rom approximately 983096983092 to 983096983093983092983091
4 Discussion and Future Work
Computer-assisted analysis o EEG has tremendous potentialor assisting the clinicians in diagnosis A very importantand novel phase o our system is user adaptation mechanismor retraining mechanism Introduction o this phase hasimportance in many aspects In this phase system tries toadapt its classi1047297cation according to users desire Moreoverthis technique personalizes the classi1047297ers classi1047297cation It has
983137983138983148983141 983094 First column shows the channel label the second columnshows the initial training accuracy and third one shows the markedcorrection by a neurologistand thelast one shows the1047297nal accuracy
been cited that sometimes even the expert neurologists havesome disagreementovera certain observation o an EEGdataTis system will be useul or disagreeing users and it will alsohelp them in comparing their results with each other
Tere is also a threat o over1047297tting by the classi1047297erIn order to keep the classi1047297er improving its perormancewith the encounter o more and more examples we haveintroduced this user adaptive mechanism in our system Weconsider the existing systems as dead because these cannotimprove their classi1047297cation rate afer initial training (duringsofware development) Te sel-improving mechanism aferdeployment makes our tool alive Tis system can be madepart o the whole epileptic diagnosis process It will highlight
983137983138983148983141 983095 First column shows the channel label the second columnshows the initial training accuracy and third one shows the markedcorrection by a neurologistand thelast one shows the1047297nal accuracy
the epileptic spikes among the whole EEG thus leading toreduced atigue and time consumption o a user We obtainedhigh classi1047297cation accuracy on datasets obtained rom twodifferent sites which indicates reproducibility o our resultsand robustness o our approach
In the uture we are planning to make this a web basedapplication neurologists can log in and consult each otherrsquosreviews about a particular subject Tis will make our systemexperience a whole versatility o examples and learn rom allo them Integration o the video and its automatic analysis(video EEG) can help a neurologist in diagnosing epilepsy ina better way whereas this can also help him in distinguishingbetween psychogenic and epileptic seizuresWewouldalso be
investigating how much over1047297tting is an issue in the reportedperormances which are now even touching 983089983088983088 based onsome claims Tere is a need or methodcriteria which couldlimit these algorithms improving their detection on a limitednumber o available examples
Tissystem is made keeping in mind thatwe haveto acil-itate the neurologist by supplementing him in the analysis o the EEG We do not want to enorce the classi1047297cation o theEEG data on a user
In the uture we will also include a slider in the systemwhich will allow the user to adjust the sensitivity and speci-1047297city beore retraining Tis assisting system is more like adetection tool which is continuously learning with encounter
983137983138983148983141 983096 Classi1047297cation rate improvement caused by retraining Firstcolumn shows thechannellabel thesecond columnshows theinitialtraining accuracy and third one shows the marked correction by aneurologist and the last one shows the 1047297nal accuracy
o better examples More and better examples will certainly improve its perormance Te agreement between differentneurologists over the EEG readings is low to moderate I we could 1047297nd the agreement on at least ew o the epilepticpatterns correspondence with epileptic disease then we cantake this tool urther ahead and use it or diagnosis instead o
just assistanceOne o the biggest limitations to this study is the unavail-
ability o non-983091 Hz spike and wave data Even though we haveincluded the data eatures o the entire epileptic requency ranges exclusive to each other proo testing on the data will
certainly prove worthy orthe progresso these assisting toolstoward a diagnostic tool
Conflict of Interests
Te authors declare that there is no con1047298ict o interests
regarding the publication o this paper
Acknowledgment
Te authors would like to extend their sincere appreciation tothe Deanship o Scienti1047297c Research at King Saud University or unding this research through Research Group Project(RG no 983089983092983091983093-983088983093983089)
References
[983089] H Adelia Z Zhoub and N Dadmehrc ldquoAnalysis o EEGrecords in an epileptic patient using wavelet transormrdquo Journal of Neuroscience Methods vol 983089983090983091 no 983089 pp 983094983097ndash983096983095 983090983088983088983091
[983090] M A Ahmad W Majeed and N A Khan ldquoAdvancements incomputer aided methods or EEG-based epileptic detectionrdquo inProceedings of the 983095th International Conference on Bio-Inspired Systems and Signal Processing (BIOSIGNALS 983089983092) AngersFrance 983090983088983089983092
[983091] S Noachtar and J R emi ldquoTe role o EEG in epilepsy a criticalreviewrdquo Epilepsy and Behavior vol 983089983093 no 983089 pp 983090983090ndash983091983091 983090983088983088983097
[983092] E B Petersen J Duun-Henriksen A Mazzaretto W Kjar CE Tomsen and H B D Sorensen ldquoGeneric single-channeldetection o absence seizuresrdquo in Proceedings of the Annual International Conference of the IEEE Engineering in Medicineand Biology Society (EMBS rsquo983089983089) pp 983092983096983090983088ndash983092983096983090983091 Boston MassUSA September 983090983088983089983089
[983093] M Kaleem A Guergachi and S Krishnan ldquoEEG seizure
detection and epilepsy diagnosis using a novel variation o Empirical Mode Decompositionrdquo in Proceedings of the 983091983093th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC rsquo983089983091) pp 983092983091983089983092ndash983092983091983089983095 OsakaJapan July 983090983088983089983091
[983094] S M S Alam and M I H Bhuiyan ldquoDetection o seizure andepilepsy usinghigher order statistics in the EMD domainrdquo IEEE Journal of Biomedical and Health Informatics vol 983089983095 no 983090 pp983091983089983090ndash983091983089983096 983090983088983089983091
[983095] H Ocbagabir K A I Aboalayon and M FaezipourldquoEfficientEEG analysis or seizure monitoring in epileptic patientsrdquo inProceedings of the Long Island Systems Applications and Tech-nology Conference (LISAT rsquo983089983091) pp 983089ndash983094 IEEE Farmingdale NYUSA May 983090983088983089983091
[983096] A A Abdullah S A Rahim and A Ibrahim ldquoDevelopment o EEG-based epileptic detection using arti1047297cial neural networkrdquoin Proceedings of the International Conference on Biomedical Engineering (ICoBE rsquo983089983090) pp 983094983088983093ndash983094983089983088 Penang Malaysia 983090983088983089983090
[983097] P Guo J Wang X Z Gao and J M A anskanen ldquoEpilepticEEG signal classi1047297cation with marching pursuit based on har-mony search methodrdquo in Proceedings of the IEEE International Conference on Systems Man and Cybernetics (SMC rsquo983089983090) pp983090983096983091ndash983090983096983096 Seoul Republic o Korea October 983090983088983089983090
[983089983088] A Shoeb ldquoCHB-MI scalp EEG databaserdquo 983090983088983088983088 httpphysionetorgpn983094chbmit
[983089983089] A S M Murugavel S Ramakrishnan U Maheswari and BS Sabetha ldquoCombined Seizure Index with Adaptive Multi-Class SVM or epileptic EEG classi1047297cationrdquo in Proceedings
of the International Conference on Emerging Trends in VLSIEmbedded System Nano Electronics and TelecommunicationSystem (ICEVENT rsquo983089983091) pp 983089ndash983093 Tiruvannammalai IndiaJanuary 983090983088983089983091
[983089983090] M H Abdullah J M Abdullah and M Z Abdullah ldquoSeizuredetection by means o hidden Markov model and station-ary wavelet transorm o electroencephalograph signalsrdquo inProceedings of the IEEE-EMBS International Conference onBiomedical and Health Informatics (BHI rsquo983089983090) pp 983094983090ndash983094983093 HongKong January 983090983088983089983090
[983089983091] A Subasi and M I Gursoy ldquoEEG signal classi1047297cation usingPCA ICA LDA and support vector machinesrdquo Expert Systemswith Applications vol 983091983095 no 983089983090 pp 983096983094983093983097ndash983096983094983094983094 983090983088983089983088
[983089983092] E Sezer H Isik and E Saracoglu ldquoEmployment and compari-son o different arti1047297cial neural networks or epilepsy diagnosisrom EEG signalsrdquo Journal of Medical Systems vol 983091983094 no 983089 pp983091983092983095ndash983091983094983090 983090983088983089983090
[983089983093] Brain Products GmbHProducts and ApplicationsAnalyzer 983090httpwwwbrainproductscomproductdetailsphpid=983089983095
[983089983094] NeuroExplorer Home httpwwwneuroexplorercom
[983089983096] C H Seng R Demirli L Khuon and D Bolger ldquoSeizuredetection in EEG signals using support vector machinesrdquo inProceedings of the 983091983096th Annual Northeast Bioengineering Con- ference (NEBEC rsquo983089983090) pp 983090983091983089ndash983090983091983090 Philadelphia Pa USA March983090983088983089983090
[983089983097] Z A Khan S B Mansoor M A Ahmad and M M MalikldquoInput devices or virtual surgical simulations a comparativestudyrdquo in Proceedings of the 983089983094th International Multi Topic Con- ference (INMIC rsquo983089983091) pp 983089983096983097ndash983089983097983092 Lahore Pakistan December983090983088983089983091
[983090983088] A L Goldberger L A Amaral L Glass et al ldquoPhysioBankPhysiooolkit and PhysioNet components o a new research
[983090983089] S Nasehi and H Pourghassem ldquoEpileptic seizure onset detec-tion algorithm using dynamic cascade eed-orward neural net-worksrdquo in Proceedings of the International Conference on Intelli- gent Computation and Bio-Medical Instrumentation (ICBMI rsquo983089983089)pp 983089983097983094ndash983089983097983097 December 983090983088983089983089
multiplied it with the standardized statistical eatures o theblind test data and then ed the top 983097 eatures to classi1047297er
983090983091 Classi1047297cation Classi1047297cation is a machine learning tech-nique in which new observations belonging to a category are identi1047297ed Tis identi1047297cation is based on the training set
which contains the observations with known labelling o theircategory Tese observations are also termed as eatures Wetried three types o classi1047297cation methods (983089) SVM (983090) QDAand (983091) ANN (Figure 983091)
Te reduced eatures were ed to these classi1047297ers Herethe reduced eatures mean that those statistical eatures o the selected wavelet coefficients are reduced using PCA asdescribed in previous section All o the three processingparts were exclusive or each channel and each epilepticpattern So like previous parts the classi1047297ers were also trainedand tested exclusively or each channel
Our system requires individual labelling o channelsTere is a separate classi1047297er or each channel and or each
epileptic pattern type So the total number o classi1047297ers isequalto the product o totalnumbero channels by ten whereten represents the number o epileptic pattern described by Noachtar and R emi [983091]
(e) Support Vector Machine Support vector machine (SVM)is a supervised learning models machine learning techniqueSVM tries to represent the examples as points in space whichare mapped in a way that points o different categories can bedivided by a clear gap that is as wide as possible Aferwardsthat division is used to categorise the new test examples basedon which side they all on
analysis (QDA) is a widely used machine learning methodamong statistics pattern recognition and signal process-ing to 1047297nd a quadratic combination o eatures which areresponsible or characterizing an example into two or morecategories QDAs combination o discriminating quadraticmultiplication actors is used or both classi1047297cation anddimensionality reduction
(g) Arti1047297cial Neural Network Arti1047297cial neural network (ANN) is a computational model which is inspired rom ani-mals central nervous system Tat is why ANN is representedby a system o interconnected neurons which are capable o computing values as per their inputs In ANN training the
weights associated with the neurons are iteratively adjustedaccording to the inputs and the difference between theoutputs with expected outputs Te iteration gets stoppedwhen either the combination o neurons starts generating theexpected results within an error o a tolerable error range orthe iteration limit 1047297nishes up
983090983092 Adaption Phase (RetrainingUser Adaptation Mecha-nism) In order to keep the classi1047297er improving its peror-mance with the encounter o more and more examples wehave introduced a user adaptive mechanism in our systemOur system allows the user to interactively select epochso his choice by simply clicking on the correction button
While using our system when a user thinks that a certainepoch is alsely labelledcategorised oursystem allows him tointeractively mark mark that label as a mistake Tese detailswill be saved in a log in the background and they will be usedto retrain the classi1047297er to improve its classi1047297cation rate andadapt itsel according to the user with the passage o time
When the user is going to select the retraining option in oursystem then classi1047297ers willretrain themselves on thepreviousand the newly logged training examples As every user has tolog in with his personal ID every corrective marking detailwill only be saved in that userrsquos older and only classi1047297er willupdate itsel or that user Hence the systems classi1047297er tries toadapt itsel according to that user without damaging anyoneelse classi1047297cation
Te concept behind the inclusion o the retraining is thati there is more than one example with same attributes butdifferent labels the classi1047297er is going to get trained to theone with most population Te userrsquos corrective marking willincrease theexampleso hischoice thus making that classi1047297eradapt itsel to the userrsquos choice in a trivial way Every userwill have exclusive classi1047297ers trained or him and his markingwill not affect other usersrsquo classi1047297er As we know the userssometimes do not agree on the choice o the epileptic patternor its type Te exclusive processing or each user will help thesame sofware keep the system trained or every user and itwill also let different users compare their markings with eachother
We do not have any standard right now to measure whichneurologist is the most righteous among a disagreeing groupo neurologist users So we kept the corrective markings o each user to his account so that it may not interere withthe one who may not agree on his choice So the developedsystem is used to acilitate the neurologistrsquos selection to theuser according to his own choice and afer initial training onevery retraining it tries to adopt more users Tis system doesnot want to dictate to the neurologist but rather learn romhim to adapt him to save his time
We want the classi1047297er to think like the user and supple-menthim by highlighting theepochso his choice so the goldstandard afer ew retraining mechanisms will be the userhimsel Already tested examples with new labels inclusion inthe training examplesor the retraining willbias the classi1047297erschoice in avour o user
3 Experimentation
In this section we will discuss the results in detail At 1047297rstwe will describe the datasets which we used to train test and
validate our method Ten we will discuss their versatility (Figure 983095)
983091983089 Dataset wo labelled datasets o epilepsy suspectedsurace EEG data were available to us Both o these datasetshave lots o versatility in between them in terms o ethnicityage gender and equipment Te datasets available to us wereabout generalised absence seizure which is characterized by the 983091 Hz spike and wave epileptic pattern in almost eachchannel Tat is why we have classi1047297cation results availableonly or one type o epilepsy which is absence seizure
983137983138983148983141 983089 Tis table describes the affiliation o detailed coefficientswith epileptic requency band o interest or 983090983093983094 Hz sampled CHB-MI dataset
Epileptic requency range Detailed coefficientsrsquo level
Beta (907317) CD983091 (983091983090 Hz to 983089983094 Hz)
Alpha (1103925) CD983092 (983089983094 Hz to 983096 Hz)
Teta (1038389) CD983093 (983096 Hz to 983092 Hz)
Delta () CD983094 (983092 Hz to 983090 Hz)
Delta () CD983095 (983090 Hz to 983089 Hz)
983137983138983148983141 983090 Tis table describes the affiliation o detailed coefficientswith epileptic requency band o interest or 983093983089983090 Hz sampled PIMHdataset
Epileptic requency range Detailed coefficientsrsquo level
Beta (907317) CD983092 (983091983089983090 Hz to 983089983093983094 Hz)
Alpha (1103925) CD983093 (983089983093983094 Hz to 983095983096 Hz)
Teta (1038389) CD983094 (983095983096 Hz to 983091983097 Hz)
Delta () CD983095 (983091983097 Hz to 983090 Hz)
Delta () CD983096 (983090 Hz to 983089 Hz)
983091983089983089 CHBMIT Tis database is the online available suraceEEG dataset [983090983088] which is provided by Children HospitalBoston and Massachusetts Institute o echnology and it isavailable at physioNet website [983089983088] It contains 983097983089983094 hours o 983090983091 channels scalp EEG recording rom 983090983092 epilepsy suspectedpatients Tis ECG recording is sampledat 983090983093983094Hz with 983089983094-bitresolution Te 983090983091rd channel is same as 983089983093th channel (able 983089)
983091983089983090 PIMH Te second database o EEG datasets is pro- vided by our collaborator at Punjab Institute o Mental Health(PIMH) Lahore Its sampling requency is 983093983088983088 Hz and it wasrecorded on 983092983091 channels (among which 983091983091 channels are orEEG) Tis dataset consists o 983090983089 patients EEG recording
983091983090 Features
983091983090983089 Feature Extraction Data which interests us lies inbetween the requency range o 983088983091 Hz to 983091983088 Hz So aferapplying DW with db983092 mother wavelet we have to selectdetailed coefficients with this requency range So in case o 983090983093983094 Hz sampled CHBMI dataset we have to go to at least983091 levels o decomposition and discard the earlier two as it isdemonstrated in Figure 983090 In order to get the discriminating
inormation between different types o epileptic patterns andidentiying them correctly without mistaking them with eachother decomposition o this detailed coefficient urther inBeta Alpha Teta and Delta is hugely helpul So we urtherdecomposed them until the 983095th level Hence we used theDWs detailed coefficients o levels 983091 983092 983093 983094 and 983095 or 983090983093983094Hzsampled CHB-MI dataset (able 983090)
Afer the selection o the wavelet coefficients we calcu-lated the statistical eature out o them Te statistical eatureswere the mean power and standard deviation o all o theselected coefficients
In case o 983093983089983090 Hz sampled PIMH dataset we used theDWs detailed coefficients o levels 983092 983093 983094 983095 and 983096
Afer the selection o detailed coefficients we calculatedthe statistical eature out o them Te statistical eatureswere the mean power and standard deviation o all o theshortlisted detailed coefficients
983091983090983090 Standardization During training stage we 1047297rst used
simple -score normalization to standardize the eatures [983089983097]beore applying eature reduction But the real issue arosewhen we tried to normalize them during testing stage Oneway o doing this is that we keep all o the examples andapply -score on them along with the new test data Insteado this time taking process we made an assumption onour observation that mean and standard deviation does notdeviate a lot It is analysed in this study that the EEG timeseries are assumed to be stationary over a small length o thesegments So we used the mean and standard deviation o thetraining examples rom the training stage to normalize thetest examples Figures 983093 and 983094 illustrate our observation inwhich you can see that there is not much deviation in trainand train + test examples mean and standard deviation
983091983091 Classi1047297er Classi1047297cation is used in machine learningto reer to the problem o identiying a discrete category to which a new observation belongs Observations withknown labels are used to train a classi1047297cation algorithm orclassi1047297er using eatures associated with the observation ForCHBMI database we had to train 983090983090983088 classi1047297ers in initialtraining stage Te calculation behind 983090983090983088 is the 983090983090 channelsmultiplied by 983089983088 types o epileptic pattern Te 983090983091rd channelwas same as 983089983093th channel For PIMH dataset 983091983091983088 classi1047297erswere trained where 983091983091 channels o EEG were utilized Wetried three different classi1047297ers and ound SVM to be the mostaccurate
We have used blind validation mechanism or the tendifferent eature data distributions to estimate the classi1047297-cation perormance Tese 983089983088 different and separate blinddata distributions were taken rom a huge set o EEGdataset Tese 983089983088 data distributions we randomly dividedinto two groups We trained our classi1047297er on one hal o thedistribution and tested it on the other hal We repeated thaton all ten distributions Ten we calculated the average o theclassi1047297cation rate or the all ten distributions
983091983090983090983097 out o 983091983090983097983095983094983088983088 epochs were randomly taken or tentimes rom CHBMI dataset Each time hal o them wereused to train and hal o them were used to test the initialclassi1047297cation Te average o the sensitivity speci1047297city and
accuracy or these ten distributionsis considered as the initialtraining phase perormance
Same approach wasapplied on PIMH datasetswhere 983091983090983090983097out o 983090983092983088983097983095 epochs were randomly taken rom PIMH datasetor the six times instead o ten times
Due to unavailability o the non-983091 Hz spike and waveepileptic EEG data currently we have only classi1047297cation ratesor generalized absence seizure
983091983091983089 Exclusive Processing In this study we have analysedthat even in the case o absence seizure epileptic patterns donot appear in the exact same way in each channel Handlingo each channel exclusive to each other was also another very
F983145983143983157983154983141 983090 Selection o DW detailed coefficients or a 983090983093983094 Hz sampled 983089 sec wide epochs EEG signal
important decision We tested the classi1047297cation in both waysthat is one classi1047297er or all o the channels at once versus oneseparate classi1047297er or each channel (Figure 983096)
Tis processing o each channel exclusive to each otherimproved over average accuracy rom approximately 983097983089 toapproximately 983097983093 in case o SVM So or SVM there is
a signi1047297cant improvement o 983092 by this change In case o QDA accuracy rose rom 983097983089 to 983097983092 with an improvemento 983091 and in case o ANN it rose rom 983097983089983096 to 983097983090983097 withan improvement o 983089983089
Results show that SVMsuites ourmethodin the most effi-cient way ANN has a lesser classi1047297cation time and LDA has
a lesser training time as compared to SVM but consideringthe sensitivity and classi1047297cation improvement through cor-rective marking we think that SVM is the better choice thanLDA and ANN In upcoming sections we have shown theresults or all three types o classi1047297er
0 5 10 15 20 250
24
6
8
10
12
14
16times10
4
Mean o the training examplesMean o the training + test examples
F983145983143983157983154983141 983093 Relationship between channelrsquos number and mean value(test + training examples)
(a) Adaptation Mechanism o test the adaptation mechanism983096983088983095corrective epochs were markedby the user ora CHBMI
F983145983143983157983154983141 983094 Relationship between channelrsquos number and standard deviation value (test + training examples)
EEG
Channel 1 DWTD1
A1 D2
A2
Mean
Standard deviation
Power
Mean
Standard deviation
Power
Mean
Standard deviation
Power
SVM or pattern 1
SVM or pattern 2
SVM or pattern 3
SVM or pattern 10
OPor
channel1
Channel 2 DWTD1
A1 D2
A2
Mean
Standard deviation
Power
Mean
Standard deviation
Power
MeanStandard deviation
Power
SVM or pattern 1
SVM or pattern 2
SVM or pattern 3
SVM or pattern 10
OPor
channel2
DWTD1
A1 D2
A2
Mean
Standard deviation
Power
Mean
Standard deviation
Power
Mean
Standard deviation
Power
SVM or pattern 1
SVM or pattern 2
SVM or pattern 3
SVM or pattern 10
Features
Features
PCA
Reduction
Standardization
PCA
Standardization
Reduction
FeaturesPCA
Reduction
Standardization
Surgeonrsquos marking
Surgeonrsquos marking
Surgeonrsquos marking
OPor
channeln
Al
Dl
Al
Al
Dl
Dl
Z-score
Z-score
Z-scoreChannel n
F983145983143983157983154983141 983095 Flowchart
dataset 1047297le and he marked the same amount o epochs oreach channel Tese corrective markings were saved in hislog as training examples Tese corrective markings as thenew examples along with the 983091983090983090983097983088 epochs o initial trainingstage were used to retrain the classi1047297er Te number 983091983090983090983097983088 hascome rom the 983091983090983090983097 randomly selected epochs rom whole o the CHBMI dataset or the ten separate times during initial
training phase Ten later the perormance o the classi1047297erafer retraining was judged again on another random 983091983088983088983088epochs (Figure 983089983092)
In case o PIMH dataset 983093983095 corrective epochs wereselected or PIMH dataset Tis time 983089983097983091983095983092 epochs o thePIMH dataset were used along with the 983093983095 corrective mark-ings as orthe PIMH we randomly selected the 983091983090983090983097 numbers
Processing all channels simultaneously with single classi1047297er
Processing all channels separately with separate classi1047297er oreach channel
F983145983143983157983154983141 983096 Accuracy relationship o different classi1047297ers and theirclassi1047297cation rate
84
86
88
90
92
94
96
98
100
Accuracy afer initial training ()
Accuracy afer retraining ()
ldquo F P 1 F 7 rdquo
ldquo F 7 T 7 rdquo
ldquo T 7 P 7 rdquo
ldquo P 7 O 1 rdquo
ldquo F P 1 F 3 rdquo
ldquo F 3 C 3 rdquo
ldquo C 3 P 3 rdquo
ldquo P 3 O 1 rdquo
ldquo F P 2 F 4 rdquo
ldquo F 4 C 4 rdquo
ldquo C 4 P 4 rdquo
ldquo P 4 O 2 rdquo
ldquo F P 2 F 8 rdquo
ldquo F 8 T 8 rdquo
ldquo T 8 P 8 rdquo
ldquo P 8 O 2 rdquo
ldquo F Z C Z rdquo
ldquo C Z P Z rdquo
ldquo P 7 T 7 rdquo
ldquo T 7 F T 9 rdquo
ldquo F T 9 F T 1 0 rdquo
ldquo F T 1 0 T 8 rdquo
F983145983143983157983154983141 983097 Relation between average classi1047297cation rate and accuracy
o the channel afer initial training and retaining
o epochs or the six times Te retrained classi1047297er was testedon the 983090983091983094983089 remaining epochs
(b) Support Vector Machine We used the support vectormachine classi1047297er package available in MALAB Bioinor-matics oolbox We ound linear kernel to be the mostaccurate SVM kernel with 983093983088 as the box constraint
(c) CHBMIT For CHBMI dataset initial training o theclassi1047297er resulted in 983097983094983091 average accuracy 983097983095983092 average
0
20
40
60
80
100
120
Accuracy afer initial training ()
Accuracy afer retraining ()
ldquo F p 1 rdquo
ldquo F p 2 rdquo
ldquo F 3 rdquo
ldquo F 4 rdquo
ldquo C 3 rdquo
ldquo C 4 rdquo
ldquo P 3 rdquo
ldquo P 4 rdquo
ldquo O 1 rdquo
ldquo O 2 rdquo
ldquo F 7 rdquo
ldquo F 8 rdquo
ldquo T 3 rdquo
ldquo T 4 rdquo
ldquo T 5 rdquo
ldquo T 6 rdquo
ldquo F z rdquo
ldquo C z rdquo
ldquo P z rdquo
ldquo E rdquo
ldquo P G 2 rdquo
ldquo A 1 rdquo
ldquo A 2 rdquo
ldquo T 1 rdquo
ldquo T 2 rdquo
ldquo X 1 rdquo
ldquo X 2 rdquo
ldquo X 3 rdquo
ldquo X 4 rdquo
ldquo X 5 rdquo
ldquo X 6 rdquo
ldquo X 7 rdquo
ldquo P G 1 rdquo
F983145983143983157983154983141 983089983088 Relation between average classi1047297cation rate and accuracy o the channel afer initial training and retaining
82
84
86
88
90
92
94
96
98
Accuracy afer initial training ()Accuracy afer retraining ()
ldquo F P 1 F 7 rdquo
ldquo F 7 T 7 rdquo
ldquo T 7 P 7 rdquo
ldquo P 7 O 1 rdquo
ldquo F P 1 F 3 rdquo
ldquo F 3 C 3 rdquo
ldquo C 3 P 3 rdquo
ldquo P 3 O 1 rdquo
ldquo F P 2 F 4 rdquo
ldquo F 4 C 4 rdquo
ldquo C 4 P 4 rdquo
ldquo P 4 O 2 rdquo
ldquo F P 2 F 8 rdquo
ldquo F 8 T 8 rdquo
ldquo T 8 P 8 rdquo
ldquo P 8 O 2 rdquo
ldquo F Z C Z rdquo
ldquo C Z P Z rdquo
ldquo P 7 T 7 rdquo
ldquo T 7 F T 9 rdquo
ldquo F T 9 F T 1 0 rdquo
ldquo F T 1 0 T 8 rdquo
F983145983143983157983154983141 983089983089 Relation between average classi1047297cation rate and accuracy o the channel afer initial training and retaining
speci1047297city and 983097983091983093 average sensitivity or 983091 Hz spike andwave which is a characteristic o absence seizure Aferinitial training our speci1047297city is better than that o Shoeb[983089983088] and Nasehi and Pourghassem [983090983089] who used the samedataset to validate their technique with different eaturesand application technique Tis shows that our technique
is providing better results even at the initial training phase(Figure 983089983089)
In able 983091 we have shown the average initial classi1047297cationand retrained classi1047297cation results o our system or eachchannel In this system we have shown that afer correctiono ew epochs there is a visible improvement in the systemsclassi1047297cation Te average accuracy o the system rose rom983097983093983093 to 983097983094983091
(d) PIMH For PIMH dataset initial training o the classi1047297erresulted in 983097983088 average accuracy 983097983092 average speci1047297city and983096983088 average sensitivity or 983091 Hz spike and wave which is acharacteristic o absence seizure (Figure 983089983090)
F983145983143983157983154983141 983089983090 Relation between average classi1047297cation rateand accuracy o the channel afer initial training and retaining
80
82
84
86
88
90
92
94
96
98
Accuracy afer initial training ()
Accuracy afer retraining ()
ldquo F P 1 F 7 rdquo
ldquo F 7 T 7 rdquo
ldquo T 7 P 7 rdquo
ldquo P 7 O 1 rdquo
ldquo F P 1 F 3 rdquo
ldquo F 3 C 3 rdquo
ldquo C 3 P 3 rdquo
ldquo P 3 O 1 rdquo
ldquo F P 2 F 4 rdquo
ldquo F 4 C 4 rdquo
ldquo C 4 P 4 rdquo
ldquo P 4 O 2 rdquo
ldquo F P 2 F 8 rdquo
ldquo F 8 T 8 rdquo
ldquo T 8 P 8 rdquo
ldquo P 8 O 2 rdquo
ldquo F Z C Z rdquo
ldquo C Z P Z rdquo
ldquo P 7 T 7 rdquo
ldquo T 7 F T 9 rdquo
ldquo F T 9 F T 1 0 rdquo
ldquo F T 1 0 T 8 rdquo
F983145983143983157983154983141 983089983091 Relation between average classi1047297cation rate and accuracy o the channel afer initial training and retaining
In able 983092 we have shown the average initial classi1047297cationand retrained classi1047297cation results o our system or eachchannel able 983092 shows that our technique is robust and itworks also on a different dataset Te average accuracy o thesystem rose rom approximately 983096983097 to 983097983088
983091983091983090 Discriminate Analysis We used the discriminant anal-
ysis package available in MALAB Statistics oolbox Weound pseudoquadratic to be the best perorming discrimi-nate type with uniorm probability
(a) CHBMIT For CHBMI dataset initial training o theclassi1047297er resulted in 983097983092 average accuracy 983097983094 averagespeci1047297city and 983097983088 average sensitivity or 983091 Hz spike andwave which is a characteristic o absence seizure Afer initialtraining our speci1047297city is better than that o Shoeb [983089983088] andNasehi and Pourghassem [983090983089] (Figure 983089983091)
In able 983093 we have shown the average initial classi1047297cationand retrained classi1047297cation results o our system or eachchannel In this system we have shown that afer correction
010
2030405060708090
100
Accuracy afer initial training ()
Accuracy afer retraining ()
ldquo F 3 rdquo
ldquo F 4 rdquo
ldquo C 3 rdquo
ldquo C 4 rdquo
ldquo P 3 rdquo
ldquo P 4 rdquo
ldquo O 1 rdquo
ldquo O 2 rdquo
ldquo F 7 rdquo
ldquo F 8 rdquo
ldquo T 3 rdquo
ldquo T 4 rdquo
ldquo T 5 rdquo
ldquo T 6 rdquo
ldquo F z rdquo
ldquo C z rdquo
ldquo P z rdquo
ldquo E rdquo
ldquo P G 2 rdquo
ldquo T 1 rdquo
ldquo T 2 rdquo
ldquo X 1 rdquo
ldquo X 2 rdquo
ldquo X 3 rdquo
ldquo X 4 rdquo
ldquo X 5 rdquo
ldquo X 6 rdquo
ldquo X 7 rdquo
ldquo P G 1 rdquo
ldquo A 1 rdquo
ldquo A 2 rdquo
ldquo F p 1 rdquo
ldquo F p 2 rdquo
F983145983143983157983154983141 983089983092 Relation between average classi1047297cation rate and accuracy o the channel afer initial training and retaining
983137983138983148983141 983091 First column shows the channel label the second columnshows the initial training accuracy and third one shows the markedcorrection by a neurologistand thelast one shows the1047297nal accuracy
o ew epochs there is visible improvement in the systemsclassi1047297cation Te average accuracy o the system rose rom983097983092 to 983097983093
(b) PIMH For PIMH dataset initial training o the classi1047297erresulted in 983097983088 average accuracy 983097983093 average speci1047297city and983095983091 average sensitivity or 983091 Hz spike and wave which is acharacteristic o absence seizure
In able 983094 we have shown the average initialclassi1047297cationand retrained classi1047297cation results o our system or each
983137983138983148983141 983092 First column shows the channel label the second columnshows the initial training accuracy and third one shows the markedcorrection by a neurologistand thelast one shows the1047297nal accuracy
channel able 983094 shows that our technique is robust and itworks also on a different dataset Te average accuracy o thesystem rose rom approximately 983096983097 to 983097983088
983091983091983091 Arti1047297cial Neural Network We used eedorward back-propagation package available in MALAB Neural Network oolbox and ound Levenberg-Marquardt to be the bestmethod with 983088983088983093 learning rate
(a) CHBMIT For CHBMI dataset initial training o theclassi1047297er resulted in 983097983090983096983096 average accuracy 983097983096983094983094 averagespeci1047297city and 983095983093983095983093 average sensitivity or 983091 Hz spike andwave which is a characteristic o absence seizure (Figure 983097)
983137983138983148983141 983093 First column shows the channel label the second columnshows the initial training accuracy and third one shows the markedcorrection by a neurologistand thelast one shows the1047297nal accuracy
In able 983095 we have shown the average initial classi1047297cationand retrained classi1047297cation results o our system or eachchannel In this system we have shown that afer correctiono ew epochs there is visible improvement in the systemsclassi1047297cation Te average accuracy o the system rose rom983097983090983096983096 to 983097983091983097983094
(b) PIMH For PIMH dataset initial training o the classi1047297erresulted in 983096983092 average accuracy 983097983092983096 average speci1047297cityand 983093983094983093 average sensitivity or 983091 Hz spike and wave whichis a characteristic o absence seizure (Figure 983089983088)
In able 983096 we have shown the average initial classi1047297cationand retrained classi1047297cation results o our system or each
channel able 983096 shows that our technique is robust and itworks also on a different dataset Te average accuracy o thesystem rose rom approximately 983096983092 to 983096983093983092983091
4 Discussion and Future Work
Computer-assisted analysis o EEG has tremendous potentialor assisting the clinicians in diagnosis A very importantand novel phase o our system is user adaptation mechanismor retraining mechanism Introduction o this phase hasimportance in many aspects In this phase system tries toadapt its classi1047297cation according to users desire Moreoverthis technique personalizes the classi1047297ers classi1047297cation It has
983137983138983148983141 983094 First column shows the channel label the second columnshows the initial training accuracy and third one shows the markedcorrection by a neurologistand thelast one shows the1047297nal accuracy
been cited that sometimes even the expert neurologists havesome disagreementovera certain observation o an EEGdataTis system will be useul or disagreeing users and it will alsohelp them in comparing their results with each other
Tere is also a threat o over1047297tting by the classi1047297erIn order to keep the classi1047297er improving its perormancewith the encounter o more and more examples we haveintroduced this user adaptive mechanism in our system Weconsider the existing systems as dead because these cannotimprove their classi1047297cation rate afer initial training (duringsofware development) Te sel-improving mechanism aferdeployment makes our tool alive Tis system can be madepart o the whole epileptic diagnosis process It will highlight
983137983138983148983141 983095 First column shows the channel label the second columnshows the initial training accuracy and third one shows the markedcorrection by a neurologistand thelast one shows the1047297nal accuracy
the epileptic spikes among the whole EEG thus leading toreduced atigue and time consumption o a user We obtainedhigh classi1047297cation accuracy on datasets obtained rom twodifferent sites which indicates reproducibility o our resultsand robustness o our approach
In the uture we are planning to make this a web basedapplication neurologists can log in and consult each otherrsquosreviews about a particular subject Tis will make our systemexperience a whole versatility o examples and learn rom allo them Integration o the video and its automatic analysis(video EEG) can help a neurologist in diagnosing epilepsy ina better way whereas this can also help him in distinguishingbetween psychogenic and epileptic seizuresWewouldalso be
investigating how much over1047297tting is an issue in the reportedperormances which are now even touching 983089983088983088 based onsome claims Tere is a need or methodcriteria which couldlimit these algorithms improving their detection on a limitednumber o available examples
Tissystem is made keeping in mind thatwe haveto acil-itate the neurologist by supplementing him in the analysis o the EEG We do not want to enorce the classi1047297cation o theEEG data on a user
In the uture we will also include a slider in the systemwhich will allow the user to adjust the sensitivity and speci-1047297city beore retraining Tis assisting system is more like adetection tool which is continuously learning with encounter
983137983138983148983141 983096 Classi1047297cation rate improvement caused by retraining Firstcolumn shows thechannellabel thesecond columnshows theinitialtraining accuracy and third one shows the marked correction by aneurologist and the last one shows the 1047297nal accuracy
o better examples More and better examples will certainly improve its perormance Te agreement between differentneurologists over the EEG readings is low to moderate I we could 1047297nd the agreement on at least ew o the epilepticpatterns correspondence with epileptic disease then we cantake this tool urther ahead and use it or diagnosis instead o
just assistanceOne o the biggest limitations to this study is the unavail-
ability o non-983091 Hz spike and wave data Even though we haveincluded the data eatures o the entire epileptic requency ranges exclusive to each other proo testing on the data will
certainly prove worthy orthe progresso these assisting toolstoward a diagnostic tool
Conflict of Interests
Te authors declare that there is no con1047298ict o interests
regarding the publication o this paper
Acknowledgment
Te authors would like to extend their sincere appreciation tothe Deanship o Scienti1047297c Research at King Saud University or unding this research through Research Group Project(RG no 983089983092983091983093-983088983093983089)
References
[983089] H Adelia Z Zhoub and N Dadmehrc ldquoAnalysis o EEGrecords in an epileptic patient using wavelet transormrdquo Journal of Neuroscience Methods vol 983089983090983091 no 983089 pp 983094983097ndash983096983095 983090983088983088983091
[983090] M A Ahmad W Majeed and N A Khan ldquoAdvancements incomputer aided methods or EEG-based epileptic detectionrdquo inProceedings of the 983095th International Conference on Bio-Inspired Systems and Signal Processing (BIOSIGNALS 983089983092) AngersFrance 983090983088983089983092
[983091] S Noachtar and J R emi ldquoTe role o EEG in epilepsy a criticalreviewrdquo Epilepsy and Behavior vol 983089983093 no 983089 pp 983090983090ndash983091983091 983090983088983088983097
[983092] E B Petersen J Duun-Henriksen A Mazzaretto W Kjar CE Tomsen and H B D Sorensen ldquoGeneric single-channeldetection o absence seizuresrdquo in Proceedings of the Annual International Conference of the IEEE Engineering in Medicineand Biology Society (EMBS rsquo983089983089) pp 983092983096983090983088ndash983092983096983090983091 Boston MassUSA September 983090983088983089983089
[983093] M Kaleem A Guergachi and S Krishnan ldquoEEG seizure
detection and epilepsy diagnosis using a novel variation o Empirical Mode Decompositionrdquo in Proceedings of the 983091983093th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC rsquo983089983091) pp 983092983091983089983092ndash983092983091983089983095 OsakaJapan July 983090983088983089983091
[983094] S M S Alam and M I H Bhuiyan ldquoDetection o seizure andepilepsy usinghigher order statistics in the EMD domainrdquo IEEE Journal of Biomedical and Health Informatics vol 983089983095 no 983090 pp983091983089983090ndash983091983089983096 983090983088983089983091
[983095] H Ocbagabir K A I Aboalayon and M FaezipourldquoEfficientEEG analysis or seizure monitoring in epileptic patientsrdquo inProceedings of the Long Island Systems Applications and Tech-nology Conference (LISAT rsquo983089983091) pp 983089ndash983094 IEEE Farmingdale NYUSA May 983090983088983089983091
[983096] A A Abdullah S A Rahim and A Ibrahim ldquoDevelopment o EEG-based epileptic detection using arti1047297cial neural networkrdquoin Proceedings of the International Conference on Biomedical Engineering (ICoBE rsquo983089983090) pp 983094983088983093ndash983094983089983088 Penang Malaysia 983090983088983089983090
[983097] P Guo J Wang X Z Gao and J M A anskanen ldquoEpilepticEEG signal classi1047297cation with marching pursuit based on har-mony search methodrdquo in Proceedings of the IEEE International Conference on Systems Man and Cybernetics (SMC rsquo983089983090) pp983090983096983091ndash983090983096983096 Seoul Republic o Korea October 983090983088983089983090
[983089983088] A Shoeb ldquoCHB-MI scalp EEG databaserdquo 983090983088983088983088 httpphysionetorgpn983094chbmit
[983089983089] A S M Murugavel S Ramakrishnan U Maheswari and BS Sabetha ldquoCombined Seizure Index with Adaptive Multi-Class SVM or epileptic EEG classi1047297cationrdquo in Proceedings
of the International Conference on Emerging Trends in VLSIEmbedded System Nano Electronics and TelecommunicationSystem (ICEVENT rsquo983089983091) pp 983089ndash983093 Tiruvannammalai IndiaJanuary 983090983088983089983091
[983089983090] M H Abdullah J M Abdullah and M Z Abdullah ldquoSeizuredetection by means o hidden Markov model and station-ary wavelet transorm o electroencephalograph signalsrdquo inProceedings of the IEEE-EMBS International Conference onBiomedical and Health Informatics (BHI rsquo983089983090) pp 983094983090ndash983094983093 HongKong January 983090983088983089983090
[983089983091] A Subasi and M I Gursoy ldquoEEG signal classi1047297cation usingPCA ICA LDA and support vector machinesrdquo Expert Systemswith Applications vol 983091983095 no 983089983090 pp 983096983094983093983097ndash983096983094983094983094 983090983088983089983088
[983089983092] E Sezer H Isik and E Saracoglu ldquoEmployment and compari-son o different arti1047297cial neural networks or epilepsy diagnosisrom EEG signalsrdquo Journal of Medical Systems vol 983091983094 no 983089 pp983091983092983095ndash983091983094983090 983090983088983089983090
[983089983093] Brain Products GmbHProducts and ApplicationsAnalyzer 983090httpwwwbrainproductscomproductdetailsphpid=983089983095
[983089983094] NeuroExplorer Home httpwwwneuroexplorercom
[983089983096] C H Seng R Demirli L Khuon and D Bolger ldquoSeizuredetection in EEG signals using support vector machinesrdquo inProceedings of the 983091983096th Annual Northeast Bioengineering Con- ference (NEBEC rsquo983089983090) pp 983090983091983089ndash983090983091983090 Philadelphia Pa USA March983090983088983089983090
[983089983097] Z A Khan S B Mansoor M A Ahmad and M M MalikldquoInput devices or virtual surgical simulations a comparativestudyrdquo in Proceedings of the 983089983094th International Multi Topic Con- ference (INMIC rsquo983089983091) pp 983089983096983097ndash983089983097983092 Lahore Pakistan December983090983088983089983091
[983090983088] A L Goldberger L A Amaral L Glass et al ldquoPhysioBankPhysiooolkit and PhysioNet components o a new research
[983090983089] S Nasehi and H Pourghassem ldquoEpileptic seizure onset detec-tion algorithm using dynamic cascade eed-orward neural net-worksrdquo in Proceedings of the International Conference on Intelli- gent Computation and Bio-Medical Instrumentation (ICBMI rsquo983089983089)pp 983089983097983094ndash983089983097983097 December 983090983088983089983089
983137983138983148983141 983089 Tis table describes the affiliation o detailed coefficientswith epileptic requency band o interest or 983090983093983094 Hz sampled CHB-MI dataset
Epileptic requency range Detailed coefficientsrsquo level
Beta (907317) CD983091 (983091983090 Hz to 983089983094 Hz)
Alpha (1103925) CD983092 (983089983094 Hz to 983096 Hz)
Teta (1038389) CD983093 (983096 Hz to 983092 Hz)
Delta () CD983094 (983092 Hz to 983090 Hz)
Delta () CD983095 (983090 Hz to 983089 Hz)
983137983138983148983141 983090 Tis table describes the affiliation o detailed coefficientswith epileptic requency band o interest or 983093983089983090 Hz sampled PIMHdataset
Epileptic requency range Detailed coefficientsrsquo level
Beta (907317) CD983092 (983091983089983090 Hz to 983089983093983094 Hz)
Alpha (1103925) CD983093 (983089983093983094 Hz to 983095983096 Hz)
Teta (1038389) CD983094 (983095983096 Hz to 983091983097 Hz)
Delta () CD983095 (983091983097 Hz to 983090 Hz)
Delta () CD983096 (983090 Hz to 983089 Hz)
983091983089983089 CHBMIT Tis database is the online available suraceEEG dataset [983090983088] which is provided by Children HospitalBoston and Massachusetts Institute o echnology and it isavailable at physioNet website [983089983088] It contains 983097983089983094 hours o 983090983091 channels scalp EEG recording rom 983090983092 epilepsy suspectedpatients Tis ECG recording is sampledat 983090983093983094Hz with 983089983094-bitresolution Te 983090983091rd channel is same as 983089983093th channel (able 983089)
983091983089983090 PIMH Te second database o EEG datasets is pro- vided by our collaborator at Punjab Institute o Mental Health(PIMH) Lahore Its sampling requency is 983093983088983088 Hz and it wasrecorded on 983092983091 channels (among which 983091983091 channels are orEEG) Tis dataset consists o 983090983089 patients EEG recording
983091983090 Features
983091983090983089 Feature Extraction Data which interests us lies inbetween the requency range o 983088983091 Hz to 983091983088 Hz So aferapplying DW with db983092 mother wavelet we have to selectdetailed coefficients with this requency range So in case o 983090983093983094 Hz sampled CHBMI dataset we have to go to at least983091 levels o decomposition and discard the earlier two as it isdemonstrated in Figure 983090 In order to get the discriminating
inormation between different types o epileptic patterns andidentiying them correctly without mistaking them with eachother decomposition o this detailed coefficient urther inBeta Alpha Teta and Delta is hugely helpul So we urtherdecomposed them until the 983095th level Hence we used theDWs detailed coefficients o levels 983091 983092 983093 983094 and 983095 or 983090983093983094Hzsampled CHB-MI dataset (able 983090)
Afer the selection o the wavelet coefficients we calcu-lated the statistical eature out o them Te statistical eatureswere the mean power and standard deviation o all o theselected coefficients
In case o 983093983089983090 Hz sampled PIMH dataset we used theDWs detailed coefficients o levels 983092 983093 983094 983095 and 983096
Afer the selection o detailed coefficients we calculatedthe statistical eature out o them Te statistical eatureswere the mean power and standard deviation o all o theshortlisted detailed coefficients
983091983090983090 Standardization During training stage we 1047297rst used
simple -score normalization to standardize the eatures [983089983097]beore applying eature reduction But the real issue arosewhen we tried to normalize them during testing stage Oneway o doing this is that we keep all o the examples andapply -score on them along with the new test data Insteado this time taking process we made an assumption onour observation that mean and standard deviation does notdeviate a lot It is analysed in this study that the EEG timeseries are assumed to be stationary over a small length o thesegments So we used the mean and standard deviation o thetraining examples rom the training stage to normalize thetest examples Figures 983093 and 983094 illustrate our observation inwhich you can see that there is not much deviation in trainand train + test examples mean and standard deviation
983091983091 Classi1047297er Classi1047297cation is used in machine learningto reer to the problem o identiying a discrete category to which a new observation belongs Observations withknown labels are used to train a classi1047297cation algorithm orclassi1047297er using eatures associated with the observation ForCHBMI database we had to train 983090983090983088 classi1047297ers in initialtraining stage Te calculation behind 983090983090983088 is the 983090983090 channelsmultiplied by 983089983088 types o epileptic pattern Te 983090983091rd channelwas same as 983089983093th channel For PIMH dataset 983091983091983088 classi1047297erswere trained where 983091983091 channels o EEG were utilized Wetried three different classi1047297ers and ound SVM to be the mostaccurate
We have used blind validation mechanism or the tendifferent eature data distributions to estimate the classi1047297-cation perormance Tese 983089983088 different and separate blinddata distributions were taken rom a huge set o EEGdataset Tese 983089983088 data distributions we randomly dividedinto two groups We trained our classi1047297er on one hal o thedistribution and tested it on the other hal We repeated thaton all ten distributions Ten we calculated the average o theclassi1047297cation rate or the all ten distributions
983091983090983090983097 out o 983091983090983097983095983094983088983088 epochs were randomly taken or tentimes rom CHBMI dataset Each time hal o them wereused to train and hal o them were used to test the initialclassi1047297cation Te average o the sensitivity speci1047297city and
accuracy or these ten distributionsis considered as the initialtraining phase perormance
Same approach wasapplied on PIMH datasetswhere 983091983090983090983097out o 983090983092983088983097983095 epochs were randomly taken rom PIMH datasetor the six times instead o ten times
Due to unavailability o the non-983091 Hz spike and waveepileptic EEG data currently we have only classi1047297cation ratesor generalized absence seizure
983091983091983089 Exclusive Processing In this study we have analysedthat even in the case o absence seizure epileptic patterns donot appear in the exact same way in each channel Handlingo each channel exclusive to each other was also another very
F983145983143983157983154983141 983090 Selection o DW detailed coefficients or a 983090983093983094 Hz sampled 983089 sec wide epochs EEG signal
important decision We tested the classi1047297cation in both waysthat is one classi1047297er or all o the channels at once versus oneseparate classi1047297er or each channel (Figure 983096)
Tis processing o each channel exclusive to each otherimproved over average accuracy rom approximately 983097983089 toapproximately 983097983093 in case o SVM So or SVM there is
a signi1047297cant improvement o 983092 by this change In case o QDA accuracy rose rom 983097983089 to 983097983092 with an improvemento 983091 and in case o ANN it rose rom 983097983089983096 to 983097983090983097 withan improvement o 983089983089
Results show that SVMsuites ourmethodin the most effi-cient way ANN has a lesser classi1047297cation time and LDA has
a lesser training time as compared to SVM but consideringthe sensitivity and classi1047297cation improvement through cor-rective marking we think that SVM is the better choice thanLDA and ANN In upcoming sections we have shown theresults or all three types o classi1047297er
0 5 10 15 20 250
24
6
8
10
12
14
16times10
4
Mean o the training examplesMean o the training + test examples
F983145983143983157983154983141 983093 Relationship between channelrsquos number and mean value(test + training examples)
(a) Adaptation Mechanism o test the adaptation mechanism983096983088983095corrective epochs were markedby the user ora CHBMI
F983145983143983157983154983141 983094 Relationship between channelrsquos number and standard deviation value (test + training examples)
EEG
Channel 1 DWTD1
A1 D2
A2
Mean
Standard deviation
Power
Mean
Standard deviation
Power
Mean
Standard deviation
Power
SVM or pattern 1
SVM or pattern 2
SVM or pattern 3
SVM or pattern 10
OPor
channel1
Channel 2 DWTD1
A1 D2
A2
Mean
Standard deviation
Power
Mean
Standard deviation
Power
MeanStandard deviation
Power
SVM or pattern 1
SVM or pattern 2
SVM or pattern 3
SVM or pattern 10
OPor
channel2
DWTD1
A1 D2
A2
Mean
Standard deviation
Power
Mean
Standard deviation
Power
Mean
Standard deviation
Power
SVM or pattern 1
SVM or pattern 2
SVM or pattern 3
SVM or pattern 10
Features
Features
PCA
Reduction
Standardization
PCA
Standardization
Reduction
FeaturesPCA
Reduction
Standardization
Surgeonrsquos marking
Surgeonrsquos marking
Surgeonrsquos marking
OPor
channeln
Al
Dl
Al
Al
Dl
Dl
Z-score
Z-score
Z-scoreChannel n
F983145983143983157983154983141 983095 Flowchart
dataset 1047297le and he marked the same amount o epochs oreach channel Tese corrective markings were saved in hislog as training examples Tese corrective markings as thenew examples along with the 983091983090983090983097983088 epochs o initial trainingstage were used to retrain the classi1047297er Te number 983091983090983090983097983088 hascome rom the 983091983090983090983097 randomly selected epochs rom whole o the CHBMI dataset or the ten separate times during initial
training phase Ten later the perormance o the classi1047297erafer retraining was judged again on another random 983091983088983088983088epochs (Figure 983089983092)
In case o PIMH dataset 983093983095 corrective epochs wereselected or PIMH dataset Tis time 983089983097983091983095983092 epochs o thePIMH dataset were used along with the 983093983095 corrective mark-ings as orthe PIMH we randomly selected the 983091983090983090983097 numbers
Processing all channels simultaneously with single classi1047297er
Processing all channels separately with separate classi1047297er oreach channel
F983145983143983157983154983141 983096 Accuracy relationship o different classi1047297ers and theirclassi1047297cation rate
84
86
88
90
92
94
96
98
100
Accuracy afer initial training ()
Accuracy afer retraining ()
ldquo F P 1 F 7 rdquo
ldquo F 7 T 7 rdquo
ldquo T 7 P 7 rdquo
ldquo P 7 O 1 rdquo
ldquo F P 1 F 3 rdquo
ldquo F 3 C 3 rdquo
ldquo C 3 P 3 rdquo
ldquo P 3 O 1 rdquo
ldquo F P 2 F 4 rdquo
ldquo F 4 C 4 rdquo
ldquo C 4 P 4 rdquo
ldquo P 4 O 2 rdquo
ldquo F P 2 F 8 rdquo
ldquo F 8 T 8 rdquo
ldquo T 8 P 8 rdquo
ldquo P 8 O 2 rdquo
ldquo F Z C Z rdquo
ldquo C Z P Z rdquo
ldquo P 7 T 7 rdquo
ldquo T 7 F T 9 rdquo
ldquo F T 9 F T 1 0 rdquo
ldquo F T 1 0 T 8 rdquo
F983145983143983157983154983141 983097 Relation between average classi1047297cation rate and accuracy
o the channel afer initial training and retaining
o epochs or the six times Te retrained classi1047297er was testedon the 983090983091983094983089 remaining epochs
(b) Support Vector Machine We used the support vectormachine classi1047297er package available in MALAB Bioinor-matics oolbox We ound linear kernel to be the mostaccurate SVM kernel with 983093983088 as the box constraint
(c) CHBMIT For CHBMI dataset initial training o theclassi1047297er resulted in 983097983094983091 average accuracy 983097983095983092 average
0
20
40
60
80
100
120
Accuracy afer initial training ()
Accuracy afer retraining ()
ldquo F p 1 rdquo
ldquo F p 2 rdquo
ldquo F 3 rdquo
ldquo F 4 rdquo
ldquo C 3 rdquo
ldquo C 4 rdquo
ldquo P 3 rdquo
ldquo P 4 rdquo
ldquo O 1 rdquo
ldquo O 2 rdquo
ldquo F 7 rdquo
ldquo F 8 rdquo
ldquo T 3 rdquo
ldquo T 4 rdquo
ldquo T 5 rdquo
ldquo T 6 rdquo
ldquo F z rdquo
ldquo C z rdquo
ldquo P z rdquo
ldquo E rdquo
ldquo P G 2 rdquo
ldquo A 1 rdquo
ldquo A 2 rdquo
ldquo T 1 rdquo
ldquo T 2 rdquo
ldquo X 1 rdquo
ldquo X 2 rdquo
ldquo X 3 rdquo
ldquo X 4 rdquo
ldquo X 5 rdquo
ldquo X 6 rdquo
ldquo X 7 rdquo
ldquo P G 1 rdquo
F983145983143983157983154983141 983089983088 Relation between average classi1047297cation rate and accuracy o the channel afer initial training and retaining
82
84
86
88
90
92
94
96
98
Accuracy afer initial training ()Accuracy afer retraining ()
ldquo F P 1 F 7 rdquo
ldquo F 7 T 7 rdquo
ldquo T 7 P 7 rdquo
ldquo P 7 O 1 rdquo
ldquo F P 1 F 3 rdquo
ldquo F 3 C 3 rdquo
ldquo C 3 P 3 rdquo
ldquo P 3 O 1 rdquo
ldquo F P 2 F 4 rdquo
ldquo F 4 C 4 rdquo
ldquo C 4 P 4 rdquo
ldquo P 4 O 2 rdquo
ldquo F P 2 F 8 rdquo
ldquo F 8 T 8 rdquo
ldquo T 8 P 8 rdquo
ldquo P 8 O 2 rdquo
ldquo F Z C Z rdquo
ldquo C Z P Z rdquo
ldquo P 7 T 7 rdquo
ldquo T 7 F T 9 rdquo
ldquo F T 9 F T 1 0 rdquo
ldquo F T 1 0 T 8 rdquo
F983145983143983157983154983141 983089983089 Relation between average classi1047297cation rate and accuracy o the channel afer initial training and retaining
speci1047297city and 983097983091983093 average sensitivity or 983091 Hz spike andwave which is a characteristic o absence seizure Aferinitial training our speci1047297city is better than that o Shoeb[983089983088] and Nasehi and Pourghassem [983090983089] who used the samedataset to validate their technique with different eaturesand application technique Tis shows that our technique
is providing better results even at the initial training phase(Figure 983089983089)
In able 983091 we have shown the average initial classi1047297cationand retrained classi1047297cation results o our system or eachchannel In this system we have shown that afer correctiono ew epochs there is a visible improvement in the systemsclassi1047297cation Te average accuracy o the system rose rom983097983093983093 to 983097983094983091
(d) PIMH For PIMH dataset initial training o the classi1047297erresulted in 983097983088 average accuracy 983097983092 average speci1047297city and983096983088 average sensitivity or 983091 Hz spike and wave which is acharacteristic o absence seizure (Figure 983089983090)
F983145983143983157983154983141 983089983090 Relation between average classi1047297cation rateand accuracy o the channel afer initial training and retaining
80
82
84
86
88
90
92
94
96
98
Accuracy afer initial training ()
Accuracy afer retraining ()
ldquo F P 1 F 7 rdquo
ldquo F 7 T 7 rdquo
ldquo T 7 P 7 rdquo
ldquo P 7 O 1 rdquo
ldquo F P 1 F 3 rdquo
ldquo F 3 C 3 rdquo
ldquo C 3 P 3 rdquo
ldquo P 3 O 1 rdquo
ldquo F P 2 F 4 rdquo
ldquo F 4 C 4 rdquo
ldquo C 4 P 4 rdquo
ldquo P 4 O 2 rdquo
ldquo F P 2 F 8 rdquo
ldquo F 8 T 8 rdquo
ldquo T 8 P 8 rdquo
ldquo P 8 O 2 rdquo
ldquo F Z C Z rdquo
ldquo C Z P Z rdquo
ldquo P 7 T 7 rdquo
ldquo T 7 F T 9 rdquo
ldquo F T 9 F T 1 0 rdquo
ldquo F T 1 0 T 8 rdquo
F983145983143983157983154983141 983089983091 Relation between average classi1047297cation rate and accuracy o the channel afer initial training and retaining
In able 983092 we have shown the average initial classi1047297cationand retrained classi1047297cation results o our system or eachchannel able 983092 shows that our technique is robust and itworks also on a different dataset Te average accuracy o thesystem rose rom approximately 983096983097 to 983097983088
983091983091983090 Discriminate Analysis We used the discriminant anal-
ysis package available in MALAB Statistics oolbox Weound pseudoquadratic to be the best perorming discrimi-nate type with uniorm probability
(a) CHBMIT For CHBMI dataset initial training o theclassi1047297er resulted in 983097983092 average accuracy 983097983094 averagespeci1047297city and 983097983088 average sensitivity or 983091 Hz spike andwave which is a characteristic o absence seizure Afer initialtraining our speci1047297city is better than that o Shoeb [983089983088] andNasehi and Pourghassem [983090983089] (Figure 983089983091)
In able 983093 we have shown the average initial classi1047297cationand retrained classi1047297cation results o our system or eachchannel In this system we have shown that afer correction
010
2030405060708090
100
Accuracy afer initial training ()
Accuracy afer retraining ()
ldquo F 3 rdquo
ldquo F 4 rdquo
ldquo C 3 rdquo
ldquo C 4 rdquo
ldquo P 3 rdquo
ldquo P 4 rdquo
ldquo O 1 rdquo
ldquo O 2 rdquo
ldquo F 7 rdquo
ldquo F 8 rdquo
ldquo T 3 rdquo
ldquo T 4 rdquo
ldquo T 5 rdquo
ldquo T 6 rdquo
ldquo F z rdquo
ldquo C z rdquo
ldquo P z rdquo
ldquo E rdquo
ldquo P G 2 rdquo
ldquo T 1 rdquo
ldquo T 2 rdquo
ldquo X 1 rdquo
ldquo X 2 rdquo
ldquo X 3 rdquo
ldquo X 4 rdquo
ldquo X 5 rdquo
ldquo X 6 rdquo
ldquo X 7 rdquo
ldquo P G 1 rdquo
ldquo A 1 rdquo
ldquo A 2 rdquo
ldquo F p 1 rdquo
ldquo F p 2 rdquo
F983145983143983157983154983141 983089983092 Relation between average classi1047297cation rate and accuracy o the channel afer initial training and retaining
983137983138983148983141 983091 First column shows the channel label the second columnshows the initial training accuracy and third one shows the markedcorrection by a neurologistand thelast one shows the1047297nal accuracy
o ew epochs there is visible improvement in the systemsclassi1047297cation Te average accuracy o the system rose rom983097983092 to 983097983093
(b) PIMH For PIMH dataset initial training o the classi1047297erresulted in 983097983088 average accuracy 983097983093 average speci1047297city and983095983091 average sensitivity or 983091 Hz spike and wave which is acharacteristic o absence seizure
In able 983094 we have shown the average initialclassi1047297cationand retrained classi1047297cation results o our system or each
983137983138983148983141 983092 First column shows the channel label the second columnshows the initial training accuracy and third one shows the markedcorrection by a neurologistand thelast one shows the1047297nal accuracy
channel able 983094 shows that our technique is robust and itworks also on a different dataset Te average accuracy o thesystem rose rom approximately 983096983097 to 983097983088
983091983091983091 Arti1047297cial Neural Network We used eedorward back-propagation package available in MALAB Neural Network oolbox and ound Levenberg-Marquardt to be the bestmethod with 983088983088983093 learning rate
(a) CHBMIT For CHBMI dataset initial training o theclassi1047297er resulted in 983097983090983096983096 average accuracy 983097983096983094983094 averagespeci1047297city and 983095983093983095983093 average sensitivity or 983091 Hz spike andwave which is a characteristic o absence seizure (Figure 983097)
983137983138983148983141 983093 First column shows the channel label the second columnshows the initial training accuracy and third one shows the markedcorrection by a neurologistand thelast one shows the1047297nal accuracy
In able 983095 we have shown the average initial classi1047297cationand retrained classi1047297cation results o our system or eachchannel In this system we have shown that afer correctiono ew epochs there is visible improvement in the systemsclassi1047297cation Te average accuracy o the system rose rom983097983090983096983096 to 983097983091983097983094
(b) PIMH For PIMH dataset initial training o the classi1047297erresulted in 983096983092 average accuracy 983097983092983096 average speci1047297cityand 983093983094983093 average sensitivity or 983091 Hz spike and wave whichis a characteristic o absence seizure (Figure 983089983088)
In able 983096 we have shown the average initial classi1047297cationand retrained classi1047297cation results o our system or each
channel able 983096 shows that our technique is robust and itworks also on a different dataset Te average accuracy o thesystem rose rom approximately 983096983092 to 983096983093983092983091
4 Discussion and Future Work
Computer-assisted analysis o EEG has tremendous potentialor assisting the clinicians in diagnosis A very importantand novel phase o our system is user adaptation mechanismor retraining mechanism Introduction o this phase hasimportance in many aspects In this phase system tries toadapt its classi1047297cation according to users desire Moreoverthis technique personalizes the classi1047297ers classi1047297cation It has
983137983138983148983141 983094 First column shows the channel label the second columnshows the initial training accuracy and third one shows the markedcorrection by a neurologistand thelast one shows the1047297nal accuracy
been cited that sometimes even the expert neurologists havesome disagreementovera certain observation o an EEGdataTis system will be useul or disagreeing users and it will alsohelp them in comparing their results with each other
Tere is also a threat o over1047297tting by the classi1047297erIn order to keep the classi1047297er improving its perormancewith the encounter o more and more examples we haveintroduced this user adaptive mechanism in our system Weconsider the existing systems as dead because these cannotimprove their classi1047297cation rate afer initial training (duringsofware development) Te sel-improving mechanism aferdeployment makes our tool alive Tis system can be madepart o the whole epileptic diagnosis process It will highlight
983137983138983148983141 983095 First column shows the channel label the second columnshows the initial training accuracy and third one shows the markedcorrection by a neurologistand thelast one shows the1047297nal accuracy
the epileptic spikes among the whole EEG thus leading toreduced atigue and time consumption o a user We obtainedhigh classi1047297cation accuracy on datasets obtained rom twodifferent sites which indicates reproducibility o our resultsand robustness o our approach
In the uture we are planning to make this a web basedapplication neurologists can log in and consult each otherrsquosreviews about a particular subject Tis will make our systemexperience a whole versatility o examples and learn rom allo them Integration o the video and its automatic analysis(video EEG) can help a neurologist in diagnosing epilepsy ina better way whereas this can also help him in distinguishingbetween psychogenic and epileptic seizuresWewouldalso be
investigating how much over1047297tting is an issue in the reportedperormances which are now even touching 983089983088983088 based onsome claims Tere is a need or methodcriteria which couldlimit these algorithms improving their detection on a limitednumber o available examples
Tissystem is made keeping in mind thatwe haveto acil-itate the neurologist by supplementing him in the analysis o the EEG We do not want to enorce the classi1047297cation o theEEG data on a user
In the uture we will also include a slider in the systemwhich will allow the user to adjust the sensitivity and speci-1047297city beore retraining Tis assisting system is more like adetection tool which is continuously learning with encounter
983137983138983148983141 983096 Classi1047297cation rate improvement caused by retraining Firstcolumn shows thechannellabel thesecond columnshows theinitialtraining accuracy and third one shows the marked correction by aneurologist and the last one shows the 1047297nal accuracy
o better examples More and better examples will certainly improve its perormance Te agreement between differentneurologists over the EEG readings is low to moderate I we could 1047297nd the agreement on at least ew o the epilepticpatterns correspondence with epileptic disease then we cantake this tool urther ahead and use it or diagnosis instead o
just assistanceOne o the biggest limitations to this study is the unavail-
ability o non-983091 Hz spike and wave data Even though we haveincluded the data eatures o the entire epileptic requency ranges exclusive to each other proo testing on the data will
certainly prove worthy orthe progresso these assisting toolstoward a diagnostic tool
Conflict of Interests
Te authors declare that there is no con1047298ict o interests
regarding the publication o this paper
Acknowledgment
Te authors would like to extend their sincere appreciation tothe Deanship o Scienti1047297c Research at King Saud University or unding this research through Research Group Project(RG no 983089983092983091983093-983088983093983089)
References
[983089] H Adelia Z Zhoub and N Dadmehrc ldquoAnalysis o EEGrecords in an epileptic patient using wavelet transormrdquo Journal of Neuroscience Methods vol 983089983090983091 no 983089 pp 983094983097ndash983096983095 983090983088983088983091
[983090] M A Ahmad W Majeed and N A Khan ldquoAdvancements incomputer aided methods or EEG-based epileptic detectionrdquo inProceedings of the 983095th International Conference on Bio-Inspired Systems and Signal Processing (BIOSIGNALS 983089983092) AngersFrance 983090983088983089983092
[983091] S Noachtar and J R emi ldquoTe role o EEG in epilepsy a criticalreviewrdquo Epilepsy and Behavior vol 983089983093 no 983089 pp 983090983090ndash983091983091 983090983088983088983097
[983092] E B Petersen J Duun-Henriksen A Mazzaretto W Kjar CE Tomsen and H B D Sorensen ldquoGeneric single-channeldetection o absence seizuresrdquo in Proceedings of the Annual International Conference of the IEEE Engineering in Medicineand Biology Society (EMBS rsquo983089983089) pp 983092983096983090983088ndash983092983096983090983091 Boston MassUSA September 983090983088983089983089
[983093] M Kaleem A Guergachi and S Krishnan ldquoEEG seizure
detection and epilepsy diagnosis using a novel variation o Empirical Mode Decompositionrdquo in Proceedings of the 983091983093th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC rsquo983089983091) pp 983092983091983089983092ndash983092983091983089983095 OsakaJapan July 983090983088983089983091
[983094] S M S Alam and M I H Bhuiyan ldquoDetection o seizure andepilepsy usinghigher order statistics in the EMD domainrdquo IEEE Journal of Biomedical and Health Informatics vol 983089983095 no 983090 pp983091983089983090ndash983091983089983096 983090983088983089983091
[983095] H Ocbagabir K A I Aboalayon and M FaezipourldquoEfficientEEG analysis or seizure monitoring in epileptic patientsrdquo inProceedings of the Long Island Systems Applications and Tech-nology Conference (LISAT rsquo983089983091) pp 983089ndash983094 IEEE Farmingdale NYUSA May 983090983088983089983091
[983096] A A Abdullah S A Rahim and A Ibrahim ldquoDevelopment o EEG-based epileptic detection using arti1047297cial neural networkrdquoin Proceedings of the International Conference on Biomedical Engineering (ICoBE rsquo983089983090) pp 983094983088983093ndash983094983089983088 Penang Malaysia 983090983088983089983090
[983097] P Guo J Wang X Z Gao and J M A anskanen ldquoEpilepticEEG signal classi1047297cation with marching pursuit based on har-mony search methodrdquo in Proceedings of the IEEE International Conference on Systems Man and Cybernetics (SMC rsquo983089983090) pp983090983096983091ndash983090983096983096 Seoul Republic o Korea October 983090983088983089983090
[983089983088] A Shoeb ldquoCHB-MI scalp EEG databaserdquo 983090983088983088983088 httpphysionetorgpn983094chbmit
[983089983089] A S M Murugavel S Ramakrishnan U Maheswari and BS Sabetha ldquoCombined Seizure Index with Adaptive Multi-Class SVM or epileptic EEG classi1047297cationrdquo in Proceedings
of the International Conference on Emerging Trends in VLSIEmbedded System Nano Electronics and TelecommunicationSystem (ICEVENT rsquo983089983091) pp 983089ndash983093 Tiruvannammalai IndiaJanuary 983090983088983089983091
[983089983090] M H Abdullah J M Abdullah and M Z Abdullah ldquoSeizuredetection by means o hidden Markov model and station-ary wavelet transorm o electroencephalograph signalsrdquo inProceedings of the IEEE-EMBS International Conference onBiomedical and Health Informatics (BHI rsquo983089983090) pp 983094983090ndash983094983093 HongKong January 983090983088983089983090
[983089983091] A Subasi and M I Gursoy ldquoEEG signal classi1047297cation usingPCA ICA LDA and support vector machinesrdquo Expert Systemswith Applications vol 983091983095 no 983089983090 pp 983096983094983093983097ndash983096983094983094983094 983090983088983089983088
[983089983092] E Sezer H Isik and E Saracoglu ldquoEmployment and compari-son o different arti1047297cial neural networks or epilepsy diagnosisrom EEG signalsrdquo Journal of Medical Systems vol 983091983094 no 983089 pp983091983092983095ndash983091983094983090 983090983088983089983090
[983089983093] Brain Products GmbHProducts and ApplicationsAnalyzer 983090httpwwwbrainproductscomproductdetailsphpid=983089983095
[983089983094] NeuroExplorer Home httpwwwneuroexplorercom
[983089983096] C H Seng R Demirli L Khuon and D Bolger ldquoSeizuredetection in EEG signals using support vector machinesrdquo inProceedings of the 983091983096th Annual Northeast Bioengineering Con- ference (NEBEC rsquo983089983090) pp 983090983091983089ndash983090983091983090 Philadelphia Pa USA March983090983088983089983090
[983089983097] Z A Khan S B Mansoor M A Ahmad and M M MalikldquoInput devices or virtual surgical simulations a comparativestudyrdquo in Proceedings of the 983089983094th International Multi Topic Con- ference (INMIC rsquo983089983091) pp 983089983096983097ndash983089983097983092 Lahore Pakistan December983090983088983089983091
[983090983088] A L Goldberger L A Amaral L Glass et al ldquoPhysioBankPhysiooolkit and PhysioNet components o a new research
[983090983089] S Nasehi and H Pourghassem ldquoEpileptic seizure onset detec-tion algorithm using dynamic cascade eed-orward neural net-worksrdquo in Proceedings of the International Conference on Intelli- gent Computation and Bio-Medical Instrumentation (ICBMI rsquo983089983089)pp 983089983097983094ndash983089983097983097 December 983090983088983089983089
F983145983143983157983154983141 983090 Selection o DW detailed coefficients or a 983090983093983094 Hz sampled 983089 sec wide epochs EEG signal
important decision We tested the classi1047297cation in both waysthat is one classi1047297er or all o the channels at once versus oneseparate classi1047297er or each channel (Figure 983096)
Tis processing o each channel exclusive to each otherimproved over average accuracy rom approximately 983097983089 toapproximately 983097983093 in case o SVM So or SVM there is
a signi1047297cant improvement o 983092 by this change In case o QDA accuracy rose rom 983097983089 to 983097983092 with an improvemento 983091 and in case o ANN it rose rom 983097983089983096 to 983097983090983097 withan improvement o 983089983089
Results show that SVMsuites ourmethodin the most effi-cient way ANN has a lesser classi1047297cation time and LDA has
a lesser training time as compared to SVM but consideringthe sensitivity and classi1047297cation improvement through cor-rective marking we think that SVM is the better choice thanLDA and ANN In upcoming sections we have shown theresults or all three types o classi1047297er
0 5 10 15 20 250
24
6
8
10
12
14
16times10
4
Mean o the training examplesMean o the training + test examples
F983145983143983157983154983141 983093 Relationship between channelrsquos number and mean value(test + training examples)
(a) Adaptation Mechanism o test the adaptation mechanism983096983088983095corrective epochs were markedby the user ora CHBMI
F983145983143983157983154983141 983094 Relationship between channelrsquos number and standard deviation value (test + training examples)
EEG
Channel 1 DWTD1
A1 D2
A2
Mean
Standard deviation
Power
Mean
Standard deviation
Power
Mean
Standard deviation
Power
SVM or pattern 1
SVM or pattern 2
SVM or pattern 3
SVM or pattern 10
OPor
channel1
Channel 2 DWTD1
A1 D2
A2
Mean
Standard deviation
Power
Mean
Standard deviation
Power
MeanStandard deviation
Power
SVM or pattern 1
SVM or pattern 2
SVM or pattern 3
SVM or pattern 10
OPor
channel2
DWTD1
A1 D2
A2
Mean
Standard deviation
Power
Mean
Standard deviation
Power
Mean
Standard deviation
Power
SVM or pattern 1
SVM or pattern 2
SVM or pattern 3
SVM or pattern 10
Features
Features
PCA
Reduction
Standardization
PCA
Standardization
Reduction
FeaturesPCA
Reduction
Standardization
Surgeonrsquos marking
Surgeonrsquos marking
Surgeonrsquos marking
OPor
channeln
Al
Dl
Al
Al
Dl
Dl
Z-score
Z-score
Z-scoreChannel n
F983145983143983157983154983141 983095 Flowchart
dataset 1047297le and he marked the same amount o epochs oreach channel Tese corrective markings were saved in hislog as training examples Tese corrective markings as thenew examples along with the 983091983090983090983097983088 epochs o initial trainingstage were used to retrain the classi1047297er Te number 983091983090983090983097983088 hascome rom the 983091983090983090983097 randomly selected epochs rom whole o the CHBMI dataset or the ten separate times during initial
training phase Ten later the perormance o the classi1047297erafer retraining was judged again on another random 983091983088983088983088epochs (Figure 983089983092)
In case o PIMH dataset 983093983095 corrective epochs wereselected or PIMH dataset Tis time 983089983097983091983095983092 epochs o thePIMH dataset were used along with the 983093983095 corrective mark-ings as orthe PIMH we randomly selected the 983091983090983090983097 numbers
Processing all channels simultaneously with single classi1047297er
Processing all channels separately with separate classi1047297er oreach channel
F983145983143983157983154983141 983096 Accuracy relationship o different classi1047297ers and theirclassi1047297cation rate
84
86
88
90
92
94
96
98
100
Accuracy afer initial training ()
Accuracy afer retraining ()
ldquo F P 1 F 7 rdquo
ldquo F 7 T 7 rdquo
ldquo T 7 P 7 rdquo
ldquo P 7 O 1 rdquo
ldquo F P 1 F 3 rdquo
ldquo F 3 C 3 rdquo
ldquo C 3 P 3 rdquo
ldquo P 3 O 1 rdquo
ldquo F P 2 F 4 rdquo
ldquo F 4 C 4 rdquo
ldquo C 4 P 4 rdquo
ldquo P 4 O 2 rdquo
ldquo F P 2 F 8 rdquo
ldquo F 8 T 8 rdquo
ldquo T 8 P 8 rdquo
ldquo P 8 O 2 rdquo
ldquo F Z C Z rdquo
ldquo C Z P Z rdquo
ldquo P 7 T 7 rdquo
ldquo T 7 F T 9 rdquo
ldquo F T 9 F T 1 0 rdquo
ldquo F T 1 0 T 8 rdquo
F983145983143983157983154983141 983097 Relation between average classi1047297cation rate and accuracy
o the channel afer initial training and retaining
o epochs or the six times Te retrained classi1047297er was testedon the 983090983091983094983089 remaining epochs
(b) Support Vector Machine We used the support vectormachine classi1047297er package available in MALAB Bioinor-matics oolbox We ound linear kernel to be the mostaccurate SVM kernel with 983093983088 as the box constraint
(c) CHBMIT For CHBMI dataset initial training o theclassi1047297er resulted in 983097983094983091 average accuracy 983097983095983092 average
0
20
40
60
80
100
120
Accuracy afer initial training ()
Accuracy afer retraining ()
ldquo F p 1 rdquo
ldquo F p 2 rdquo
ldquo F 3 rdquo
ldquo F 4 rdquo
ldquo C 3 rdquo
ldquo C 4 rdquo
ldquo P 3 rdquo
ldquo P 4 rdquo
ldquo O 1 rdquo
ldquo O 2 rdquo
ldquo F 7 rdquo
ldquo F 8 rdquo
ldquo T 3 rdquo
ldquo T 4 rdquo
ldquo T 5 rdquo
ldquo T 6 rdquo
ldquo F z rdquo
ldquo C z rdquo
ldquo P z rdquo
ldquo E rdquo
ldquo P G 2 rdquo
ldquo A 1 rdquo
ldquo A 2 rdquo
ldquo T 1 rdquo
ldquo T 2 rdquo
ldquo X 1 rdquo
ldquo X 2 rdquo
ldquo X 3 rdquo
ldquo X 4 rdquo
ldquo X 5 rdquo
ldquo X 6 rdquo
ldquo X 7 rdquo
ldquo P G 1 rdquo
F983145983143983157983154983141 983089983088 Relation between average classi1047297cation rate and accuracy o the channel afer initial training and retaining
82
84
86
88
90
92
94
96
98
Accuracy afer initial training ()Accuracy afer retraining ()
ldquo F P 1 F 7 rdquo
ldquo F 7 T 7 rdquo
ldquo T 7 P 7 rdquo
ldquo P 7 O 1 rdquo
ldquo F P 1 F 3 rdquo
ldquo F 3 C 3 rdquo
ldquo C 3 P 3 rdquo
ldquo P 3 O 1 rdquo
ldquo F P 2 F 4 rdquo
ldquo F 4 C 4 rdquo
ldquo C 4 P 4 rdquo
ldquo P 4 O 2 rdquo
ldquo F P 2 F 8 rdquo
ldquo F 8 T 8 rdquo
ldquo T 8 P 8 rdquo
ldquo P 8 O 2 rdquo
ldquo F Z C Z rdquo
ldquo C Z P Z rdquo
ldquo P 7 T 7 rdquo
ldquo T 7 F T 9 rdquo
ldquo F T 9 F T 1 0 rdquo
ldquo F T 1 0 T 8 rdquo
F983145983143983157983154983141 983089983089 Relation between average classi1047297cation rate and accuracy o the channel afer initial training and retaining
speci1047297city and 983097983091983093 average sensitivity or 983091 Hz spike andwave which is a characteristic o absence seizure Aferinitial training our speci1047297city is better than that o Shoeb[983089983088] and Nasehi and Pourghassem [983090983089] who used the samedataset to validate their technique with different eaturesand application technique Tis shows that our technique
is providing better results even at the initial training phase(Figure 983089983089)
In able 983091 we have shown the average initial classi1047297cationand retrained classi1047297cation results o our system or eachchannel In this system we have shown that afer correctiono ew epochs there is a visible improvement in the systemsclassi1047297cation Te average accuracy o the system rose rom983097983093983093 to 983097983094983091
(d) PIMH For PIMH dataset initial training o the classi1047297erresulted in 983097983088 average accuracy 983097983092 average speci1047297city and983096983088 average sensitivity or 983091 Hz spike and wave which is acharacteristic o absence seizure (Figure 983089983090)
F983145983143983157983154983141 983089983090 Relation between average classi1047297cation rateand accuracy o the channel afer initial training and retaining
80
82
84
86
88
90
92
94
96
98
Accuracy afer initial training ()
Accuracy afer retraining ()
ldquo F P 1 F 7 rdquo
ldquo F 7 T 7 rdquo
ldquo T 7 P 7 rdquo
ldquo P 7 O 1 rdquo
ldquo F P 1 F 3 rdquo
ldquo F 3 C 3 rdquo
ldquo C 3 P 3 rdquo
ldquo P 3 O 1 rdquo
ldquo F P 2 F 4 rdquo
ldquo F 4 C 4 rdquo
ldquo C 4 P 4 rdquo
ldquo P 4 O 2 rdquo
ldquo F P 2 F 8 rdquo
ldquo F 8 T 8 rdquo
ldquo T 8 P 8 rdquo
ldquo P 8 O 2 rdquo
ldquo F Z C Z rdquo
ldquo C Z P Z rdquo
ldquo P 7 T 7 rdquo
ldquo T 7 F T 9 rdquo
ldquo F T 9 F T 1 0 rdquo
ldquo F T 1 0 T 8 rdquo
F983145983143983157983154983141 983089983091 Relation between average classi1047297cation rate and accuracy o the channel afer initial training and retaining
In able 983092 we have shown the average initial classi1047297cationand retrained classi1047297cation results o our system or eachchannel able 983092 shows that our technique is robust and itworks also on a different dataset Te average accuracy o thesystem rose rom approximately 983096983097 to 983097983088
983091983091983090 Discriminate Analysis We used the discriminant anal-
ysis package available in MALAB Statistics oolbox Weound pseudoquadratic to be the best perorming discrimi-nate type with uniorm probability
(a) CHBMIT For CHBMI dataset initial training o theclassi1047297er resulted in 983097983092 average accuracy 983097983094 averagespeci1047297city and 983097983088 average sensitivity or 983091 Hz spike andwave which is a characteristic o absence seizure Afer initialtraining our speci1047297city is better than that o Shoeb [983089983088] andNasehi and Pourghassem [983090983089] (Figure 983089983091)
In able 983093 we have shown the average initial classi1047297cationand retrained classi1047297cation results o our system or eachchannel In this system we have shown that afer correction
010
2030405060708090
100
Accuracy afer initial training ()
Accuracy afer retraining ()
ldquo F 3 rdquo
ldquo F 4 rdquo
ldquo C 3 rdquo
ldquo C 4 rdquo
ldquo P 3 rdquo
ldquo P 4 rdquo
ldquo O 1 rdquo
ldquo O 2 rdquo
ldquo F 7 rdquo
ldquo F 8 rdquo
ldquo T 3 rdquo
ldquo T 4 rdquo
ldquo T 5 rdquo
ldquo T 6 rdquo
ldquo F z rdquo
ldquo C z rdquo
ldquo P z rdquo
ldquo E rdquo
ldquo P G 2 rdquo
ldquo T 1 rdquo
ldquo T 2 rdquo
ldquo X 1 rdquo
ldquo X 2 rdquo
ldquo X 3 rdquo
ldquo X 4 rdquo
ldquo X 5 rdquo
ldquo X 6 rdquo
ldquo X 7 rdquo
ldquo P G 1 rdquo
ldquo A 1 rdquo
ldquo A 2 rdquo
ldquo F p 1 rdquo
ldquo F p 2 rdquo
F983145983143983157983154983141 983089983092 Relation between average classi1047297cation rate and accuracy o the channel afer initial training and retaining
983137983138983148983141 983091 First column shows the channel label the second columnshows the initial training accuracy and third one shows the markedcorrection by a neurologistand thelast one shows the1047297nal accuracy
o ew epochs there is visible improvement in the systemsclassi1047297cation Te average accuracy o the system rose rom983097983092 to 983097983093
(b) PIMH For PIMH dataset initial training o the classi1047297erresulted in 983097983088 average accuracy 983097983093 average speci1047297city and983095983091 average sensitivity or 983091 Hz spike and wave which is acharacteristic o absence seizure
In able 983094 we have shown the average initialclassi1047297cationand retrained classi1047297cation results o our system or each
983137983138983148983141 983092 First column shows the channel label the second columnshows the initial training accuracy and third one shows the markedcorrection by a neurologistand thelast one shows the1047297nal accuracy
channel able 983094 shows that our technique is robust and itworks also on a different dataset Te average accuracy o thesystem rose rom approximately 983096983097 to 983097983088
983091983091983091 Arti1047297cial Neural Network We used eedorward back-propagation package available in MALAB Neural Network oolbox and ound Levenberg-Marquardt to be the bestmethod with 983088983088983093 learning rate
(a) CHBMIT For CHBMI dataset initial training o theclassi1047297er resulted in 983097983090983096983096 average accuracy 983097983096983094983094 averagespeci1047297city and 983095983093983095983093 average sensitivity or 983091 Hz spike andwave which is a characteristic o absence seizure (Figure 983097)
983137983138983148983141 983093 First column shows the channel label the second columnshows the initial training accuracy and third one shows the markedcorrection by a neurologistand thelast one shows the1047297nal accuracy
In able 983095 we have shown the average initial classi1047297cationand retrained classi1047297cation results o our system or eachchannel In this system we have shown that afer correctiono ew epochs there is visible improvement in the systemsclassi1047297cation Te average accuracy o the system rose rom983097983090983096983096 to 983097983091983097983094
(b) PIMH For PIMH dataset initial training o the classi1047297erresulted in 983096983092 average accuracy 983097983092983096 average speci1047297cityand 983093983094983093 average sensitivity or 983091 Hz spike and wave whichis a characteristic o absence seizure (Figure 983089983088)
In able 983096 we have shown the average initial classi1047297cationand retrained classi1047297cation results o our system or each
channel able 983096 shows that our technique is robust and itworks also on a different dataset Te average accuracy o thesystem rose rom approximately 983096983092 to 983096983093983092983091
4 Discussion and Future Work
Computer-assisted analysis o EEG has tremendous potentialor assisting the clinicians in diagnosis A very importantand novel phase o our system is user adaptation mechanismor retraining mechanism Introduction o this phase hasimportance in many aspects In this phase system tries toadapt its classi1047297cation according to users desire Moreoverthis technique personalizes the classi1047297ers classi1047297cation It has
983137983138983148983141 983094 First column shows the channel label the second columnshows the initial training accuracy and third one shows the markedcorrection by a neurologistand thelast one shows the1047297nal accuracy
been cited that sometimes even the expert neurologists havesome disagreementovera certain observation o an EEGdataTis system will be useul or disagreeing users and it will alsohelp them in comparing their results with each other
Tere is also a threat o over1047297tting by the classi1047297erIn order to keep the classi1047297er improving its perormancewith the encounter o more and more examples we haveintroduced this user adaptive mechanism in our system Weconsider the existing systems as dead because these cannotimprove their classi1047297cation rate afer initial training (duringsofware development) Te sel-improving mechanism aferdeployment makes our tool alive Tis system can be madepart o the whole epileptic diagnosis process It will highlight
983137983138983148983141 983095 First column shows the channel label the second columnshows the initial training accuracy and third one shows the markedcorrection by a neurologistand thelast one shows the1047297nal accuracy
the epileptic spikes among the whole EEG thus leading toreduced atigue and time consumption o a user We obtainedhigh classi1047297cation accuracy on datasets obtained rom twodifferent sites which indicates reproducibility o our resultsand robustness o our approach
In the uture we are planning to make this a web basedapplication neurologists can log in and consult each otherrsquosreviews about a particular subject Tis will make our systemexperience a whole versatility o examples and learn rom allo them Integration o the video and its automatic analysis(video EEG) can help a neurologist in diagnosing epilepsy ina better way whereas this can also help him in distinguishingbetween psychogenic and epileptic seizuresWewouldalso be
investigating how much over1047297tting is an issue in the reportedperormances which are now even touching 983089983088983088 based onsome claims Tere is a need or methodcriteria which couldlimit these algorithms improving their detection on a limitednumber o available examples
Tissystem is made keeping in mind thatwe haveto acil-itate the neurologist by supplementing him in the analysis o the EEG We do not want to enorce the classi1047297cation o theEEG data on a user
In the uture we will also include a slider in the systemwhich will allow the user to adjust the sensitivity and speci-1047297city beore retraining Tis assisting system is more like adetection tool which is continuously learning with encounter
983137983138983148983141 983096 Classi1047297cation rate improvement caused by retraining Firstcolumn shows thechannellabel thesecond columnshows theinitialtraining accuracy and third one shows the marked correction by aneurologist and the last one shows the 1047297nal accuracy
o better examples More and better examples will certainly improve its perormance Te agreement between differentneurologists over the EEG readings is low to moderate I we could 1047297nd the agreement on at least ew o the epilepticpatterns correspondence with epileptic disease then we cantake this tool urther ahead and use it or diagnosis instead o
just assistanceOne o the biggest limitations to this study is the unavail-
ability o non-983091 Hz spike and wave data Even though we haveincluded the data eatures o the entire epileptic requency ranges exclusive to each other proo testing on the data will
certainly prove worthy orthe progresso these assisting toolstoward a diagnostic tool
Conflict of Interests
Te authors declare that there is no con1047298ict o interests
regarding the publication o this paper
Acknowledgment
Te authors would like to extend their sincere appreciation tothe Deanship o Scienti1047297c Research at King Saud University or unding this research through Research Group Project(RG no 983089983092983091983093-983088983093983089)
References
[983089] H Adelia Z Zhoub and N Dadmehrc ldquoAnalysis o EEGrecords in an epileptic patient using wavelet transormrdquo Journal of Neuroscience Methods vol 983089983090983091 no 983089 pp 983094983097ndash983096983095 983090983088983088983091
[983090] M A Ahmad W Majeed and N A Khan ldquoAdvancements incomputer aided methods or EEG-based epileptic detectionrdquo inProceedings of the 983095th International Conference on Bio-Inspired Systems and Signal Processing (BIOSIGNALS 983089983092) AngersFrance 983090983088983089983092
[983091] S Noachtar and J R emi ldquoTe role o EEG in epilepsy a criticalreviewrdquo Epilepsy and Behavior vol 983089983093 no 983089 pp 983090983090ndash983091983091 983090983088983088983097
[983092] E B Petersen J Duun-Henriksen A Mazzaretto W Kjar CE Tomsen and H B D Sorensen ldquoGeneric single-channeldetection o absence seizuresrdquo in Proceedings of the Annual International Conference of the IEEE Engineering in Medicineand Biology Society (EMBS rsquo983089983089) pp 983092983096983090983088ndash983092983096983090983091 Boston MassUSA September 983090983088983089983089
[983093] M Kaleem A Guergachi and S Krishnan ldquoEEG seizure
detection and epilepsy diagnosis using a novel variation o Empirical Mode Decompositionrdquo in Proceedings of the 983091983093th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC rsquo983089983091) pp 983092983091983089983092ndash983092983091983089983095 OsakaJapan July 983090983088983089983091
[983094] S M S Alam and M I H Bhuiyan ldquoDetection o seizure andepilepsy usinghigher order statistics in the EMD domainrdquo IEEE Journal of Biomedical and Health Informatics vol 983089983095 no 983090 pp983091983089983090ndash983091983089983096 983090983088983089983091
[983095] H Ocbagabir K A I Aboalayon and M FaezipourldquoEfficientEEG analysis or seizure monitoring in epileptic patientsrdquo inProceedings of the Long Island Systems Applications and Tech-nology Conference (LISAT rsquo983089983091) pp 983089ndash983094 IEEE Farmingdale NYUSA May 983090983088983089983091
[983096] A A Abdullah S A Rahim and A Ibrahim ldquoDevelopment o EEG-based epileptic detection using arti1047297cial neural networkrdquoin Proceedings of the International Conference on Biomedical Engineering (ICoBE rsquo983089983090) pp 983094983088983093ndash983094983089983088 Penang Malaysia 983090983088983089983090
[983097] P Guo J Wang X Z Gao and J M A anskanen ldquoEpilepticEEG signal classi1047297cation with marching pursuit based on har-mony search methodrdquo in Proceedings of the IEEE International Conference on Systems Man and Cybernetics (SMC rsquo983089983090) pp983090983096983091ndash983090983096983096 Seoul Republic o Korea October 983090983088983089983090
[983089983088] A Shoeb ldquoCHB-MI scalp EEG databaserdquo 983090983088983088983088 httpphysionetorgpn983094chbmit
[983089983089] A S M Murugavel S Ramakrishnan U Maheswari and BS Sabetha ldquoCombined Seizure Index with Adaptive Multi-Class SVM or epileptic EEG classi1047297cationrdquo in Proceedings
of the International Conference on Emerging Trends in VLSIEmbedded System Nano Electronics and TelecommunicationSystem (ICEVENT rsquo983089983091) pp 983089ndash983093 Tiruvannammalai IndiaJanuary 983090983088983089983091
[983089983090] M H Abdullah J M Abdullah and M Z Abdullah ldquoSeizuredetection by means o hidden Markov model and station-ary wavelet transorm o electroencephalograph signalsrdquo inProceedings of the IEEE-EMBS International Conference onBiomedical and Health Informatics (BHI rsquo983089983090) pp 983094983090ndash983094983093 HongKong January 983090983088983089983090
[983089983091] A Subasi and M I Gursoy ldquoEEG signal classi1047297cation usingPCA ICA LDA and support vector machinesrdquo Expert Systemswith Applications vol 983091983095 no 983089983090 pp 983096983094983093983097ndash983096983094983094983094 983090983088983089983088
[983089983092] E Sezer H Isik and E Saracoglu ldquoEmployment and compari-son o different arti1047297cial neural networks or epilepsy diagnosisrom EEG signalsrdquo Journal of Medical Systems vol 983091983094 no 983089 pp983091983092983095ndash983091983094983090 983090983088983089983090
[983089983093] Brain Products GmbHProducts and ApplicationsAnalyzer 983090httpwwwbrainproductscomproductdetailsphpid=983089983095
[983089983094] NeuroExplorer Home httpwwwneuroexplorercom
[983089983096] C H Seng R Demirli L Khuon and D Bolger ldquoSeizuredetection in EEG signals using support vector machinesrdquo inProceedings of the 983091983096th Annual Northeast Bioengineering Con- ference (NEBEC rsquo983089983090) pp 983090983091983089ndash983090983091983090 Philadelphia Pa USA March983090983088983089983090
[983089983097] Z A Khan S B Mansoor M A Ahmad and M M MalikldquoInput devices or virtual surgical simulations a comparativestudyrdquo in Proceedings of the 983089983094th International Multi Topic Con- ference (INMIC rsquo983089983091) pp 983089983096983097ndash983089983097983092 Lahore Pakistan December983090983088983089983091
[983090983088] A L Goldberger L A Amaral L Glass et al ldquoPhysioBankPhysiooolkit and PhysioNet components o a new research
[983090983089] S Nasehi and H Pourghassem ldquoEpileptic seizure onset detec-tion algorithm using dynamic cascade eed-orward neural net-worksrdquo in Proceedings of the International Conference on Intelli- gent Computation and Bio-Medical Instrumentation (ICBMI rsquo983089983089)pp 983089983097983094ndash983089983097983097 December 983090983088983089983089
a lesser training time as compared to SVM but consideringthe sensitivity and classi1047297cation improvement through cor-rective marking we think that SVM is the better choice thanLDA and ANN In upcoming sections we have shown theresults or all three types o classi1047297er
0 5 10 15 20 250
24
6
8
10
12
14
16times10
4
Mean o the training examplesMean o the training + test examples
F983145983143983157983154983141 983093 Relationship between channelrsquos number and mean value(test + training examples)
(a) Adaptation Mechanism o test the adaptation mechanism983096983088983095corrective epochs were markedby the user ora CHBMI
F983145983143983157983154983141 983094 Relationship between channelrsquos number and standard deviation value (test + training examples)
EEG
Channel 1 DWTD1
A1 D2
A2
Mean
Standard deviation
Power
Mean
Standard deviation
Power
Mean
Standard deviation
Power
SVM or pattern 1
SVM or pattern 2
SVM or pattern 3
SVM or pattern 10
OPor
channel1
Channel 2 DWTD1
A1 D2
A2
Mean
Standard deviation
Power
Mean
Standard deviation
Power
MeanStandard deviation
Power
SVM or pattern 1
SVM or pattern 2
SVM or pattern 3
SVM or pattern 10
OPor
channel2
DWTD1
A1 D2
A2
Mean
Standard deviation
Power
Mean
Standard deviation
Power
Mean
Standard deviation
Power
SVM or pattern 1
SVM or pattern 2
SVM or pattern 3
SVM or pattern 10
Features
Features
PCA
Reduction
Standardization
PCA
Standardization
Reduction
FeaturesPCA
Reduction
Standardization
Surgeonrsquos marking
Surgeonrsquos marking
Surgeonrsquos marking
OPor
channeln
Al
Dl
Al
Al
Dl
Dl
Z-score
Z-score
Z-scoreChannel n
F983145983143983157983154983141 983095 Flowchart
dataset 1047297le and he marked the same amount o epochs oreach channel Tese corrective markings were saved in hislog as training examples Tese corrective markings as thenew examples along with the 983091983090983090983097983088 epochs o initial trainingstage were used to retrain the classi1047297er Te number 983091983090983090983097983088 hascome rom the 983091983090983090983097 randomly selected epochs rom whole o the CHBMI dataset or the ten separate times during initial
training phase Ten later the perormance o the classi1047297erafer retraining was judged again on another random 983091983088983088983088epochs (Figure 983089983092)
In case o PIMH dataset 983093983095 corrective epochs wereselected or PIMH dataset Tis time 983089983097983091983095983092 epochs o thePIMH dataset were used along with the 983093983095 corrective mark-ings as orthe PIMH we randomly selected the 983091983090983090983097 numbers
Processing all channels simultaneously with single classi1047297er
Processing all channels separately with separate classi1047297er oreach channel
F983145983143983157983154983141 983096 Accuracy relationship o different classi1047297ers and theirclassi1047297cation rate
84
86
88
90
92
94
96
98
100
Accuracy afer initial training ()
Accuracy afer retraining ()
ldquo F P 1 F 7 rdquo
ldquo F 7 T 7 rdquo
ldquo T 7 P 7 rdquo
ldquo P 7 O 1 rdquo
ldquo F P 1 F 3 rdquo
ldquo F 3 C 3 rdquo
ldquo C 3 P 3 rdquo
ldquo P 3 O 1 rdquo
ldquo F P 2 F 4 rdquo
ldquo F 4 C 4 rdquo
ldquo C 4 P 4 rdquo
ldquo P 4 O 2 rdquo
ldquo F P 2 F 8 rdquo
ldquo F 8 T 8 rdquo
ldquo T 8 P 8 rdquo
ldquo P 8 O 2 rdquo
ldquo F Z C Z rdquo
ldquo C Z P Z rdquo
ldquo P 7 T 7 rdquo
ldquo T 7 F T 9 rdquo
ldquo F T 9 F T 1 0 rdquo
ldquo F T 1 0 T 8 rdquo
F983145983143983157983154983141 983097 Relation between average classi1047297cation rate and accuracy
o the channel afer initial training and retaining
o epochs or the six times Te retrained classi1047297er was testedon the 983090983091983094983089 remaining epochs
(b) Support Vector Machine We used the support vectormachine classi1047297er package available in MALAB Bioinor-matics oolbox We ound linear kernel to be the mostaccurate SVM kernel with 983093983088 as the box constraint
(c) CHBMIT For CHBMI dataset initial training o theclassi1047297er resulted in 983097983094983091 average accuracy 983097983095983092 average
0
20
40
60
80
100
120
Accuracy afer initial training ()
Accuracy afer retraining ()
ldquo F p 1 rdquo
ldquo F p 2 rdquo
ldquo F 3 rdquo
ldquo F 4 rdquo
ldquo C 3 rdquo
ldquo C 4 rdquo
ldquo P 3 rdquo
ldquo P 4 rdquo
ldquo O 1 rdquo
ldquo O 2 rdquo
ldquo F 7 rdquo
ldquo F 8 rdquo
ldquo T 3 rdquo
ldquo T 4 rdquo
ldquo T 5 rdquo
ldquo T 6 rdquo
ldquo F z rdquo
ldquo C z rdquo
ldquo P z rdquo
ldquo E rdquo
ldquo P G 2 rdquo
ldquo A 1 rdquo
ldquo A 2 rdquo
ldquo T 1 rdquo
ldquo T 2 rdquo
ldquo X 1 rdquo
ldquo X 2 rdquo
ldquo X 3 rdquo
ldquo X 4 rdquo
ldquo X 5 rdquo
ldquo X 6 rdquo
ldquo X 7 rdquo
ldquo P G 1 rdquo
F983145983143983157983154983141 983089983088 Relation between average classi1047297cation rate and accuracy o the channel afer initial training and retaining
82
84
86
88
90
92
94
96
98
Accuracy afer initial training ()Accuracy afer retraining ()
ldquo F P 1 F 7 rdquo
ldquo F 7 T 7 rdquo
ldquo T 7 P 7 rdquo
ldquo P 7 O 1 rdquo
ldquo F P 1 F 3 rdquo
ldquo F 3 C 3 rdquo
ldquo C 3 P 3 rdquo
ldquo P 3 O 1 rdquo
ldquo F P 2 F 4 rdquo
ldquo F 4 C 4 rdquo
ldquo C 4 P 4 rdquo
ldquo P 4 O 2 rdquo
ldquo F P 2 F 8 rdquo
ldquo F 8 T 8 rdquo
ldquo T 8 P 8 rdquo
ldquo P 8 O 2 rdquo
ldquo F Z C Z rdquo
ldquo C Z P Z rdquo
ldquo P 7 T 7 rdquo
ldquo T 7 F T 9 rdquo
ldquo F T 9 F T 1 0 rdquo
ldquo F T 1 0 T 8 rdquo
F983145983143983157983154983141 983089983089 Relation between average classi1047297cation rate and accuracy o the channel afer initial training and retaining
speci1047297city and 983097983091983093 average sensitivity or 983091 Hz spike andwave which is a characteristic o absence seizure Aferinitial training our speci1047297city is better than that o Shoeb[983089983088] and Nasehi and Pourghassem [983090983089] who used the samedataset to validate their technique with different eaturesand application technique Tis shows that our technique
is providing better results even at the initial training phase(Figure 983089983089)
In able 983091 we have shown the average initial classi1047297cationand retrained classi1047297cation results o our system or eachchannel In this system we have shown that afer correctiono ew epochs there is a visible improvement in the systemsclassi1047297cation Te average accuracy o the system rose rom983097983093983093 to 983097983094983091
(d) PIMH For PIMH dataset initial training o the classi1047297erresulted in 983097983088 average accuracy 983097983092 average speci1047297city and983096983088 average sensitivity or 983091 Hz spike and wave which is acharacteristic o absence seizure (Figure 983089983090)
F983145983143983157983154983141 983089983090 Relation between average classi1047297cation rateand accuracy o the channel afer initial training and retaining
80
82
84
86
88
90
92
94
96
98
Accuracy afer initial training ()
Accuracy afer retraining ()
ldquo F P 1 F 7 rdquo
ldquo F 7 T 7 rdquo
ldquo T 7 P 7 rdquo
ldquo P 7 O 1 rdquo
ldquo F P 1 F 3 rdquo
ldquo F 3 C 3 rdquo
ldquo C 3 P 3 rdquo
ldquo P 3 O 1 rdquo
ldquo F P 2 F 4 rdquo
ldquo F 4 C 4 rdquo
ldquo C 4 P 4 rdquo
ldquo P 4 O 2 rdquo
ldquo F P 2 F 8 rdquo
ldquo F 8 T 8 rdquo
ldquo T 8 P 8 rdquo
ldquo P 8 O 2 rdquo
ldquo F Z C Z rdquo
ldquo C Z P Z rdquo
ldquo P 7 T 7 rdquo
ldquo T 7 F T 9 rdquo
ldquo F T 9 F T 1 0 rdquo
ldquo F T 1 0 T 8 rdquo
F983145983143983157983154983141 983089983091 Relation between average classi1047297cation rate and accuracy o the channel afer initial training and retaining
In able 983092 we have shown the average initial classi1047297cationand retrained classi1047297cation results o our system or eachchannel able 983092 shows that our technique is robust and itworks also on a different dataset Te average accuracy o thesystem rose rom approximately 983096983097 to 983097983088
983091983091983090 Discriminate Analysis We used the discriminant anal-
ysis package available in MALAB Statistics oolbox Weound pseudoquadratic to be the best perorming discrimi-nate type with uniorm probability
(a) CHBMIT For CHBMI dataset initial training o theclassi1047297er resulted in 983097983092 average accuracy 983097983094 averagespeci1047297city and 983097983088 average sensitivity or 983091 Hz spike andwave which is a characteristic o absence seizure Afer initialtraining our speci1047297city is better than that o Shoeb [983089983088] andNasehi and Pourghassem [983090983089] (Figure 983089983091)
In able 983093 we have shown the average initial classi1047297cationand retrained classi1047297cation results o our system or eachchannel In this system we have shown that afer correction
010
2030405060708090
100
Accuracy afer initial training ()
Accuracy afer retraining ()
ldquo F 3 rdquo
ldquo F 4 rdquo
ldquo C 3 rdquo
ldquo C 4 rdquo
ldquo P 3 rdquo
ldquo P 4 rdquo
ldquo O 1 rdquo
ldquo O 2 rdquo
ldquo F 7 rdquo
ldquo F 8 rdquo
ldquo T 3 rdquo
ldquo T 4 rdquo
ldquo T 5 rdquo
ldquo T 6 rdquo
ldquo F z rdquo
ldquo C z rdquo
ldquo P z rdquo
ldquo E rdquo
ldquo P G 2 rdquo
ldquo T 1 rdquo
ldquo T 2 rdquo
ldquo X 1 rdquo
ldquo X 2 rdquo
ldquo X 3 rdquo
ldquo X 4 rdquo
ldquo X 5 rdquo
ldquo X 6 rdquo
ldquo X 7 rdquo
ldquo P G 1 rdquo
ldquo A 1 rdquo
ldquo A 2 rdquo
ldquo F p 1 rdquo
ldquo F p 2 rdquo
F983145983143983157983154983141 983089983092 Relation between average classi1047297cation rate and accuracy o the channel afer initial training and retaining
983137983138983148983141 983091 First column shows the channel label the second columnshows the initial training accuracy and third one shows the markedcorrection by a neurologistand thelast one shows the1047297nal accuracy
o ew epochs there is visible improvement in the systemsclassi1047297cation Te average accuracy o the system rose rom983097983092 to 983097983093
(b) PIMH For PIMH dataset initial training o the classi1047297erresulted in 983097983088 average accuracy 983097983093 average speci1047297city and983095983091 average sensitivity or 983091 Hz spike and wave which is acharacteristic o absence seizure
In able 983094 we have shown the average initialclassi1047297cationand retrained classi1047297cation results o our system or each
983137983138983148983141 983092 First column shows the channel label the second columnshows the initial training accuracy and third one shows the markedcorrection by a neurologistand thelast one shows the1047297nal accuracy
channel able 983094 shows that our technique is robust and itworks also on a different dataset Te average accuracy o thesystem rose rom approximately 983096983097 to 983097983088
983091983091983091 Arti1047297cial Neural Network We used eedorward back-propagation package available in MALAB Neural Network oolbox and ound Levenberg-Marquardt to be the bestmethod with 983088983088983093 learning rate
(a) CHBMIT For CHBMI dataset initial training o theclassi1047297er resulted in 983097983090983096983096 average accuracy 983097983096983094983094 averagespeci1047297city and 983095983093983095983093 average sensitivity or 983091 Hz spike andwave which is a characteristic o absence seizure (Figure 983097)
983137983138983148983141 983093 First column shows the channel label the second columnshows the initial training accuracy and third one shows the markedcorrection by a neurologistand thelast one shows the1047297nal accuracy
In able 983095 we have shown the average initial classi1047297cationand retrained classi1047297cation results o our system or eachchannel In this system we have shown that afer correctiono ew epochs there is visible improvement in the systemsclassi1047297cation Te average accuracy o the system rose rom983097983090983096983096 to 983097983091983097983094
(b) PIMH For PIMH dataset initial training o the classi1047297erresulted in 983096983092 average accuracy 983097983092983096 average speci1047297cityand 983093983094983093 average sensitivity or 983091 Hz spike and wave whichis a characteristic o absence seizure (Figure 983089983088)
In able 983096 we have shown the average initial classi1047297cationand retrained classi1047297cation results o our system or each
channel able 983096 shows that our technique is robust and itworks also on a different dataset Te average accuracy o thesystem rose rom approximately 983096983092 to 983096983093983092983091
4 Discussion and Future Work
Computer-assisted analysis o EEG has tremendous potentialor assisting the clinicians in diagnosis A very importantand novel phase o our system is user adaptation mechanismor retraining mechanism Introduction o this phase hasimportance in many aspects In this phase system tries toadapt its classi1047297cation according to users desire Moreoverthis technique personalizes the classi1047297ers classi1047297cation It has
983137983138983148983141 983094 First column shows the channel label the second columnshows the initial training accuracy and third one shows the markedcorrection by a neurologistand thelast one shows the1047297nal accuracy
been cited that sometimes even the expert neurologists havesome disagreementovera certain observation o an EEGdataTis system will be useul or disagreeing users and it will alsohelp them in comparing their results with each other
Tere is also a threat o over1047297tting by the classi1047297erIn order to keep the classi1047297er improving its perormancewith the encounter o more and more examples we haveintroduced this user adaptive mechanism in our system Weconsider the existing systems as dead because these cannotimprove their classi1047297cation rate afer initial training (duringsofware development) Te sel-improving mechanism aferdeployment makes our tool alive Tis system can be madepart o the whole epileptic diagnosis process It will highlight
983137983138983148983141 983095 First column shows the channel label the second columnshows the initial training accuracy and third one shows the markedcorrection by a neurologistand thelast one shows the1047297nal accuracy
the epileptic spikes among the whole EEG thus leading toreduced atigue and time consumption o a user We obtainedhigh classi1047297cation accuracy on datasets obtained rom twodifferent sites which indicates reproducibility o our resultsand robustness o our approach
In the uture we are planning to make this a web basedapplication neurologists can log in and consult each otherrsquosreviews about a particular subject Tis will make our systemexperience a whole versatility o examples and learn rom allo them Integration o the video and its automatic analysis(video EEG) can help a neurologist in diagnosing epilepsy ina better way whereas this can also help him in distinguishingbetween psychogenic and epileptic seizuresWewouldalso be
investigating how much over1047297tting is an issue in the reportedperormances which are now even touching 983089983088983088 based onsome claims Tere is a need or methodcriteria which couldlimit these algorithms improving their detection on a limitednumber o available examples
Tissystem is made keeping in mind thatwe haveto acil-itate the neurologist by supplementing him in the analysis o the EEG We do not want to enorce the classi1047297cation o theEEG data on a user
In the uture we will also include a slider in the systemwhich will allow the user to adjust the sensitivity and speci-1047297city beore retraining Tis assisting system is more like adetection tool which is continuously learning with encounter
983137983138983148983141 983096 Classi1047297cation rate improvement caused by retraining Firstcolumn shows thechannellabel thesecond columnshows theinitialtraining accuracy and third one shows the marked correction by aneurologist and the last one shows the 1047297nal accuracy
o better examples More and better examples will certainly improve its perormance Te agreement between differentneurologists over the EEG readings is low to moderate I we could 1047297nd the agreement on at least ew o the epilepticpatterns correspondence with epileptic disease then we cantake this tool urther ahead and use it or diagnosis instead o
just assistanceOne o the biggest limitations to this study is the unavail-
ability o non-983091 Hz spike and wave data Even though we haveincluded the data eatures o the entire epileptic requency ranges exclusive to each other proo testing on the data will
certainly prove worthy orthe progresso these assisting toolstoward a diagnostic tool
Conflict of Interests
Te authors declare that there is no con1047298ict o interests
regarding the publication o this paper
Acknowledgment
Te authors would like to extend their sincere appreciation tothe Deanship o Scienti1047297c Research at King Saud University or unding this research through Research Group Project(RG no 983089983092983091983093-983088983093983089)
References
[983089] H Adelia Z Zhoub and N Dadmehrc ldquoAnalysis o EEGrecords in an epileptic patient using wavelet transormrdquo Journal of Neuroscience Methods vol 983089983090983091 no 983089 pp 983094983097ndash983096983095 983090983088983088983091
[983090] M A Ahmad W Majeed and N A Khan ldquoAdvancements incomputer aided methods or EEG-based epileptic detectionrdquo inProceedings of the 983095th International Conference on Bio-Inspired Systems and Signal Processing (BIOSIGNALS 983089983092) AngersFrance 983090983088983089983092
[983091] S Noachtar and J R emi ldquoTe role o EEG in epilepsy a criticalreviewrdquo Epilepsy and Behavior vol 983089983093 no 983089 pp 983090983090ndash983091983091 983090983088983088983097
[983092] E B Petersen J Duun-Henriksen A Mazzaretto W Kjar CE Tomsen and H B D Sorensen ldquoGeneric single-channeldetection o absence seizuresrdquo in Proceedings of the Annual International Conference of the IEEE Engineering in Medicineand Biology Society (EMBS rsquo983089983089) pp 983092983096983090983088ndash983092983096983090983091 Boston MassUSA September 983090983088983089983089
[983093] M Kaleem A Guergachi and S Krishnan ldquoEEG seizure
detection and epilepsy diagnosis using a novel variation o Empirical Mode Decompositionrdquo in Proceedings of the 983091983093th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC rsquo983089983091) pp 983092983091983089983092ndash983092983091983089983095 OsakaJapan July 983090983088983089983091
[983094] S M S Alam and M I H Bhuiyan ldquoDetection o seizure andepilepsy usinghigher order statistics in the EMD domainrdquo IEEE Journal of Biomedical and Health Informatics vol 983089983095 no 983090 pp983091983089983090ndash983091983089983096 983090983088983089983091
[983095] H Ocbagabir K A I Aboalayon and M FaezipourldquoEfficientEEG analysis or seizure monitoring in epileptic patientsrdquo inProceedings of the Long Island Systems Applications and Tech-nology Conference (LISAT rsquo983089983091) pp 983089ndash983094 IEEE Farmingdale NYUSA May 983090983088983089983091
[983096] A A Abdullah S A Rahim and A Ibrahim ldquoDevelopment o EEG-based epileptic detection using arti1047297cial neural networkrdquoin Proceedings of the International Conference on Biomedical Engineering (ICoBE rsquo983089983090) pp 983094983088983093ndash983094983089983088 Penang Malaysia 983090983088983089983090
[983097] P Guo J Wang X Z Gao and J M A anskanen ldquoEpilepticEEG signal classi1047297cation with marching pursuit based on har-mony search methodrdquo in Proceedings of the IEEE International Conference on Systems Man and Cybernetics (SMC rsquo983089983090) pp983090983096983091ndash983090983096983096 Seoul Republic o Korea October 983090983088983089983090
[983089983088] A Shoeb ldquoCHB-MI scalp EEG databaserdquo 983090983088983088983088 httpphysionetorgpn983094chbmit
[983089983089] A S M Murugavel S Ramakrishnan U Maheswari and BS Sabetha ldquoCombined Seizure Index with Adaptive Multi-Class SVM or epileptic EEG classi1047297cationrdquo in Proceedings
of the International Conference on Emerging Trends in VLSIEmbedded System Nano Electronics and TelecommunicationSystem (ICEVENT rsquo983089983091) pp 983089ndash983093 Tiruvannammalai IndiaJanuary 983090983088983089983091
[983089983090] M H Abdullah J M Abdullah and M Z Abdullah ldquoSeizuredetection by means o hidden Markov model and station-ary wavelet transorm o electroencephalograph signalsrdquo inProceedings of the IEEE-EMBS International Conference onBiomedical and Health Informatics (BHI rsquo983089983090) pp 983094983090ndash983094983093 HongKong January 983090983088983089983090
[983089983091] A Subasi and M I Gursoy ldquoEEG signal classi1047297cation usingPCA ICA LDA and support vector machinesrdquo Expert Systemswith Applications vol 983091983095 no 983089983090 pp 983096983094983093983097ndash983096983094983094983094 983090983088983089983088
[983089983092] E Sezer H Isik and E Saracoglu ldquoEmployment and compari-son o different arti1047297cial neural networks or epilepsy diagnosisrom EEG signalsrdquo Journal of Medical Systems vol 983091983094 no 983089 pp983091983092983095ndash983091983094983090 983090983088983089983090
[983089983093] Brain Products GmbHProducts and ApplicationsAnalyzer 983090httpwwwbrainproductscomproductdetailsphpid=983089983095
[983089983094] NeuroExplorer Home httpwwwneuroexplorercom
[983089983096] C H Seng R Demirli L Khuon and D Bolger ldquoSeizuredetection in EEG signals using support vector machinesrdquo inProceedings of the 983091983096th Annual Northeast Bioengineering Con- ference (NEBEC rsquo983089983090) pp 983090983091983089ndash983090983091983090 Philadelphia Pa USA March983090983088983089983090
[983089983097] Z A Khan S B Mansoor M A Ahmad and M M MalikldquoInput devices or virtual surgical simulations a comparativestudyrdquo in Proceedings of the 983089983094th International Multi Topic Con- ference (INMIC rsquo983089983091) pp 983089983096983097ndash983089983097983092 Lahore Pakistan December983090983088983089983091
[983090983088] A L Goldberger L A Amaral L Glass et al ldquoPhysioBankPhysiooolkit and PhysioNet components o a new research
[983090983089] S Nasehi and H Pourghassem ldquoEpileptic seizure onset detec-tion algorithm using dynamic cascade eed-orward neural net-worksrdquo in Proceedings of the International Conference on Intelli- gent Computation and Bio-Medical Instrumentation (ICBMI rsquo983089983089)pp 983089983097983094ndash983089983097983097 December 983090983088983089983089
F983145983143983157983154983141 983094 Relationship between channelrsquos number and standard deviation value (test + training examples)
EEG
Channel 1 DWTD1
A1 D2
A2
Mean
Standard deviation
Power
Mean
Standard deviation
Power
Mean
Standard deviation
Power
SVM or pattern 1
SVM or pattern 2
SVM or pattern 3
SVM or pattern 10
OPor
channel1
Channel 2 DWTD1
A1 D2
A2
Mean
Standard deviation
Power
Mean
Standard deviation
Power
MeanStandard deviation
Power
SVM or pattern 1
SVM or pattern 2
SVM or pattern 3
SVM or pattern 10
OPor
channel2
DWTD1
A1 D2
A2
Mean
Standard deviation
Power
Mean
Standard deviation
Power
Mean
Standard deviation
Power
SVM or pattern 1
SVM or pattern 2
SVM or pattern 3
SVM or pattern 10
Features
Features
PCA
Reduction
Standardization
PCA
Standardization
Reduction
FeaturesPCA
Reduction
Standardization
Surgeonrsquos marking
Surgeonrsquos marking
Surgeonrsquos marking
OPor
channeln
Al
Dl
Al
Al
Dl
Dl
Z-score
Z-score
Z-scoreChannel n
F983145983143983157983154983141 983095 Flowchart
dataset 1047297le and he marked the same amount o epochs oreach channel Tese corrective markings were saved in hislog as training examples Tese corrective markings as thenew examples along with the 983091983090983090983097983088 epochs o initial trainingstage were used to retrain the classi1047297er Te number 983091983090983090983097983088 hascome rom the 983091983090983090983097 randomly selected epochs rom whole o the CHBMI dataset or the ten separate times during initial
training phase Ten later the perormance o the classi1047297erafer retraining was judged again on another random 983091983088983088983088epochs (Figure 983089983092)
In case o PIMH dataset 983093983095 corrective epochs wereselected or PIMH dataset Tis time 983089983097983091983095983092 epochs o thePIMH dataset were used along with the 983093983095 corrective mark-ings as orthe PIMH we randomly selected the 983091983090983090983097 numbers
Processing all channels simultaneously with single classi1047297er
Processing all channels separately with separate classi1047297er oreach channel
F983145983143983157983154983141 983096 Accuracy relationship o different classi1047297ers and theirclassi1047297cation rate
84
86
88
90
92
94
96
98
100
Accuracy afer initial training ()
Accuracy afer retraining ()
ldquo F P 1 F 7 rdquo
ldquo F 7 T 7 rdquo
ldquo T 7 P 7 rdquo
ldquo P 7 O 1 rdquo
ldquo F P 1 F 3 rdquo
ldquo F 3 C 3 rdquo
ldquo C 3 P 3 rdquo
ldquo P 3 O 1 rdquo
ldquo F P 2 F 4 rdquo
ldquo F 4 C 4 rdquo
ldquo C 4 P 4 rdquo
ldquo P 4 O 2 rdquo
ldquo F P 2 F 8 rdquo
ldquo F 8 T 8 rdquo
ldquo T 8 P 8 rdquo
ldquo P 8 O 2 rdquo
ldquo F Z C Z rdquo
ldquo C Z P Z rdquo
ldquo P 7 T 7 rdquo
ldquo T 7 F T 9 rdquo
ldquo F T 9 F T 1 0 rdquo
ldquo F T 1 0 T 8 rdquo
F983145983143983157983154983141 983097 Relation between average classi1047297cation rate and accuracy
o the channel afer initial training and retaining
o epochs or the six times Te retrained classi1047297er was testedon the 983090983091983094983089 remaining epochs
(b) Support Vector Machine We used the support vectormachine classi1047297er package available in MALAB Bioinor-matics oolbox We ound linear kernel to be the mostaccurate SVM kernel with 983093983088 as the box constraint
(c) CHBMIT For CHBMI dataset initial training o theclassi1047297er resulted in 983097983094983091 average accuracy 983097983095983092 average
0
20
40
60
80
100
120
Accuracy afer initial training ()
Accuracy afer retraining ()
ldquo F p 1 rdquo
ldquo F p 2 rdquo
ldquo F 3 rdquo
ldquo F 4 rdquo
ldquo C 3 rdquo
ldquo C 4 rdquo
ldquo P 3 rdquo
ldquo P 4 rdquo
ldquo O 1 rdquo
ldquo O 2 rdquo
ldquo F 7 rdquo
ldquo F 8 rdquo
ldquo T 3 rdquo
ldquo T 4 rdquo
ldquo T 5 rdquo
ldquo T 6 rdquo
ldquo F z rdquo
ldquo C z rdquo
ldquo P z rdquo
ldquo E rdquo
ldquo P G 2 rdquo
ldquo A 1 rdquo
ldquo A 2 rdquo
ldquo T 1 rdquo
ldquo T 2 rdquo
ldquo X 1 rdquo
ldquo X 2 rdquo
ldquo X 3 rdquo
ldquo X 4 rdquo
ldquo X 5 rdquo
ldquo X 6 rdquo
ldquo X 7 rdquo
ldquo P G 1 rdquo
F983145983143983157983154983141 983089983088 Relation between average classi1047297cation rate and accuracy o the channel afer initial training and retaining
82
84
86
88
90
92
94
96
98
Accuracy afer initial training ()Accuracy afer retraining ()
ldquo F P 1 F 7 rdquo
ldquo F 7 T 7 rdquo
ldquo T 7 P 7 rdquo
ldquo P 7 O 1 rdquo
ldquo F P 1 F 3 rdquo
ldquo F 3 C 3 rdquo
ldquo C 3 P 3 rdquo
ldquo P 3 O 1 rdquo
ldquo F P 2 F 4 rdquo
ldquo F 4 C 4 rdquo
ldquo C 4 P 4 rdquo
ldquo P 4 O 2 rdquo
ldquo F P 2 F 8 rdquo
ldquo F 8 T 8 rdquo
ldquo T 8 P 8 rdquo
ldquo P 8 O 2 rdquo
ldquo F Z C Z rdquo
ldquo C Z P Z rdquo
ldquo P 7 T 7 rdquo
ldquo T 7 F T 9 rdquo
ldquo F T 9 F T 1 0 rdquo
ldquo F T 1 0 T 8 rdquo
F983145983143983157983154983141 983089983089 Relation between average classi1047297cation rate and accuracy o the channel afer initial training and retaining
speci1047297city and 983097983091983093 average sensitivity or 983091 Hz spike andwave which is a characteristic o absence seizure Aferinitial training our speci1047297city is better than that o Shoeb[983089983088] and Nasehi and Pourghassem [983090983089] who used the samedataset to validate their technique with different eaturesand application technique Tis shows that our technique
is providing better results even at the initial training phase(Figure 983089983089)
In able 983091 we have shown the average initial classi1047297cationand retrained classi1047297cation results o our system or eachchannel In this system we have shown that afer correctiono ew epochs there is a visible improvement in the systemsclassi1047297cation Te average accuracy o the system rose rom983097983093983093 to 983097983094983091
(d) PIMH For PIMH dataset initial training o the classi1047297erresulted in 983097983088 average accuracy 983097983092 average speci1047297city and983096983088 average sensitivity or 983091 Hz spike and wave which is acharacteristic o absence seizure (Figure 983089983090)
F983145983143983157983154983141 983089983090 Relation between average classi1047297cation rateand accuracy o the channel afer initial training and retaining
80
82
84
86
88
90
92
94
96
98
Accuracy afer initial training ()
Accuracy afer retraining ()
ldquo F P 1 F 7 rdquo
ldquo F 7 T 7 rdquo
ldquo T 7 P 7 rdquo
ldquo P 7 O 1 rdquo
ldquo F P 1 F 3 rdquo
ldquo F 3 C 3 rdquo
ldquo C 3 P 3 rdquo
ldquo P 3 O 1 rdquo
ldquo F P 2 F 4 rdquo
ldquo F 4 C 4 rdquo
ldquo C 4 P 4 rdquo
ldquo P 4 O 2 rdquo
ldquo F P 2 F 8 rdquo
ldquo F 8 T 8 rdquo
ldquo T 8 P 8 rdquo
ldquo P 8 O 2 rdquo
ldquo F Z C Z rdquo
ldquo C Z P Z rdquo
ldquo P 7 T 7 rdquo
ldquo T 7 F T 9 rdquo
ldquo F T 9 F T 1 0 rdquo
ldquo F T 1 0 T 8 rdquo
F983145983143983157983154983141 983089983091 Relation between average classi1047297cation rate and accuracy o the channel afer initial training and retaining
In able 983092 we have shown the average initial classi1047297cationand retrained classi1047297cation results o our system or eachchannel able 983092 shows that our technique is robust and itworks also on a different dataset Te average accuracy o thesystem rose rom approximately 983096983097 to 983097983088
983091983091983090 Discriminate Analysis We used the discriminant anal-
ysis package available in MALAB Statistics oolbox Weound pseudoquadratic to be the best perorming discrimi-nate type with uniorm probability
(a) CHBMIT For CHBMI dataset initial training o theclassi1047297er resulted in 983097983092 average accuracy 983097983094 averagespeci1047297city and 983097983088 average sensitivity or 983091 Hz spike andwave which is a characteristic o absence seizure Afer initialtraining our speci1047297city is better than that o Shoeb [983089983088] andNasehi and Pourghassem [983090983089] (Figure 983089983091)
In able 983093 we have shown the average initial classi1047297cationand retrained classi1047297cation results o our system or eachchannel In this system we have shown that afer correction
010
2030405060708090
100
Accuracy afer initial training ()
Accuracy afer retraining ()
ldquo F 3 rdquo
ldquo F 4 rdquo
ldquo C 3 rdquo
ldquo C 4 rdquo
ldquo P 3 rdquo
ldquo P 4 rdquo
ldquo O 1 rdquo
ldquo O 2 rdquo
ldquo F 7 rdquo
ldquo F 8 rdquo
ldquo T 3 rdquo
ldquo T 4 rdquo
ldquo T 5 rdquo
ldquo T 6 rdquo
ldquo F z rdquo
ldquo C z rdquo
ldquo P z rdquo
ldquo E rdquo
ldquo P G 2 rdquo
ldquo T 1 rdquo
ldquo T 2 rdquo
ldquo X 1 rdquo
ldquo X 2 rdquo
ldquo X 3 rdquo
ldquo X 4 rdquo
ldquo X 5 rdquo
ldquo X 6 rdquo
ldquo X 7 rdquo
ldquo P G 1 rdquo
ldquo A 1 rdquo
ldquo A 2 rdquo
ldquo F p 1 rdquo
ldquo F p 2 rdquo
F983145983143983157983154983141 983089983092 Relation between average classi1047297cation rate and accuracy o the channel afer initial training and retaining
983137983138983148983141 983091 First column shows the channel label the second columnshows the initial training accuracy and third one shows the markedcorrection by a neurologistand thelast one shows the1047297nal accuracy
o ew epochs there is visible improvement in the systemsclassi1047297cation Te average accuracy o the system rose rom983097983092 to 983097983093
(b) PIMH For PIMH dataset initial training o the classi1047297erresulted in 983097983088 average accuracy 983097983093 average speci1047297city and983095983091 average sensitivity or 983091 Hz spike and wave which is acharacteristic o absence seizure
In able 983094 we have shown the average initialclassi1047297cationand retrained classi1047297cation results o our system or each
983137983138983148983141 983092 First column shows the channel label the second columnshows the initial training accuracy and third one shows the markedcorrection by a neurologistand thelast one shows the1047297nal accuracy
channel able 983094 shows that our technique is robust and itworks also on a different dataset Te average accuracy o thesystem rose rom approximately 983096983097 to 983097983088
983091983091983091 Arti1047297cial Neural Network We used eedorward back-propagation package available in MALAB Neural Network oolbox and ound Levenberg-Marquardt to be the bestmethod with 983088983088983093 learning rate
(a) CHBMIT For CHBMI dataset initial training o theclassi1047297er resulted in 983097983090983096983096 average accuracy 983097983096983094983094 averagespeci1047297city and 983095983093983095983093 average sensitivity or 983091 Hz spike andwave which is a characteristic o absence seizure (Figure 983097)
983137983138983148983141 983093 First column shows the channel label the second columnshows the initial training accuracy and third one shows the markedcorrection by a neurologistand thelast one shows the1047297nal accuracy
In able 983095 we have shown the average initial classi1047297cationand retrained classi1047297cation results o our system or eachchannel In this system we have shown that afer correctiono ew epochs there is visible improvement in the systemsclassi1047297cation Te average accuracy o the system rose rom983097983090983096983096 to 983097983091983097983094
(b) PIMH For PIMH dataset initial training o the classi1047297erresulted in 983096983092 average accuracy 983097983092983096 average speci1047297cityand 983093983094983093 average sensitivity or 983091 Hz spike and wave whichis a characteristic o absence seizure (Figure 983089983088)
In able 983096 we have shown the average initial classi1047297cationand retrained classi1047297cation results o our system or each
channel able 983096 shows that our technique is robust and itworks also on a different dataset Te average accuracy o thesystem rose rom approximately 983096983092 to 983096983093983092983091
4 Discussion and Future Work
Computer-assisted analysis o EEG has tremendous potentialor assisting the clinicians in diagnosis A very importantand novel phase o our system is user adaptation mechanismor retraining mechanism Introduction o this phase hasimportance in many aspects In this phase system tries toadapt its classi1047297cation according to users desire Moreoverthis technique personalizes the classi1047297ers classi1047297cation It has
983137983138983148983141 983094 First column shows the channel label the second columnshows the initial training accuracy and third one shows the markedcorrection by a neurologistand thelast one shows the1047297nal accuracy
been cited that sometimes even the expert neurologists havesome disagreementovera certain observation o an EEGdataTis system will be useul or disagreeing users and it will alsohelp them in comparing their results with each other
Tere is also a threat o over1047297tting by the classi1047297erIn order to keep the classi1047297er improving its perormancewith the encounter o more and more examples we haveintroduced this user adaptive mechanism in our system Weconsider the existing systems as dead because these cannotimprove their classi1047297cation rate afer initial training (duringsofware development) Te sel-improving mechanism aferdeployment makes our tool alive Tis system can be madepart o the whole epileptic diagnosis process It will highlight
983137983138983148983141 983095 First column shows the channel label the second columnshows the initial training accuracy and third one shows the markedcorrection by a neurologistand thelast one shows the1047297nal accuracy
the epileptic spikes among the whole EEG thus leading toreduced atigue and time consumption o a user We obtainedhigh classi1047297cation accuracy on datasets obtained rom twodifferent sites which indicates reproducibility o our resultsand robustness o our approach
In the uture we are planning to make this a web basedapplication neurologists can log in and consult each otherrsquosreviews about a particular subject Tis will make our systemexperience a whole versatility o examples and learn rom allo them Integration o the video and its automatic analysis(video EEG) can help a neurologist in diagnosing epilepsy ina better way whereas this can also help him in distinguishingbetween psychogenic and epileptic seizuresWewouldalso be
investigating how much over1047297tting is an issue in the reportedperormances which are now even touching 983089983088983088 based onsome claims Tere is a need or methodcriteria which couldlimit these algorithms improving their detection on a limitednumber o available examples
Tissystem is made keeping in mind thatwe haveto acil-itate the neurologist by supplementing him in the analysis o the EEG We do not want to enorce the classi1047297cation o theEEG data on a user
In the uture we will also include a slider in the systemwhich will allow the user to adjust the sensitivity and speci-1047297city beore retraining Tis assisting system is more like adetection tool which is continuously learning with encounter
983137983138983148983141 983096 Classi1047297cation rate improvement caused by retraining Firstcolumn shows thechannellabel thesecond columnshows theinitialtraining accuracy and third one shows the marked correction by aneurologist and the last one shows the 1047297nal accuracy
o better examples More and better examples will certainly improve its perormance Te agreement between differentneurologists over the EEG readings is low to moderate I we could 1047297nd the agreement on at least ew o the epilepticpatterns correspondence with epileptic disease then we cantake this tool urther ahead and use it or diagnosis instead o
just assistanceOne o the biggest limitations to this study is the unavail-
ability o non-983091 Hz spike and wave data Even though we haveincluded the data eatures o the entire epileptic requency ranges exclusive to each other proo testing on the data will
certainly prove worthy orthe progresso these assisting toolstoward a diagnostic tool
Conflict of Interests
Te authors declare that there is no con1047298ict o interests
regarding the publication o this paper
Acknowledgment
Te authors would like to extend their sincere appreciation tothe Deanship o Scienti1047297c Research at King Saud University or unding this research through Research Group Project(RG no 983089983092983091983093-983088983093983089)
References
[983089] H Adelia Z Zhoub and N Dadmehrc ldquoAnalysis o EEGrecords in an epileptic patient using wavelet transormrdquo Journal of Neuroscience Methods vol 983089983090983091 no 983089 pp 983094983097ndash983096983095 983090983088983088983091
[983090] M A Ahmad W Majeed and N A Khan ldquoAdvancements incomputer aided methods or EEG-based epileptic detectionrdquo inProceedings of the 983095th International Conference on Bio-Inspired Systems and Signal Processing (BIOSIGNALS 983089983092) AngersFrance 983090983088983089983092
[983091] S Noachtar and J R emi ldquoTe role o EEG in epilepsy a criticalreviewrdquo Epilepsy and Behavior vol 983089983093 no 983089 pp 983090983090ndash983091983091 983090983088983088983097
[983092] E B Petersen J Duun-Henriksen A Mazzaretto W Kjar CE Tomsen and H B D Sorensen ldquoGeneric single-channeldetection o absence seizuresrdquo in Proceedings of the Annual International Conference of the IEEE Engineering in Medicineand Biology Society (EMBS rsquo983089983089) pp 983092983096983090983088ndash983092983096983090983091 Boston MassUSA September 983090983088983089983089
[983093] M Kaleem A Guergachi and S Krishnan ldquoEEG seizure
detection and epilepsy diagnosis using a novel variation o Empirical Mode Decompositionrdquo in Proceedings of the 983091983093th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC rsquo983089983091) pp 983092983091983089983092ndash983092983091983089983095 OsakaJapan July 983090983088983089983091
[983094] S M S Alam and M I H Bhuiyan ldquoDetection o seizure andepilepsy usinghigher order statistics in the EMD domainrdquo IEEE Journal of Biomedical and Health Informatics vol 983089983095 no 983090 pp983091983089983090ndash983091983089983096 983090983088983089983091
[983095] H Ocbagabir K A I Aboalayon and M FaezipourldquoEfficientEEG analysis or seizure monitoring in epileptic patientsrdquo inProceedings of the Long Island Systems Applications and Tech-nology Conference (LISAT rsquo983089983091) pp 983089ndash983094 IEEE Farmingdale NYUSA May 983090983088983089983091
[983096] A A Abdullah S A Rahim and A Ibrahim ldquoDevelopment o EEG-based epileptic detection using arti1047297cial neural networkrdquoin Proceedings of the International Conference on Biomedical Engineering (ICoBE rsquo983089983090) pp 983094983088983093ndash983094983089983088 Penang Malaysia 983090983088983089983090
[983097] P Guo J Wang X Z Gao and J M A anskanen ldquoEpilepticEEG signal classi1047297cation with marching pursuit based on har-mony search methodrdquo in Proceedings of the IEEE International Conference on Systems Man and Cybernetics (SMC rsquo983089983090) pp983090983096983091ndash983090983096983096 Seoul Republic o Korea October 983090983088983089983090
[983089983088] A Shoeb ldquoCHB-MI scalp EEG databaserdquo 983090983088983088983088 httpphysionetorgpn983094chbmit
[983089983089] A S M Murugavel S Ramakrishnan U Maheswari and BS Sabetha ldquoCombined Seizure Index with Adaptive Multi-Class SVM or epileptic EEG classi1047297cationrdquo in Proceedings
of the International Conference on Emerging Trends in VLSIEmbedded System Nano Electronics and TelecommunicationSystem (ICEVENT rsquo983089983091) pp 983089ndash983093 Tiruvannammalai IndiaJanuary 983090983088983089983091
[983089983090] M H Abdullah J M Abdullah and M Z Abdullah ldquoSeizuredetection by means o hidden Markov model and station-ary wavelet transorm o electroencephalograph signalsrdquo inProceedings of the IEEE-EMBS International Conference onBiomedical and Health Informatics (BHI rsquo983089983090) pp 983094983090ndash983094983093 HongKong January 983090983088983089983090
[983089983091] A Subasi and M I Gursoy ldquoEEG signal classi1047297cation usingPCA ICA LDA and support vector machinesrdquo Expert Systemswith Applications vol 983091983095 no 983089983090 pp 983096983094983093983097ndash983096983094983094983094 983090983088983089983088
[983089983092] E Sezer H Isik and E Saracoglu ldquoEmployment and compari-son o different arti1047297cial neural networks or epilepsy diagnosisrom EEG signalsrdquo Journal of Medical Systems vol 983091983094 no 983089 pp983091983092983095ndash983091983094983090 983090983088983089983090
[983089983093] Brain Products GmbHProducts and ApplicationsAnalyzer 983090httpwwwbrainproductscomproductdetailsphpid=983089983095
[983089983094] NeuroExplorer Home httpwwwneuroexplorercom
[983089983096] C H Seng R Demirli L Khuon and D Bolger ldquoSeizuredetection in EEG signals using support vector machinesrdquo inProceedings of the 983091983096th Annual Northeast Bioengineering Con- ference (NEBEC rsquo983089983090) pp 983090983091983089ndash983090983091983090 Philadelphia Pa USA March983090983088983089983090
[983089983097] Z A Khan S B Mansoor M A Ahmad and M M MalikldquoInput devices or virtual surgical simulations a comparativestudyrdquo in Proceedings of the 983089983094th International Multi Topic Con- ference (INMIC rsquo983089983091) pp 983089983096983097ndash983089983097983092 Lahore Pakistan December983090983088983089983091
[983090983088] A L Goldberger L A Amaral L Glass et al ldquoPhysioBankPhysiooolkit and PhysioNet components o a new research
[983090983089] S Nasehi and H Pourghassem ldquoEpileptic seizure onset detec-tion algorithm using dynamic cascade eed-orward neural net-worksrdquo in Proceedings of the International Conference on Intelli- gent Computation and Bio-Medical Instrumentation (ICBMI rsquo983089983089)pp 983089983097983094ndash983089983097983097 December 983090983088983089983089
Processing all channels simultaneously with single classi1047297er
Processing all channels separately with separate classi1047297er oreach channel
F983145983143983157983154983141 983096 Accuracy relationship o different classi1047297ers and theirclassi1047297cation rate
84
86
88
90
92
94
96
98
100
Accuracy afer initial training ()
Accuracy afer retraining ()
ldquo F P 1 F 7 rdquo
ldquo F 7 T 7 rdquo
ldquo T 7 P 7 rdquo
ldquo P 7 O 1 rdquo
ldquo F P 1 F 3 rdquo
ldquo F 3 C 3 rdquo
ldquo C 3 P 3 rdquo
ldquo P 3 O 1 rdquo
ldquo F P 2 F 4 rdquo
ldquo F 4 C 4 rdquo
ldquo C 4 P 4 rdquo
ldquo P 4 O 2 rdquo
ldquo F P 2 F 8 rdquo
ldquo F 8 T 8 rdquo
ldquo T 8 P 8 rdquo
ldquo P 8 O 2 rdquo
ldquo F Z C Z rdquo
ldquo C Z P Z rdquo
ldquo P 7 T 7 rdquo
ldquo T 7 F T 9 rdquo
ldquo F T 9 F T 1 0 rdquo
ldquo F T 1 0 T 8 rdquo
F983145983143983157983154983141 983097 Relation between average classi1047297cation rate and accuracy
o the channel afer initial training and retaining
o epochs or the six times Te retrained classi1047297er was testedon the 983090983091983094983089 remaining epochs
(b) Support Vector Machine We used the support vectormachine classi1047297er package available in MALAB Bioinor-matics oolbox We ound linear kernel to be the mostaccurate SVM kernel with 983093983088 as the box constraint
(c) CHBMIT For CHBMI dataset initial training o theclassi1047297er resulted in 983097983094983091 average accuracy 983097983095983092 average
0
20
40
60
80
100
120
Accuracy afer initial training ()
Accuracy afer retraining ()
ldquo F p 1 rdquo
ldquo F p 2 rdquo
ldquo F 3 rdquo
ldquo F 4 rdquo
ldquo C 3 rdquo
ldquo C 4 rdquo
ldquo P 3 rdquo
ldquo P 4 rdquo
ldquo O 1 rdquo
ldquo O 2 rdquo
ldquo F 7 rdquo
ldquo F 8 rdquo
ldquo T 3 rdquo
ldquo T 4 rdquo
ldquo T 5 rdquo
ldquo T 6 rdquo
ldquo F z rdquo
ldquo C z rdquo
ldquo P z rdquo
ldquo E rdquo
ldquo P G 2 rdquo
ldquo A 1 rdquo
ldquo A 2 rdquo
ldquo T 1 rdquo
ldquo T 2 rdquo
ldquo X 1 rdquo
ldquo X 2 rdquo
ldquo X 3 rdquo
ldquo X 4 rdquo
ldquo X 5 rdquo
ldquo X 6 rdquo
ldquo X 7 rdquo
ldquo P G 1 rdquo
F983145983143983157983154983141 983089983088 Relation between average classi1047297cation rate and accuracy o the channel afer initial training and retaining
82
84
86
88
90
92
94
96
98
Accuracy afer initial training ()Accuracy afer retraining ()
ldquo F P 1 F 7 rdquo
ldquo F 7 T 7 rdquo
ldquo T 7 P 7 rdquo
ldquo P 7 O 1 rdquo
ldquo F P 1 F 3 rdquo
ldquo F 3 C 3 rdquo
ldquo C 3 P 3 rdquo
ldquo P 3 O 1 rdquo
ldquo F P 2 F 4 rdquo
ldquo F 4 C 4 rdquo
ldquo C 4 P 4 rdquo
ldquo P 4 O 2 rdquo
ldquo F P 2 F 8 rdquo
ldquo F 8 T 8 rdquo
ldquo T 8 P 8 rdquo
ldquo P 8 O 2 rdquo
ldquo F Z C Z rdquo
ldquo C Z P Z rdquo
ldquo P 7 T 7 rdquo
ldquo T 7 F T 9 rdquo
ldquo F T 9 F T 1 0 rdquo
ldquo F T 1 0 T 8 rdquo
F983145983143983157983154983141 983089983089 Relation between average classi1047297cation rate and accuracy o the channel afer initial training and retaining
speci1047297city and 983097983091983093 average sensitivity or 983091 Hz spike andwave which is a characteristic o absence seizure Aferinitial training our speci1047297city is better than that o Shoeb[983089983088] and Nasehi and Pourghassem [983090983089] who used the samedataset to validate their technique with different eaturesand application technique Tis shows that our technique
is providing better results even at the initial training phase(Figure 983089983089)
In able 983091 we have shown the average initial classi1047297cationand retrained classi1047297cation results o our system or eachchannel In this system we have shown that afer correctiono ew epochs there is a visible improvement in the systemsclassi1047297cation Te average accuracy o the system rose rom983097983093983093 to 983097983094983091
(d) PIMH For PIMH dataset initial training o the classi1047297erresulted in 983097983088 average accuracy 983097983092 average speci1047297city and983096983088 average sensitivity or 983091 Hz spike and wave which is acharacteristic o absence seizure (Figure 983089983090)
F983145983143983157983154983141 983089983090 Relation between average classi1047297cation rateand accuracy o the channel afer initial training and retaining
80
82
84
86
88
90
92
94
96
98
Accuracy afer initial training ()
Accuracy afer retraining ()
ldquo F P 1 F 7 rdquo
ldquo F 7 T 7 rdquo
ldquo T 7 P 7 rdquo
ldquo P 7 O 1 rdquo
ldquo F P 1 F 3 rdquo
ldquo F 3 C 3 rdquo
ldquo C 3 P 3 rdquo
ldquo P 3 O 1 rdquo
ldquo F P 2 F 4 rdquo
ldquo F 4 C 4 rdquo
ldquo C 4 P 4 rdquo
ldquo P 4 O 2 rdquo
ldquo F P 2 F 8 rdquo
ldquo F 8 T 8 rdquo
ldquo T 8 P 8 rdquo
ldquo P 8 O 2 rdquo
ldquo F Z C Z rdquo
ldquo C Z P Z rdquo
ldquo P 7 T 7 rdquo
ldquo T 7 F T 9 rdquo
ldquo F T 9 F T 1 0 rdquo
ldquo F T 1 0 T 8 rdquo
F983145983143983157983154983141 983089983091 Relation between average classi1047297cation rate and accuracy o the channel afer initial training and retaining
In able 983092 we have shown the average initial classi1047297cationand retrained classi1047297cation results o our system or eachchannel able 983092 shows that our technique is robust and itworks also on a different dataset Te average accuracy o thesystem rose rom approximately 983096983097 to 983097983088
983091983091983090 Discriminate Analysis We used the discriminant anal-
ysis package available in MALAB Statistics oolbox Weound pseudoquadratic to be the best perorming discrimi-nate type with uniorm probability
(a) CHBMIT For CHBMI dataset initial training o theclassi1047297er resulted in 983097983092 average accuracy 983097983094 averagespeci1047297city and 983097983088 average sensitivity or 983091 Hz spike andwave which is a characteristic o absence seizure Afer initialtraining our speci1047297city is better than that o Shoeb [983089983088] andNasehi and Pourghassem [983090983089] (Figure 983089983091)
In able 983093 we have shown the average initial classi1047297cationand retrained classi1047297cation results o our system or eachchannel In this system we have shown that afer correction
010
2030405060708090
100
Accuracy afer initial training ()
Accuracy afer retraining ()
ldquo F 3 rdquo
ldquo F 4 rdquo
ldquo C 3 rdquo
ldquo C 4 rdquo
ldquo P 3 rdquo
ldquo P 4 rdquo
ldquo O 1 rdquo
ldquo O 2 rdquo
ldquo F 7 rdquo
ldquo F 8 rdquo
ldquo T 3 rdquo
ldquo T 4 rdquo
ldquo T 5 rdquo
ldquo T 6 rdquo
ldquo F z rdquo
ldquo C z rdquo
ldquo P z rdquo
ldquo E rdquo
ldquo P G 2 rdquo
ldquo T 1 rdquo
ldquo T 2 rdquo
ldquo X 1 rdquo
ldquo X 2 rdquo
ldquo X 3 rdquo
ldquo X 4 rdquo
ldquo X 5 rdquo
ldquo X 6 rdquo
ldquo X 7 rdquo
ldquo P G 1 rdquo
ldquo A 1 rdquo
ldquo A 2 rdquo
ldquo F p 1 rdquo
ldquo F p 2 rdquo
F983145983143983157983154983141 983089983092 Relation between average classi1047297cation rate and accuracy o the channel afer initial training and retaining
983137983138983148983141 983091 First column shows the channel label the second columnshows the initial training accuracy and third one shows the markedcorrection by a neurologistand thelast one shows the1047297nal accuracy
o ew epochs there is visible improvement in the systemsclassi1047297cation Te average accuracy o the system rose rom983097983092 to 983097983093
(b) PIMH For PIMH dataset initial training o the classi1047297erresulted in 983097983088 average accuracy 983097983093 average speci1047297city and983095983091 average sensitivity or 983091 Hz spike and wave which is acharacteristic o absence seizure
In able 983094 we have shown the average initialclassi1047297cationand retrained classi1047297cation results o our system or each
983137983138983148983141 983092 First column shows the channel label the second columnshows the initial training accuracy and third one shows the markedcorrection by a neurologistand thelast one shows the1047297nal accuracy
channel able 983094 shows that our technique is robust and itworks also on a different dataset Te average accuracy o thesystem rose rom approximately 983096983097 to 983097983088
983091983091983091 Arti1047297cial Neural Network We used eedorward back-propagation package available in MALAB Neural Network oolbox and ound Levenberg-Marquardt to be the bestmethod with 983088983088983093 learning rate
(a) CHBMIT For CHBMI dataset initial training o theclassi1047297er resulted in 983097983090983096983096 average accuracy 983097983096983094983094 averagespeci1047297city and 983095983093983095983093 average sensitivity or 983091 Hz spike andwave which is a characteristic o absence seizure (Figure 983097)
983137983138983148983141 983093 First column shows the channel label the second columnshows the initial training accuracy and third one shows the markedcorrection by a neurologistand thelast one shows the1047297nal accuracy
In able 983095 we have shown the average initial classi1047297cationand retrained classi1047297cation results o our system or eachchannel In this system we have shown that afer correctiono ew epochs there is visible improvement in the systemsclassi1047297cation Te average accuracy o the system rose rom983097983090983096983096 to 983097983091983097983094
(b) PIMH For PIMH dataset initial training o the classi1047297erresulted in 983096983092 average accuracy 983097983092983096 average speci1047297cityand 983093983094983093 average sensitivity or 983091 Hz spike and wave whichis a characteristic o absence seizure (Figure 983089983088)
In able 983096 we have shown the average initial classi1047297cationand retrained classi1047297cation results o our system or each
channel able 983096 shows that our technique is robust and itworks also on a different dataset Te average accuracy o thesystem rose rom approximately 983096983092 to 983096983093983092983091
4 Discussion and Future Work
Computer-assisted analysis o EEG has tremendous potentialor assisting the clinicians in diagnosis A very importantand novel phase o our system is user adaptation mechanismor retraining mechanism Introduction o this phase hasimportance in many aspects In this phase system tries toadapt its classi1047297cation according to users desire Moreoverthis technique personalizes the classi1047297ers classi1047297cation It has
983137983138983148983141 983094 First column shows the channel label the second columnshows the initial training accuracy and third one shows the markedcorrection by a neurologistand thelast one shows the1047297nal accuracy
been cited that sometimes even the expert neurologists havesome disagreementovera certain observation o an EEGdataTis system will be useul or disagreeing users and it will alsohelp them in comparing their results with each other
Tere is also a threat o over1047297tting by the classi1047297erIn order to keep the classi1047297er improving its perormancewith the encounter o more and more examples we haveintroduced this user adaptive mechanism in our system Weconsider the existing systems as dead because these cannotimprove their classi1047297cation rate afer initial training (duringsofware development) Te sel-improving mechanism aferdeployment makes our tool alive Tis system can be madepart o the whole epileptic diagnosis process It will highlight
983137983138983148983141 983095 First column shows the channel label the second columnshows the initial training accuracy and third one shows the markedcorrection by a neurologistand thelast one shows the1047297nal accuracy
the epileptic spikes among the whole EEG thus leading toreduced atigue and time consumption o a user We obtainedhigh classi1047297cation accuracy on datasets obtained rom twodifferent sites which indicates reproducibility o our resultsand robustness o our approach
In the uture we are planning to make this a web basedapplication neurologists can log in and consult each otherrsquosreviews about a particular subject Tis will make our systemexperience a whole versatility o examples and learn rom allo them Integration o the video and its automatic analysis(video EEG) can help a neurologist in diagnosing epilepsy ina better way whereas this can also help him in distinguishingbetween psychogenic and epileptic seizuresWewouldalso be
investigating how much over1047297tting is an issue in the reportedperormances which are now even touching 983089983088983088 based onsome claims Tere is a need or methodcriteria which couldlimit these algorithms improving their detection on a limitednumber o available examples
Tissystem is made keeping in mind thatwe haveto acil-itate the neurologist by supplementing him in the analysis o the EEG We do not want to enorce the classi1047297cation o theEEG data on a user
In the uture we will also include a slider in the systemwhich will allow the user to adjust the sensitivity and speci-1047297city beore retraining Tis assisting system is more like adetection tool which is continuously learning with encounter
983137983138983148983141 983096 Classi1047297cation rate improvement caused by retraining Firstcolumn shows thechannellabel thesecond columnshows theinitialtraining accuracy and third one shows the marked correction by aneurologist and the last one shows the 1047297nal accuracy
o better examples More and better examples will certainly improve its perormance Te agreement between differentneurologists over the EEG readings is low to moderate I we could 1047297nd the agreement on at least ew o the epilepticpatterns correspondence with epileptic disease then we cantake this tool urther ahead and use it or diagnosis instead o
just assistanceOne o the biggest limitations to this study is the unavail-
ability o non-983091 Hz spike and wave data Even though we haveincluded the data eatures o the entire epileptic requency ranges exclusive to each other proo testing on the data will
certainly prove worthy orthe progresso these assisting toolstoward a diagnostic tool
Conflict of Interests
Te authors declare that there is no con1047298ict o interests
regarding the publication o this paper
Acknowledgment
Te authors would like to extend their sincere appreciation tothe Deanship o Scienti1047297c Research at King Saud University or unding this research through Research Group Project(RG no 983089983092983091983093-983088983093983089)
References
[983089] H Adelia Z Zhoub and N Dadmehrc ldquoAnalysis o EEGrecords in an epileptic patient using wavelet transormrdquo Journal of Neuroscience Methods vol 983089983090983091 no 983089 pp 983094983097ndash983096983095 983090983088983088983091
[983090] M A Ahmad W Majeed and N A Khan ldquoAdvancements incomputer aided methods or EEG-based epileptic detectionrdquo inProceedings of the 983095th International Conference on Bio-Inspired Systems and Signal Processing (BIOSIGNALS 983089983092) AngersFrance 983090983088983089983092
[983091] S Noachtar and J R emi ldquoTe role o EEG in epilepsy a criticalreviewrdquo Epilepsy and Behavior vol 983089983093 no 983089 pp 983090983090ndash983091983091 983090983088983088983097
[983092] E B Petersen J Duun-Henriksen A Mazzaretto W Kjar CE Tomsen and H B D Sorensen ldquoGeneric single-channeldetection o absence seizuresrdquo in Proceedings of the Annual International Conference of the IEEE Engineering in Medicineand Biology Society (EMBS rsquo983089983089) pp 983092983096983090983088ndash983092983096983090983091 Boston MassUSA September 983090983088983089983089
[983093] M Kaleem A Guergachi and S Krishnan ldquoEEG seizure
detection and epilepsy diagnosis using a novel variation o Empirical Mode Decompositionrdquo in Proceedings of the 983091983093th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC rsquo983089983091) pp 983092983091983089983092ndash983092983091983089983095 OsakaJapan July 983090983088983089983091
[983094] S M S Alam and M I H Bhuiyan ldquoDetection o seizure andepilepsy usinghigher order statistics in the EMD domainrdquo IEEE Journal of Biomedical and Health Informatics vol 983089983095 no 983090 pp983091983089983090ndash983091983089983096 983090983088983089983091
[983095] H Ocbagabir K A I Aboalayon and M FaezipourldquoEfficientEEG analysis or seizure monitoring in epileptic patientsrdquo inProceedings of the Long Island Systems Applications and Tech-nology Conference (LISAT rsquo983089983091) pp 983089ndash983094 IEEE Farmingdale NYUSA May 983090983088983089983091
[983096] A A Abdullah S A Rahim and A Ibrahim ldquoDevelopment o EEG-based epileptic detection using arti1047297cial neural networkrdquoin Proceedings of the International Conference on Biomedical Engineering (ICoBE rsquo983089983090) pp 983094983088983093ndash983094983089983088 Penang Malaysia 983090983088983089983090
[983097] P Guo J Wang X Z Gao and J M A anskanen ldquoEpilepticEEG signal classi1047297cation with marching pursuit based on har-mony search methodrdquo in Proceedings of the IEEE International Conference on Systems Man and Cybernetics (SMC rsquo983089983090) pp983090983096983091ndash983090983096983096 Seoul Republic o Korea October 983090983088983089983090
[983089983088] A Shoeb ldquoCHB-MI scalp EEG databaserdquo 983090983088983088983088 httpphysionetorgpn983094chbmit
[983089983089] A S M Murugavel S Ramakrishnan U Maheswari and BS Sabetha ldquoCombined Seizure Index with Adaptive Multi-Class SVM or epileptic EEG classi1047297cationrdquo in Proceedings
of the International Conference on Emerging Trends in VLSIEmbedded System Nano Electronics and TelecommunicationSystem (ICEVENT rsquo983089983091) pp 983089ndash983093 Tiruvannammalai IndiaJanuary 983090983088983089983091
[983089983090] M H Abdullah J M Abdullah and M Z Abdullah ldquoSeizuredetection by means o hidden Markov model and station-ary wavelet transorm o electroencephalograph signalsrdquo inProceedings of the IEEE-EMBS International Conference onBiomedical and Health Informatics (BHI rsquo983089983090) pp 983094983090ndash983094983093 HongKong January 983090983088983089983090
[983089983091] A Subasi and M I Gursoy ldquoEEG signal classi1047297cation usingPCA ICA LDA and support vector machinesrdquo Expert Systemswith Applications vol 983091983095 no 983089983090 pp 983096983094983093983097ndash983096983094983094983094 983090983088983089983088
[983089983092] E Sezer H Isik and E Saracoglu ldquoEmployment and compari-son o different arti1047297cial neural networks or epilepsy diagnosisrom EEG signalsrdquo Journal of Medical Systems vol 983091983094 no 983089 pp983091983092983095ndash983091983094983090 983090983088983089983090
[983089983093] Brain Products GmbHProducts and ApplicationsAnalyzer 983090httpwwwbrainproductscomproductdetailsphpid=983089983095
[983089983094] NeuroExplorer Home httpwwwneuroexplorercom
[983089983096] C H Seng R Demirli L Khuon and D Bolger ldquoSeizuredetection in EEG signals using support vector machinesrdquo inProceedings of the 983091983096th Annual Northeast Bioengineering Con- ference (NEBEC rsquo983089983090) pp 983090983091983089ndash983090983091983090 Philadelphia Pa USA March983090983088983089983090
[983089983097] Z A Khan S B Mansoor M A Ahmad and M M MalikldquoInput devices or virtual surgical simulations a comparativestudyrdquo in Proceedings of the 983089983094th International Multi Topic Con- ference (INMIC rsquo983089983091) pp 983089983096983097ndash983089983097983092 Lahore Pakistan December983090983088983089983091
[983090983088] A L Goldberger L A Amaral L Glass et al ldquoPhysioBankPhysiooolkit and PhysioNet components o a new research
[983090983089] S Nasehi and H Pourghassem ldquoEpileptic seizure onset detec-tion algorithm using dynamic cascade eed-orward neural net-worksrdquo in Proceedings of the International Conference on Intelli- gent Computation and Bio-Medical Instrumentation (ICBMI rsquo983089983089)pp 983089983097983094ndash983089983097983097 December 983090983088983089983089
F983145983143983157983154983141 983089983090 Relation between average classi1047297cation rateand accuracy o the channel afer initial training and retaining
80
82
84
86
88
90
92
94
96
98
Accuracy afer initial training ()
Accuracy afer retraining ()
ldquo F P 1 F 7 rdquo
ldquo F 7 T 7 rdquo
ldquo T 7 P 7 rdquo
ldquo P 7 O 1 rdquo
ldquo F P 1 F 3 rdquo
ldquo F 3 C 3 rdquo
ldquo C 3 P 3 rdquo
ldquo P 3 O 1 rdquo
ldquo F P 2 F 4 rdquo
ldquo F 4 C 4 rdquo
ldquo C 4 P 4 rdquo
ldquo P 4 O 2 rdquo
ldquo F P 2 F 8 rdquo
ldquo F 8 T 8 rdquo
ldquo T 8 P 8 rdquo
ldquo P 8 O 2 rdquo
ldquo F Z C Z rdquo
ldquo C Z P Z rdquo
ldquo P 7 T 7 rdquo
ldquo T 7 F T 9 rdquo
ldquo F T 9 F T 1 0 rdquo
ldquo F T 1 0 T 8 rdquo
F983145983143983157983154983141 983089983091 Relation between average classi1047297cation rate and accuracy o the channel afer initial training and retaining
In able 983092 we have shown the average initial classi1047297cationand retrained classi1047297cation results o our system or eachchannel able 983092 shows that our technique is robust and itworks also on a different dataset Te average accuracy o thesystem rose rom approximately 983096983097 to 983097983088
983091983091983090 Discriminate Analysis We used the discriminant anal-
ysis package available in MALAB Statistics oolbox Weound pseudoquadratic to be the best perorming discrimi-nate type with uniorm probability
(a) CHBMIT For CHBMI dataset initial training o theclassi1047297er resulted in 983097983092 average accuracy 983097983094 averagespeci1047297city and 983097983088 average sensitivity or 983091 Hz spike andwave which is a characteristic o absence seizure Afer initialtraining our speci1047297city is better than that o Shoeb [983089983088] andNasehi and Pourghassem [983090983089] (Figure 983089983091)
In able 983093 we have shown the average initial classi1047297cationand retrained classi1047297cation results o our system or eachchannel In this system we have shown that afer correction
010
2030405060708090
100
Accuracy afer initial training ()
Accuracy afer retraining ()
ldquo F 3 rdquo
ldquo F 4 rdquo
ldquo C 3 rdquo
ldquo C 4 rdquo
ldquo P 3 rdquo
ldquo P 4 rdquo
ldquo O 1 rdquo
ldquo O 2 rdquo
ldquo F 7 rdquo
ldquo F 8 rdquo
ldquo T 3 rdquo
ldquo T 4 rdquo
ldquo T 5 rdquo
ldquo T 6 rdquo
ldquo F z rdquo
ldquo C z rdquo
ldquo P z rdquo
ldquo E rdquo
ldquo P G 2 rdquo
ldquo T 1 rdquo
ldquo T 2 rdquo
ldquo X 1 rdquo
ldquo X 2 rdquo
ldquo X 3 rdquo
ldquo X 4 rdquo
ldquo X 5 rdquo
ldquo X 6 rdquo
ldquo X 7 rdquo
ldquo P G 1 rdquo
ldquo A 1 rdquo
ldquo A 2 rdquo
ldquo F p 1 rdquo
ldquo F p 2 rdquo
F983145983143983157983154983141 983089983092 Relation between average classi1047297cation rate and accuracy o the channel afer initial training and retaining
983137983138983148983141 983091 First column shows the channel label the second columnshows the initial training accuracy and third one shows the markedcorrection by a neurologistand thelast one shows the1047297nal accuracy
o ew epochs there is visible improvement in the systemsclassi1047297cation Te average accuracy o the system rose rom983097983092 to 983097983093
(b) PIMH For PIMH dataset initial training o the classi1047297erresulted in 983097983088 average accuracy 983097983093 average speci1047297city and983095983091 average sensitivity or 983091 Hz spike and wave which is acharacteristic o absence seizure
In able 983094 we have shown the average initialclassi1047297cationand retrained classi1047297cation results o our system or each
983137983138983148983141 983092 First column shows the channel label the second columnshows the initial training accuracy and third one shows the markedcorrection by a neurologistand thelast one shows the1047297nal accuracy
channel able 983094 shows that our technique is robust and itworks also on a different dataset Te average accuracy o thesystem rose rom approximately 983096983097 to 983097983088
983091983091983091 Arti1047297cial Neural Network We used eedorward back-propagation package available in MALAB Neural Network oolbox and ound Levenberg-Marquardt to be the bestmethod with 983088983088983093 learning rate
(a) CHBMIT For CHBMI dataset initial training o theclassi1047297er resulted in 983097983090983096983096 average accuracy 983097983096983094983094 averagespeci1047297city and 983095983093983095983093 average sensitivity or 983091 Hz spike andwave which is a characteristic o absence seizure (Figure 983097)
983137983138983148983141 983093 First column shows the channel label the second columnshows the initial training accuracy and third one shows the markedcorrection by a neurologistand thelast one shows the1047297nal accuracy
In able 983095 we have shown the average initial classi1047297cationand retrained classi1047297cation results o our system or eachchannel In this system we have shown that afer correctiono ew epochs there is visible improvement in the systemsclassi1047297cation Te average accuracy o the system rose rom983097983090983096983096 to 983097983091983097983094
(b) PIMH For PIMH dataset initial training o the classi1047297erresulted in 983096983092 average accuracy 983097983092983096 average speci1047297cityand 983093983094983093 average sensitivity or 983091 Hz spike and wave whichis a characteristic o absence seizure (Figure 983089983088)
In able 983096 we have shown the average initial classi1047297cationand retrained classi1047297cation results o our system or each
channel able 983096 shows that our technique is robust and itworks also on a different dataset Te average accuracy o thesystem rose rom approximately 983096983092 to 983096983093983092983091
4 Discussion and Future Work
Computer-assisted analysis o EEG has tremendous potentialor assisting the clinicians in diagnosis A very importantand novel phase o our system is user adaptation mechanismor retraining mechanism Introduction o this phase hasimportance in many aspects In this phase system tries toadapt its classi1047297cation according to users desire Moreoverthis technique personalizes the classi1047297ers classi1047297cation It has
983137983138983148983141 983094 First column shows the channel label the second columnshows the initial training accuracy and third one shows the markedcorrection by a neurologistand thelast one shows the1047297nal accuracy
been cited that sometimes even the expert neurologists havesome disagreementovera certain observation o an EEGdataTis system will be useul or disagreeing users and it will alsohelp them in comparing their results with each other
Tere is also a threat o over1047297tting by the classi1047297erIn order to keep the classi1047297er improving its perormancewith the encounter o more and more examples we haveintroduced this user adaptive mechanism in our system Weconsider the existing systems as dead because these cannotimprove their classi1047297cation rate afer initial training (duringsofware development) Te sel-improving mechanism aferdeployment makes our tool alive Tis system can be madepart o the whole epileptic diagnosis process It will highlight
983137983138983148983141 983095 First column shows the channel label the second columnshows the initial training accuracy and third one shows the markedcorrection by a neurologistand thelast one shows the1047297nal accuracy
the epileptic spikes among the whole EEG thus leading toreduced atigue and time consumption o a user We obtainedhigh classi1047297cation accuracy on datasets obtained rom twodifferent sites which indicates reproducibility o our resultsand robustness o our approach
In the uture we are planning to make this a web basedapplication neurologists can log in and consult each otherrsquosreviews about a particular subject Tis will make our systemexperience a whole versatility o examples and learn rom allo them Integration o the video and its automatic analysis(video EEG) can help a neurologist in diagnosing epilepsy ina better way whereas this can also help him in distinguishingbetween psychogenic and epileptic seizuresWewouldalso be
investigating how much over1047297tting is an issue in the reportedperormances which are now even touching 983089983088983088 based onsome claims Tere is a need or methodcriteria which couldlimit these algorithms improving their detection on a limitednumber o available examples
Tissystem is made keeping in mind thatwe haveto acil-itate the neurologist by supplementing him in the analysis o the EEG We do not want to enorce the classi1047297cation o theEEG data on a user
In the uture we will also include a slider in the systemwhich will allow the user to adjust the sensitivity and speci-1047297city beore retraining Tis assisting system is more like adetection tool which is continuously learning with encounter
983137983138983148983141 983096 Classi1047297cation rate improvement caused by retraining Firstcolumn shows thechannellabel thesecond columnshows theinitialtraining accuracy and third one shows the marked correction by aneurologist and the last one shows the 1047297nal accuracy
o better examples More and better examples will certainly improve its perormance Te agreement between differentneurologists over the EEG readings is low to moderate I we could 1047297nd the agreement on at least ew o the epilepticpatterns correspondence with epileptic disease then we cantake this tool urther ahead and use it or diagnosis instead o
just assistanceOne o the biggest limitations to this study is the unavail-
ability o non-983091 Hz spike and wave data Even though we haveincluded the data eatures o the entire epileptic requency ranges exclusive to each other proo testing on the data will
certainly prove worthy orthe progresso these assisting toolstoward a diagnostic tool
Conflict of Interests
Te authors declare that there is no con1047298ict o interests
regarding the publication o this paper
Acknowledgment
Te authors would like to extend their sincere appreciation tothe Deanship o Scienti1047297c Research at King Saud University or unding this research through Research Group Project(RG no 983089983092983091983093-983088983093983089)
References
[983089] H Adelia Z Zhoub and N Dadmehrc ldquoAnalysis o EEGrecords in an epileptic patient using wavelet transormrdquo Journal of Neuroscience Methods vol 983089983090983091 no 983089 pp 983094983097ndash983096983095 983090983088983088983091
[983090] M A Ahmad W Majeed and N A Khan ldquoAdvancements incomputer aided methods or EEG-based epileptic detectionrdquo inProceedings of the 983095th International Conference on Bio-Inspired Systems and Signal Processing (BIOSIGNALS 983089983092) AngersFrance 983090983088983089983092
[983091] S Noachtar and J R emi ldquoTe role o EEG in epilepsy a criticalreviewrdquo Epilepsy and Behavior vol 983089983093 no 983089 pp 983090983090ndash983091983091 983090983088983088983097
[983092] E B Petersen J Duun-Henriksen A Mazzaretto W Kjar CE Tomsen and H B D Sorensen ldquoGeneric single-channeldetection o absence seizuresrdquo in Proceedings of the Annual International Conference of the IEEE Engineering in Medicineand Biology Society (EMBS rsquo983089983089) pp 983092983096983090983088ndash983092983096983090983091 Boston MassUSA September 983090983088983089983089
[983093] M Kaleem A Guergachi and S Krishnan ldquoEEG seizure
detection and epilepsy diagnosis using a novel variation o Empirical Mode Decompositionrdquo in Proceedings of the 983091983093th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC rsquo983089983091) pp 983092983091983089983092ndash983092983091983089983095 OsakaJapan July 983090983088983089983091
[983094] S M S Alam and M I H Bhuiyan ldquoDetection o seizure andepilepsy usinghigher order statistics in the EMD domainrdquo IEEE Journal of Biomedical and Health Informatics vol 983089983095 no 983090 pp983091983089983090ndash983091983089983096 983090983088983089983091
[983095] H Ocbagabir K A I Aboalayon and M FaezipourldquoEfficientEEG analysis or seizure monitoring in epileptic patientsrdquo inProceedings of the Long Island Systems Applications and Tech-nology Conference (LISAT rsquo983089983091) pp 983089ndash983094 IEEE Farmingdale NYUSA May 983090983088983089983091
[983096] A A Abdullah S A Rahim and A Ibrahim ldquoDevelopment o EEG-based epileptic detection using arti1047297cial neural networkrdquoin Proceedings of the International Conference on Biomedical Engineering (ICoBE rsquo983089983090) pp 983094983088983093ndash983094983089983088 Penang Malaysia 983090983088983089983090
[983097] P Guo J Wang X Z Gao and J M A anskanen ldquoEpilepticEEG signal classi1047297cation with marching pursuit based on har-mony search methodrdquo in Proceedings of the IEEE International Conference on Systems Man and Cybernetics (SMC rsquo983089983090) pp983090983096983091ndash983090983096983096 Seoul Republic o Korea October 983090983088983089983090
[983089983088] A Shoeb ldquoCHB-MI scalp EEG databaserdquo 983090983088983088983088 httpphysionetorgpn983094chbmit
[983089983089] A S M Murugavel S Ramakrishnan U Maheswari and BS Sabetha ldquoCombined Seizure Index with Adaptive Multi-Class SVM or epileptic EEG classi1047297cationrdquo in Proceedings
of the International Conference on Emerging Trends in VLSIEmbedded System Nano Electronics and TelecommunicationSystem (ICEVENT rsquo983089983091) pp 983089ndash983093 Tiruvannammalai IndiaJanuary 983090983088983089983091
[983089983090] M H Abdullah J M Abdullah and M Z Abdullah ldquoSeizuredetection by means o hidden Markov model and station-ary wavelet transorm o electroencephalograph signalsrdquo inProceedings of the IEEE-EMBS International Conference onBiomedical and Health Informatics (BHI rsquo983089983090) pp 983094983090ndash983094983093 HongKong January 983090983088983089983090
[983089983091] A Subasi and M I Gursoy ldquoEEG signal classi1047297cation usingPCA ICA LDA and support vector machinesrdquo Expert Systemswith Applications vol 983091983095 no 983089983090 pp 983096983094983093983097ndash983096983094983094983094 983090983088983089983088
[983089983092] E Sezer H Isik and E Saracoglu ldquoEmployment and compari-son o different arti1047297cial neural networks or epilepsy diagnosisrom EEG signalsrdquo Journal of Medical Systems vol 983091983094 no 983089 pp983091983092983095ndash983091983094983090 983090983088983089983090
[983089983093] Brain Products GmbHProducts and ApplicationsAnalyzer 983090httpwwwbrainproductscomproductdetailsphpid=983089983095
[983089983094] NeuroExplorer Home httpwwwneuroexplorercom
[983089983096] C H Seng R Demirli L Khuon and D Bolger ldquoSeizuredetection in EEG signals using support vector machinesrdquo inProceedings of the 983091983096th Annual Northeast Bioengineering Con- ference (NEBEC rsquo983089983090) pp 983090983091983089ndash983090983091983090 Philadelphia Pa USA March983090983088983089983090
[983089983097] Z A Khan S B Mansoor M A Ahmad and M M MalikldquoInput devices or virtual surgical simulations a comparativestudyrdquo in Proceedings of the 983089983094th International Multi Topic Con- ference (INMIC rsquo983089983091) pp 983089983096983097ndash983089983097983092 Lahore Pakistan December983090983088983089983091
[983090983088] A L Goldberger L A Amaral L Glass et al ldquoPhysioBankPhysiooolkit and PhysioNet components o a new research
[983090983089] S Nasehi and H Pourghassem ldquoEpileptic seizure onset detec-tion algorithm using dynamic cascade eed-orward neural net-worksrdquo in Proceedings of the International Conference on Intelli- gent Computation and Bio-Medical Instrumentation (ICBMI rsquo983089983089)pp 983089983097983094ndash983089983097983097 December 983090983088983089983089
983137983138983148983141 983092 First column shows the channel label the second columnshows the initial training accuracy and third one shows the markedcorrection by a neurologistand thelast one shows the1047297nal accuracy
channel able 983094 shows that our technique is robust and itworks also on a different dataset Te average accuracy o thesystem rose rom approximately 983096983097 to 983097983088
983091983091983091 Arti1047297cial Neural Network We used eedorward back-propagation package available in MALAB Neural Network oolbox and ound Levenberg-Marquardt to be the bestmethod with 983088983088983093 learning rate
(a) CHBMIT For CHBMI dataset initial training o theclassi1047297er resulted in 983097983090983096983096 average accuracy 983097983096983094983094 averagespeci1047297city and 983095983093983095983093 average sensitivity or 983091 Hz spike andwave which is a characteristic o absence seizure (Figure 983097)
983137983138983148983141 983093 First column shows the channel label the second columnshows the initial training accuracy and third one shows the markedcorrection by a neurologistand thelast one shows the1047297nal accuracy
In able 983095 we have shown the average initial classi1047297cationand retrained classi1047297cation results o our system or eachchannel In this system we have shown that afer correctiono ew epochs there is visible improvement in the systemsclassi1047297cation Te average accuracy o the system rose rom983097983090983096983096 to 983097983091983097983094
(b) PIMH For PIMH dataset initial training o the classi1047297erresulted in 983096983092 average accuracy 983097983092983096 average speci1047297cityand 983093983094983093 average sensitivity or 983091 Hz spike and wave whichis a characteristic o absence seizure (Figure 983089983088)
In able 983096 we have shown the average initial classi1047297cationand retrained classi1047297cation results o our system or each
channel able 983096 shows that our technique is robust and itworks also on a different dataset Te average accuracy o thesystem rose rom approximately 983096983092 to 983096983093983092983091
4 Discussion and Future Work
Computer-assisted analysis o EEG has tremendous potentialor assisting the clinicians in diagnosis A very importantand novel phase o our system is user adaptation mechanismor retraining mechanism Introduction o this phase hasimportance in many aspects In this phase system tries toadapt its classi1047297cation according to users desire Moreoverthis technique personalizes the classi1047297ers classi1047297cation It has
983137983138983148983141 983094 First column shows the channel label the second columnshows the initial training accuracy and third one shows the markedcorrection by a neurologistand thelast one shows the1047297nal accuracy
been cited that sometimes even the expert neurologists havesome disagreementovera certain observation o an EEGdataTis system will be useul or disagreeing users and it will alsohelp them in comparing their results with each other
Tere is also a threat o over1047297tting by the classi1047297erIn order to keep the classi1047297er improving its perormancewith the encounter o more and more examples we haveintroduced this user adaptive mechanism in our system Weconsider the existing systems as dead because these cannotimprove their classi1047297cation rate afer initial training (duringsofware development) Te sel-improving mechanism aferdeployment makes our tool alive Tis system can be madepart o the whole epileptic diagnosis process It will highlight
983137983138983148983141 983095 First column shows the channel label the second columnshows the initial training accuracy and third one shows the markedcorrection by a neurologistand thelast one shows the1047297nal accuracy
the epileptic spikes among the whole EEG thus leading toreduced atigue and time consumption o a user We obtainedhigh classi1047297cation accuracy on datasets obtained rom twodifferent sites which indicates reproducibility o our resultsand robustness o our approach
In the uture we are planning to make this a web basedapplication neurologists can log in and consult each otherrsquosreviews about a particular subject Tis will make our systemexperience a whole versatility o examples and learn rom allo them Integration o the video and its automatic analysis(video EEG) can help a neurologist in diagnosing epilepsy ina better way whereas this can also help him in distinguishingbetween psychogenic and epileptic seizuresWewouldalso be
investigating how much over1047297tting is an issue in the reportedperormances which are now even touching 983089983088983088 based onsome claims Tere is a need or methodcriteria which couldlimit these algorithms improving their detection on a limitednumber o available examples
Tissystem is made keeping in mind thatwe haveto acil-itate the neurologist by supplementing him in the analysis o the EEG We do not want to enorce the classi1047297cation o theEEG data on a user
In the uture we will also include a slider in the systemwhich will allow the user to adjust the sensitivity and speci-1047297city beore retraining Tis assisting system is more like adetection tool which is continuously learning with encounter
983137983138983148983141 983096 Classi1047297cation rate improvement caused by retraining Firstcolumn shows thechannellabel thesecond columnshows theinitialtraining accuracy and third one shows the marked correction by aneurologist and the last one shows the 1047297nal accuracy
o better examples More and better examples will certainly improve its perormance Te agreement between differentneurologists over the EEG readings is low to moderate I we could 1047297nd the agreement on at least ew o the epilepticpatterns correspondence with epileptic disease then we cantake this tool urther ahead and use it or diagnosis instead o
just assistanceOne o the biggest limitations to this study is the unavail-
ability o non-983091 Hz spike and wave data Even though we haveincluded the data eatures o the entire epileptic requency ranges exclusive to each other proo testing on the data will
certainly prove worthy orthe progresso these assisting toolstoward a diagnostic tool
Conflict of Interests
Te authors declare that there is no con1047298ict o interests
regarding the publication o this paper
Acknowledgment
Te authors would like to extend their sincere appreciation tothe Deanship o Scienti1047297c Research at King Saud University or unding this research through Research Group Project(RG no 983089983092983091983093-983088983093983089)
References
[983089] H Adelia Z Zhoub and N Dadmehrc ldquoAnalysis o EEGrecords in an epileptic patient using wavelet transormrdquo Journal of Neuroscience Methods vol 983089983090983091 no 983089 pp 983094983097ndash983096983095 983090983088983088983091
[983090] M A Ahmad W Majeed and N A Khan ldquoAdvancements incomputer aided methods or EEG-based epileptic detectionrdquo inProceedings of the 983095th International Conference on Bio-Inspired Systems and Signal Processing (BIOSIGNALS 983089983092) AngersFrance 983090983088983089983092
[983091] S Noachtar and J R emi ldquoTe role o EEG in epilepsy a criticalreviewrdquo Epilepsy and Behavior vol 983089983093 no 983089 pp 983090983090ndash983091983091 983090983088983088983097
[983092] E B Petersen J Duun-Henriksen A Mazzaretto W Kjar CE Tomsen and H B D Sorensen ldquoGeneric single-channeldetection o absence seizuresrdquo in Proceedings of the Annual International Conference of the IEEE Engineering in Medicineand Biology Society (EMBS rsquo983089983089) pp 983092983096983090983088ndash983092983096983090983091 Boston MassUSA September 983090983088983089983089
[983093] M Kaleem A Guergachi and S Krishnan ldquoEEG seizure
detection and epilepsy diagnosis using a novel variation o Empirical Mode Decompositionrdquo in Proceedings of the 983091983093th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC rsquo983089983091) pp 983092983091983089983092ndash983092983091983089983095 OsakaJapan July 983090983088983089983091
[983094] S M S Alam and M I H Bhuiyan ldquoDetection o seizure andepilepsy usinghigher order statistics in the EMD domainrdquo IEEE Journal of Biomedical and Health Informatics vol 983089983095 no 983090 pp983091983089983090ndash983091983089983096 983090983088983089983091
[983095] H Ocbagabir K A I Aboalayon and M FaezipourldquoEfficientEEG analysis or seizure monitoring in epileptic patientsrdquo inProceedings of the Long Island Systems Applications and Tech-nology Conference (LISAT rsquo983089983091) pp 983089ndash983094 IEEE Farmingdale NYUSA May 983090983088983089983091
[983096] A A Abdullah S A Rahim and A Ibrahim ldquoDevelopment o EEG-based epileptic detection using arti1047297cial neural networkrdquoin Proceedings of the International Conference on Biomedical Engineering (ICoBE rsquo983089983090) pp 983094983088983093ndash983094983089983088 Penang Malaysia 983090983088983089983090
[983097] P Guo J Wang X Z Gao and J M A anskanen ldquoEpilepticEEG signal classi1047297cation with marching pursuit based on har-mony search methodrdquo in Proceedings of the IEEE International Conference on Systems Man and Cybernetics (SMC rsquo983089983090) pp983090983096983091ndash983090983096983096 Seoul Republic o Korea October 983090983088983089983090
[983089983088] A Shoeb ldquoCHB-MI scalp EEG databaserdquo 983090983088983088983088 httpphysionetorgpn983094chbmit
[983089983089] A S M Murugavel S Ramakrishnan U Maheswari and BS Sabetha ldquoCombined Seizure Index with Adaptive Multi-Class SVM or epileptic EEG classi1047297cationrdquo in Proceedings
of the International Conference on Emerging Trends in VLSIEmbedded System Nano Electronics and TelecommunicationSystem (ICEVENT rsquo983089983091) pp 983089ndash983093 Tiruvannammalai IndiaJanuary 983090983088983089983091
[983089983090] M H Abdullah J M Abdullah and M Z Abdullah ldquoSeizuredetection by means o hidden Markov model and station-ary wavelet transorm o electroencephalograph signalsrdquo inProceedings of the IEEE-EMBS International Conference onBiomedical and Health Informatics (BHI rsquo983089983090) pp 983094983090ndash983094983093 HongKong January 983090983088983089983090
[983089983091] A Subasi and M I Gursoy ldquoEEG signal classi1047297cation usingPCA ICA LDA and support vector machinesrdquo Expert Systemswith Applications vol 983091983095 no 983089983090 pp 983096983094983093983097ndash983096983094983094983094 983090983088983089983088
[983089983092] E Sezer H Isik and E Saracoglu ldquoEmployment and compari-son o different arti1047297cial neural networks or epilepsy diagnosisrom EEG signalsrdquo Journal of Medical Systems vol 983091983094 no 983089 pp983091983092983095ndash983091983094983090 983090983088983089983090
[983089983093] Brain Products GmbHProducts and ApplicationsAnalyzer 983090httpwwwbrainproductscomproductdetailsphpid=983089983095
[983089983094] NeuroExplorer Home httpwwwneuroexplorercom
[983089983096] C H Seng R Demirli L Khuon and D Bolger ldquoSeizuredetection in EEG signals using support vector machinesrdquo inProceedings of the 983091983096th Annual Northeast Bioengineering Con- ference (NEBEC rsquo983089983090) pp 983090983091983089ndash983090983091983090 Philadelphia Pa USA March983090983088983089983090
[983089983097] Z A Khan S B Mansoor M A Ahmad and M M MalikldquoInput devices or virtual surgical simulations a comparativestudyrdquo in Proceedings of the 983089983094th International Multi Topic Con- ference (INMIC rsquo983089983091) pp 983089983096983097ndash983089983097983092 Lahore Pakistan December983090983088983089983091
[983090983088] A L Goldberger L A Amaral L Glass et al ldquoPhysioBankPhysiooolkit and PhysioNet components o a new research
[983090983089] S Nasehi and H Pourghassem ldquoEpileptic seizure onset detec-tion algorithm using dynamic cascade eed-orward neural net-worksrdquo in Proceedings of the International Conference on Intelli- gent Computation and Bio-Medical Instrumentation (ICBMI rsquo983089983089)pp 983089983097983094ndash983089983097983097 December 983090983088983089983089
983137983138983148983141 983094 First column shows the channel label the second columnshows the initial training accuracy and third one shows the markedcorrection by a neurologistand thelast one shows the1047297nal accuracy
been cited that sometimes even the expert neurologists havesome disagreementovera certain observation o an EEGdataTis system will be useul or disagreeing users and it will alsohelp them in comparing their results with each other
Tere is also a threat o over1047297tting by the classi1047297erIn order to keep the classi1047297er improving its perormancewith the encounter o more and more examples we haveintroduced this user adaptive mechanism in our system Weconsider the existing systems as dead because these cannotimprove their classi1047297cation rate afer initial training (duringsofware development) Te sel-improving mechanism aferdeployment makes our tool alive Tis system can be madepart o the whole epileptic diagnosis process It will highlight
983137983138983148983141 983095 First column shows the channel label the second columnshows the initial training accuracy and third one shows the markedcorrection by a neurologistand thelast one shows the1047297nal accuracy
the epileptic spikes among the whole EEG thus leading toreduced atigue and time consumption o a user We obtainedhigh classi1047297cation accuracy on datasets obtained rom twodifferent sites which indicates reproducibility o our resultsand robustness o our approach
In the uture we are planning to make this a web basedapplication neurologists can log in and consult each otherrsquosreviews about a particular subject Tis will make our systemexperience a whole versatility o examples and learn rom allo them Integration o the video and its automatic analysis(video EEG) can help a neurologist in diagnosing epilepsy ina better way whereas this can also help him in distinguishingbetween psychogenic and epileptic seizuresWewouldalso be
investigating how much over1047297tting is an issue in the reportedperormances which are now even touching 983089983088983088 based onsome claims Tere is a need or methodcriteria which couldlimit these algorithms improving their detection on a limitednumber o available examples
Tissystem is made keeping in mind thatwe haveto acil-itate the neurologist by supplementing him in the analysis o the EEG We do not want to enorce the classi1047297cation o theEEG data on a user
In the uture we will also include a slider in the systemwhich will allow the user to adjust the sensitivity and speci-1047297city beore retraining Tis assisting system is more like adetection tool which is continuously learning with encounter
983137983138983148983141 983096 Classi1047297cation rate improvement caused by retraining Firstcolumn shows thechannellabel thesecond columnshows theinitialtraining accuracy and third one shows the marked correction by aneurologist and the last one shows the 1047297nal accuracy
o better examples More and better examples will certainly improve its perormance Te agreement between differentneurologists over the EEG readings is low to moderate I we could 1047297nd the agreement on at least ew o the epilepticpatterns correspondence with epileptic disease then we cantake this tool urther ahead and use it or diagnosis instead o
just assistanceOne o the biggest limitations to this study is the unavail-
ability o non-983091 Hz spike and wave data Even though we haveincluded the data eatures o the entire epileptic requency ranges exclusive to each other proo testing on the data will
certainly prove worthy orthe progresso these assisting toolstoward a diagnostic tool
Conflict of Interests
Te authors declare that there is no con1047298ict o interests
regarding the publication o this paper
Acknowledgment
Te authors would like to extend their sincere appreciation tothe Deanship o Scienti1047297c Research at King Saud University or unding this research through Research Group Project(RG no 983089983092983091983093-983088983093983089)
References
[983089] H Adelia Z Zhoub and N Dadmehrc ldquoAnalysis o EEGrecords in an epileptic patient using wavelet transormrdquo Journal of Neuroscience Methods vol 983089983090983091 no 983089 pp 983094983097ndash983096983095 983090983088983088983091
[983090] M A Ahmad W Majeed and N A Khan ldquoAdvancements incomputer aided methods or EEG-based epileptic detectionrdquo inProceedings of the 983095th International Conference on Bio-Inspired Systems and Signal Processing (BIOSIGNALS 983089983092) AngersFrance 983090983088983089983092
[983091] S Noachtar and J R emi ldquoTe role o EEG in epilepsy a criticalreviewrdquo Epilepsy and Behavior vol 983089983093 no 983089 pp 983090983090ndash983091983091 983090983088983088983097
[983092] E B Petersen J Duun-Henriksen A Mazzaretto W Kjar CE Tomsen and H B D Sorensen ldquoGeneric single-channeldetection o absence seizuresrdquo in Proceedings of the Annual International Conference of the IEEE Engineering in Medicineand Biology Society (EMBS rsquo983089983089) pp 983092983096983090983088ndash983092983096983090983091 Boston MassUSA September 983090983088983089983089
[983093] M Kaleem A Guergachi and S Krishnan ldquoEEG seizure
detection and epilepsy diagnosis using a novel variation o Empirical Mode Decompositionrdquo in Proceedings of the 983091983093th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC rsquo983089983091) pp 983092983091983089983092ndash983092983091983089983095 OsakaJapan July 983090983088983089983091
[983094] S M S Alam and M I H Bhuiyan ldquoDetection o seizure andepilepsy usinghigher order statistics in the EMD domainrdquo IEEE Journal of Biomedical and Health Informatics vol 983089983095 no 983090 pp983091983089983090ndash983091983089983096 983090983088983089983091
[983095] H Ocbagabir K A I Aboalayon and M FaezipourldquoEfficientEEG analysis or seizure monitoring in epileptic patientsrdquo inProceedings of the Long Island Systems Applications and Tech-nology Conference (LISAT rsquo983089983091) pp 983089ndash983094 IEEE Farmingdale NYUSA May 983090983088983089983091
[983096] A A Abdullah S A Rahim and A Ibrahim ldquoDevelopment o EEG-based epileptic detection using arti1047297cial neural networkrdquoin Proceedings of the International Conference on Biomedical Engineering (ICoBE rsquo983089983090) pp 983094983088983093ndash983094983089983088 Penang Malaysia 983090983088983089983090
[983097] P Guo J Wang X Z Gao and J M A anskanen ldquoEpilepticEEG signal classi1047297cation with marching pursuit based on har-mony search methodrdquo in Proceedings of the IEEE International Conference on Systems Man and Cybernetics (SMC rsquo983089983090) pp983090983096983091ndash983090983096983096 Seoul Republic o Korea October 983090983088983089983090
[983089983088] A Shoeb ldquoCHB-MI scalp EEG databaserdquo 983090983088983088983088 httpphysionetorgpn983094chbmit
[983089983089] A S M Murugavel S Ramakrishnan U Maheswari and BS Sabetha ldquoCombined Seizure Index with Adaptive Multi-Class SVM or epileptic EEG classi1047297cationrdquo in Proceedings
of the International Conference on Emerging Trends in VLSIEmbedded System Nano Electronics and TelecommunicationSystem (ICEVENT rsquo983089983091) pp 983089ndash983093 Tiruvannammalai IndiaJanuary 983090983088983089983091
[983089983090] M H Abdullah J M Abdullah and M Z Abdullah ldquoSeizuredetection by means o hidden Markov model and station-ary wavelet transorm o electroencephalograph signalsrdquo inProceedings of the IEEE-EMBS International Conference onBiomedical and Health Informatics (BHI rsquo983089983090) pp 983094983090ndash983094983093 HongKong January 983090983088983089983090
[983089983091] A Subasi and M I Gursoy ldquoEEG signal classi1047297cation usingPCA ICA LDA and support vector machinesrdquo Expert Systemswith Applications vol 983091983095 no 983089983090 pp 983096983094983093983097ndash983096983094983094983094 983090983088983089983088
[983089983092] E Sezer H Isik and E Saracoglu ldquoEmployment and compari-son o different arti1047297cial neural networks or epilepsy diagnosisrom EEG signalsrdquo Journal of Medical Systems vol 983091983094 no 983089 pp983091983092983095ndash983091983094983090 983090983088983089983090
[983089983093] Brain Products GmbHProducts and ApplicationsAnalyzer 983090httpwwwbrainproductscomproductdetailsphpid=983089983095
[983089983094] NeuroExplorer Home httpwwwneuroexplorercom
[983089983096] C H Seng R Demirli L Khuon and D Bolger ldquoSeizuredetection in EEG signals using support vector machinesrdquo inProceedings of the 983091983096th Annual Northeast Bioengineering Con- ference (NEBEC rsquo983089983090) pp 983090983091983089ndash983090983091983090 Philadelphia Pa USA March983090983088983089983090
[983089983097] Z A Khan S B Mansoor M A Ahmad and M M MalikldquoInput devices or virtual surgical simulations a comparativestudyrdquo in Proceedings of the 983089983094th International Multi Topic Con- ference (INMIC rsquo983089983091) pp 983089983096983097ndash983089983097983092 Lahore Pakistan December983090983088983089983091
[983090983088] A L Goldberger L A Amaral L Glass et al ldquoPhysioBankPhysiooolkit and PhysioNet components o a new research
[983090983089] S Nasehi and H Pourghassem ldquoEpileptic seizure onset detec-tion algorithm using dynamic cascade eed-orward neural net-worksrdquo in Proceedings of the International Conference on Intelli- gent Computation and Bio-Medical Instrumentation (ICBMI rsquo983089983089)pp 983089983097983094ndash983089983097983097 December 983090983088983089983089
983137983138983148983141 983096 Classi1047297cation rate improvement caused by retraining Firstcolumn shows thechannellabel thesecond columnshows theinitialtraining accuracy and third one shows the marked correction by aneurologist and the last one shows the 1047297nal accuracy
o better examples More and better examples will certainly improve its perormance Te agreement between differentneurologists over the EEG readings is low to moderate I we could 1047297nd the agreement on at least ew o the epilepticpatterns correspondence with epileptic disease then we cantake this tool urther ahead and use it or diagnosis instead o
just assistanceOne o the biggest limitations to this study is the unavail-
ability o non-983091 Hz spike and wave data Even though we haveincluded the data eatures o the entire epileptic requency ranges exclusive to each other proo testing on the data will
certainly prove worthy orthe progresso these assisting toolstoward a diagnostic tool
Conflict of Interests
Te authors declare that there is no con1047298ict o interests
regarding the publication o this paper
Acknowledgment
Te authors would like to extend their sincere appreciation tothe Deanship o Scienti1047297c Research at King Saud University or unding this research through Research Group Project(RG no 983089983092983091983093-983088983093983089)
References
[983089] H Adelia Z Zhoub and N Dadmehrc ldquoAnalysis o EEGrecords in an epileptic patient using wavelet transormrdquo Journal of Neuroscience Methods vol 983089983090983091 no 983089 pp 983094983097ndash983096983095 983090983088983088983091
[983090] M A Ahmad W Majeed and N A Khan ldquoAdvancements incomputer aided methods or EEG-based epileptic detectionrdquo inProceedings of the 983095th International Conference on Bio-Inspired Systems and Signal Processing (BIOSIGNALS 983089983092) AngersFrance 983090983088983089983092
[983091] S Noachtar and J R emi ldquoTe role o EEG in epilepsy a criticalreviewrdquo Epilepsy and Behavior vol 983089983093 no 983089 pp 983090983090ndash983091983091 983090983088983088983097
[983092] E B Petersen J Duun-Henriksen A Mazzaretto W Kjar CE Tomsen and H B D Sorensen ldquoGeneric single-channeldetection o absence seizuresrdquo in Proceedings of the Annual International Conference of the IEEE Engineering in Medicineand Biology Society (EMBS rsquo983089983089) pp 983092983096983090983088ndash983092983096983090983091 Boston MassUSA September 983090983088983089983089
[983093] M Kaleem A Guergachi and S Krishnan ldquoEEG seizure
detection and epilepsy diagnosis using a novel variation o Empirical Mode Decompositionrdquo in Proceedings of the 983091983093th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC rsquo983089983091) pp 983092983091983089983092ndash983092983091983089983095 OsakaJapan July 983090983088983089983091
[983094] S M S Alam and M I H Bhuiyan ldquoDetection o seizure andepilepsy usinghigher order statistics in the EMD domainrdquo IEEE Journal of Biomedical and Health Informatics vol 983089983095 no 983090 pp983091983089983090ndash983091983089983096 983090983088983089983091
[983095] H Ocbagabir K A I Aboalayon and M FaezipourldquoEfficientEEG analysis or seizure monitoring in epileptic patientsrdquo inProceedings of the Long Island Systems Applications and Tech-nology Conference (LISAT rsquo983089983091) pp 983089ndash983094 IEEE Farmingdale NYUSA May 983090983088983089983091
[983096] A A Abdullah S A Rahim and A Ibrahim ldquoDevelopment o EEG-based epileptic detection using arti1047297cial neural networkrdquoin Proceedings of the International Conference on Biomedical Engineering (ICoBE rsquo983089983090) pp 983094983088983093ndash983094983089983088 Penang Malaysia 983090983088983089983090
[983097] P Guo J Wang X Z Gao and J M A anskanen ldquoEpilepticEEG signal classi1047297cation with marching pursuit based on har-mony search methodrdquo in Proceedings of the IEEE International Conference on Systems Man and Cybernetics (SMC rsquo983089983090) pp983090983096983091ndash983090983096983096 Seoul Republic o Korea October 983090983088983089983090
[983089983088] A Shoeb ldquoCHB-MI scalp EEG databaserdquo 983090983088983088983088 httpphysionetorgpn983094chbmit
[983089983089] A S M Murugavel S Ramakrishnan U Maheswari and BS Sabetha ldquoCombined Seizure Index with Adaptive Multi-Class SVM or epileptic EEG classi1047297cationrdquo in Proceedings
of the International Conference on Emerging Trends in VLSIEmbedded System Nano Electronics and TelecommunicationSystem (ICEVENT rsquo983089983091) pp 983089ndash983093 Tiruvannammalai IndiaJanuary 983090983088983089983091
[983089983090] M H Abdullah J M Abdullah and M Z Abdullah ldquoSeizuredetection by means o hidden Markov model and station-ary wavelet transorm o electroencephalograph signalsrdquo inProceedings of the IEEE-EMBS International Conference onBiomedical and Health Informatics (BHI rsquo983089983090) pp 983094983090ndash983094983093 HongKong January 983090983088983089983090
[983089983091] A Subasi and M I Gursoy ldquoEEG signal classi1047297cation usingPCA ICA LDA and support vector machinesrdquo Expert Systemswith Applications vol 983091983095 no 983089983090 pp 983096983094983093983097ndash983096983094983094983094 983090983088983089983088
[983089983092] E Sezer H Isik and E Saracoglu ldquoEmployment and compari-son o different arti1047297cial neural networks or epilepsy diagnosisrom EEG signalsrdquo Journal of Medical Systems vol 983091983094 no 983089 pp983091983092983095ndash983091983094983090 983090983088983089983090
[983089983093] Brain Products GmbHProducts and ApplicationsAnalyzer 983090httpwwwbrainproductscomproductdetailsphpid=983089983095
[983089983094] NeuroExplorer Home httpwwwneuroexplorercom
[983089983096] C H Seng R Demirli L Khuon and D Bolger ldquoSeizuredetection in EEG signals using support vector machinesrdquo inProceedings of the 983091983096th Annual Northeast Bioengineering Con- ference (NEBEC rsquo983089983090) pp 983090983091983089ndash983090983091983090 Philadelphia Pa USA March983090983088983089983090
[983089983097] Z A Khan S B Mansoor M A Ahmad and M M MalikldquoInput devices or virtual surgical simulations a comparativestudyrdquo in Proceedings of the 983089983094th International Multi Topic Con- ference (INMIC rsquo983089983091) pp 983089983096983097ndash983089983097983092 Lahore Pakistan December983090983088983089983091
[983090983088] A L Goldberger L A Amaral L Glass et al ldquoPhysioBankPhysiooolkit and PhysioNet components o a new research
[983090983089] S Nasehi and H Pourghassem ldquoEpileptic seizure onset detec-tion algorithm using dynamic cascade eed-orward neural net-worksrdquo in Proceedings of the International Conference on Intelli- gent Computation and Bio-Medical Instrumentation (ICBMI rsquo983089983089)pp 983089983097983094ndash983089983097983097 December 983090983088983089983089
of the International Conference on Emerging Trends in VLSIEmbedded System Nano Electronics and TelecommunicationSystem (ICEVENT rsquo983089983091) pp 983089ndash983093 Tiruvannammalai IndiaJanuary 983090983088983089983091
[983089983090] M H Abdullah J M Abdullah and M Z Abdullah ldquoSeizuredetection by means o hidden Markov model and station-ary wavelet transorm o electroencephalograph signalsrdquo inProceedings of the IEEE-EMBS International Conference onBiomedical and Health Informatics (BHI rsquo983089983090) pp 983094983090ndash983094983093 HongKong January 983090983088983089983090
[983089983091] A Subasi and M I Gursoy ldquoEEG signal classi1047297cation usingPCA ICA LDA and support vector machinesrdquo Expert Systemswith Applications vol 983091983095 no 983089983090 pp 983096983094983093983097ndash983096983094983094983094 983090983088983089983088
[983089983092] E Sezer H Isik and E Saracoglu ldquoEmployment and compari-son o different arti1047297cial neural networks or epilepsy diagnosisrom EEG signalsrdquo Journal of Medical Systems vol 983091983094 no 983089 pp983091983092983095ndash983091983094983090 983090983088983089983090
[983089983093] Brain Products GmbHProducts and ApplicationsAnalyzer 983090httpwwwbrainproductscomproductdetailsphpid=983089983095
[983089983094] NeuroExplorer Home httpwwwneuroexplorercom
[983089983096] C H Seng R Demirli L Khuon and D Bolger ldquoSeizuredetection in EEG signals using support vector machinesrdquo inProceedings of the 983091983096th Annual Northeast Bioengineering Con- ference (NEBEC rsquo983089983090) pp 983090983091983089ndash983090983091983090 Philadelphia Pa USA March983090983088983089983090
[983089983097] Z A Khan S B Mansoor M A Ahmad and M M MalikldquoInput devices or virtual surgical simulations a comparativestudyrdquo in Proceedings of the 983089983094th International Multi Topic Con- ference (INMIC rsquo983089983091) pp 983089983096983097ndash983089983097983092 Lahore Pakistan December983090983088983089983091
[983090983088] A L Goldberger L A Amaral L Glass et al ldquoPhysioBankPhysiooolkit and PhysioNet components o a new research
[983090983089] S Nasehi and H Pourghassem ldquoEpileptic seizure onset detec-tion algorithm using dynamic cascade eed-orward neural net-worksrdquo in Proceedings of the International Conference on Intelli- gent Computation and Bio-Medical Instrumentation (ICBMI rsquo983089983089)pp 983089983097983094ndash983089983097983097 December 983090983088983089983089
