Pattern recognition of epileptic EEG graphoelements with adaptive segmentation, supervised and...

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Pattern recognition of epileptic EEG graphoelements with adaptive segmentation,

supervised and unsupervised learning algorithms

Vladimir Krajca1, Jiri Hozman1, Jitka Mohylová2, Svojmil Petránek3

1Czech Technical University in Prague, Faculty of Biomedical Engineering, Czech Republic,

vladimir.krajca@fbmi.cvut.cz2 VŠB-Technical University of Ostrava, Faculty of Electrical Engineering and

Computer Science, Czech Republic,

jitka.mohylova@vsb.cz 3 Hospital Na Bulovce, Dept. Neurology, Prague, ebupetranek@seznam.cz

IntroductionIntroduction The electroencephalogram (EEG) provides markers of brain disturbances

in the field of epilepsy.

In short duration EEG data recordings, the epileptic graphoelements may not manifest itself.

The visual analysis of lengthy signals is a tedious task. It is necessary to track the EEG activity on the computer screen and to detect the epileptiform graphoelements.

The automation of the process is needed.

The EEG wave classification both by supervised and unsupervised learning algorithms will be compared.

Combination of the above algorithms will be used2

Aim of studyAim of study To show, that artificial neural networks (ANN) exhibit

better precision of classification of EEG graphoelements, then cluster analysis used perviously

Cluster analysis can be used in preprocessing – in semi-automatic creation of etalons for learning classifiers

Etalons can be extracted both manually and automatically from original EEG recordings – from segments detected by adaptive segmentation and described by a feature set from the time, frequency, and entropic domains.

Automatic identification of EEG graphoelementsAutomatic identification of EEG graphoelements

In different areas of EEG processing, as

– Brain maturation assesemnt of the newborns– Monitoring and detection of epileptic seizures in adults

computerized analysis of micro- and macrostructure of EEG is desirable.

EEG microstructure – identification of single graphoelements and /or frequency bands, EEG bursts, artefacts, etc.

Macrostructure – trends, detection of significant events, behavioral states, sleep stages, reveals hidden information in long-term EEG processing

Cluster analysis and adaptive segmentation yield color identification of the classes. It reflects microstructure (short events).

Temporal profiles reflect macrostructure, classs membership in the course of a time

Macrostructure is reflected in temporal profiles (example: time scale 15 min/page)

SIGNIFICANT EVENT (artefact)

SIGNIFICANT EVENT (epi paroxysms)

Cursor in profile (15 min/page) selects event in original EEG recording (at that position). Example: muscle artefacts (blue color)

ORIGINAL EEG 10s/page

CURSORPROFILE (15min/page)

Example – epi event at cursor positionExample – epi event at cursor position

Example – epileptic events are reflected in temporal profileExample – epileptic events are reflected in temporal profile

Adaptive segmentation and identified clusters improve feature extraction and etalons Adaptive segmentation and identified clusters improve feature extraction and etalons selection (we can use as a guide segment boundaries and types/classes of segments)selection (we can use as a guide segment boundaries and types/classes of segments)

Cluster analysisCluster analysis

Advantages: unsupervised learning („push the button and wait for results“), classes are ordered according the increasing amplitude of segment

Disadvantages: classes (clusters) selected by a computer

Last (red class) can consist of genuine epileptic spikes, or there can be artefacts

Learning (supervised) classifersLearning (supervised) classifers

Advantages: by supervised learning we can ourselves decide, which class is the first, second, etc. We can decide (by teaching) which types of graphoelements we are looking for. One class can consist of moving artefacts, which can be later eliminated

Disadvantages: teaching of classifier and etalons (prototypes) selection is a tedious work, requiring a skilled expert.

Expert in semi-automatic etalons selectionExpert in semi-automatic etalons selection

Best compromise between visual and full-automatized EEG analysis is semi -automatic method, using both machine learning and expertise of the physician

As a first, preprocessing step, cluster analysis is used for etalons extraction: it is effective, but the classes are created independently on a user wishes. They can be inhomogeneous.

Learning classifier

Teaching is tedious.

Etalons – typical representatives of the desired classes must be created/selected by a teacher.

Etalons are submitted to classifier during a learning process. At least 50-100 prototypes/class are necessary (personal experience)

Manual prototypes selection is time-consuming: but we can exploit class centers of the clusters for automatic prototype selection – outliers are edited by an expert.

Automatic classification of EEG graphoelements Automatic classification of EEG graphoelements by a cluster analysisby a cluster analysis

Efficient, without necessity of learning

Hybrid segments with overlapping classes exhibiting features of several classes can be misclassified.

No posiibility to influence classification – to specify uswer defined classes (artefacts in last class etc.) Clusters are created by „natural“ data structure

Clusters have spheric shape in the feature space, are formed without the user intervention.

Testing the methodology on the real Testing the methodology on the real datadata EEG record of patient with the diagnosis

epilepsy (length 31 min , 8 classes)

Both epileptic graphoelements and impulse artefacts have similar parameters (features).

Noise/muscle artefacts are misclassified into blue (6th) class of impulse artefacts. See its position in temporal profiles.

Note the good identification of continuous impulse artefacts in 13th channel.

Misclassified „hybrid“ segments exhibiting features of both classes. Blue and violet are the class colors

Fuzzy cluster analysis might help to improve to eliminate the hybrid segments

Cluster analysis can be used for semi-Cluster analysis can be used for semi-automatic extraction of etalons from the raw, automatic extraction of etalons from the raw, original EEG original EEG Typical, representative segments of the cluster

are positioned in feature space near the center of gravity.

They are typical members of the class (etalons) of the class, closest to the class center .

Because cluster analysis works relatively quicky, we have at our disposal the candidates for etalons . Only minimum effort is needed for final editing of the etalons set.

Representative segments , closest to the Representative segments , closest to the center of cluster = etalons for teaching of the center of cluster = etalons for teaching of the learning classifier (neural network)learning classifier (neural network)

Learning classifiers could provide the Learning classifiers could provide the solution/improvement to the above mentioned problems. solution/improvement to the above mentioned problems. Method: Method:

1. User specifies what to search for

2. Realisation is performed by ANN (artificial neural networks)

3. Learning by GA (genetic algorithms)

4. Weights initializing (to avoid local minimum) - simulated annealing

ANN 24-12-8ANN 24-12-8

24 inputs - features 12 neurons in hidden

layer (input features combining , set empirically – try and mistake approach

8 outputs (8 classes)

Improvement of cluster Improvement of cluster analysis method – analysis method – impulse and noisy impulse and noisy artefacts are artefacts are distinguished now. distinguished now.

ANN, 3-layer ANN, 3-layer perceptronperceptron

Classes are more homogeneous now

ANN, 3-layer perceptronANN, 3-layer perceptron

How to select etalons?How to select etalons?

1. Expert selects etalons with a mouse on the computer screen

2. (semi) automatically by cluster analysis (minor editing of the etalons database)

Example of etalons selection – by mouse within the range (boundaries) of adaptive segmentation segments

ANN, etalons selectionANN, etalons selection

SPECTRUMFEATURES

ETALON

1 - etalon selection 2 – etalon identification (class number entered by a teacher)

3 – click on the etalon – features histogram (4) and spectrum (5)

Parameters are compared in small window (6).

Average features and average spectrum for each class (7)

ETALON SELECTION FROM EEG

DATABASE EDITING

Epileptic prototypes and artefacts in two different channels

ANN, summary sheetsANN, summary sheets

Visualization – different types of activity can be Visualization – different types of activity can be identified by a color directly in the real EEG/temporal identified by a color directly in the real EEG/temporal profiles under the cursor positionprofiles under the cursor position

Results visualizationResults visualization

Scatterogram AP- SigmaScatterogram AP- Sigma (amplitude vs. (amplitude vs. sigma frequency band)sigma frequency band)

Features evaluation

ANN - MLPANN - MLP(3- layer perceptron)(3- layer perceptron)

ConclusionConclusionANN with genetic algorithm and simulated annealing can learn to recognize the EG graphoelements much better than unsupervised learning algorithm. The types of graphoelements of classes can be specified by an user.

Cluster analysis provides "natural" clusters, it is not possible to specify, that class number six, for example, consists of artifacts

Cluster analysis can be used in the first step of processing - for etalons specification

Adaptive segmentation can be used for manual selection of etalons from EEG for segment boundaries plotting in the graph 35

Fuzzy shluková analýza Fuzzy shluková analýza (algoritmus FCM). (algoritmus FCM). Impulsy chybně Impulsy chybně zařazeny do třídy epi zařazeny do třídy epi grafoelementů. Práh 0.3grafoelementů. Práh 0.3

SHLUKOVÁ SHLUKOVÁ ANALÝZAANALÝZA

ZLEPŠENÍ HOMOGENITY ZLEPŠENÍ HOMOGENITY DAT. DAT. NETYPICKÉ SEGMENTY NETYPICKÉ SEGMENTY JSOU VYLOUČENYJSOU VYLOUČENY

Fuzzy shluková analýza - eliminace outliers s Fuzzy shluková analýza - eliminace outliers s menším členstvím než 0.3menším členstvím než 0.3

Pro hodnocení kvality navržených příznaků lze užít histogram Pro hodnocení kvality navržených příznaků lze užít histogram příznaků a spektrum. Z obr. 11 je opět patrné, že třídy č. 5 a 7 příznaků a spektrum. Z obr. 11 je opět patrné, že třídy č. 5 a 7 (počítáno od nuly) by se měly sloučit.(počítáno od nuly) by se měly sloučit.

ANN - MLPANN - MLP(3-vrtstvý perceptron)(3-vrtstvý perceptron)

Ale Ale !!! !!! : : A Artefakty - mohou spadnout do poslední třídy, stejně jako rtefakty - mohou spadnout do poslední třídy, stejně jako pomalá vysokovoltážní aktivita a poškodit přesnost detekcepomalá vysokovoltážní aktivita a poškodit přesnost detekce

ARTEFAKTY

Automatic classification and Automatic classification and visualization of epileptic EEG by visualization of epileptic EEG by

supervised and unsupervised supervised and unsupervised algorithmsalgorithms

MOTIVACE – vedlejší MOTIVACE – vedlejší paroxysmusparoxysmus

Srovnání fuzzy k-NN a Shlukové Srovnání fuzzy k-NN a Shlukové analýzyanalýzy

ClusterCluster

Fuzzy k-NNFuzzy k-NN

Problems to be solvedProblems to be solved Etalons description – features selection Database of prototypes Generalization – presented examples based on etalons

extracted from the beginning of the same recording Robust identification Optimal MLP structure (number of hidden neurons) Modern better classifiers inspired by a nature (genetic

algorithms, ant colony optimization,…).

Pattern recognition of epileptic EEG graphoelements with adaptive segmentation, supervised and...

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