QRS-complex of ECG-based Biometrics in aTwo-Level Classifier

Loh Sik Hou, Khazaimatol S Subari and Syed SyahrilFaculty of EngineeringMultimedia University

63100 Cyberjaya, SelangorMalaysia

Email: lexloh86@gmail.com, khazaimatol@mmu.edu.my, syahril@mmu.edu.my

Abstract—This research is based on an ECG biometrics systemwhich segments the QRS-complex, extracts the non-fiducialfeatures and sends the data to a two-level classifier. For spectralanalysis, the discrete Fourier transform (DFT), and discretecosine transform (DCT) were used to transform the signal, beforeprincipal component analysis (PCA) is used to reduce the featurevectors. From here, statistical parameters were computed for theclassifier, where the first level is denoted called feature matching(FM) and the second level is the Neural Networks algorithm(NN). The system is tested on two databases. Database I consistsof 45 subjects with 10 recordings each (recorded on the sameday) while Database II consists of 35 subjects with 20 recordingseach (recorded on separate days). The accuracy measures wereis 99.176% and 96.67% respectively.

I. INTRODUCTION

The electrocardiogram (ECG) is investigated here as themajor component of a biometrics system. Current securitysystems use passwords or pins for authentication which can beforgotten or stolen. Security systems that are based on who youare instead of what you possess, uses physiological, anatomicalor behavioral features for identification and authentication.These systems, also known as biometric systems, are basedon fingerprinting, face and/or voice recognition modalities,among others. However, these features can still be falsified,stolen and forged. Face recognition systems can be fooled by apicture, fingerprints can be recreated and voice can be imitatedor prerecorded [1].

The ECG is the transthoracic interpretation of the electricalactivity of the heart, measured using external electrodes placedat various locations on the body. The ECG works by detectingand amplifying the tiny electrical charges on the skin asthe heart muscles depolarize during each cycle of a heartbeat. The characteristics of the ECG is therefore influencedby the subject’s sex, age and body habitus, as well theunique geometrical and physiological features of the heart,thus making it a suitable candidate as input to a biometricssystem. Previous studies that have considered the ECG asa biometric measure such as Biel et al. (2001) and Shen etal. (2002) observed that the fiducial features of the signalwere unique for each person, suggesting that ECG data carriesenough genetic information for identification purposes [2], [3].

It is important to note that although the ECG looks repet-itive, it is not a periodic signal. The fiducial points of an

ECG wave is highly dependent on the time of day the signalwas measured, the subject’s physical state, mental state andmany other factors. These issues must be considered whendetermining the features of the signal we wish to extract.

II. BACKGROUND REVIEW

In the past, classification of the ECG signal was mainlyfocused on determining the health condition of the subject’sheart (e.g., [4]–[6]). These studies generally extracted thefiducial features of the ECG waveform as input to a neuralnetworks classifier. It wasn’t until recently, that quite a numberof studies were dedicated for the use of ECG as a subjectidentifier and for person verification purposes.

There are two types of features that can be extracted fromthe ECG waveform, i.e., fiducial and non-fiducial features,and previous research can be divided according to these twocategories. Most studies are based on fiducial features, andindeed, some have shown relatively high accuracy scores(e.g., [2], [7]–[13]), though studies which address the issueof subject invariability are quite limited [9], [14]. Studies ofnon-fiducial features usually extract spectral features of thesignal and are therefore more robust to falsification, althoughsuch systems may be sensitive to slight variations in the signalthus resulting in lower accuracy measures [15], [16].

It has been observed that the high-level security offered byECG-based biometrics systems can be compromised if onlyfiducial parameters are extracted as features, because a falsesignal with all the intended features can be generated in placeof an actual ECG signal. It was also observed that taking allfiducial points as features do not increase the accuracy of thesystem, indeed, it results in the opposite affect as some featuresare common across subjects [13]. However, as previouslymentioned, using an all non-fiducial features modality, whileperhaps giving increased robustness to the system, does nottake into account changes in heart rate and subject invariabilityetc, thus reducing accuracy measures [16].

A study by Gahi et al. (2008) found that the top twofiducial features of the ECG wave that resulted in the highestclassification rates are RS and QR [13]. Israel et al. (2005)noted that for a given subject, the QRS-complex is known tohave the least variability in time under different measurementconditions [9], and should provide a robust representation

978-1-4577-0255-6/11/$26.00 ©2011 IEEE 1159 TENCON 2011

of the subject’s heartbeat conditions. Tsao (2007) noted thatincluding the subtle changes in the signal in between thefiducial points are important for robustness and accuracy[11]. Following these observations, the features selected forthis study will be extracted from the QRS-complex of theECG signal, henceforth referred to as the QRS vector, v[n].Subsequently, the said vector will be transformed to frequencydomain to obtain the non-fiducial features, before computingthe principle components and consequently the statistical pa-rameters. Therefore, it can be said that the features extractedhere are a combination of both fiducial and non-fiducialparameters, intended to increase the level of robustness againstfalsification while at the same time taking into account theuniqueness of the signal for each subject and differences inheart rate.

For classification of the ECG features, template matchingvia means of distance computation (e.g., [10], [17]), andneural networks are generally used (e.g., [6]). An approachthat combined both these techniques was proposed by Shenet al. (2002) which scored a 100% recognition rate for adatabase of 20 subjects. The proposed algorithm is also atwo-level classifier with slight modifications: it consists ofa feature matching (FM) algorithm, and a neural network(NN) classifier. If more than one possible subject is identifiedthrough the FM level, all identified subjects will be passed tothe NN to determine the final identity. This will be explainedin more detail in the following sections.

III. METHODOLOGY

ECG signals were recorded using a gMobilab+ console byGuger Technologies that was connected to a computer andcaptured using the Matlab Data Acquisition Toolbox witha sampling frequency of 256 Hz. A single lead ECG, i.e.,recorded with three electrodes, was captured following theconnections depicted in Figure 1.

Two sets of data were collected for this study. For Dataset I,the ECG of 45 subjects were recorded for approximately300 s in a single recording session, with breaks in the record-ing every 30 s, resulting in 10 recordings per subject. ForDataset II, the ECG of 35 subjects were recorded over twoseparate sessions on different days, for approximately 300 s,again with breaks in the recording every 30 s, resulting in 10recordings per subject per day. This database was created totest the robustness of the proposed algorithm against subjectvariability.

The subjects of this investigation were all healthy males whowere students at Multimedia University between the ages of18 to 22. Prior to the ECG measurement, subjects underwenta health questionnaire to ensure their health condition, andwere asked to refrain from consuming coffee before their ECGmeasurements were taken.

A. Preprocessing

The first 2 s and the last 1 s of each recording was discardedand subsequently filtered with a 50 Hz notch filter to removetransmission-line noise. An example of a single cycle of the

Fig. 1. Location of electrodes for one lead ECG measurement.

Fig. 2. PQRST locations of one cycle of ECG signal.

ECG signal after filtering is shown in Figure 2. Following thetechnique presented in a previous study [18], the QRS complexis automatically segmented from the signal to create the QRSvector, v[n], which is subsequently multiplied to a Hanningwindow of the same length. Note that after the segmentationprocess, the signal has a reduced number of samples. Theprocess underwent by each recording is illustrated in Figure 3.

B. Feature Extraction

The QRS vector was transformed to obtain two sets ofspectral coefficients via the discrete Fourier transform anddiscrete cosine transform as shown respectively below:

V [k] =N−1∑n=0

v[n]e−2π(nk/N) (1)

for k = 0, 1 . . . , N − 1

W [k] =N−1∑n=0

v[n] cos[π

N(n+

1

2)k] (2)

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Fig. 3. ECG waveform (a) after noise removal, (b) after segmentation of QRS-complex, (c) after windowing.

TABLE IFEATURES EXTRACTED FROM EACH TECHNIQUE

DFT DCT PCA of FFT PCA of DCTMax point Max point Max point

Max point Min point Min point Min pointStd dev Std dev Std dev Std devMean Mean Mean Mean

for k = 0, 1 . . . , N − 1

The spectral coefficients are used to compute the principlecomponent models. Principle component analysis is a mathe-matical transform which is used to explain the variance in thespectral data. Once the principle components are computed,the statistical parameters from each recording were extracted,as listed in Table I. For each recording, a total of 118 featureswere extracted for the classifier per recording per subject,henceforth denoted f [n]. For Dataset I, half of the ECG datawill be used as testing data, ftest[n], while the remainder willbe used as training data, fref[n]. For Dataset II, the recordingsfrom session one will be used as training data, while therecordings from session two will be used as the test data.

C. Two-Level Classifier

Figure 4 is the proposed two-level classifier. It is an au-tomated system that will compare the test features to thefeatures trained and stored in the database, starting at the FMlevel and followed by the NN classifier. Data is randomlypicked for testing. The technique used for the FM level isas follows. The numerical values of the test data and itscorresponding value in the training data are directly compared.If the difference between the values is less than 17.5%, thecounter, C, will increment by 1 to a maximum value of 118points per recording.

D[k] = |fref[n]− ftest[n]| (3)

C = find(D[k]) < 10% (4)

Fig. 4. Two-level classification process.

In the event that a single person was identified at the FMlevel, the NN classifier is bypassed and the identificationroutine is complete. If, however, more than one possibleidentity is returned by FM, e.g., person A, person B, andperson C, the NN will automatically extract and train the storedfeatures of the three suggested people and match these to thetest features. If the extreme case where the FM is unableto identify the test data, the system will terminate with anintruder alert warning.

The NN algorithm employed here consists of three layers:input, hidden and output layer. The input layer is the featureset to be trained or classified f [n]. The hidden layer consistsof any interconnected artificial neurons working in parallel tomodel the relationship between the input layer and the outputlayer. The output layer is the result obtained after runningthe input layer through the hidden layer. A back-propagationneural network is adopted whereby the signals travel from boththe input to the output and vice versa and the weights of theneurons are contiously changed until the performance goal ofthe system is met or the system reaches equilibrium.

The training data is normalised to a mean of 0 and astandard deviation of 1 before training. The same process usedto normalise the training set will be used to normalise thetesting set during the testing of the system. Two hidden layerswith 100 nodes in each layer are employed and the trainingfunction used was the scaled conjugate gradient. The training

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TABLE IISIMULATION RESULTS OF THE TWO-LEVEL CLASSIFIER. FM DENOTES

FEATURE MATCHING AND NN DENOTES NEURAL NETWORKS.

Classifer (%)Database # (subjects) FM FM+NN Accuracy (%)

1 (45) 62 38 99.182 (35) 48 52 96.67

is deemed succesful only when the performance of the systemcalculated using mean square error reaches or falls below10−3. Otherwise the system is retrained to achieve optimalperformance.

The advantage of a two-level classifier is obvious. The NNalgorithm, although robust and computationally efficient, takesa while to train and classify data, especially for a large samplesize of subjects. The fact that the possible identities of thesubject have been identified beforehand significantly reducescomputational costs and time.

IV. RESULTS AND DISCUSSION

Table 2 shows the simulation results of 100 random testruns of the proposed system for Dataset I and II. The secondand third columns list the amount (in percent) of times thesystem used the two-level classifier. The last column lists theaccuracy of the system, i.e., the number of times the test datawas correctly identified. It can be seen for Dataset I, that 62%of the time, the feature matching algorithm was sufficient toidentify the test subject, while the remainder 38% had to gothrough the FM and NN network. For Dataset II, only 48%was able to be identified via FM.

The higher percentage in the FM technique for Dataset Icompared to Dataset II is due to the fact that the ECG record-ings that were used for testing and training were recorded onthe same day in the same session, even though the test andtraining data were different. Recall that for Dataset II, theECG recordings spanned several days, and the affect this hason the results are significant. Although Dataset II has 10 lesssubjects, which would infer higher accuracy rates, the accuracyhas somewhat decreased, although perhaps not significantly,caused by the subtle changes in the ECG waveform. Figure 5shows the test data of a subject from two different recordingsessions. From the figure, the differences in the data, althoughvery small, have affected the outcome of the classifier.

Based on these observations, we have found that biometricsystems for ECG data taken over several days is a validchallenge. The extraction of the fiducial features throughsegmentation may have compensated for the variability inheart rate but a closer look at other aspects that affect thesignal is necessary, though we speculate that the choice ofthe QRS-complex was the correct decision, as it resulted in afairly high 96.67% accuracy for Database II.

V. CONCLUSION

The ECG signal was analyzed as a biometrics featureusing a two-level classification system. The QRS-complex ofthe ECG waveform was extracted, and spectral analysis was

Fig. 5. Extracted features of the same subject recorded from (a) first session,(b) second session.

applied to obtain the features of interest. The signal thenunderwent a feature matching process. If more than one userwas identified as the potential subject, the NN will triggerand begin training. For Dataset I, an accuracy of 99.18% wasachieved while for Dataset II, with ECG data compiled in twosessions on different days, an accuracy measure of 96.67% wasachieved, which is still relatively high. The proposed methodnot only focuses in classification but also observed that theQRS-complex is stable for a subject, different across subjectsand is a reliable feature to use for ECG-based identification.

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