J. C. Bansal et al. (eds.), Proceedings of Seventh International Conference on Bio-InspiredComputing: Theories and Applications (BIC-TA 2012), Advances in Intelligent Systemsand Computing 201, DOI: 10.1007/978-81-322-1038-2_8, � Springer India 2013

Human Identification using Heartbeat IntervalFeatures and ECG Morphology

Yogendra Narain Singh and Sanjay Kumar Singh

Abstract This paper presents a novel method to characterize the ECG signal forhuman identification. The characterization process utilizes the analytical and ap-pearance based techniques to analyze the ECG signal with an aim to make the mea-surements insensitive to noise and non-signal artifacts. We extract heartbeat intervalfeatures and interbeat interval features using analytical based technique and usethem as a complementary information with the morphological features that are ex-tracted using appearance based technique for improved identification accuracy. Weperform identification using one-to-many comparisons based on match scores thatare generated using statistical pattern matching technique. Results demonstrate thatthe proposed method for automated characterization of the ECG signal is efficientlyused in identifying the normal as well as the arrhythmia subjects. In particular, therecognition accuracy for the subjects of MIT-BIH Arrhythmia database is reportedto 87.37% whereas the subjects of our IIT(BHU) database are recognized with anaccuracy of 92.88%.

Key words: Human identification, electrocardiogram, biometrics, signal process-ing and pattern recognition.

1 Introduction

Heartbeat is normally used in diagnosing intraventricular conduction disturbancesand arrhythmia [1]. The heart is a muscular organ, which is electrically polarized.

Y. N. SinghDepartment of Computer Science & Engineering, Institute of Engineering & Technology,Guatam Buddh Technical University, Lucknow - 226 021, India. e-mail: singhyn@gmail.com

S. K. SinghDepartment of Computer Engineering, Indian Institute of TechnologyBanaras Hindu University, Varanasi - 225 021, India.

87

The polarization of cardiac cells cause the heart to beat. The electrocardiogram(ECG) is used to record the electrical activity of the heart. It measures rate andregularity of the heartbeats. More formally, an ECG signal is a transthoracic inter-pretation of the electrical activity of the heart over a period of time. Recent, studies[2]-[10] have suggested that the ECG acquired from different individuals show het-erogeneous characteristics. The heterogeneity has also been marked in the studiesconducted for diagnosing arrhythmia present in the heart function [11]. Therefore,the ECG signal of an individual can be characterized by specific patterns that aresufficient to discriminate his/her identify from others.

The distinctiveness of the ECG signal among individuals is generally resulteddue to the change in ionic potential, the time of ionic potential to spread from differ-ent parts of heart muscle, the plasma levels of electrolytes (e.g., potassium, calciumand magnesium etc.) and the rhythmic differences. The difference in heart structuresuch as, chest geometry, position, size, and physical condition can also manifestunique characteristic in the rhythm of an individual heartbeats. These distinctionsare reflected in the change in morphology, difference in amplitudes and the varia-tion in time intervals of the dominant fiducials in an individual heartbeats. The mainadvantage of using the ECG signal as a biometric is the robustness to circumven-tion, replay and obfuscation attacks, however these are prime concerns associatedto the conventional biometrics [12], [13]. An ECG signal exploits the physiologicalfeatures that exist in all (live) humans and as such, it is naturally secured. It has aninherent feature of vitality that signifies the life signs [14]. It is difficult to mimic,and hard to be copied or stolen. Therefore, the ECG has the strong credentials tosuccessfully address the security and privacy issues of an individual. In a multibio-metric system, an ECG signal can also be combined with other and independentbiometric modalities as a complementary information to enable secure and efficientindividual authentication [15]. The ECG signal as a biometric can solve the problemof identity theft and therefore, it can be used as a tool for information security [16].

The issue of using the ECG signal as a biometric includes the variation present inthe signal that makes the data representation more difficult [17], [18]. The variationsin the signal are resulted due to noise and non-signal artifacts such as 50/60 Hzpower line interference, muscle contractions close to the electrodes and the motionartifacts. In addition, fluctuation in isoelectric line caused by respiration and motionof the subjects degrade the quality of the ECG signal significantly [19].

In this paper, we present a novel method to characterize heartbeat features forhuman identification that is insensitive to signal variations and non-signal artifacts.The method performs the ECG characterization using analytical and appearancebased techniques. Using analytical technique, we extract clinically dominant fidu-cials from the selected heartbeats that include interval features and amplitude fea-tures while the appearance based technique extracts the morphological features fromthe beats. The advantage of using the analytic based features is that it captures localinformation of a heartbeat. The drawback of analytical based technique is that it isnot robust in analyzing all types of ECG traces. In order to overcome from its lim-itation, we use an appearance based feature extraction technique that captures theheartbeat features in a holistic manner such that the complete information of a heart-

88 Y. N. Singh and S. K. Singh

TP Segment

P

Q

R

S

T

QRS Complex

PR Interval

QT Interval

T Wave

P Wave

RR Interval STSegment

Fig. 1 A typical ECG signal that includes three successive heartbeats and information lying in P,Q, R, S and T waves.

beat can be preserved. We utilize the analytical based features as a complementaryinformation and combined with the morphological features for improved recogni-tion accuracy. In order to avoid the sudden changes in an ECG signal the afore-mentioned characterization selects a sequence of heartbeats such that the currentbeat can be analyzed only if its predecessor and the successor beats are segmentedcorrectly.

At appearance level, the two stage procedure is employed to extract the ECGmorphological features. In the first stage, segmented ECG morphological featuresare extracted from the beats that show consistent characteristics using the informa-tion of analytical based features. In the second stage, fixed interval ECG morpho-logical features are extracted from the selected beats. In this stage the effect of noiseand motion artifacts are minimized by scaling the signal using Pareto normalization[20]. For the identification experiment, the classification is performed using statisti-cal pattern matching technique on the basis of match scores. The performance of theproposed method is evaluated on 73 ECG recordings selected from publically avail-able MIT-BIH Arrhythmia database of PhysioBank [21] and our IIT(BHU) data-base. The identification results confirm the effectiveness of proposed characteriza-tion of the ECG signal and support the presence of distinct physiological featuresthat can be used for human recognition.

The rest of the paper is organized as follows. Section 2 presents the methodsused for ECG characterization and feature extraction. In section to follow the de-scription of statistical technique that generates match scores from derived featuresof the ECG signal is presented. The description of a human identification system us-ing the ECG signal is given in Section 3. The experimental results that prove theefficacy of the proposed biometric system on the publically available database andon our database are presented in Section 4. Finally, some conclusions are drawnin Section 5.

Human Identification using Heartbeat Interval Features and ECG Morphology 89

2 Methods

The normal cycle of a ECG signal contains P, Q, R, S and T waves as shown in Fig.1. The P wave is a representation of contraction of the atrial muscle and has durationof 60-100 milliseconds (ms). It has low amplitude morphology of 0.1-0.25 millivolts(mV) and usually found in the beginning of the heartbeat. The QRS complex is theresult of depolarization of the messy ventricles. It is a sharp biphasic or triphasicwave of 80-120 ms duration and shows a significant amplitude deflection that variesfrom person to person. The time taken for ionic potential to spread from sinus node,through the atrial muscle and entering the ventricles is 120-200 ms and known asPR interval. The ventricles have a relatively long ionic potential duration of 300-420 ms known as the QT interval. The plateau part of ionic potential of 80-120 msafter the QRS and known as the ST segment. The return of the ventricular muscleto its resting ionic state causes the T wave that has an amplitude of 0.1-0.5 mV andduration of 120-180 ms. The duration from resting of ventricles to the beginning ofthe next cycle of atrial contraction is known as TP segment which is a long plateaupart of negligible elevation.

Prior to use the ECG signal in the subsequent processing of heartbeat segmenta-tion and features extraction all signals are passed through a two-stage median filtersof width 200 ms and 600 ms, respectively to remove the baseline wander. The firstmedian filter suppressed the QRS complexes and P waves while the second me-dian filter suppressed the T waves. The resulting signal is then subtracted from theoriginal signal to produce the baseline corrected ECG signal [22].

2.1 Heartbeat Detection and Segmentation

The heartbeats are detected from the ECG signal using the QRS complex delin-eator. We employed the technique proposed by Pan and Tompkins [23] with someimprovements. It uses digital analysis of slope, amplitude and width informationof the ECG waveforms. The beginning and end of the QRS complex i.e., QRSonsetand QRSo f f set time instances (fiducials), respectively are delineated according to thelocation and convexity of the R peak.

Once the heartbeat is detected, temporal time windows are defined heuristicallybefore and after the QRS complex fiducials to seek for the P and the T waves. Thetechnique proposed in [24] is used to determine the Ponset and the Po f f set fiducialsfrom the P wave, while the technique proposed in [25] is used to determine the Tonsetand the To f f set fiducials from the T wave including their peak fiducials. The delin-eation results of the ECG characteristic waves obtained by the employed methodsare shown in Fig. 2. From computed fiducials of a heartbeat, we derive three differ-ent classes of features such as, (1) heartbeat interval features, (2) interbeat intervalfeatures and (3) ECG morphological features.

90 Y. N. Singh and S. K. Singh

Fig. 2 Delineation of ECGcharacteristic waveforms andtheir clinically dominantfiducials.

2.2 Feature Extraction

2.2.1 Heartbeat Interval Features

Five features related to the heartbeat intervals are computed after heartbeat segmen-tation. The QRS width is the duration between the QRSonset and the QRSo f f set , fidu-cials. The T wave duration is defined as the time interval from QRSo f f set to To f f setfiducials. The PQ segment is defined as the time interval from Ponset to QRSonsetfiducials. The pre-TP segment is defined as the duration between the current beatPonset and the previous beat To f f set fiducials. Similarly, the post-TP segment is de-fined as the duration between the current beat To f f set and the following beat Ponsetfiducials.

2.2.2 Interbeat Interval Features

Ten features related to inter heartbeat intervals are computed after segmentation ofthe heartbeats. These features include PP, QQ, SS, TT and RR sequence that areextracted from successive heartbeats. The pre-PP (post-PP) interval is the durationbetween the Ponset of the current heartbeat and the Ponset of the previous (following)heartbeat. The pre-QQ (post-QQ) interval is the duration between the QPeak of thecurrent beat and the Qpeak of the previous (following) beat. The pre-SS (post-SS)interval is the duration between the Speak of the current beat and the Speak of theprevious (following) beat. The pre-TT (post-TT) offset interval is the duration be-tween the To f f set of the current beat and the To f f set of the previous (following) beat.Similarly, the pre-RR (post-RR) interval is defined as the RR interval between thecurrent heartbeat and the previous (following) heartbeat.

Human Identification using Heartbeat Interval Features and ECG Morphology 91

Fig. 3 Extraction of ECGmorphological features from aheartbeat where fiducial point(FP) represents the position ofR peak.

FP QRS offset

Ponset

Toffset

QRS onset

Ponset+120ms

2.2.3 ECG Morphological Features

We divided the ECG morphological features into two groups where both groupscontained the amplitude values of the segmented heartbeats of an ECG trace. Themain distinction between the groups lie on the method used to extract the ampli-tude features. The first group extracts the morphological features from a heartbeatusing the segmented information derived by the analytical method within the sam-pling windows e.g., the P wave, the QRS complex and the T wave as shown inFig. 3. In total, thirty-three features are derived within the sampling windows. Thefirst window is set between the QRSonset and the QRSo f f set fiducials. Five featuresare extracted corresponding to the QRSonset , the Qpeak, the Rpeak, the Speak and theQRSo f f set fiducials. The boundaries of the second window is set as such so that itapproximately cover the P wave. It contains the portion of heartbeat between thePonset and the Ponset + 120 ms fiducials. Using linear interpolation method, thirteenfeatures are estimated uniformly within the sampling window. Similarly, the thirdwindow is bounded between the QRSo f f set and the To f f set fiducials. Fifteen ampli-tude features are derived uniformly within this window using linear interpolation.

The second group extracts the morphological features from a scaled ECG signal.It contains twenty-eight features which are extracted from a heartbeat on fixed in-terval basis. In a scaled signal the amplitude difference from xnT to the mean, µ ismeasured in units of standard deviation σ such as,

xnT′ =

xnT −µ√σ

(1)

where xnT represents the data sample of size n at discrete time instance T [20]. Theaim of scaling is to reduce the sensitivity of the signal, both to noise and non-signalartifacts that are contaminated to them. In this group the sampling windows are

92 Y. N. Singh and S. K. Singh

Fig. 4 Extraction of ECGmorphological features fromthe scaled samples of a heart-beat.

FP

+100ms-80ms

FP-240ms +150ms FP+420ms

defined with respect to the location of the heartbeat fiducial point (FP) as shown inFig. 4. The first window approximately covered the QRS complex and contained theportion of the ECG signal between FP−80 ms and FP+100 ms. Nine features areresulted from this window. The second window approximately covered the P waveand extended from FP− 80 ms to FP− 240 ms towards its left. Nine features areresulted within this window. The third window approximately covered the T waveand extended from FP+150 ms to FP+420 ms. Ten amplitude features are derivedfrom this window. In the described windows the features are derived from uniformlydistributed sample positions using linear interpolation method.

2.3 Generation of Match Scores

In order to generate the match scores from feature vectors of the gallery and theprobe samples of the ECG signal, statistical framework technique is adopted [15].Consider, the subject i has an ECG signal of length t unit of time. The m sub signalsof length l unit of time (l < t) are arbitrarily selected from the complete trace ofthe ECG signal. For each sub signal, a vector of d-dimension is prepared from thesuccessive occurrence of the selected beats by taking average of attributes of theirfeature vectors. Let P(i) be the pattern matrix consisting of m vectors of the subjecti of size m×d can be defined as,

P(i) =

f1,1 f1,2 . . . f1,df2,1 f2,2 . . . f2,d...

......

fm,1 fm,2 . . . fm,d

(2)

where element f j,k represents the kth feature of jth sub dataset. The values of m andd are set to 10 and 74, respectively in this experiment. The purpose of arbitrarilyselection of subdata set is to analyze the signal statistically for the variation presentin different heartbeats of an individual ECG. Consider, the population size is N,so there are N different ECG signals. Thus, N different pattern matrices P(i) aregenerated in the database where, 1≤ i≤ N.

Similarly, a probe sample, Q is prepared from the testing dataset of an individualECG and a feature vector f ′, where f ′ = { f ′1, f ′2, . . . , f ′d} is generated. Statistically,

Human Identification using Heartbeat Interval Features and ECG Morphology 93

the distance between the attributes of a probe feature vector and the attributes of thegallery feature vectors for subject i is computed using Euclidean distance as follows,

d(i)j =

(| f j,1− f ′1| | f j,2− f ′2| . . . | f j,d − f ′d |)

(3)

where 1 ≤ j ≤ m. Sum of the computed Euclidean distances return the distancescore measure between the attributes of a probe and the gallery feature vectors foran individual i such as

s(i)j =

d

∑k=1| f j,k− f ′k| (4)

In order to acknowledge the variation present in an ECG signal for the subject i, themean of the distance scores, s(i) can be computed and determined as follows

s(i) =1m

m

∑j=1

s(i)j (5)

A smaller value of distance score indicates a good match while a higher value ofdistance score indicates a poor match.

3 Identification System

The schematic representation of the proposed automatic system of ECG character-ization for human identification is shown in Fig. 5. The identification process is anoutcome of the processing of three different stages such as, a preprocessing stage,a data representation stage and a decision making stage. First, the ECG signal isacquired from individuals and preprocessed. It utilizes a filtering unit that makesnecessary correction of the signal from noise and non-signal artifacts. The data rep-resentation stage consists of heartbeat detection and feature extraction modules. Theheartbeat detection module attempts to locate all heartbeats with heartbeat segmen-tation. The heartbeat segmentation includes the detection of the P, Q, R. S, andT waves and determination of their end fiducials. The feature extraction includesthe determination of heartbeat interval features, interbeat interval features and ECGmorphological features from a set of heartbeats. From the derived features, a vectorof measurement is prepared as template and stored in the database. A similar processis adopted for generating a vector of measurement from the probe ECG signal. Fi-nally, the probe information is compared with the templates stored in the database.The identification decision can be taken on the basis of the generated match scoresusing 1 : N matching criterion under the predefined threshold.

94 Y. N. Singh and S. K. Singh

Preprocessing Data Representation

Signal correction from noise and non-signal artifacts

HeartbeatSegmentation

Gallery ECG Signal

Decision Making

Heartbeat(fiducial point)Detection

HeartbeatIntervalFeatures

InterbeatIntervalFeatures

Maching

SystemDatabase

HeartbeatSegmentation

ProbeECG Signal

Heartbeat(fiducial point)Detection

HeartbeatIntervalFeatures

InterbeatIntervalFeatures

Enrollment Phase

Match/Non-match

Identification Phase

ECG Morphological Features

ECG Morphological Features

Signal correction from noise and non-signal artifacts

Fig. 5 Schematic representation of automated characterization of ECG for human identification.

4 Results

The efficacy of the proposed characterization of the ECG signal for human identi-fication is tested on two different database. The first database is prepared from thepublically available PhysioBank archives [21], in particular MIT-BIH Arrhythmiadatabase is used. It contains 48 half-hour excerts of two-channel ECG recordingsof the subjects aged 23 to 89 years whereas over 50% of the included subjects aresuffered with clinically significant arrhythmia. Forty-four ECG recordings are ran-domly selected from this database in this study. The second database is prepared inour laboratory at the School of Biomedical Engineering, Indian Institute of Tech-nology, IIT(BHU), using the PowerLab 4/25 of AD-Instruments. The total 29 vol-unteers aged 20 to 56 years participated in data enrollment process and the data isacquired in multiple sessions across a period of one year. Each session contains theECG recording of five minutes for each subjects whereas the subjects do not haveany known cardiac arrhythmia. We perform the data acquisition in a more simplisticmanner, with subjects merely sitting on a wooden stool under relax condition andthe clamp electrodes are fixed to both wrist and left ankles. The data are bandpassfiltered at 0.3−50 Hz and sampled at 1000 Hz.

Human Identification using Heartbeat Interval Features and ECG Morphology 95

Fig. 6 ROC curve repre-senting the identificationperformance for the subjectsof MIT-BIH Arrhythmia data-base and IIT(BHU) database.

52

60

70

80

90

98

0 2 4 6 8 10 12

Gen

uine

Mat

ch R

ate

(GM

R)

False Match Rate (FMR)

IIT(BHU)MIT-BIH

The MIT-BIH Arrhythmia database contains one ECG recording of about 30minutes for each subjects, therefore the whole recording of the ECG signal is di-vided into two halves. The first half of the ECG recording is labeled as the galleryand the other half of the ECG recording is labeled as the probe, for each subject. Inthe IIT(BHU) database, different sessions of recordings are used for the gallery andthe probe purpose. Ten sets of heartbeats are randomly selected from the gallery dataand the features are derived such that they meet the requirement of the delineatorsin each set.

The aim of testing two different ECG database is to evaluate the effectivenessof the proposed method of ECG characterization and subsequently the performanceof the aforementioned human identification system on the normal subjects and thesubjects suffering with cardiac arrhythmia. The performance of the system is eval-uated on equal error rate (EER) and receiver operating characteristic (ROC) curve[26]. An EER is the error rate where the likelihood of fraudulent matches (FMR)and the likelihood of non-matches of individuals who should be correctly verified(FNMR) assume the same value. The EER can be adopted as a unique measurefor characterizing the security standard of a biometric system. The ROC curve is atwo dimensional measure of classification performance that plots the likelihood offraudulent matches against the likelihood of genuine matches (GMR).

The identification system reports the EER values of 7.12% and 12.63%, respec-tively for the subjects of IIT(BHU) database and MIT-BIH Arrhythmia database.The ROC curves representing the classification accuracy on both the database areshown in Fig. 6. The subjects of IIT(BHU) database achieves better classificationaccuracy of 92.88% than the subjects of MIT-BIH Arrhythmia database that areclassified with an accuracy of 87.37%. Further, the subjects of IIT(BHU) databaseare genuinely matched with the rate of 55% when no fraudulent match is allowed bythe system. The system achieves the GMR of 61% at the FMR of 3%. Subsequently,the system achieves higher GMR of 88%, 94% and 98%, respectively at the FMRof 7%, 10% and 12%. The subjects of MIT-BIH Arrhythmia database are genuinelymatched with the rate of 54% when there is no fraudulent matched. Performanceof the system raises further to the GMR of 64%, 80% and 88%, respectively at the

96 Y. N. Singh and S. K. Singh

FMR of 7%, 9% and 12%. One of the reasons of reporting better identification per-formance for the subjects of IIT(BHU) database is because, it contained the ECGrecordings of healthy subjects that are acquired under normal condition.

5 Conclusion

This study has proposed a novel method for automated characterization of the ECGsignal for human identification. The characterization process has involved the ana-lytical based method and the appearance based method that made the ECG analysisinsensitive to noise and non-signal artifacts. The vector of measurement has derivedfrom the analytical based features that include interval features and amplitude fea-tures while the appearance based features include morphological features derivedfrom the successive heartbeats. The aforementioned system has performed the iden-tification on the basis of match scores that are statistically generated using patternmatching technique through one-to-many comparisons. The system has performedefficiently for the subjects of test database, in particular the identification resultson the normal ECG recordings of IIT(BHU) are found better. The results have alsoproved that there exists a number of heartbeat features that are remained consistentin each individual inspite that they are suffering with significant cardiac arrhythmia.

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