Individual Identification Using ECG SignalsW


The electrocardiogram (ECG) signal used for diagnosis and patient monitoring, has recently emerged as a biometric recognition tool. Indeed, ECG signal changes from one person to another according to health status, heart geometry and anatomy among other factors. This paper forms a comparative study between different identification techniques and their performances. Previous works in this field referred to methodologies implementing either set of fiducial or set non-fiducial features. In this study we show a comparison of the same data using a fiducial feature set and a non-fiducial feature set based on statistical calculation of wavelet coefficient. High identification rates were measured in both cases, non-fiducial using Discrete Meyer (dmey) wavelet outperformed the rest at 98.65.

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Diab, M. , Seif, A. , El-Abed, M. and Sabbah, M. (2018) Individual Identification Using ECG SignalsW. Journal of Computer and Communications, 6, 74-80. doi: 10.4236/jcc.2018.61008.

1. Introduction

Biometric recognition systems identify or verify individuals according to unique physiological or behavioral characteristics. Physiological characteristics are based on intrinsic morphological qualities such as fingerprint, face and iris recognition [1]. Behavioral characteristics are based on learnt qualities such as signatures, gait analysis and keystroke dynamics [1]. A major drawback for the use of physiological characteristics is the ability of copying by intruders. For example fingerprints could be lifted and masked into an artificial print. Even though physiological characteristics are more unique than behavioral characteristics, there is a possibility of falsification. Therefore there is a need of physiological characteristic that cannot be falsified or captured. ECG signals fulfill this need as they are confidential for each individual and cannot be lifted for masking [2]. Moreover ECG biometrics detects the aliveness of the individual while verifying. All the other biometric requirements are fulfilled. Those are: the universality, the uniqueness, the collectability, the permanence and the circumvention of the characteristics.

The objective of this paper is to compare the identification rate between two feature extraction methodologies. The outline of this paper is defined as follows. In Section 2, a literature review of the previous work is presented. In Section 3, the methodology is explained. In Section 4, the results are displayed and discussed. Finally Section 5 concludes with the future perspective.

2. Literature Review

Biel et al. pioneered in the field of ECG biometric and proved that lead Iis sufficient for individual recognition [1]. The extracted feature sets could either be fiducial features based or non-fiducial features based. The advantage of using non-fiducial features over the fiducial is less computation time, even though their performance is comparable.

Fiducial features are based on characteristic peaks of the ECG signal. The detection of the peaks is due to maxima or minima points in a window. The fiducial features are based on angle, amplitude and time interval (R-R) peaks. Different algorithms could be applied to detect the peaks which could lead to inconsistency between different studies. Palaniappan et al. [3] used only 6 fiducial features and obtained identification performance of 97.6% when tested onten different subjects. Gahi et al. [4] extracted a larger feature set of 24 fiducial features to obtain 100% identification rate on a sample of 16 subjects. The increase of fiducial features improves the performance but increases computational time. Israel et al. [5] compromised between performance and feature set size. They used a feature set of 15 fiducial features to obtain 98% identification rate on a sample of 29 subjects.

The non-fiducial based approaches detect either only QRS peaks or no peaks at all. they could be either model based approaches or transform based ones[3]. Agrafioti et al. [6] demonstrate an autocorrelation based feature extraction approach, in conjunction with the Discrete Cosine Transform and obtained rate of 92.3% in a sample size of 52. Wan et al. [7] applied discrete wavelet transform and extracted features corresponding to wavelet coefficient. This resulted in identification rate of 10% for a sample of 15 subjects.

3. Methodology

The methodology is composed of three parts: 1) preprocessing, 2) feature extraction and selection, and 3) classification, the block diagram linking the process’s different phases and stages is illustrated in Figure 1.

3.1. Preprocessing

The applied database corresponds to acquired ECG signals during 10 seconds.

Figure 1. Flow chart showing methodology used.

The ten seconds ECG recordings are filtered before any further processing, in order to remove undesired components of power line interference, muscle noise and baseline wander. the filter consisted of a 3rd order Butterworth low pass filter with 3 dB cut-off at 15 Hz allowing elimination of 50 Hz power line interference and high frequency interferences using a 3rd order Baseline wander is due to subject movement, perspiration or chest hair [8]. This type of noise has a very low frequency range. Therefore to remove it, the baseline noise is extracted. This is done using a 3rd order low-pass Butterworth digital filter with cut-off at 5 Hz. When the baseline is detected, the clean signal is obtained by deducting the baseline signal from the original signal [9].

3.2. Feature Extraction

Fiducial feature sets are calculated from peaks detected during ECG sequence. The non-fiducial feature sets are calculated from the entire beat or recording.. Some methodologies require the detection of only the R-peaks. In this paper both forms of feature extraction methodologies are tested using Matlab.

The fiducial feature set is obtained by calculating 13 different features from 5 different heart beats. The selected beats should not be the first or the last beats in the recording to ensure that all selected beats are complete beats. Nine duration features and four amplitude features were extracted into the feature vector. Table 1 summaries the fiducial features extracted from the ECG recordings

Firstly, Pan Tompkin’s algorithm [10] was implemented for detection of R peaks. The R-peaks are detected using a differential filter and squaring of the signal. Two sets of thresholds are used. The first one is being applied to the filtered signal while the second one is used for the signal resulting from the moving window. After applying the adaptive threshold on the filtered signal the R-peaks are detected [8]. The other ECG peaks (P-QRS-T) peaks were detected by windowing of the signal around the R-peaks. The Q peak was detected as the first downward deflection trough by tracing back 50 ms from R peak. In addition, S peak was detected as the first upward deflection peak by tracing forward

Table 1. Fiducial features extracted.

50 ms from R peak. The features like R amplitude, QR amplitude and RS amplitude were computed. R-R interval was obtained from two consecutive beats.

The second feature extraction technique is performed using non-fiducial transform based set. In such case, no peak detection is required. The use of discrete wavelet transform (DWT) in biomedical signal analysis increased greatly due to its ability to analyze non-stationary signals in frequency domain and time domain. The decomposition of the signal in both domains provides information in low and high frequencies for short and long time intervals. The frequencies in ECG signal vary with time therefore applying Fourier transform does not provide all the information in the signal. The DWT decomposes the signal into approximations and details through a series of cascading low pass and high pass filters, respectively. The approximations are further decomposed. The decomposition level chosen based on the dominant frequency components of the signal and it is limited by the length of the signal. In our work the signal was decomposed to level 6. The two approaches behind the choice of a certain mother wavelet are either theoretically by the similarity of the signal to the mother wavelet or experimentally by testing the performance of different mother wavelets on the signal. The similarity could be quantified using either signal energy or entropy. In this work we looked into mother wavelets that have high energy and/or low entropy with respect to the ECG signal. Then their performance was tested to ensure the highest identification rate is being reached.

The tested mother wavelets are:

o Daubechies 2 (db2)

o Haar

o Biorthogonal 6.8 (bior6.8)

o Symlets 5 (sym5)

o Coiflets 5 (coif)

o Discrete Meyer (dmey)

Statistical features over coefficients of the details and the approximation were calculated to decrease the dimensionality of the wavelet feature vector. Those statistical features formed the feature set used for the classification. The following features were used to represent the time frequency distribution of the ECG signal:

o Absolute mean of the coefficients in each sub-band

o Average power of the coefficients in each sub-band

o Standard deviation of the coefficients in each sub-band

For feature selection, Kruskel-Wallis H test was applied on both feature sets, the fiducial feature set and the non-fiducial feature set. It ensures that features show inter-subject variability and intra-subject stability. The Kruskel-Wallis H test is a rank-based nonparametric test that can be used to determine if there are statistically significant differences between two or more groups of an independent variable on a continuous or ordinal dependent variable. The Kruskel-Wallis H test was performed on IBM SPSS Statistics.

3.3. Classification

In our work, we attempted to further investigate classifiers that are not the most frequently used in ECG biometrics. Neural network classifiers are used for their extensive ability to learn complex relationships between feature vectors. Each artificial neuron is itself a classifier, only a simpler one whose accuracy in principle has limitations when used for complex problems therefore they are connected to form a network. A single artificial neuron is perceptron or a logistic regression unit. Simply an artificial neuron accepts a number of inputs that collectively describe an item to be classified and outputs the class it believes the item belongs to. The neurons are organized in a form of layers. Its input layer consists of a number of ANs that depends on the number of input features while the output layer consists of a number of ANs equal to the number of classes. In between the input and output layers consists several hidden layers as shown in Figure 2. Classification for both feature sets was performed using the Machine learning software Weka [11].

4. Experimental Results

The methodology is tested on a publicly available database. Physikalisch-Tech- nische Bundesanstalt (PTB) database is provided by the National Metrology Institute of Germany [12]. It contains recordings for 52 healthy female and male subjects of age ranging from 17 to 57. Each signal is digitized at 1000 samples per second, with 16 bit resolution over a range of ±16.384 mV. Each record includes the 12 leads but our work is tested on only lead 1.

For all the features included in both fiducial and non-fiducial sets, the kruskalwallis test obtained p < 0.05. This indicates that there is a significant difference between different subjects. Therefore all the features were included in the two respective feature sets. The classification was implemented using Weka software and its results are displayed in Table 2. Neural network multi-layer

Figure 2. Decomposition of neural network Multilayer preceptron.

Table 2. Identification performance.

perceptron was applied using 10-fold cross-validation. The identification rates obtained using both approaches are high showing a great promise in the use of ECG for biometrics. The non-fiducial approach using Discrete Meyer mother wavelet outperformed all the other mother wavelets.

5. Conclusion and Perspective

We have presented in this paper a comparative study of the feature extraction methodologies for ECG biometrics. The compared methodologies included 13 fiducial based features obtained from detecting all ECG peaks as well as non-fiducial statistical features associated to the wavelet decomposition coefficients. All the presented methodologies obtained high identification performance, and further confirmed the possibility of applying ECG for individual verification and identification. Nonetheless, the non-fiducial based features slightly outperformed the fiducial features. Moreover this paper presented a comparison between the possible mother wavelets. Even though all mother wavelets had high identification performance some outranked others with the Discrete Meyer (dmey) obtaining the highest rate 98.6%. To further improve the identification rate it is possible to form a hybrid feature set composed of the fiducial features and the non-fiducial wavelet features. In addition, both methodologies should be tested on a database of subjects with a range of cardiac disorders to ensure its inclusivity.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Biel, L., Pettersson, O., Philipson, L. and Wide, P. (2001) ECG Analysis: A New Approach in Human Identification. IEEE Transactions on Instrumentation and Measurement, 50, 808-812.
[2] Belgacem, N. and Bereksi-Reguig, F. (2012) Person Identification System Based on Electrocardiogram Signal Using LabView. International Journal on Computer Science and Engineering (IJCSE) Person, 4, 974-981.
[3] Palaniappan, R. and Krishnan, S.M. (2004) Identifying Individuals Using ECG Beats. International Conference on Signal Processing and Communications, SPCOM’04, 569-572.
[4] Gahi, Y., Lamrani, M., Zoglat, A., Guennoun, M., Kapralos, B. and El-Khatib, K. (2008) Biometric Identification System Based on Electrocardiogram Data. New Technologies, Mobility and Security, 1-4.
[5] Israela, S., Irvineb, J., Chengb, A., Wiederholdc, M. and Wiederholdd, B. (2004) ECG to Identify Individuals. Pattern Recognition Society, 38, 133-142.
[6] Agrafioti, F. and Hatzinakos, D. (2010) Signal Validation for Cardiac Biometrics. 2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 1734-1737.
[7] Wan, Y. and Yao, J. (2008) A Neural Network to Identify Human Subjects with Electrocardiogram Signals. Proceedings of the Worlds Congress on Engineering and Computer Science, San Francisco.
[8] Wang, Y., Agrafioti, F., Hatzinakos, D. and Plataniotis, K. (2008) Analysis of Human Electrocardiogram for Biometric Recognition. EURASIP Journal on Advances in Signal Processing.
[9] Palaniappan, R. and Krishnan, S.M. (2004) Identifying Individuals Using ECG Beats. Int. Conference on Signal Processing and Communications, SPCOM’04, 569-572.
[10] Pan, J. and Tompkins, W. (1985) A Real-Time QRS Detection Algorithm. IEEE Transactions on Biomedical Engineering, 32, 230-236.
[11] Data Mining: Practical Machine Learning Tools and Techniques.
[12] “The PTB Diagnostic ECG Database”,, 2017.

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