Multimedia Tools and Applications https://doi.org/10.1007/s11042-019-7152-0 Biometric human recognition system based on ECG Sahar A. El_Rahman 1,2 Received: 9 February 2018 / Revised: 21 December 2018 / Accepted: 2 January 2019 # Springer Science+Business Media, LLC, part of Springer Nature 2019 Abstract The ECG (electrocardiogram) is an emerging technology for biometric human identification. In this paper, the performance of an ECG biometric recognition system is evaluated. Signal processing techniques are utilized to extract the ECG features. In preprocessing stage, digital filters eliminate the noises and hence improve the signal to noise ratio. The process of ventricular complex (QRS Complex) detection depends on Pan and Tompkins approach that achieves an efficient QRS detection, and hence enhancing the feature extraction process. The main classifiers applied to the extracted features are Neural Network (NN), Fuzzy Logic (FL), Nearest Mean Classifier (NMC), Linear Discriminant Analysis (LDA), and Euclidean Distance (ED) are utilized to classify QRS fragments. ECG of an unknown subject is acquired; the classifiers are applied to wavelet coefficient features set between the unknown subject and all enrolled subjects. The Performance of the different approaches is evaluated by utilizing Sensitivity, Specificity, and efficiency, EER (Equal Error Rate) and ROC (Receiver Operating Characteristic) curve. The experiments are conducted on 112 individuals MIT-BIH database and the accuracy is up to 98.99%. Keywords ECG . Human recognition . ECG biometrics . QRS complex . QRS detection . Individual identification 1 Introduction The biometric is the individual identification based on the behavioral or/and physiological features such as fingerprint, gait, face, retina, voice and vein. Biometrics are truly identifying the actual subjects than other traditional approaches such as passwords and tokens. Although these approaches aren’t robust whereas the private biometric credentials are not secured, spoofing attack will happen * Sahar A. El_Rahman sahr_ar@yahoo.com 1 Electrical Engineering Department, Faculty of Engineering-Shoubra, Benha University, Cairo, Egypt 2 Computer Science Department, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia Multimedia Tools and Applications either digitally or physically. For example, the fingerprints could be on glass, doors and face image may be stolen from any surveillance system. The researchers have proposed ECG (electrocardiogram) as a biometric approach to identify the subjects [29]. The analysis of ECG is not only a very useful diagnostic tool for the diagnosis and monitoring of heart, but also is being utilized from a biometric point of view [2, 31]. Due to the unique feature of ECG it is more difficult to falsify. In addition to this, ECG signals can be used for aliveness detection as well. The heartbeat signal offers direct solutions to the liveness detection [25] as it cannot be captured from deceased body parts, fake finger, or a high-resolution video. It is also difficult to steal and replicate a heartbeat signal. So the imposters will face a greater challenge to collect an illicit copy of the heartbeat signal of the actual user. As, the heartbeat signal of a person holds a unique signature and it is stable for a long time [4, 6], it can be considered as a unique liveness property for each individual. Moreover, good-quality heartbeat signal for authentication purpose can be captured from fingers [6, 7, 13]. The ECG is a recording of time-varying bio-electric potential generated by the electrical activity of the heart [15, 23]. An ECG signal is a representation of the bio-electrical activity caused by cyclical contractions and relaxations of the heart muscles. A normal cycle of ECG produces the specific waves or complex corresponding to atrial / ventricle depolarization / repolarization [19, 24]. The ECG may roughly be divided into the phases of depolarization and repolarization of the muscle fibers making up the heart. The depolarization phases correspond to the P wave (atrial depolarization) and QRS-waves (ventricular depolarization). The repolarization phases correspond to the T-wave and U-wave (ventricular repolarization). A typical ECG signal and the wave fiducials are shown in Fig. 1 [8, 14]. Where, a normal ECG signal is composed of the QRS complex, T wave, and P wave. P wave corresponds to low frequency spectral components (10-15 Hz) and it is created when the left and right atria of the heart are depolarized [30]. The QRS complex corresponds the depolarization of the left and right ventricles. It has much steeper slopes and its spectrum is concentrated in the interval of 10– 40 Hz that higher than the frequency of the other ECG waves. Finally, the T wave reflects ventricular repolarization and expands around 300 ms next to the QRS complex. Atrial repolarization is less commonly observed in ECG traces and is labeled as a U wave [12, 23, 24, 30, 31]. Temporal information was used as heartbeat biometric features requiring delineation of different waves (e.g. P wave, QRS-complex, and T-wave) which is often difficult [8]. 2 Related work ECG as a biometric has been studied last years and several approaches have been proposed to achieve high accuracy and a reliable ECG authentication system. Recently, Singh and Gupta [20] have discovered the ECG signals conveniently for human recognition. ECG waveforms are delineated from each heartbeat by utilizing signal processing techniques. Where, 19 features based on angle, interval, and amplitude are extracted from each heartbeat. The experiments are conducted on 50 individuals Physionet database and the accuracy is up to 99%. Also, Singh and Gupta [21, 22] found that the ECG conveniently to be an identification system. The researchers delineate fiducials of ECG signal from every heartbeat by signal processing approaches. The delineators of P and T wave are utilized with the QRS complex for feature extraction. The results of delineation are found better than other published approaches. The final stage in their system is the matching process based on the correlation. The proposed scheme achieves accuracy nearly 98%. Wang et al. [30] presented a two-stage fiducial Multimedia Tools and Applications Fig. 1 A typical ECG signal [8] detection model which extracts appearance and analytic features of the heartbeat. The approach utilized for feature extraction is based on the combination of DCT and autocorrelation that is free from fiducial detection. The performance of the combination approach is between 94.47% and 97.8%. In Belgacem et al. [3], Discrete Wavelet Transform (DWT) is utilized for extracting features, then ECG authentication was performed by the Random Forest. Preliminary experiments are conducted on 80 individuals from the Physionet database and the findings indicate that the system is accurate and can achieve a low false negative rate, low false positive rate and accuracy 100%. Castro et al. [5] propose a novel algorithm to extract ECG features based on the wavelet transform. They implemented an algorithm to select an optimal mother wavelet from the biorthogonal and orthogonal wavelet filter banks using the best correlated ECG signal. They divided each cycle coefficient into 3 segments that related to T-wave, QRS complex, and P wave. The feature vector is generated from the summation of the segment values. Barra et al. [2] propose a multimodal biometric recognition system based on the fusion of six various bands of the electroencephalogram (EEG) with the first lead of ECG. The ECG fiducial features combined with EEG spectrum features are extracted. The signals of two benchmark datasets are composed to create the dataset. The findings indicate a good recognition performance using different measurements. In [16], a single lead ECG recognition system is presented. The approach extracts the main features from ECG signal after correcting it from noise. Whereas, a Finite Impulse Response equiripple high pass filter is utilized for denoising the signal and Haar wavelet transform is utilized to detect the R peaks. The ECG feasibility as a new recognition system is tested on the data size of 100 recordings of PTB database that reports the accuracy to 97.12%. To summarize, the existing classification schemes depends on the feature vectors which are extracted from the ECG signal waves. 3 Pan and Tompkins’s QRS detection algorithm A real time QRS complex detection algorithm presented in [17]. It detects QRS complex using the amplitude, slope, and width analysis. Figure 2 indicates the different filters of QRS Multimedia Tools and Applications Fig. 2 Filter stages of the QRS detector [27] detector, where. x(n) is the differentiated signal, y(n) is the band-passed signal, and z(n) is the time averaged ECG signal. In order to improve the signal to noise ratio, a bandpass filter is applied to ECG signal and allows the utilization of threshold lower than would be on the unfiltered ECG signal. So, the detection sensitivity will be increased. The processing steps are the signal differentiation to recognize QRS complex from other waves. Next, squaring of the signal samples to convert all the data positive before subsequent time averaging of the signal [17, 27]. 3.1 Band pass filter QRS detection algorithm utilizes a band pass filter to attenuate noise in the ECG signal based on the spectrum identification of the average QRS complex. Consequently, it reduces noise caused by 60-Hz interference, muscle noise, T-wave interference, and baseline wander. To maximize the QRS energy, the desired pass band range is approximately 5–15 Hz. in this algorithm, the filter developed is a recursive integer filter where the zeros on the z plane unit circle are canceled by the location of the poles. Thus, the high pass and low pass filters are cascaded to obtain a 3 dB pass band from nearby 5–12 Hz, reasonably close to the design goal [17, 18, 27]. 3.1.1 Low pass filter (LPF) LPF is an integer coefficient filter with transfer function H(z) of the 2nd order LPF as indicated in Eq. (1), the amplitude response in Eq. (2), and the filter difference equation in (3) [17, 18, 27]:
2
2 HðzÞ ¼ 1−z−6 = 1−z−1 jH ðwT Þj ¼ sin2 ð3wT Þ sin2 ðwT=2Þ yðnTÞ ¼ 2yðnT−TÞ−yðnT−2TÞ þ xðnTÞ−2xðnT−6TÞ þ xðnT−12TÞ ð1Þ ð2Þ ð3Þ where T is the period of sampling, the gain is 36, the processing delay of the filter is 6 samples, and the cutoff frequency is nearby 11 Hz. Multimedia Tools and Applications 3.1.2 High pass filter (HPF) The implementation of HPF based on subtracting 1st LPF output from an all-pass filter (i.e., the original signal samples) with delay. The transfer function H(z) for HPF is indicated in Eq. (4), the amplitude response in Eq. (5), and the filter difference equation in (6) [17, 18, 27]:

HðzÞ ¼ −1 þ 32z−16 þ z−32 = 1 þ z−1 jH ðwT Þj ¼ ½256 þ sin2 ð16wT ފ cos ðwT=2Þ ð4Þ 1=2 ð5Þ yðnTÞ ¼ 32xðnT−16TÞ−½yðnT−TÞ þ xðnTÞ−xðnT−32Tފ ð6Þ where the gain is 32, the processing delay of the filter is 16 samples, and the low cutoff frequency is nearby 5 Hz. 3.2 Derivative After applying BPF filter, the output filtered signal is differentiated for providing the information of QRS complex slope. The transfer function in Eq. (7) is used, the amplitude response in Eq. (8) and the resultant derivative is implemented using the difference equation in (9) [17, 18, 27]. HðzÞ ¼ ð1=8T Þ −z−2 –2z−1 þ 2z1 þ z2 jH ðwT Þj ¼
h sinð2wT Þ þ 2 sinðwT Þ 4T yðnTÞ ¼ ð1=8TÞ½−xðnT−2TÞ−2xðnT−TÞ þ 2xðnT þ TÞ þ xðnT þ 2Tފ ð7Þ ð8Þ ð9Þ 3.3 Squaring function In this operation, squaring the signal point by point by using Eq. (10), where the operations of the QRS detector are linear processing except the operation of squaring function is nonlinear [17, 18, 27]: yðnT Þ ¼ ½xðnT ފ2 ð10Þ Multimedia Tools and Applications 3.4 Moving window integration (MWI) MWI purpose is to obtain features information of the waveform and the R wave slope. It is computed from Eq. (11) [17, 18, 27]: yðnT Þ ¼ ð1=N Þ½xðnT−ðN −1ÞT Þ þ xðnT−ðN −2ÞT Þ þ … þ xðnT ފ ð11Þ where N is the samples number in the integration window width. 4 ECG biometric recognition system The scheme of the ECG biometric recognition system is explained in Fig. 3. In preprocessing stage, the ECG signal correction is performed to remove the noise and artifacts. QRS Complex is the most distinctive feature between all ECG features. The challenges of QRS detection are the QRS complexes physiological variability and the different types of noise which may be existing in the ECG signal. The sources of noise are artifacts due to electrode motion, muscle noise, baseline wander, power line interference, and high frequency features of T waves that similar to the QRS complex [17, 27]. In the proposed algorithm, digital filters eliminate the noises and hence the ratio between the signal and the noise gets better. In the proposed algorithm, the algorithm in Pan and Tompkins [17] is used for the detection of QRS, that achieves efficient QRS detection, and hence enhancing the feature extraction process. The Fig. 3 ECG biometric recognition system ECG signal Signal correction BPF (LPF+HPF) Feature Extraction DF Wavelet Decomposion Squaring MWI QRS Detecon FL Classification and Identification NN LDA ED NMC Multimedia Tools and Applications processing of QRS detection steps are linear digital filtering, nonlinear transformation, and decision rule algorithm [17]. Whereas, linear processes include a Band Pass Filter (BPF), a Derivative Filter (DF), and a Moving Window Integration (MWI). The nonlinear transformation that we use is the squaring of signal amplitude. T-wave discrimination and adaptive thresholds techniques support the decision [17]. 4.1 Signal correction The raw ECG signal should be preprocessed to eliminate the non-signal artifacts. The first step is the noise source identification, then the filter is applied to the raw signal based on the structure of these noise sources (see Fig. 4). This filter reduces the high frequencies related to electromyographic noise and power line interference, reduces the low frequencies of T and P waves and baseline drift, and also isolates the primary QRS energy. BPF is LPF combined with HPF and is used to discard the different types of noise and artifacts. To maximize the QRS energy, The desirable cutoff low frequency to eliminate the baseline wander is 5 Hz and cutoff frequency to get rid of the high frequency noise is 15 Hz [17, 18, 27]. 4.2 Feature extraction and space formation The second stage in classification, after signal correction, is to detect certain features of ECG signals mostly QRS complex, P and T waves. The QRS complex is the most unique among them. The P wave has a low amplitude and can be greatly distracted by noise and the T wave position depends on heart rate. The initial space formation process starts with the extraction of a set of R peak synchronized PQRST-fragments. PR, QRS and especially QT intervals vary in length, because they based on heart rate and subjects physiology. A further descriptions in particular for feature extraction is explained in the subsequent subsections. 4.2.1 Derivative filter To get the slope information of QRS complex after filtering, the signal is differentiated. 5-point derivative is used with the transfer function H(z) as indicated in Eq. (7), where T is the sampling period [17, 18, 27]. (a) Before Filtering (b) After Filtering Fig. 4 ECG signal (a) Before Filtering and (b) After Filtering Multimedia Tools and Applications 4.3 Squaring function The signal is squared point by point after differentiation. Nonlinearly squaring enhance the dominant peaks, where this produces positive data points and makes nonlinear amplifying of the derivative output intensifying the higher frequencies that are mainly the frequencies of ECG. The process equation that used is indicated in Eq. (10) [17, 18, 27]: 4.3.1 Moving window integration (MWI) The QRS complex to the MWI Waveform relationship is indicated in Fig. 5 according to Eq. (11). The sample number N in the MWI is important. Generally, the window width must be nearly exactly the widen QRS complex. If the window is too narrow, some QRS complex will make several peaks in the integrator waveform. If it is too wide, the integrator waveform will combine the QRS complex and T wave together. Then, the subsequent QRS detection process is more difficult. The window width is defined empirically, for the sample rate of 200 samples/ s, the width is 150 ms (30 samples wide) [17, 18, 27]. 4.3.2 Wavelet decomposition In the proposed system, the wavelet decomposition by Discrete Wavelet Transform (DWT) with ‘Haar’ wavelet decomposition at level 9, the wavelet coefficients decomposed from the signal were used as the biometric of the individual. 4.4 Classification and identification The classifier is applied to the extracted features, so, it can be called as feature matching. The mainly features matching techniques utilized in the ECG biometric recognition system are Neural Network (NN), Hidden Markov Modeling (HMM), and Dynamic Time Warping (DTW). In the proposed system, Fuzzy Logic (FL), Nearest Mean Classifier (NMC), Linear (a) (b) Fig. 5 The QRS complex to the MWI Waveform relationship (where QS is QRS width. W is the width of MWI). (a) ECG signal and (b) MWI output [17, 27] Multimedia Tools and Applications Discriminant Analysis (LDA), Euclidean Distance (ED), and NN classifiers are utilized to classify QRS fragments. ECG of an unknown subject is acquired; the classifiers are applied to wavelet coefficient features set between the unknown subject and all enrolled subjects. Finally, authentication is decided by comparing the stored template and the query sample. For all techniques, briefly, some information is explained in the subsequent subsections. A further descriptions in particular for these techniques can be gotten from the references presented in subsequent subsections. 4.4.1 NN classifier NN classifier that is adopted is SOM (Self Organizing Feature Map) in [11]. SOM has n dimensional input vectors and maps them to 2-D output plane with lower dimension. It has n input and m by m output nodes. Each input node i in SOM network connected to each output node j with connection weight wij between them. SOM has two process phases the training and testing phases. SOM training steps [11]: 1. Initiates the weights wij by small random values. 2. From the space of samples, choose a vector x as inputs. 3. The winning output node dwin by Eq. (12): d win ¼ min j  x−w j ð12Þ where ‖x − wj‖ is an Euclidean norm and wj is the weight vector. 5 Adapt the weight vector according to the following adaption Eq. (13)   wij ðt þ 1Þ ¼ wij ðtÞ þ ηðtÞ xi ðt Þ−wij ðtÞ N ð j; tÞ ð13Þ where wij(t) is the ith component of the weight vector wj, N(j, t) is neighborhood function, and η(t) is the learning rate. 6 Repeat from step 2 until no considerable variations take place in the weights When training is achieved, the classification is applied to the input vector to be classified. The winning output node defines the class of the input vector [11]. SOM utilized in this work use batch weight/ bias rules training sequence and mean squared error as the algorithm to get the best network possible for the system. Where, the training will end when the training achieves 0.00001 mean squared error or the training reaches 50,000 iterations. Table 1 shows the parameters that are used to achieve a significantly high reliability of the neural network. Multimedia Tools and Applications Table 1 Parameters in training neural network Parameter Training Used Performance Epochs Performance value Target Value/Algorithm batch weight/ bias rules Mean squared error 50,000 0.00001 6.1 LDA classifier LDA coefficients is defined by the following Eqs. (14) and (15) [28], TP ¼ ∑ll¼1 Tl ¼ ∑0∈Dl ðyo −nl Þðyo −nl Þu nl ¼ 1 ∑ y Ol 0∈Dl o ð14Þ ð15Þ where: yo is DWT Coefficient, Ol is patterns number in the class Dl, and l is class total number. The between Class Covariance Matrix is obtained by Eq. (16), and the data global mean by Eq. (17) [28], TC ¼ ∑ll¼1 Ol ðnl −nÞðnl −nÞu n¼ ð16Þ 1 O 1 ∑O¼1 yo ¼ ∑ll¼1 Ol nl O O ð17Þ Then, Total Covariance Matrix is computed from Eq. (18), the projection matrix is from Eq. (19), and LDA coefficients from Eq. (20) [28], T U ¼ TP þ TC P ¼ arg x max n PTP PU
−1 ð18Þ PTC PU
o z ¼ PU y ð19Þ ð20Þ where y is DWT coefficient vector, and z is LDA coefficients vector. 6.1.1 FL classifier Fuzzy integrals are considered as nonlinear functions stated by fuzzy measures [26]. The fuzzy integrals can integrate the multiple data sources results [1]. Sets function g:2x-(0.1) is defined as a fuzzy measure If: g ðAÞ ≤gðBÞ if A⊂B g ð0Þ ¼ 0 and gðxÞ ¼ 1 fAi giα ¼ 1 Multimedia Tools and Applications is a sequence of the measurable set increments Then   lim g ðAiÞ ¼ g lim Ai i→∞ i→∞ As a consequence, g is not necessary additive, the additive property of the ordinary measure can replace this property [1]. In this work, the fuzzy Sugeno integral in [26] is adopted, where Bλg –fuzzy^ measure is introduced that satisfies the property in Eq. (21), where the measure of A∪ B (two disjunction sets) can be computed from the individual measures, For every A, B ⊂ X and A ∩ B = θ, gðA∪BÞ ¼ g ðAÞ þ gðBÞ þ λ gðAÞgðBÞ; for some value of λ > −1: ð21Þ 7 Performance evaluation The performance measures for classification and identification during testing are Accuracy, Sensitivity, Specificity, and EER, which are defined by the following formulas [10, 28, 29]. & Sensitivity (True Positive Rate): it measures the proportion of positives that are correctly identified as shown in Eq. (22). Sensitivity ¼ True Positive Rate ¼ TP TP þ FN ð22Þ where TP is True Positive and FN is False Negative. & Specificity (True Negative Rate): it measures the proportion of negatives that are correctly identified as shown in Eq. (23). Specificity ¼ True Negative Rate ¼ TN TN þ FP ð23Þ where TN is True Negative and FP is False Positive. & Efficiency: it measures the times that the test provides the correct result compared to the total numbers of tests as shown in Eq. (24). Efficiency ¼ TP þ TN TP þ TN þ FP þ FN ð24Þ Multimedia Tools and Applications Fig. 6 ECG signal at processing steps. (a) The signal after Band pass filter. (b) The signal after Adaptive filter. (c) The squared signal. (d) Averaged with 30 sample length, Noise (Black), Adaptive Threshold (Green), Signal Level (Red), QRS adaptive threshold (Red circles) Fig. 7 The processing steps of QRS detector for a noisy ECG from MIT-BIH database: (a) QRS on Filtered Signal. (b) QRS on MWI signal and Noise level(black),Signal Level (red) and Adaptive Threshold(Green). (c) Pulse train of the found QRS on ECG signal Multimedia Tools and Applications (a) ROC Analysis for NN Classifier (b) ROC Analysis for FL Classifier (c) ROC Analysis for ED Classifier Fig. 8 ROC analysis for (NN, FL, ED, LDA, NMC) classifiers Multimedia Tools and Applications (d) ROC Analysis for LDA Classifier (e) ROC Analysis for NMC Classifier Fig. 8 continued. & FAR, FRR and EER: EER (Equal Error Rate) is the error rate equates to the point at it FRR (False Acceptance Rate) in Eq. (25) cross FAR (False Rejection Rate) in Eq. (26) (i.e. FAR ≈ FRR). FAR ¼ FP ¼ Nnmber of false acceptance Number of identification attempt ð25Þ FRR ¼ FN ¼ Nnmber of false rejection Number of identification attempt ð26Þ 100 0.04 80 0.03 60 0.02 EER AUC(%) Multimedia Tools and Applications 40 0.01 20 0 0 NN FL ED LDA NMC NN FL ED LDA NMC Classifier Classifier (a) (b) Fig. 9 AUC and EER for (NN, FL, ED, LDA, NMC) classifiers 8 Results and discussion In this paper, the algorithm in Pan and Tompkins [17] is used for the QRS detection after signal correction. The results of QRS detection processing steps for a noisy ECG from the MIT-BIH database is indicated in Fig. 6 and Fig. 7. Where Fig. 6(a) indicates the signal after Band pass filter, Fig. 6 (b) the signal after Adaptive filter, Fig. 6(c) the squared signal, and Fig. 6(d) Averaged with 30 sample length, Noise (Black), Adaptive Threshold (Green), Signal Level (Red), QRS adaptive threshold (Red circles). Also, Fig. 7 indicates the processing steps of QRS detector for a noisy ECG from MIT-BIH database, where Fig. 7(a) QRS on Filtered Signal, Fig. 7(b) QRS on MWI signal and Noise level(black),Signal Level (red) and Adaptive Threshold(Green), and Fig. 7 (c) Pulse train of the found QRS on ECG signal. In this work, we investigated several classifiers with the same MIT_BIT dataset. The yielded results demonstrate that the performance of all algorithms is high enough. Although the NN classifier achieves performance better than the other algorithms (FL, NMC, LDA, and ED). The performance of these classifiers is evaluated using Sensitivity, Specificity, efficiency, ROC curve and EER as indicated in Fig. 8, Fig. 9 and Table 2. The proposed system indicates a similar performance of those good systems. Mostly, compared to other systems, the proposed system indicates a better performance. Table 3 summarizes the features of some of the most common ECG biometric systems. Table 2 AUC and EER Classifier AUC (%) EER NN FL ED LDA NMC 98.98591 89.98591 87.98347 87.79034 78.98347 0.00835 0.02630 0.02874 0.02896 0.03716 Multimedia Tools and Applications Table 3 Biometric systems performance comparison Authors Techniques Sample Size + Dataset Accuracy Falconi [9] Singh [19] Template Matching Eigen beat features + matching based on nearest neighbor criterion. 73 (Physionet database) 44 (MIT-BIH arrhythmia dataset) 65 (IIT (BHU) dataset) 81.82% 85.7% (MIT-BIH arrhythmia dataset) 92.49% (IIT (BHU) dataset) 79.55% (MIT-BIH arrhythmia dataset) 84.9%, (IIT (BHU) dataset) up to 99% 94.47% (PTB dataset) and 97.8% (MI-TBIH dataset) 100% 97.12% EER 1.33% 96.15% up to 98.99%. support vector machine Singh and Gupta [20] Wang et al. [30] signal processing techniques DCT and autocorrelation 50 (Physionet dataset) 13 (PTB dataset) 13 (MITBIH dataset) Belgacem et al. [3] Pal and Singh [16] Barra et al. [2] DWT Random Forest Haar wavelet transform 80 (Physionet dataset) 100 (PTB dataset) 52 (PTB dataset) Proposed study NN FL ED LDA NMC 112 (MIT-BIH dataset) 9 Conclusion This paper presented a comparative study based on ECG signals for a biometric human recognition system based on ECG using Neural Network, Fuzzy Logic, Nearest Mean Classifier, Linear Discriminant Analysis, and Euclidean Distance classification approaches with wavelet decomposition coefficients can effectively identify subjects. Further experiments are being performed to evaluate the proposed system with 112 subjects from MIT-BIH database. The experimental findings are found that Neural Network classifier has yielded comparatively better results than other approaches. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. References 1. 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Varatharajan R, Manogaran G, Priyan MK (2017) A big data classification approach using LDA with an enhanced SVM method for ECG signals in cloud computing. Journal of multimedia tools and applications, © Springer, 1–21. doi:https://doi.org/10.1007/s11042-017-5318-1 29. Venkatesh N, Jayaraman S (2010) Human electrocardiogram for biometrics using DTW and FLDA. International conference on pattern recognition. © IEEE Comput Soc: 3838–3841 30. Wang Y, Agrafioti F, Hatzinakos D, Plataniotis KN (2008) Analysis of human electrocardiogram for biometric recognition. EURASIP J Adv Sign Process 148658:1–11 31. Zokaee S, Faez K (2012) Human identification based on electrocardiogram and Palmprint. Int J Electric Comput Eng (IJECE) 2(2):261–266 Multimedia Tools and Applications Sahar Abd El_Rahman has received her M.Sc. (2003) in an AI Technique Applied to Machine Aided Translation, and PhD (2008) in Reconstruction of High-Resolution Image from a Set of Low-Resolution Images, from the Faculty of Engineering- Shoubra, Benha University, Cairo, Egypt. She is currently Assistant Professor, College of Computer and Information System, Princess Nourah Bint Abdulrahman University (Saudia Arabia). Also, she is Assistant Professor from 2008 till now at Faculty of Engineering-Shoubra, Benha University, Cairo, Egypt. She has published many papers in national and international journals and conferences. Her research interests include Computer Vision, Image Processing, Signal Processing, Information Security, Human Computer Interaction, E-Health, Big Data and Cloud Computing.