A survey on various machine learning approaches for human electrocardiograms identification

Document Type : Research Paper


Department of Computer Science, College of Sciences, University of Diyala, Iraq


Human identification is a critical function that can aid in data security protection. Developing deep learning models for human identification from electrocardiogram (ECG) data is one of the most promising strategies. It has a number of specific advantages, including the identification of liveness, insensitivity, ease of collecting, and greater security. On the other hand, present classifier-based methods can only identify closed sets, whilst existing matching-based methods are computationally intensive. Additionally, virtually all algorithms analyze only one-shot identification, which is subject to noise. In light of the fact that the electrocardiogram (ECG) is the most often used diagnostic tool for monitoring electrical activity in the heart, it is critical to use it to find early detection and diagnosis signals. The rapid growth and adoption of electronic health records, which include a systematized collection of various types of digitalized medical data, along with the development of new methods for quickly evaluating this massive amount of data, has resurrected interest in the fields of machine learning and deep learning in recent decades. The purpose of this article is to provide an overview of the EKG's significance in terms of learning approaches, as well as a comparison of the most well-known research and technical phrases relating to the electrocardiogram.


[1] S.A. Abdelghani, T.M. Rosenthal and D.P. Morin, Surface electrocardiogram predictors of sudden cardiac arrest,
Ochsner J. 16(3) (2016) 280-–289.
[2] J.S. Arteaga-Falconi, H. Al Osman and A. El Saddik, ECG authentication for mobile devices, IEEE Trans.
Instrum. Meas. 65(3) (2016) 591–600.
[3] M.M. Bassiouni, E.S.A. El-Dahshan, W. Khalefa and A.M. Salem, Intelligent hybrid approaches for human ECG
signals identification, Signal, Image Video Process. 12(5) (2018) 941-–949.
[4] N. Belgacem, R. Fournier, A. Nait-Ali and F. Bereksi-Reguig, A novel biometric authentication approach using
ECG and EMG signals, J. Med. Eng. Technol. 39(4) (2015) 226—238.[5] M. Boussaa, I. Atouf, M. Atibi and A. Bennis, ECG signals classification using MFCC coefficients and ANN
classifier, Proc. 2016 Int. Conf. Electr. Inf. Technol. ICEIT, (2016) 480-–484.
[6] R. Bousseljot, D. Kreiseler and A. Schnabel, Nutzung der EKG-Signaldatenbank CARDIODAT der PTB ¨uber das
Internet, De Gruyter, Biomedizinische Technik, 40(s1) (1995) 317–318.
[7] D. Bzdok, M. Krzywinski and N. Altman, Points of significance: Machine learning: Supervised methods, Nat.
Methods 15(1) (2018) 5-–6.
[8] C. Camara, P. Peris-Lopez and J.E. Tapiador, Human identification using compressed ECG signals, J. Med. Syst.
39(11) (2015).
[9] C. Camara, P. Peris-Lopez, J.E. Tapiador and G. Suarez-Tangil, Non-invasive multi-modal human identification
system combining ECG, GSR, and airflow biosignals, J. Med. Biol. Eng. 35(6) (2015) 735-–748.
[10] S. Chandra’, A. Sharma and G.K. Singh, A comparative analysis of performance of several wavelet-based ECG
data compression methodologies, Irbm 42(4) (2021) 227—244.
[11] G. Cicceri, F. De Vita, D. Bruneo, G. Merlino and A. Puliafito, A deep learning approach for pressure ulcer
prevention using wearable computing, Human-centric Comput. Info. Sci. 10(1) (2020) 1–21.
[12] G.D. Clifford, AF classification from a short single lead ECG recording: The physionet/computing in cardiology
challenge 2017, Comput. Cardiol. 44 (2017) 1-–4.
[13] M. Cluitmans, D.H. Brooks, R. MacLeod, O. D¨ossel, M.S. Guillem, P.M. Van Dam, J. Svehlikova, B. He, J. Sapp,
L. Wang and L. Bear, Validation and opportunities of electrocardiographic imaging: From technical achievements
to clinical applications, Front. Physiol. 9 (2018) 1305.
[14] C. Cortes, P. Haffner and M. Mohri, A Machine Learning Framework for Spoken-Dialog Classification, Springer
Handbooks, 2008.
[15] S. Dalal and V.P. Vishwakarma, GA based KELM optimization for ECG classification, Proc. Comput. Sci. 167
(2020) 580—588.
[16] P.H.B. de Melo, A brief review on electrocardiogram analysis and classification techniques with machine learning
approaches, U. Porto J. Eng. 7(4) (2021) 153—162.
[17] X. Dong, W. Si, W. Huang, ECG-based identity recognition via deterministic learning, Biotechnology & Biotechnological Equipment, 32(3) (2018) 769-–777.
[18] R. Donida Labati, E. Mu˜noz, V. Piuri, R. Sassi and F. Scotti, Deep-ECG: convolutional neural networks for ECG
biometric recognition, Pattern Recognit. Lett. 126 (2019) 78-–85.
[19] J. Dubochet, J. Frank and R. Henderson, The nobel prize in chemistry 2017, Nobel Media AB, 2017.
[20] F.A. Elhaj, N. Salim, A.R. Harris, T.T. Swee and T. Ahmed, Arrhythmia recognition and classification using
combined linear and nonlinear features of ECG signals, Comput. Methods Programs Biomed. 127 (2016) 52-–63.
[21] A. Fratini, M. Sansone, P. Bifulco and M. Cesarelli, Individual identification via electrocardiogram analysis,
Biomed. Eng. 14(1) (2015) 1-–23.
[22] S. Gutta and Q. Cheng, Joint feature extraction and classifier design for ECG-based biometric recognition, IEEE
J. Biomed. Heal. Informatics, 20(2) (2015) 460—468.
[23] M. Hammad, Y. Liu and K. Wang, Multimodal biometric authentication systems using convolution neural network
based on the different level fusion of ECG and fingerprint, IEEE Access 7 (2019) 25527-–25542.
[24] H. He and Y. Tan, Automatic pattern recognition of ECG signals using entropy-based adaptive dimensionality
reduction and clustering, Appl. Soft Comput. J. 55 (2017) 238-–252.
[25] S. Hong, C. Wang and Z. Fu, CardioID: learning to identification from electrocardiogram data, Neurocomputing,
412 (2020) 11-–18.
[26] S. Hong, Y. Zhou, J. Shang, C. Xiao and J. Sun, Opportunities and challenges of deep learning methods for
electrocardiogram data: A systematic review, Comput. Biol. Med. 122 (2020) 103801.
[27] T.M. Hossain, J. Wataada, M. Hermana and I.A. Aziz, Supervised machine learning in electrofacies classification:
A rough set theory approach, J. Phys. Conf. Ser. 1529(5) (2020).
[28] S.H. Jambukia, V.K. Dabhi and H.B. Prajapati, Classification of ECG signals using machine learning techniques:
A survey, Conf. Proceeding - 2015 Int. Conf. Adv. Comput. Eng. Appl. ICACEA, 2015, pp. 714—721.
[29] R. Krishan, Feature extraction of human electrocardiogram signal using machine learning, Department of Computer Science, Mata Sundri University Girls College, Mansa, Punjab (2021) 153–161.
[30] S. Krishnan and Y. Athavale, Trends in biomedical signal feature extraction, Biomed. Signal Process. Control, 43
(2018) 41-–63.
[31] F. Liu, An open access database for evaluating the algorithms of electrocardiogram rhythm and morphology abnormality detection, J. Med. Imaging Heal. Informatics, 8(7) (2018) 1368-–1373.
[32] H.M. Lynn, S.B. Pan and P. Kim, A deep bidirectional GRU network model for biometric electrocardiogram
classification based on recurrent neural networks, IEEE Access, 7 (2019) 145395—145405.[33] N.J. Majaj and D.G. Pelli, Deep learning-using machine learning to study biological vision, J. Vis. 18(13) (2018)
[34] L. Marˇs´anov´a, R. Sm´ıˇsek, A. Nˇemcov´a, L. Smital and M. V´ıtek, Brno university of technology ECG signal
database with annotations of P wave (BUT PDB), Res. Square, (2021).
[35] A. Matsuyama and M. Jonkman, The application of wavelet and feature vectors to ECG signals, Australasian
Phys. Eng. Sci. Med. 29(1) (2006) 13–17.
[36] A. Minchol´e, J. Camps, A. Lyon and B. Rodr´ıguez, Machine learning in the electrocardiogram, J. Electrocardiol.
57 (2019) 61—64.
[37] G. Moody, A new method for detecting atrial fibrillation using R-R intervals, Comput. Cardiol. 1983 (1983)
[38] G.B. Moody and R.G. Mark, The impact of the MIT-BIH arrhythmia database, IEEE Eng. Med. Biol. Mag. 20(3)
(2001) 45-–50.
[39] J.B. Muhlestein, Smartphone ECG for evaluation of STEMI: Results of the ST LEUIS Pilot Study, J. Electroc.
48 (2015) 249—259.
[40] Y. Nakano, E.A. Rashed, T. Nakane, I. Laakso and A. Hirata, ECG localization method based on volume conductor
model and kalman filtering, Sensors, 21(13) (2021) 1-–18.
[41] S.K. Nayak, A. Bit, A. Dey, B. Mohapatra and K. Pal, A review on the nonlinear dynamical system analysis of
electrocardiogram signal, J. Healthc. Eng. 2018 (2018).
[42] I. Odinaka, P.H. Lai, A.D. Kaplan, J.A. O’Sullivan, E.J. Sirevaag and J.W. Rohrbaugh, ECG biometric recognition: A comparative analysis, IEEE Trans. Inf. Forensics Secur. 7(6) (2012) 1812-–1824.
[43] A. Pal, A.K. Gautam and N. Singh, Evaluation of bioelectric signals for human recognition, Procedia Comput.
Sci. 48 (2015) 746—752.
[44] P. Parvathy, K. Subramaniam, G.K.D.P. Venkatesan, P. Karthikaikumar, J. Varghese and T. Jayasankar, Development of hand gesture recognition system using machine learning, J. Ambient Intell. Humaniz. Comput. 12(6)
(2021) 6793—6800.
[45] P.A. Pontes, R.O. Chaves, R.C. Castro, E.F.D. Souza, M.C. Seruffo and C.R. Francˆes, ´ Educational software
applied in teaching electrocardiogram: A systematic review, Biomed Res. Int. 2018 (2018).
[46] M. Regouid, M. Touahria, M. Benouis and A. Costen, Multimodal biometric system for ECG, ear and iris
recognition based on local descriptors, Multimedia Tools Appl. 78(16) (2019) 22509–22535.
[47] C.K. Roopa and B.S.A. Harish, Survey on various machine learning approaches for ECG analysis, Int. J. Comput.
Appl. 163(9) (2017) 25-–33.
[48] C.K. Roopa and B.S. Harish, Automated ECG analysis for localizing thrombus in culprit artery using rule based
information fuzzy network, Int. J. Interact. Multimed. Artif. Intell. 6(1) (2020) 16–25.
[49] S.M. Salerno, P.C. Alguire and H.S. Waxman, Competency in interpretation of 12-lead electrocardiograms: A
summary and appraisal of published evidence, Ann. Intern. Med. 138(9) (2003) 751-–760.
[50] R. Salloum and C.-C.J. Kuo, Ecg-based biometrics using recurrent neural networks, IEEE Int. Conf. Acoust.
Speech, Signal Process, 2017, pp. 2062—2066.
[51] A.K. Sangaiah, M. Arumugam and G. Bin Bian, An intelligent learning approach for improving ECG signal
classification and arrhythmia analysis, Artif. Intell. Med. 103 (2020) 101788.
[52] M. Sansone, R. Fusco, A. Pepino and C. Sansone, Electrocardiogram pattern recognition and analysis based on
artificial neural networks and support vector machines: A review, J. Healthc. Eng. 4 (2013) 465-–504.
[53] Y. Shahriari, R. Fidler, M.M. Pelter, Y. Bai, A. Villaroman and X. Hu, Electrocardiogram signal quality assessment
based on structural image similarity metric, IEEE Trans. Biomed. Eng. 65(4) (2018) 748-–753.
[54] A.I. Siam, Biosignal classification for human identification based on convolutional neural networks, Int. J. Commun. Syst. 34(7) (2021).
[55] R. Srivastva, A. Singh and Y.N. Singh, PlexNet: A fast and robust ECG biometric system for human recognition,
Inf. Sci. 558 (2021) 208—228.
[56] R. Tan and M. Perkowski, Toward improving electrocardiogram (ECG) biometric verification using mobile sensors:
A two-stage classifier approach, Sensors 17(2) (2017).
[57] M.M. Tantawi, K. Revett, A.B. Salem and M.F. Tolba, A wavelet feature extraction method for electrocardiogram
(ECG)-based biometric recognition, Signal, Image Video Process. 9(6) (2015) 1271-–1280.
[58] M. Usama, Unsupervised machine learning for networking: Techniques, applications, and research challenges,
IEEE Access 7 (2019) 65579—65615.
[59] S. Vluymans, Multi-label learning, Stud. Comput. Intell. 807 (2019) 189-–218.
[60] L. Wieclaw, Y. Khoma, P. Falat, D. Sabodashko and V. Herasymenko, Biometrie identification from raw ECG
signal using deep learning techniques, Proc. 2017 IEEE 9th Int. Conf. Intell. Data Acquis. Adv. Comput. Syst.Technol. Appl. IDAACS, 2017, pp. 129-–133.
[61] S.C. Wu, P.L. Hung and A.L. Swindlehurst, ECG biometric recognition: unlinkability, irreversibility, and security,
IEEE IoT J. 8(1) (2021) 487—500.
[62] Y. Xin, Machine learning and deep learning methods for cybersecurity, IEEE Access 6 (2018) 35365—35381.
[63] F. Yan, Deep learning and its application to CV and NLP, University of Surrey, 2016.
[64] X.L. Yang, G.Z. Liu, Y.H. Tong, H. Yan, Z. Xu, Q. Chen and S.H. Tan, The history, hotspots, and trends of
electrocardiogram, Journal of geriatric cardiology: JGC, 12 (2015) 448.
[65] Y. Zhang, R. Gravina, H. Lu, M. Villari and G. Fortino, PEA: Parallel electrocardiogram-based authentication
for smart healthcare systems, J. Netw. Comput. Appl. 117 (2018) 10—16.
[66] L. Zhang, J. Tan, D. Han and H. Zhu, From mahine learning to deep learning: progress in machine intelligence
for rational drug discovery, Drug Discov. Today, 22(11) (2017) 1680—1685.
[67] C. Zhang, T.I.A.N. Yang-Meng and W.A.N.G. Hong-Wei, Review of ECG signal identification research, DEStech
Trans. Comput. Sci. Eng. (2016).
[68] Y. Zhang, Z. Zhao, Y. Deng, X. Zhang and Y. Zhang, Human identification driven by deep CNN and transfer
learning based on multiview feature representations of ECG, Biomed. Signal Process. Cont. 68 (2021) 102689.
[69] Q. Zhang and D. Zhou, Deep arm/ear-ECG image learning for highly wearable biometric human identification,
Ann. Biomed. Eng. 46(1) (2018) 122-–134.
[70] Q. Zhang, D. Zhou and X. Zeng, HeartID: A multiresolution convolutional neural network for ECG-based biometric
human identification in smart health applications, IEEE Access 5 (2017) 11805—11816.
[71] S. Zokaee and K. Faez, Human identification based on ECG and palmprint, Int. J. Electr. Comput. Eng. 2(2)
Volume 13, Issue 1
March 2022
Pages 4017-4035
  • Receive Date: 03 November 2021
  • Revise Date: 11 December 2021
  • Accept Date: 04 January 2022