ECG Based Biometric by Superposition Matrix in Unrestricted Status

  • Gang ZhengEmail author
  • Xiaoxia Sun
  • Shengzhen Ji
  • Min Dai
  • Ying Sun
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10996)


The paper proposed an Electrocardiogram (ECG) feature extraction method for biometric. It relied on ECG superposition number matrix built by several single heartbeat ECG data. The target of the study was to find stable features of the ECG signal under unrestricted status for biometric. By matrix segmentation and similarity comparison, the stable feature distribution was gotten, and stable feature sets were also constructed. 13 volunteers’ ECG data collected by self-made ECG device in different status were gotten, the collecting period was lasting for half year; 28 healthy individuals’ ECG data under calm status were also collected; Besides that, 14 subjects’ ECG data in MIT-BIH were also involved in study. From the result of experiments, the average True Positive Rate (TPR) reached 83.21%, 83.93% and 80% on MIT data set, ECG data set in calm status and ECG data in different status respectively. It is also found that along with the increasing amount of ECG single heartbeat used to build superposition matrix, the stable features of one’s ECG were gradually revealed and this helped ECG based biometric effectively.


ECG biometric Unrestricted status Identity authentication Superposition matrix 



The paper is supported by TianJin National Science Foundation 16JCYBJC15300 (2016.04-2019.03).


  1. 1.
    Ghazi, M.M., Ekenel, H.K.: A comprehensive analysis of deep learning based representation for face recognition. In: Computer Vision and Pattern Recognition Workshops, pp. 102–109 (2016)Google Scholar
  2. 2.
    Ali, M.M.H., Mahale, V.H., Yannawar, P., Gaikwad, A.T.: Fingerprint recognition for person identification and verification based on minutiae matching. IEEE International Conference on Advanced Computing, pp. 332–339 (2016)Google Scholar
  3. 3.
    Garagad, V.G., Iyer, N.C.: A novel technique of iris identification for biometric systems. In: International Conference on Advances in Computing, pp. 973–978 (2014)Google Scholar
  4. 4.
    Ramos, J., Ausín, J.L., Lorido, A.M., Redondo, F., Duque-Carrillo, J.F.: A wireless multi-channel bioimpedance measurement system for personalized healthcare and lifestyle. Stud. Health Technol. Inf. 189, 59–67 (2013)Google Scholar
  5. 5.
    Odinaka, I., Lai, P.H., Kaplan, A.D., O’Sullivan, J.A., Sirevaag, E.J.: ECG biometric recognition: a comparative analysis. IEEE Trans. Inf. Forensics Secur. 7, 1812–1814 (2012)Google Scholar
  6. 6.
    Singh, Y.N.: Human recognition using Fisher’s discriminant analysis of heartbeat interval features and ECG morphology. Neurocomputing 167, 322–335 (2015)Google Scholar
  7. 7.
    Hamdi, T., Ben Slimane, A., Ben Khalifa, A.: A novel feature extraction method in ECG biometrics. In: Image Processing, Applications and Systems Conference, pp. 1–5 (2014)Google Scholar
  8. 8.
    Paulet, M.V., Salceanu, A., Salceanu, A.: Automatic recognition of the person by ECG signals characteristics. In: International Symposium on Advanced Topics in Electrical Engineering (ATEE), pp. 281–284 (2015)Google Scholar
  9. 9.
    Choi, H.S., Lee, B., Yoon, S.: Biometric authentication using noisy electrocardiograms acquired by mobile sensors. IEEE Access 4, 1266–1273 (2016)Google Scholar
  10. 10.
    Zhang, Y., Shi, Y.: A new method for ECG biometric recognition using a hierarchical scheme classifier. In: IEEE International Conference on Software Engineering and Service Science, pp. 457–460 (2015)Google Scholar
  11. 11.
    Tantawi, M.M., Revett, K., Salem, A.B., Tolba, M.F.: A wavelet feature extraction method for electrocardiogram (ECG)-based biometric recognition. Sig. Image Video Process. 9, 1271–1280 (2015)Google Scholar
  12. 12.
    Page, A., Kulkarni, A., Mohsenin, T.: Utilizing deep neural nets for an embedded ECG-based biometric authentication system. In: Biomedical Circuits and Systems Conference, pp. 1–4 (2015)Google Scholar
  13. 13.
    Jahiruzzaman, M., Hossain, A.B.M.A.: ECG based biometric human identification using chaotic encryption. In: International Conference on Electrical Engineering and Information Communication Technology (2015)Google Scholar
  14. 14.
    Zheng, G., Chen, Y., Dai, M.: HRV based stress recognizing by random forest. Fuzzy Syst. Data Min. II, 444–458 (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Gang Zheng
    • 1
    Email author
  • Xiaoxia Sun
    • 1
  • Shengzhen Ji
    • 1
  • Min Dai
    • 1
  • Ying Sun
    • 1
  1. 1.TianJin Key Laboratory of Intelligence Computing and Novel Software TechnologyTianjin University of TechnologyTianjinPeople’s Republic of China

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