Biometrics Method for Human Identification Using Electrocardiogram

  • Yogendra Narain Singh
  • P. Gupta
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5558)

Abstract

This work exploits the feasibility of physiological signal electrocardiogram (ECG) to aid in human identification. Signal processing methods for analysis of ECG are discussed. Using ECG signal as biometrics, a total of 19 features based on time interval, amplitudes and angles between clinically dominant fiducials are extracted from each heartbeat. A test set of 250 ECG recordings prepared from 50 subjects ECG from Physionet are evaluated on proposed identification system, designed on template matching and adaptive thresholding. The matching decisions are evaluated on the basis of correlation between features. As a result, encouraging performance is obtained, for instance, the achieved equal error rate is smaller than 1.01 and the accuracy of the system is 99%.

Keywords

Template Match Search Window Equal Error Rate High Frequency Noise Adaptive Thresholding 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Kligfield, P.: The Centennial of the Einthoven Electrocardiogram. Journal of Electrocardiology 35, 123–129 (2002)Google Scholar
  2. 2.
    Biel, L., Pettersson, O., Lennart, P., Peter, W.: ECG Analysis: A New Approach in Human Identification. IEEE Transaction on Instrumentation and Measurement 50(3), 808–812 (2001)Google Scholar
  3. 3.
    Shen, T.W., Tompkins, W.J.: One-Lead ECG for Identity Verification. In: Proceedings of the Second Joint EMBS/BMES Conference, pp. 62–63 (2002)Google Scholar
  4. 4.
    Israel, S.A., Irvine, J.M., Andrew, C., Mark, D.W., Brenda, K.W.: ECG to Identify Individuals. Pattern Recognition 38(1), 133–142 (2005)Google Scholar
  5. 5.
    Kors, J.A., Bemmel, J.H., Zywietz, C.: Signal analysis for ECG interpretation. Methods Inf. Med. 29(4), 317–329 (1990)Google Scholar
  6. 6.
    Friesen, G.M., Thomas, C.J., Manal, A.J., Stanford, L.Y., Stephen, R.Q., Troy, N.: A Comparison of the Noise Sensitivity of Nine QRS Detection Algorithms. IEEE Transaction on Biomedical Engineering 37(1), 85–98 (1990)Google Scholar
  7. 7.
    Pan, J., Tompkins, W.J.: A Real Time QRS Detection Algorithm. IEEE Transactions on Biomedical Engineering 33(3), 230–236 (1985)Google Scholar
  8. 8.
    Bazett, H.C.: An Analysis of the Time-Relations of Electrocardiograms. Heart 7, 353–370 (1920)Google Scholar
  9. 9.
    Laguna, P., Mark, R.G., Goldberger, A.L., Moody, G.B.: A Database for Evaluation of Algorithms for Measurement of QT and Other Waveform Intervals in the ECG. In: Computers in Cardiology, pp. 673–676 (1997)Google Scholar
  10. 10.
    Rijnbeek, P.R., Witsenburg, M., Schrama, E., Hess, J., Kors, J.A.: New Normal Limits for the Pediatric Electrocardiogram. European Heart Journal 22, 702–711 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Yogendra Narain Singh
    • 1
  • P. Gupta
    • 2
  1. 1.Institute of Engineering & TechnologyLucknowIndia
  2. 2.Indian Institute of Technology KanpurKanpurIndia

Personalised recommendations