Identifying Individuals Using Fourier and Discriminant Analysis of Electrocardiogram

  • Ranjeet Srivastva
  • Yogendra Narain Singh
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 834)


From the last one and a half decades, the electrocardiogram (ECG) has emerged as a new modality for human identification. The research shows that the people heartbeats recorded using diagnostic method called ECG exhibit discriminatory features that can distinguish themselves. The ECG as a biometric inherently provides liveness detection and robustness against falsification. This paper presents a novel method of ECG analysis for human identification using Fourier and linear discriminant analysis, which does not require detection of fiducial points of ECG wave. The method utilizes autocorrelation coefficients of filtered ECG signal, to extract significant features of it. The performance of the proposed method is evaluated on MIT-BIH arrhythmia and QT database of physionet. The experimental results show the equal error rate (EER) of 0.17% and 0.03% on MIT-BIH arrhythmia and QT database, respectively that outperform the other methods on these databases.


Individual identification Electrocardiogram Fourier transform Discriminant analysis 


  1. 1.
    Pouryayevali, S.: ECG biometrics: new algorithm and multimodal biometric system. Master of Applied Science thesis, University of Toronto (2015)Google Scholar
  2. 2.
    Singh, Y.N., Gupta, P.: ECG to individual identification. In: Proceedings of 2nd IEEE International Conference on Biometrics: Theory, Applications and Systems, BTAS 2008, pp. 1–8, October 2008Google Scholar
  3. 3.
    Singh, Y.N., Gupta, P.: Correlation based classification of heartbeats for individual identification. Soft Comput. 15(3), 449–460 (2009)CrossRefGoogle Scholar
  4. 4.
    Singh, Y.N., Singh, S.K.: Identifying individuals using eigenbeat features of electrocardiogram. J. Eng. 2013, 1–8 (2013)Google Scholar
  5. 5.
    Singh, Y.N., Singh, S.K., Gupta, P.: Fusion of electrocardiogram with unobtrusive biometrics: an efficient individual authentication system. Pattern Recognit. Lett. 33(14), 1932–1941 (2012)CrossRefGoogle 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)CrossRefGoogle Scholar
  7. 7.
    Biel, L., Pettersson, O., Philipson, L., Wide, P.: ECG analysis: a new approach in human identification. IEEE Trans. Instrum. Meas. 50(3), 808–812 (2001)CrossRefGoogle Scholar
  8. 8.
    Shen, T.W., Tompkins, W.J., Hu, Y.H.: One-lead ECG for identity verification. In: 2nd Joint Conference of the IEEE Engineering in Medicine and Biology Society and the Biomedical Engineering Society, Houston, pp. 62–63 (2002)Google Scholar
  9. 9.
    Israel, S.A., Irvine, J.M., Andrew, C., Mark, D.W., Brenda, K.W.: ECG to identify individuals. Pattern Recognit. 38(1), 133–142 (2005)CrossRefGoogle Scholar
  10. 10.
    Wang, Y., Agrafioti, F., Hatzinakos, D., Plataniotis, K.N.: Analysis of human electrocardiogram for biometric recognition. EURASIP J. Adv. Signal Process. 2008, 1–11 (2008)CrossRefGoogle Scholar
  11. 11.
    Singh, Y.N., Gupta, P.: Biometrics method for human identification using electrocardiogram. In: Tistarelli, M., Nixon, M.S. (eds.) ICB 2009. LNCS, vol. 5558, pp. 1270–1279. Springer, Heidelberg (2009). Scholar
  12. 12.
    Plataniotis, K., Hatzinakos, D., Lee, J.: ECG biometric recognition without fiducial detection. In: Proceedings of Biometrics Symposiums, BSYM, Baltimore, Maryland, USA (2006)Google Scholar
  13. 13.
    Agrafioti, F., Hatzinakos, D.,: ECG based recognition using second order statistics. In: IEEE Sixth Annual Communication Networks and Services Research Conference, Canada, pp. 82–87 (2008)Google Scholar
  14. 14.
    Srivastva, R., Singh, Y.N.: ECG biometric analysis using Walsh-Hadamard transform. In: Kolhe, M.L., et al. (eds.) Advances in Data and Information Sciences. LNNS, vol. 38. Springer (2017).
  15. 15.
    Srivastva, R., Singh, Y.N.: Human recognition using discrete cosine transform and discriminant analysis of ECG. In: Proceedings of IEEE 2017 Fourth International Conference on Image Information Processing, JUIT, Solan, pp. 368–372 (2017)Google Scholar
  16. 16.
    Pan, J., Tompkins, W.J.: A real-time QRS detection algorithm. IEEE Trans. Biomed. Eng. 32(3), 230–236 (1985)CrossRefGoogle Scholar
  17. 17.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley, India (2000)zbMATHGoogle Scholar
  18. 18.
    Singh, Y.N.: Individual identification using linear projection of heartbeat features. Appl. Comput. Intell. Soft Comput. 2014, 1–14 (2014)CrossRefGoogle Scholar
  19. 19.
    Wubbeler, G., Stavridis, M., Kreiseler, D., Bousseljot, R.D., Elster, C.: Verification of humans using the electrocardiogram. Pattern Recognit. Lett. 28, 1172–1175 (2007)CrossRefGoogle Scholar
  20. 20.
    Chan, A.D.C., Hamdy, M.M., Badre, A., Badee, V.: Wavelet distance measure for person identification using electrocardiograms. IEEE Trans. Instrum. Meas. 57(2), 248–253 (2008)CrossRefGoogle Scholar
  21. 21.
    Li, M., Narayanan, S.: Robust ECG biometrics by fusing temporal and cepstral information. In: 2010 20th International Conference Pattern Recognition, ICPR, pp. 1326–1329, August 2010Google Scholar
  22. 22.
    PhysioNet: PhysioBank archives. Massachusetts Institute of Technology Cambridge.

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© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Information TechnologyBabu Banarasi Das Northern India Institute of TechnologyLucknowIndia
  2. 2.Department of Computer Science and EngineeringInstitute of Engineering and TechnologyLucknowIndia

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