Discriminant Analysis for Identifying Individuals of Electrocardiogram

  • Yogendra Narain Singh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8251)

Abstract

We present a novel method to characterize the electrocardiogram (ECG) for individual recognition. The method works on analytical and appearance features of the heartbeats. The features are analyzed using the principle of Fisher’s linear discriminant that produces well separated classes in a lower dimension subspace, under the presence of noise and muscle flexure. The biometric experiment is benchmarked using ECG recordings of the publically available QT database. The proposed method achieves the recognition accuracy of 98.9% on the evaluated subjects which is found optimum than the other best known methods.

Keywords

Identification Electrocardiogram Heartbeats Biometrics 

References

  1. 1.
    Singh, Y.N., Singh, S.K.: A Taxonomy of Biometric System Vulnerabilities and Defenses. Intl. J. of Biometrics 5(2), 137–159 (2013)CrossRefGoogle Scholar
  2. 2.
    Biel, L., Pettersson, O., Philipson, L.: ECG Analysis: A New Approach in Human Identification. IEEE Trans. on Instrumentation and Measurement 50(3), 808–812 (2001)CrossRefGoogle Scholar
  3. 3.
    Singh, Y.N., Gupta, P.: ECG to Individual Identification. In: Proc. Biometrics: Theory, Applications and Systems, Washington DC, USA, pp. 1–8 (2008)Google Scholar
  4. 4.
    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)CrossRefGoogle Scholar
  5. 5.
    Singh, Y.N., Gupta, P.: Correlation Based Classification of Heartbeats for Individual Identification. J. Soft Computing 15(3), 449–460 (2011)CrossRefGoogle Scholar
  6. 6.
    Zhao, C., Wysocki, T., Agrafioti, F., Hatzinakos, D.: Securing Handheld Devices and Fingerprint Readers with ECG Biometrics. In: Proc. IEEE Fifth Int. Conf. Biometrics: Theory, Applications and Systems, pp. 150–155 (2012)Google Scholar
  7. 7.
    Singh, Y.N., Singh, S.K., Gupta, P.: Fusion of Electrocardiogram with Unobtrusive Biometrics: An Efficient Individual Authentication System. Pattern Recognition Letters 33, 1932–1941 (2012)CrossRefGoogle Scholar
  8. 8.
    Singh, Y.N., Singh, S.K.: Evaluation of Electrocardiogram for Biometric Authentication. Journal of Information Security 3, 39–48 (2012)CrossRefGoogle Scholar
  9. 9.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley India, New Delhi (2009)Google Scholar
  10. 10.
    Laguna, P., Mark, R.G., Goldberger, A., Moody, G.B.: A Database for Evaluation of Algorithms for Measurement of QT and Other Waveform Intervals in the ECG. Comput. Cardiol. 24, 673–676 (1997)Google Scholar
  11. 11.
    Pan, J., Tompkins, W.J.: A Real Time QRS Detection Algorithm. IEEE Trans. on Biomedical Engineering 33(3), 230–236 (1985)CrossRefGoogle Scholar
  12. 12.
    Singh, Y.N., Gupta, P.: A Robust Delineation Approach of Electrocardiographic P Waves. In: Proc. 2009 IEEE Symposium on Industrial Electronics and Applications, Kuala Lumpur, Malaysia, vol. 2, pp. 846–849 (2009)Google Scholar
  13. 13.
    Singh, Y.N., Gupta, P.: A Robust and Efficient Technique of T Wave Delineation from Electrocardiogram. In: Proc. Second Int. Conf. on Bio-inspired Systems and Signal Processing, Porto, Portugal, pp. 146–154 (2009)Google Scholar
  14. 14.
    van den Berg, R.A., Hoefsloot, H.C.J., Westerhuis, J.A., Smilde, A.K., van der Werf, M.J.: Centering, Scaling, and Transformations: Improving the Biological Information Content of Metabolomics Data. BMC Genomics 7(142), 1–15 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Yogendra Narain Singh
    • 1
  1. 1.Institute of Engineering & TechnologyGautam Buddh Technical UniversityLucknowIndia

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