Evaluation of Electrocardiogram for Biometric Authentication

Yogendra Narain Singh, S K Singh

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DOI: 10.4236/jis.2012.31005                

Abstract

This paper presents an evaluation of a new biometric electrocardiogram (ECG) for individual authentication. We report the potential of ECG as a biometric and address the research concerns to use ECG-enabled biometric authentication system across a range of conditions. We present a method to delineate ECG waveforms and their end fiducials from each heartbeat. A new authentication strategy is proposed in this work, which uses the delineated features and taking decision for the identity of an individual with respect to the template database on the basis of match scores. Performance of the system is evaluated in a unimodal framework and in the multibiometric framework where ECG is combined with the face biometric and with the fingerprint biometric. The equal error rate (EER) result of the unimodal system is reported to 10.8%, while the EER results of the multibiometric systems are reported to 3.02% and 1.52%, respectively for the systems when ECG combined with the face biometric and ECG combined with the fingerprint biometric. The EER results of the combined systems prove that the ECG has an excellent source of supplementary information to a multibiometric system, despite it shows moderate performance in a unimodal framework. We critically evaluate the concerns involved to use ECG as a biometric for individual authentication such as, the lack of standardization of signal features and the presence of acquisition variations that make the data representation more difficult. In order to determine large scale performance, individuality of ECG remains to be examined.

Keywords

Electrocardiogram (ECG); Biometrics; Vitality Measures; Spoofing Attacks; Biometrics Fusion; Security

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	B. P. Simon and C. Eswaran, “An ECG Classifier Designed Using Modified Decision Based Neural Networks,” Computer and Biomedical Research, Vol. 30, No. 4, 1997, pp. 257-272. doi:10.1006/cbmr.1997.1446
[2]	S. A. Israel, J. M. Irvine, A. Cheng, M. D. Wiederhold and B. K. Wiederhold, “ECG to Identify Individuals,” Pattern Recognition, Vol. 38, No. 1, 2005, pp. 133-142. doi:10.1016/j.patcog.2004.05.014
[3]	J. M. Irvin and S. A. Israel, “A Sequential Procedure for Individual Identity Verification Using ECG,” EURASIP Journal on Advances in Signal Processing, Vol. 2009, 2009, Article ID: 243215, pp. 1-13.
[4]	L. Biel, O. Pettersson, L. Philipson and P. Wide, “ECG Analysis: A New Approach in Human Identification,” IEEE Transaction on Instrumentation and Measurement, Vol. 50, No. 3, 2001, pp. 808-812. doi:10.1109/19.930458
[5]	T. W. Shen, W. J. Tompkins and Y. H. Hu, “One-Lead ECG for Identity Verification,” Proceedings of the Second Joint EMBS/BMES Conference, Houston, 23-26 October 2002, pp. 62-63.
[6]	Y. Wang, F. Agrafioti, D. Hatzinakos and K. N. Plataniotis, “Analysis of Human Electrocardiogram for Biometric Recognition,” EURASIP Journal on Advances in Signal Processing, Vol. 2008, 2008, Article ID: 148658, pp. 1-11.
[7]	Y. N. Singh and P. Gupta, “Biometric Method for Human Identification Using Electrocardiogram,” Proceedings of the 3rd IAPR/IEEE International Conference on Biometrics, ICB 2009, LNCS, Springer-Verlag, Berlin, Vol. 5558, 2009, pp. 1270-1279.
[8]	Y. N. Singh and P. Gupta, “Correlation Based Classification of Heartbeats for Individual Identification,” Journal of Soft Computing, Vol. 15, No. 3, 2011, pp. 449-460. doi:10.1007/s00500-009-0525-y
[9]	F. Sufi and I. Khalil, “An Automated Patient Authentication System for Remote Telecardiology,” Proceedings of the Fourth International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP 2008, 15-18 December 2008, pp. 279-284.
[10]	J. Pan and W. J. Tompkins, “A Real Time QRS Detection Algorithm,” IEEE Transactions on Biomedical Engineering, Vol. 33, No. 3, 1985, pp. 230-236. doi:10.1109/TBME.1985.325532
[11]	Y. N. Singh and P. Gupta, “A Robust Delineation Approach of Electrocardiographic P Waves,” Proceedings of the 2009 IEEE Symposium on Industrial Electronics and Applications, ISIEA 2009, Vol. 2, 2009, pp. 846-849.
[12]	Y. N. Singh and P. Gupta, “A Robust and Efficient Technique of T Wave Delineation from Electrocardiogram,” Proceedings of the 2nd International Conference on Bio-Inspired Systems and Signal Processing, BIOSIGNALS, 2009, pp. 146-154.
[13]	H. C. Bazett, “An Analysis of the Time-Relations of Electrocardiograms,” Heart, Vol. 7, 1920, pp. 353-370.
[14]	R. O. Duda, P. E. Hart and D. G. Stork, “Pattern Classification,” 2nd Edition, Wiley, New Delhi.
[15]	P. Verlinde, P. Druyts, G. Cholet and M. Acheroy, “Applying Bayes Based Classifiers for Decision Fusion in a Multi-Modal Identity Verification System,” Proceedings of the International Symposium on Pattern Recognition in Memoriam Pierre Devijver, Brussels, 12 February 1999.
[16]	Physionet, “Physiobank Archives,” Massachusetts Institute of Technology, Cambridge, 2011. http://www.physionet.org/physiobank/database/#ecg
[17]	“Biometric Score Set,” National Institute of Standard and Technology, 2011. http://www.itl.nist.gov/iad/894.03/biometricscores/
[18]	M. W. Russell, I. Law, P. Sholinsky and R. R. Fabsitz, “Heritability of ECG Measurements in Adult Male Twins,” Journal of Electrocardiology, Vol. 30, No. 1, 1998, pp. 64-68. doi:10.1016/S0022-0736(98)80034-4