Heart-Rate Resistance Electrocardiogram Identification Based on Slope-Oriented Neural Networks

Authors: Tsu-Wang Shen, Shan-Chun Chang, Chih-Hsien Wang, Te-Chao Fang

Abstract:

For electrocardiogram (ECG) biometrics system, it is a tedious process to pre-install user’s high-intensity heart rate (HR) templates in ECG biometric systems. Based on only resting enrollment templates, it is a challenge to identify human by using ECG with the high-intensity HR caused from exercises and stress. This research provides a heartbeat segment method with slope-oriented neural networks against the ECG morphology changes due to high intensity HRs. The method has overall system accuracy at 97.73% which includes six levels of HR intensities. A cumulative match characteristic curve is also used to compare with other traditional ECG biometric methods.

Keywords: High-intensity heart rate, heart rate resistant, ECG human identification, decision based artificial neural network.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1127170

References:

[1] T.W. Shen, W.J. Tompkins, and Y.H. Hu, Implementation of a one-lead ECG human identification system on a normal population, Journal of Engineering and Computer Innovations, Vol. 1, no. 2, pp. 12-21, Jan. 2011.
[2] K.S. Kim, T.H. Yoon, J.W. Lee, D.J. Kim, and H.S. Koo, "A Robust Human Identification by Normalized Time-Domain Features of Electrocardiogram," in Engineering in Medicine and Biology Society, IEEE-EMBS 27th Annual International Conference, pp. 1114-1117, 2005.
[3] M. Kyoso and A. Uchiyama, "Development of an ECG identification system," in Engineering in Medicine and Biology Society, Proceedings of the 23rd Annual International Conference of the IEEE, Istanbul, Turkey, pp. 3721-3723, 2001.
[4] A.A. Ross, A.K. Jain, K. Nandakumar, Information fusion in biometrics, Handbook of Multibiometrics, p.p. 37-58, 2006
[5] S. Pankanti, B. R.M., and A. Jain, Biometrics: The future of identification, Computer, pp. 46-55, 2000.
[6] L. Biel, O. Pettersson, L. Philipson, and P. Wide, "ECG Analysis: A new approach in human identification," IEEE Trans. Instrum. Meas., vol. 50 No. 3, pp. 808-812, 2001.
[7] S. A. Israel, J. M. Irvineb, A. Chengb, M. D. Wiederholdc, and B. K. Wiederholdd, "ECG to identify individuals," in Pattern Recognition, pp. 133-142, 2005.
[8] I. Khalil, F Sufi, Legendre Polynomials based biometric authentication using QRS complex of ECG. In: Intelligent Sensors, Sensor Networks and Information Processing ISSNIP 2008. Int. Conf., on, pp. 297-302, 2008.
[9] Y. Wang, F. Agrafioti, D. Hatzinakos, and K. N. Plataniotis, "Analysis of human electrocardiogram for biometric recognition," EURASIP Journal on Advances in Signal Processing, January 2008.
[10] C.C. Chiu, C.M. Chuang, C.Y. Hsu, Discrete wavelet transform applied on personal verification with ECG signal, International Journal of Wavelets, Multiresolution and Information Processing (IJWMIP), vol.7, no.3, p.p.341-355, 2009.
[11] F. Agrafioti and D. Hatzinakos, "Fusion of ECG sources for human identification," in Communications, Control and Signal Processing, ISCCSP. 3rd, pp. 1542 – 1547, 2008.
[12] S.W. Chen, Complexity-measure-based sequential hypothesis testing for real-time detection of lethal cardiac arrhythmias, EURASIP J. Advanc. Signal Process., Article ID 20957, 2007.
[13] M. Li and S. Narayanan, "Robust ECG Biometrics by Fusing Temporal and Cepstral Information," in Pattern Recognition (ICPR), 20th International Conference, pp. 1326 – 1329, 2010.
[14] J. M. Irvine, S. A. Israel, W. T. Scruggs, and W. J. Worek, eigenPulse: Robust human identification from cardiovascular function, Pattern Recognition, vol. 41, pp. 3427-3435, 2008.
[15] T. Taneja, B. W. Mahnert, R. Passman, J. Goldberger, and A. Kadish, Effects of Sex and Age on Electrocardiographic and Cardiac Electrophysiological Properties in Adults, Pacing and Clinical Electrophysiology, vol. 24, pp. 16-21, 2001.
[16] Tsu-Wang Shen, and Willis J. Tompkins, Biometric Statistical Study of One-Lead ECG Features and Body Mass Index (BMI), 27th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC05), p.p 1529~1533, 2005.
[17] AL Goldberger, LAN Amaral, L Glass, JM Hausdorff, PC Ivanov, RG Mark, JE Mietus, GB Moody, CK Peng, HE Stanley, PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation, 101(23): e215-e220, 2000.
[18] J. T. Pan, Willis J., "A Real-Time QRS Detection Algorithm," Biomedical Engineering, IEEE Transactions on, vol. BME-32, pp. 230 - 236, 1985.
[19] C. Kamath, T.V. Ananthapadmanabhayuyu, Modeling QRS Complex in dECG. IEEE Trans. Biomed. Eng., vol. 54, no.1, p.p. 156 – 158, 2007.
[20] SM Al-Khatib, NMA LaPointe, JM Kramer, RM Califf, "What clinicians should know about the QT interval", JAMA, vol.289, p.p.2120-2127, 2003.
[21] C. Wilson, AR Hicklin, M. Bone, et. al., "Fingerprint vendor technology evaluation 2003: summary of results and analysis report." Technical Report NISTIR 7123, 2004.