A New Approach to ECG Biometric Systems: A Comparitive Study between LPC and WPD Systems
In this paper, a novel method for a biometric system based on the ECG signal is proposed, using spectral coefficients computed through linear predictive coding (LPC). ECG biometric systems have traditionally incorporated characteristics of fiducial points of the ECG signal as the feature set. These systems have been shown to contain loopholes and thus a non-fiducial system allows for tighter security. In the proposed system, incorporating non-fiducial features from the LPC spectrum produced a segment and subject recognition rate of 99.52% and 100% respectively. The recognition rates outperformed the biometric system that is based on the wavelet packet decomposition (WPD) algorithm in terms of recognition rates and computation time. This allows for LPC to be used in a practical ECG biometric system that requires fast, stringent and accurate recognition.
[1] F. Agrafioti and D. Hatzinakos. Fusion of ECG sources for human
identification. In 3rd International Symposium on Communications,
Control and Signal Processing, pages 1542 - 1547, St. Julians, Malta,
2008.
[2] L. Biel, O. Pettersson, L. Philipson, and P. Wide. ECG analysis: a
new approach in human identification. In Proceedings of the 16th IEEE
Instrumentation and Measurement Technology Conference, volume 1,
pages 557 -561, Venice, Italy, 1999.
[3] L. Biel, O. Pettersson, L. Philipson, and P. Wide. ECG analysis: a new
approach in human identification. IEEE Transactions on Instrumentation
and Measurement, 50(3):808 - 812, 2001.
[4] O. Boumbarov, Y. Velchev, and S. Sokolov. ECG personal identification
in subspaces using radial basis neural networks. In IEEE International
Workshop on Intelligent Data Acquisition and Advanced Computing
Systems: Technology and Applications, pages 446 - 451, Rende, Italy,
2009.
[5] A. D. C. Chan, M. M. Hamdy, A. Badre, and V. Badee. Person
identification using electrocardiograms. In Canadian Conference on
Electrical and Computer Engineering, pages 1 - 4, Ottawa, Canada,
2006.
[6] C.-C. Chiu, C.-M. Chuang, and C.-Y. Hsu. A novel personal identity
verification approach using a discrete wavelet transform of the ECG
signal. In International Conference on Multimedia and Ubiquitous
Engineering, pages 201 - 206, Busan, Korea, 2008.
[7] B. A. Eisenstein and R. J. Vaccaro. Feature extraction by system
identification. IEEE Transactions on Systems, Man and Cybernetics,
12(1):42 - 50, 1982.
[8] Y. Gahi, M. Lamrani, A. Zoglat, M. Guennoun, B. Kapralos, and K. El-
Khatib. Biometric identification system based on electrocardiogram
data. In New Technologies, Mobility and Security, pages 1 - 5, Tangier,
Morocco, 2008.
[9] M. Guennoun, N. Abbad, J. Talom, S. M. M. Rahman, and K. El-Khatib.
Continuous authentication by electrocardiogram data. In IEEE Toronto
International Conference Science and Technology for Humanity, pages
40 - 42, Toronto, Canada, 2009.
[10] I. Khalil and F. Sufi. Legendre polynomials based biometric authentication
using QRS complex of ECG. In International Conference on
Intelligent Sensors, Sensor Networks and Information Processing, pages
297 - 302, Sydney, Australia, 2008.
[11] 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 27th Annual International Conference of the Engineering
in Medicine and Biology Society, pages 1114 - 117, Shanghai, China,
2005.
[12] M. Kyoso. A technique for avoiding false acceptance in ECG identification.
In IEEE Engineering in Medicine and Biology Society Asian-Pacific
Conference on Biomedical Engineering, pages 190 - 191, the border of
Kyoto-Osaka-Nara, Japan, 2003.
[13] M. Kyoso and A. Uchiyama. Development of an ECG identification
system. In Proceedings of the 23rd Annual International Conference
of the IEEE Engineering in Medicine and Biology Society, volume 4,
pages 3721 - 3723, Istanbul, Turkey, 2001.
[14] R. Palaniappan and S. Krishnan. Identifying individuals using ECG
beats. In International Conference on Signal Processing and Communications,
pages 569 - 572, Bangalore, India, 2004.
[15] K. Plataniotis, D. Hatzinakos, and J. Lee. ECG biometric recognition
without fiducial detection. In Biometrics Symposium: Special Session
on Research at the Biometric Consortium Conference, pages 1 - 6,
Baltimore, MD, 2006.
[16] S. Saechia, J. Koseeyaporn, and P. Wardkein. Human identification
system based ECG signal. In IEEE TENCON, pages 1 - 4, Hong Kong,
China, 2005.
[17] T. Shen, W. Tompkins, and Y. Hu. One-lead ECG for identity verification.
In Proceedings of the Second Joint Engineering in Medicine
and Biology Society and Biomedical Engineering Society Conference,
volume 1, pages 62 - 63, Houstan, TX, 2002.
[18] Y. Singh and P. Gupta. ECG to individual identification. In 2nd
IEEE International Conference on Biometrics: Theory, Applications and
Systems, pages 1 - 8, Washington, DC, 2008.
[19] J. C. Sriram, M. Shin, T. Choudhury, and D. Kotz. Activity-aware ECGbased
patient authentication for remote health monitoring. In Proceedings
of the 2009 International Conference on Multimodal Interfaces,
pages 297 - 304, Cambridge, MA, 2009.
[20] W. Ting, Y. Guo-zheng, Y. Bang-hua, and S. Hong. EEG feature
extraction based on wavelet packet decomposition for brain computer
interface. Measurement, 41(6):618 - 625, 2008.
[21] Y.-T. Tsao, T.-W. Shen, T.-F. Ko, and T.-H. Lin. The morphology of the
electrocardiogram for evaluating ECG biometrics. In 9th International
Conference on e-Health Networking, Application and Services, pages
233 - 235, Taipei, Taiwan, 2007.
[22] Y. Wang, K. Plataniotis, and D. Hatzinakos. Integrating analytic and
appearance attributes for human identification from ECG signals. In
Biometrics Symposium: Special Session on Research at the Biometric
Consortium Conference, pages 1 - 6, Baltimore, MD, 2006.
[23] J. Yao and Y. Wan. A wavelet method for biometric identification
using wearable ECG sensors. In 5th International Summer School and
Symposium on Medical Devices and Biosensors, pages 297 - 300, Hong
Kong, China, 2008.
[24] Z. Zhang and D. Wei. A new ECG identification method using Bayes-
theorem. In IEEE TENCON, pages 1 - 4, Hong Kong, China, 2006.
[1] F. Agrafioti and D. Hatzinakos. Fusion of ECG sources for human
identification. In 3rd International Symposium on Communications,
Control and Signal Processing, pages 1542 - 1547, St. Julians, Malta,
2008.
[2] L. Biel, O. Pettersson, L. Philipson, and P. Wide. ECG analysis: a
new approach in human identification. In Proceedings of the 16th IEEE
Instrumentation and Measurement Technology Conference, volume 1,
pages 557 -561, Venice, Italy, 1999.
[3] L. Biel, O. Pettersson, L. Philipson, and P. Wide. ECG analysis: a new
approach in human identification. IEEE Transactions on Instrumentation
and Measurement, 50(3):808 - 812, 2001.
[4] O. Boumbarov, Y. Velchev, and S. Sokolov. ECG personal identification
in subspaces using radial basis neural networks. In IEEE International
Workshop on Intelligent Data Acquisition and Advanced Computing
Systems: Technology and Applications, pages 446 - 451, Rende, Italy,
2009.
[5] A. D. C. Chan, M. M. Hamdy, A. Badre, and V. Badee. Person
identification using electrocardiograms. In Canadian Conference on
Electrical and Computer Engineering, pages 1 - 4, Ottawa, Canada,
2006.
[6] C.-C. Chiu, C.-M. Chuang, and C.-Y. Hsu. A novel personal identity
verification approach using a discrete wavelet transform of the ECG
signal. In International Conference on Multimedia and Ubiquitous
Engineering, pages 201 - 206, Busan, Korea, 2008.
[7] B. A. Eisenstein and R. J. Vaccaro. Feature extraction by system
identification. IEEE Transactions on Systems, Man and Cybernetics,
12(1):42 - 50, 1982.
[8] Y. Gahi, M. Lamrani, A. Zoglat, M. Guennoun, B. Kapralos, and K. El-
Khatib. Biometric identification system based on electrocardiogram
data. In New Technologies, Mobility and Security, pages 1 - 5, Tangier,
Morocco, 2008.
[9] M. Guennoun, N. Abbad, J. Talom, S. M. M. Rahman, and K. El-Khatib.
Continuous authentication by electrocardiogram data. In IEEE Toronto
International Conference Science and Technology for Humanity, pages
40 - 42, Toronto, Canada, 2009.
[10] I. Khalil and F. Sufi. Legendre polynomials based biometric authentication
using QRS complex of ECG. In International Conference on
Intelligent Sensors, Sensor Networks and Information Processing, pages
297 - 302, Sydney, Australia, 2008.
[11] 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 27th Annual International Conference of the Engineering
in Medicine and Biology Society, pages 1114 - 117, Shanghai, China,
2005.
[12] M. Kyoso. A technique for avoiding false acceptance in ECG identification.
In IEEE Engineering in Medicine and Biology Society Asian-Pacific
Conference on Biomedical Engineering, pages 190 - 191, the border of
Kyoto-Osaka-Nara, Japan, 2003.
[13] M. Kyoso and A. Uchiyama. Development of an ECG identification
system. In Proceedings of the 23rd Annual International Conference
of the IEEE Engineering in Medicine and Biology Society, volume 4,
pages 3721 - 3723, Istanbul, Turkey, 2001.
[14] R. Palaniappan and S. Krishnan. Identifying individuals using ECG
beats. In International Conference on Signal Processing and Communications,
pages 569 - 572, Bangalore, India, 2004.
[15] K. Plataniotis, D. Hatzinakos, and J. Lee. ECG biometric recognition
without fiducial detection. In Biometrics Symposium: Special Session
on Research at the Biometric Consortium Conference, pages 1 - 6,
Baltimore, MD, 2006.
[16] S. Saechia, J. Koseeyaporn, and P. Wardkein. Human identification
system based ECG signal. In IEEE TENCON, pages 1 - 4, Hong Kong,
China, 2005.
[17] T. Shen, W. Tompkins, and Y. Hu. One-lead ECG for identity verification.
In Proceedings of the Second Joint Engineering in Medicine
and Biology Society and Biomedical Engineering Society Conference,
volume 1, pages 62 - 63, Houstan, TX, 2002.
[18] Y. Singh and P. Gupta. ECG to individual identification. In 2nd
IEEE International Conference on Biometrics: Theory, Applications and
Systems, pages 1 - 8, Washington, DC, 2008.
[19] J. C. Sriram, M. Shin, T. Choudhury, and D. Kotz. Activity-aware ECGbased
patient authentication for remote health monitoring. In Proceedings
of the 2009 International Conference on Multimodal Interfaces,
pages 297 - 304, Cambridge, MA, 2009.
[20] W. Ting, Y. Guo-zheng, Y. Bang-hua, and S. Hong. EEG feature
extraction based on wavelet packet decomposition for brain computer
interface. Measurement, 41(6):618 - 625, 2008.
[21] Y.-T. Tsao, T.-W. Shen, T.-F. Ko, and T.-H. Lin. The morphology of the
electrocardiogram for evaluating ECG biometrics. In 9th International
Conference on e-Health Networking, Application and Services, pages
233 - 235, Taipei, Taiwan, 2007.
[22] Y. Wang, K. Plataniotis, and D. Hatzinakos. Integrating analytic and
appearance attributes for human identification from ECG signals. In
Biometrics Symposium: Special Session on Research at the Biometric
Consortium Conference, pages 1 - 6, Baltimore, MD, 2006.
[23] J. Yao and Y. Wan. A wavelet method for biometric identification
using wearable ECG sensors. In 5th International Summer School and
Symposium on Medical Devices and Biosensors, pages 297 - 300, Hong
Kong, China, 2008.
[24] Z. Zhang and D. Wei. A new ECG identification method using Bayes-
theorem. In IEEE TENCON, pages 1 - 4, Hong Kong, China, 2006.
@article{"International Journal of Medical, Medicine and Health Sciences:58625", author = "Justin Leo Cheang Loong and Khazaimatol S Subari and Rosli Besar and Muhammad Kamil Abdullah", title = "A New Approach to ECG Biometric Systems: A Comparitive Study between LPC and WPD Systems", abstract = "In this paper, a novel method for a biometric system based on the ECG signal is proposed, using spectral coefficients computed through linear predictive coding (LPC). ECG biometric systems have traditionally incorporated characteristics of fiducial points of the ECG signal as the feature set. These systems have been shown to contain loopholes and thus a non-fiducial system allows for tighter security. In the proposed system, incorporating non-fiducial features from the LPC spectrum produced a segment and subject recognition rate of 99.52% and 100% respectively. The recognition rates outperformed the biometric system that is based on the wavelet packet decomposition (WPD) algorithm in terms of recognition rates and computation time. This allows for LPC to be used in a practical ECG biometric system that requires fast, stringent and accurate recognition.
", keywords = "biometric, ecg, linear predictive coding, wavelet packet decomposition", volume = "4", number = "8", pages = "338-6", }
