Author : Chinthana S 1
Date of Publication :7th May 2016
Abstract: Electrocardiogram (ECG) will be employed in clinical identification for cardiac perform. Also, because people have completely different electrocardiogram traces, therefore, they can be non-inheritable as promising biometric options for human identification. According to the utilized options, the existing ECG based mostly biometric systems is classified to fiducial and nonfiducial systems. The identification of fiducial features needs the correct detection of fiducial points that is a terribly difficult task. On the other hand, non-fiducial approaches relax the detection process however typically result in high dimension feature area. This paper presents a combined approach of these two strategies for electrocardiogram primarily based individual identification. A fiducial based approach that utilizes a feature set chosen by local features of heart beats for biometric template style. Furthermore, a non-fiducial wavelet based mostly approach is projected. To avoid the high dimensionality of the resultant wavelet coefficient structure, the structure has been investigated and reduced using principal component analysis. The proposed feature sets were examined and compared using SVM classifier. To the effectiveness of this approach, records from the ECG-ID database using single lead are used to check subject identification, yielding high accuracy in identification.
Reference :
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[1] Biel, L, Pettersson O, Philipson L, Wide P,"ECG analysis: a new approach in human identification," Instrumentation and Measurement ,IEEE Transactions on, vol.50, no.3, pp.808-812, Jun 2001.
[2] S. A. Israel, J. M. Irvine, A. Cheng , M. D. Wiederhold, Brenda K., Wiederhold, "ECG to identify individuals", Pattern Recognition, vol.38, , pp. 133-142, Issue 1, Jan2005.
[3] 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.
[4] 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, SpringerVerlag, Berlin, Vol. 5558, 2009, pp. 1270-1279.
[5] 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
[6] T. W. Shen, W. J. Tompkins, and Y. H. Hu, “One-lead ECG for identity verification,” in Proceedings of the 2nd Conf. of the IEEE Eng. in Med. and Bio. Society and the Biomed. Eng. Society, vol. 1, 2002, pp. 62–63.
[7] A. Chan, M. Hamdy, A. Badre, and V. Badee, “Wavelet distance measure for person identification using electrocardiograms,” Instrumentation and Measurement, IEEE Transactions on, vol. 57, no. 2, pp. 248 –253, Feb. 2008
[8] I. Odinaka, P.-H. Lai, A. Kaplan, J. O‟Sullivan, E. Sirevaag, S. Kristjansson, A. Sheffield, and J. Rohrbaugh, “Ecg biometrics: A robust short-time frequency analysis,” in Proceedings of IEEE International Workshop on Information Forensics and Security, Dec. 2010, pp. 1 –6.
[9] J. C. Bansal et al. (eds.), Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2012), „Human Identification using Heartbeat Interval Features and ECG Morphology‟ Advances in Intelligent Systems and Computing 201, DOI: 10.1007/978-81-322-1038-2_8, Springer India 2013
[10] M.Tantawi, A.Salem and M.F. Tolba Faculty of Computer and Information Sciences,Ain Shams University Cairo, Egypt „ECG Signals Analysis for Biometric Recognition‟ 978-1-4799-7633-1/14/2014 IEEE
[11] Jun-Jie Wu1, Yue Zhang‟ Ecg Identification Based On Neural Networks „ Department Of Automation, Graduate School At Shenzhen, Tsinghua University, The University Town, Shenzhen, 518055,China 978-1-4799- 7208-1/14/2014 IEEE
[12] Jin Wang, Mary She, Saeid Nahavandi, Senior Member, IEEE, and Abbas Kouzani, Member, IEEE „ Human Identification From ECG Signals Via Sparse Representation of Local Segments „IEEE Signal Processing Letters, Vol. 20, No. 10, October 2013
[13] „Ecg-Based Biometrics: A Real Time Classification Approach „Andr´E Lourenc¸ Hugo Silva Y And Ana Fred 2012 IEEE International Workshop On Machine Learning For Signal Processing, Sept. 23–26, 2012, Santander, Spain
[14] Siddarth Hari, Foteini Agrafioti, Dimitrios Hatzinakos„ Design Of A Hamming-Distance Classifier For Ecg Biometrics‟The Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, 978-1-4799-0356-6/13/2013 IEEE
[15]‟Fiducial feature reduction analysis for electrocardiogram (ECG) based biometric recognition‟ M. M. Tantawi · K. Revett · A. Salem · M. F. Tolba Received: 19 April 2012 / Revised: 27 June 2012 / Accepted: 27 June 2012 / Published online: 20 July 2012 © Springer
[16] H. Chen, F. Zeng, K. Tseng, H. Huang, S. Tu, J. Panl. (2012, July). ECG Human Identification with Statistical Support Vector Machines. In Computing, Measurement, Control and Sensor Network (CMCSN), 2012 International Conference on (pp. 237-240). IEEE.
[17] C. Cortes, V. Vapnik. "Support-vector networks." Machine learning 20.3 (1995): 273-297.