Copyright © 2008 The Institute of Electronics, Information and Communication Engineers
Regular Section -- Letters -- Application Information Security |
A New Approach for Personal Identification Based on dVCG
1 The authors are with the Department of Biomedical Engineering, Hanyang University, Seoul, Korea., 2 Corresponding author. E-mail: iykim{at}hanyang.ac.kr
We propose a new approach to personal identification using derived vectorcardiogram (dVCG). The dVCG was calculated from recorded ECG using inverse Dower transform. Twenty-one features were extracted from the resulting dVCG. To analyze the effect of each feature and to improve efficiency while maintaining the performance, we performed feature selection using the Relief-F algorithm using these 21 features. Each set of the eight highest ranked features and all 21 features were used in SVM learning and in tests, respectively. The classification accuracy using the entire feature set was 99.53 %. However, using only the eight highest ranked features, the classification accuracy was 99.07 %, indicating only a 0.46 % decrease in accuracy compared with the accuracy achieved using the entire feature set. Using only the eight highest ranked features, the conventional ECG method resulted in a 93 % recognition rate, whereas our method achieved >99 % recognition rate, over 6 % higher than the conventional ECG method. Our experiments show that it is possible to perform a personal identification using only eight features extracted from the dVCG.
Key Words: personal identification, ECG, dVCG, Dower transform, SVM
Manuscript received September 10, 2007. Manuscript revised December 19, 2007.
Reference
[1] D.W. John, N.M. Orlans, and P.T. Higgins, Biometrics—Identify assurance in the Information Age, pp.139–149, McGraw-Hill, USA, 2003. [2] 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. [3] T.W. Shen, W.J. Tompkins, and Y.H. Hu, "One-lead ECG for identity verification," Proc. Second Joint EMBS/BMES Conference, pp.62–63, 2002. [4] M. Kyoso and A. Uchiyama, "Development of an ECG identification system," Proc. 23rd Annual EMBS International Conference, pp.3721–3723, 2001. [5] J. Carlson, R. Havmoller, A. Herreros, P. Platonov, R. Johansson, and B. Olsson, "Can orthogonal lead indicators of propensity to atrial fibrillation be accurately assessed from the 12-lead ECG?," Europace, vol.7, suppl. 2, pp.39–48, 2005. [6] I. Christov, I. Jekova, and G. Bortolan, "Premature ventricular contraction classification by the Kth nearest-neighbours rule," Physiological Measurement, vol.26, no.2, pp.123–130, 2005. [7] P. Dilaveris, A. Pantazis, E. Gialafos, J. Gialafos, P. Toutouzas, and C. Stefanadis, "Assessment of ventricular repolarization alterations in subjects with early repolarization," International Journal of Cardiology, vol.96, no.2, pp.273–279, 2004. [8] L. Edenbrandt, A. Jakobsson, E. Lindvall, P.O. Bitzen, and O. Pahlm, "Increased prevalence of large bites in 12-lead vectorcardiograms of diabetic patients," Journal of Electrocardiology, vol.30, no.2, pp.91–96, 1997. [9] L. Edenbrandt and O. Pahlm, "Vectorcardiogram synthesized from a 12-lead ECG: Superiority of the inverse Dower matrix," Journal of Electrocardiology, vol.21, no.4, pp.87–95, 1980. [10] P.S. Hamilton and W.J. Tompkins, "Quantitative investigation of QRS detection rules using the MIT/BIH arrhythmia database," IEEE Trans. Biomed. Eng., vol.33, no.12, pp.1157–1165, 1986. [11] V. Vapnik, The nature of statistical learning theory, Springer, New York, USA, 1995. [12] C.J.C. Burges, "A tutorial on support vector machines for pattern recognition," Data Mining and Knowledge Discovery, vol.2, no.2,[–.5mm] pp.121–167, 1998. [13] T. Hastie and R. Tibshirani, "Classification by pairwise coupling," Annals of Statistics, vol.26, no.2, pp.451–471, 1998. [14] I. Kononenko, "Estimating attributes: Analysis and extensions of relief," Proc. ECML'94, pp.171–182, Catania, Italy, April 1994. [15] I.H. Witten and E. Frank, Data Mining: Practical machine learning tools and techniques, 2nd ed., Morgan Kaufman, San Francisco, USA, 2005. [16] C.C. Chang and C.J. Lin, LIBSVM: A library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm [17] R.J. Huszar, Basic Dysrhythmias : Interpretation & Management, 3rd ed., Mosby, 2001.
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