Implementation of Kernel Sparse Representation Classifier for ECG Biometric System

Authors

  • Haryati Jaafar Faculty of Engineering Technology, Universiti Malaysia Perlis, UniCITI Alam Campus, Sg. Chuchuh, 02100, Padang Besar, Perlis, Malaysia.
  • Nurhidayah Ramli Faculty of Engineering Technology, Universiti Malaysia Perlis, UniCITI Alam Campus, Sg. Chuchuh, 02100, Padang Besar, Perlis, Malaysia.
  • Aimi Salihah Abdul Nasir Faculty of Engineering Technology, Universiti Malaysia Perlis, UniCITI Alam Campus, Sg. Chuchuh, 02100, Padang Besar, Perlis, Malaysia.

Keywords:

Autocorrelation Method, ECG Signal, KSRC, SRC, STE and STAZCR.

Abstract

In this paper, a biometric human recognition system based on Electrocardiography (ECG) signal is proposed. Three processes i.e., pre-processing, feature extraction and classification is discussed. A combination of enhanced start and end point detection namely short time energy (STE) and short time average zero crossing rate (STAZCR) is employed in the pre-processing. Subsequently, an autocorrelation method is applied in feature extraction. For the classification process, the kernel sparse representation classifier (KSRC) is proposed as a classifier to increase the system performance in high dimensional feature space. 79 recorded signals from 79 subjects are used are employed in this study. To validate the performance of the KSRC, several classifiers, i.e. sparse representation classifier (SRC), k nearest neighbor (kNN) and support vector machine (SVM) are compared. An experiment based on different sizes of feature dimensions is conducted. The classification performance for four classifiers are found to be 90.93%, 92.8%, 94.24%, 62.9%, 97.23% and 95.87% for the kNN, SVM (Polynomial and RBF), SRC and KSRC (Polynomial and RBF), respectively. The results reveal that the KSRC is a promising classifier for the ECG biometric system compared to the existing reference classifiers.

References

K. A. Sidek, V. Mai, and I. Khalil, “Data mining in mobile ECG based biometric identification,” Journal of Network and Computer Applications, vol. 44, pp. 83-91, 2014.

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. 19, 2008.

J. Yin, Z. Liu, Z. Jin, and W. Yang, “Kernel sparse representation based classification,” Neurocomputing, vol. 77, pp. 120-128, 2011.

S.S. Chen, D.L. Donoho, and M.A. Saunders, “Atomic decomposition by basis pursuit,” SIAM Journal of Scientific Computing, vol. 20, no. 1, pp. 33–61, 1998.

B. L. Strum, and P. Noorzad, “On automatic genre recognition by sparse representation classification using auditory temporal modulations,” 9th International Symposium on Computer Music Modeling and Retrieval (CMMR 2012): Lecture Notes in Computer Sciences Series, pp. 379-394, 2012.

O. Vinyals, and L. Deng, “Are sparse representations rich enough for acoustic modeling?” Proceedings of Interspeech 2012, 2012.

S. Zubair, F. Yan, and W. Wang, “Dictionary learning based sparse coefficients for audio classification with max and average pooling,” Digital Signal Processing, vol. 23, pp. 960–970, 2013.

S.W. Lin, Z.L. Lee, S.C. Chen, and T.Y. Tseng, “Parameter determination of support vector machine and feature selection using simulated annealing approach,” Applied Soft Computing, vol. 8, pp. 1505–1512, 2008.

Fratini, A., Sansone, M., Bifulco, P., & Cesarelli, M. (2015). Individual identification via electrocardiogram analysis. Biomedical engineering online, 14- 78.

C. Liu, D. Springer, Q. Li, B. Moody, R.A. Juan, F.J. Chorro, F. Castells, J.M. Roig, I. Silva, A.E. Johnson, and Z. Syed, “An open access database for the evaluation of heart sound algorithms,” Physiological Measurement, vol. 37, no. 12, pp. 2181, 2016.

D.A. Ramli, and H. Jaafar, “Peak Finding Algorithm to Improve Syllable Segmentation for Noisy Bioacoustic Sound Signal,” Procedia Computer Science, vol. 96, pp.100-109, 2016.

I., Guyon, S., Gunn, M., Nikravesh, and L. A. Zadeh, Feature extraction: foundations and applications, vol. 207, Springer Eds, 2008.

W.L. Zuo, Z.Y. Wang, T. Liu, and H.L. Chen, “Effective detection of Parkinson's disease using an adaptive fuzzy k-nearest neighbor approach,” Biomedical Signal Processing and Control, vol. 8, no. 4, pp. 364-373, 2013.

J. Wright, A.Y. Yang, A. Ganesh, S.S. Sastry, and Y. Ma, “Robust face recognition via sparse representation,” IEEE Trans. Pattern Analysis Machine Intelligent, vol. 31, no. 2, pp. 210–227, 2009.

L. Zhang, W.D. Zhou, P.C. Chang, and J. Liu, “Kernel sparse representation-based classifier,” IEEE Trans. On Signal Processing, vol. 60, no. 4, pp. 1684-1695, 2012.

Downloads

Published

2018-05-29

How to Cite

Jaafar, H., Ramli, N., & Abdul Nasir, A. S. (2018). Implementation of Kernel Sparse Representation Classifier for ECG Biometric System. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(1-13), 89–94. Retrieved from https://jtec.utem.edu.my/jtec/article/view/4128