PCA and KPCA of ECG signals with binary SVM classification

2011 IEEE Workshop on Signal Processing Systems, SiPS 2011, Proceedings

Volumn , Issue , 2011, Pages 344-348

  Kanaan, L ^a   Merheb, D ^a   Kallas, M ^b,c   Francis, C ^b   Amoud, H ^b   Honeine, P ^c  

^a HOLY SPIRIT UNIVERSITY OF KASLIK USEK   (Lebanon)

^b LEBANESE UNIVERSITY   (Lebanon)

^c UNIVERSITÉ DE TECHNOLOGIE DE TROYES   (France)

Author keywords

ECG signals; Kernel PCA; PCA; SVM classification

Indexed keywords

BINARY SVM; DATA SETS; ECG SIGNALS; HIGH DIMENSIONAL SPACES; KERNEL PCA; KERNEL PRINCIPAL COMPONENT ANALYSIS; PCA; SVM CLASSIFICATION;

CLASSIFICATION (OF INFORMATION); DISEASES; ELECTROCARDIOGRAPHY; SIGNAL PROCESSING; SUPPORT VECTOR MACHINES; VECTOR SPACES;

PRINCIPAL COMPONENT ANALYSIS;

EID: 84055218483     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/SiPS.2011.6089000     Document Type: Conference Paper

References (14)

1. AMSibai, A Fletcher, MHills, and O Campbell, "Non-communicable disease mortality rates using the verbal autopsy in a cohort of middle aged and older populations in beirut during wartime, 1983-93.," J Epidemiol Community Health, vol. 55, no. 4, pp. 271-6, 2001.
2. T.H. Linh, S. Osowski, and M. Stodolski, "On-line heart beat recognition using hermite polynomials and neuro-fuzzy network," IEEE Transactions On Instrumentation And Measurement, vol. 52, no. 4, August 2003.
3. S. Osowski, L. T. Hoai, and T. Markiewicz, "Support vector machine-based expert system for reliable heartbeat recognition," IEEE Transactions On Biomedical Engineering, vol. 51, no. 4, Avril 2004.
4. W.C. Kao, C.K. Yu, C.P. Shen, W.H. Chen, and P.Y. Hsiao, "Electrocardiogram analysis with adaptive feature selection and support vector machines," in IEEE Asia Pacific Conference on Circuits and Systems, APC-CAS, Singapore, 4-7 December 2006, pp. 1783-1786.
5. I. T. Jolliffe, Principal Component Analysis, Springer, second edition, Oct. 2002.
6. F. Castells, P. Laguna, L. Sörnmo, A. Bollmann, and J.M. Roig, "Principal component analysis in ecg signal processing," EURASIP J. Appl. Signal Process., vol. 2007, pp. 98-98, January 2007.
7. H.H. Chou, Y.J. Chen, Y.C. Shiau, and T.S. Kuo, "A high performance compression algorithm for ECG with irregular periods," in Proc. IEEE International Workshop on Biomedical Circuits and Systems, 2004.
8. D. Patra, M. Kumar Das, and S. Pradhan, "Integration of FCM, PCA and neural networks for classification of ECG arrhythmias," in IAENG International Journal of Computer Science, 2010.
9. K.-R. Müller, S. Mika, G. Rätsch, S. Tsuda, and B Schölkopf, "An introduction to kernel-based learning algorithms," IEEE Transactions on Neural Networks, vol. 12, no. 2, pp. 181-202, 2001.
10. B. Schölkopf, A. Smola, and K.R. Müller, "Nonlinear component analysis as a kernel eigenvalue problem,"Neural Comput., vol. 10, pp. 1299-1319, July 1998. (Pubitemid 128463674)
11. G.H. Bakir, J. Weston, and B. Schölkopf, "Learning to find pre-images," in Advances in Neural Information Processing Systems. 2004, pp. 449-456, MIT Press.
12. B. Schölkopf, S. Mika, C.J.C. Burges, P. Knirsch, K.R. Müller, G. Rätsch, and A.J. Smola, "Input space versus feature space in kernel-based methods," IEEE Transactions On Neural Networks, vol. 10, pp. 1000-1017, 1999.
13. P. Honeine and C. Richard, "Pre-image problem in kernel-based machine learning," IEEE Signal Processing Magazine, Special Issue On Dimensionality Reduction Via Subspace and Manifold Learning, vol. 28 (2), March 2011.
14. C.J.C. Burges, "A tutorial on support vector machines for pattern recognition," Data Min. Knowl. Discov., vol. 2, pp. 121-167, June 1998. (Pubitemid 128695475)