Sparse representation-based heartbeat classification using independent component analysis

Journal of Medical Systems

Volumn 36, Issue 3, 2012, Pages 1235-1247

  Huang, Hui Fang ^a,b   Hu, Guang Shu ^a   Zhu, Li ^a  

^a TSINGHUA UNIVERSITY   (China)

^b BEIJING JIAOTONG UNIVERSITY   (China)

Author keywords

Electrocardiogram (ECG); Heartbeat classification; Independent component analysis (ICA); L 1 minimization; Sparse representation

Indexed keywords

ACCURACY; ARTICLE; CLASSIFICATION ALGORITHM; CONTROLLED STUDY; ELECTROCARDIOGRAM; HEART BEAT; INDEPENDENT COMPONENT ANALYSIS; SENSITIVITY ANALYSIS; SPARSE REPRESENTATION BASED HEARTBEAT CLASSIFICATION;

ALGORITHMS; ARRHYTHMIAS, CARDIAC; ELECTROCARDIOGRAPHY; HEART SOUNDS; HUMANS; SIGNAL PROCESSING, COMPUTER-ASSISTED;

EID: 84864060051     PISSN: 01485598     EISSN: 1573689X     Source Type: Journal    
DOI: 10.1007/s10916-010-9585-x     Document Type: Article

References (29)

1. de Chazal, P., O'Dwyer, M., and Reilly, R. B., Automatic classification of heartbeats using ECG morphology and heartbeat interval features. IEEE Trans. Biomed. Eng. 51(7):1196-1206, 2004. (Pubitemid 38787925)
2. Osowski, S., and Linh, T. H., ECG beat recognition using fuzzy hybrid neural network. IEEE Trans. Biomed. Eng. 48(11):1265-1271, 2001. (Pubitemid 32988567)
3. Yeh, Y. C., Wang, W. J., and Chiou, C. W., Feature selection algorithm for ECG signals using range-overlaps method. Expert. Syst. Appl. 37:3499-3512, 2010.
4. Minhas, F. A., and Arif, M., Robust electrocardiogram (ECG) beat classification using discrete wavelet transform. Physiol. Meas. 29:555-570, 2008. (Pubitemid 351875522)
5. Yu, S. N., and Chen, Y. H., Electrocardiogram beat classification based on wavelet transformation and probabilistic beural network. Pattern Recogn. Lett. 28(10):1142-1150, 2007.
6. Yu, S. N., and Chou, K. T., Integration of independent component analysis and neural networks for ECG beat classification. Expert. Syst. Appl. 34:2841-2846, 2008.
7. Yu, S. N., and Chou, K. T., Selection of significant independent components for ECG beat classification. Expert. Syst. Appl. 36:2088-2096, 2009.
8. Jiang, X., Zhang, L. Q., Zhao, Q. B., and Albayrak, S., ECG arrhythmias recognition system based on independent component analysis feature extraction. TENCON 2006. 2006 IEEE Region 10 Conference, Hong Kong, China, 1-4, 2006.
9. Osowski, S., Linh, T. H., and Markiewcz, T., Support vector machine-based expert system for reliable heartbeat recognition. IEEE Trans. Biomed. Eng. 51:582-589, 2004. (Pubitemid 38375133)
10. Yeh, Y. C., Wang, W. J., and Chiou, C. W., Cardiac arrhythmia diagnosis method using linear discriminant analysis on ECG signals. Measurement 42:778-789, 2009.
11. Christov, I., Jekova, I., and Bortolan, G., Premature ventricular contractionsclassification by the Kth nearest-neighbours rule. Physiol. Meas. 26:123-130, 2005. (Pubitemid 41223596)
12. Güler, I., and Übeyli, E. D., A modified mixture of experts network structure for ECG beats classification with diverse features. Eng. Appl. Artif. Intell. 18:845-856, 2005. (Pubitemid 41765029)
13. Güler, I., and Übeyli, E. D., ECG beat classifier designed by combined neural network model. Pattern Recognit. 38:199-208, 2005.
14. Übeyli, E. D., Combining recurrent neural networks with eigenvector methods for classification of ECG beats. Digit. Signal Process. 19:320-329, 2009.
15. Acir, N., A support vector machine classifier algorithm based on a perturbation method and its application to ECG beat recognition systems. Expert. Syst. Appl. 31:150-158, 2006. (Pubitemid 43344272)
16. Übeyli, E. D., ECG beats classification using multiclass support vector machines with error correcting output codes. Digit. Signal Process. 17:675-684, 2007. (Pubitemid 46551740)
17. Übeyli, E. D., Usage of eigenvector methods in implementation of automated diagnostic systems for ECG beats. Digit. Signal Process. 18:33-48, 2008. (Pubitemid 350184350)
18. Donoho, D., For most large underdetermined systems of linear equations the minimal l1-norm solution is also the sparsest solution. Commun. Pure Appl. Math. 59(6):797-829, 2006. (Pubitemid 43667226)
19. Candès, E., Romberg, J., and Tao, T., Stable signal recovery form incomplete and inaccurate measurements. Commun. Pure Appl. Math. 59(8):1207-1223, 2006. (Pubitemid 43988295)
20. Candès, E., and Tao, T., Near-optimal signal recovery from random projections: universal encoding strategies? IEEE Trans. Inf. Theory 52(12):5406-5425, 2006. (Pubitemid 46445381)
21. Wright, J., Yang, A. Y., Ganesh, A., Sastry, S. S., and Ma, Y., Robust face recognition via sparse representation. IEEE Trans. PAMI. 31(2):210-227, 2009.
22. Comon, P., Independent component analysis, A new concept? Signal Process. 36:287-314, 1994.
23. Hyvärinen, A., and Oja, E., Independent component analysis: algorithms and application. Neural Netw. 13(4-5):411-430, 2000. (Pubitemid 30447427)
24. Mark, R., and Moody, G., MIT-BIH Arrhythmia Database, available online: http://www.physionet.org/physiobank/database/mitdb/, 1997.
25. Hyvärinen, A., Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans. Neural. Netw. 10(3):626-634, 1999.
26. Amaldi, E., and Kann, V., On the approximability of minimizing nonzero variables or unsatisfied relations in linear systems. Theor. Comput. Sci. 209:237-260, 1998. (Pubitemid 128450440)
27. Figueiredo, M. A. T., Nowak, R. D., and Wright, S. J., Gradient projection for sparse reconstruction: application to compressed sensing and other inverse problem. IEEE J. Sel. Top. Signa. 1:586-597, 2007. (Pubitemid 351276802)
28. Candès, E. J., and Wakin, M. B., An introduction to compressive sampling. IEEE Signal Process. Mag. 25(2):21-30, 2008.
29. Baraniuk, R. G., Compressive sensing. IEEE Signal Process. Mag. 24(4):118-121, 2007.