Automated screening of arrhythmia using wavelet based machine learning techniques

Journal of Medical Systems

Volumn 36, Issue 2, 2012, Pages 677-688

  Martis, Roshan Joy ^a   Krishnan, M Muthu Rama ^a   Chakraborty, Chandan ^a   Pal, Sarbajit ^b   Sarkar, Debranjan ^b   Mandana, K M ^c   Ray, Ajoy Kumar ^a  

^a INDIAN INSTITUTE OF TECHNOLOGY   (India)

^b VARIABLE ENERGY CYCLOTRON CENTRE   (India)

^c AMRI HOSPITALS   (India)

Author keywords

Arrhythmia; EBPNN; ECG; GMM; Normal sinus rhythm; SVM

Indexed keywords

ALGORITHM; ARTICLE; CARDIOVASCULAR PARAMETERS; CONTROLLED STUDY; DIAGNOSTIC ACCURACY; DISCRETE WAVELET TRANSFORM; EARLY DIAGNOSIS; ELECTROCARDIOGRAM; HEART ARRHYTHMIA; KERNEL METHOD; MACHINE LEARNING; SCREENING; SINUS RHYTHM; STUDENT T TEST; ADULT; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; DECISION SUPPORT SYSTEM; ELECTROCARDIOGRAPHY; FEMALE; HUMAN; MALE; MIDDLE AGED; ORGANIZATION AND MANAGEMENT; SUPPORT VECTOR MACHINE; WAVELET ANALYSIS;

ADULT; ARRHYTHMIAS, CARDIAC; ARTIFICIAL INTELLIGENCE; DECISION SUPPORT SYSTEMS, CLINICAL; ELECTROCARDIOGRAPHY, AMBULATORY; FEMALE; HUMANS; MALE; MIDDLE AGED; NEURAL NETWORKS (COMPUTER); SUPPORT VECTOR MACHINES; WAVELET ANALYSIS;

EID: 84863214019     PISSN: 01485598     EISSN: 1573689X     Source Type: Journal    
DOI: 10.1007/s10916-010-9535-7     Document Type: Article

References (25)

1. Guyton, A. C., and Hall, J. E., Textbook of medical physiology (11th Edition). W. B. Saunders Co., 2006.
2. Fauci, A. S., Braunwald, E., Kesper, D. L., Hauser, S. L., Longo, D. L., Jamesonn, J. L., and Loscalzo, J., Harrison's principles of internal medicine, 17th Edition, Mc-Graw Hill, 2008.
3. Martinez, J. P., Almeida, R., Olmos, S., Rocha, A. P., and Laguna, P., A wavelet based ECG delineator: Evaluation on standard databases. IEEE Trans. Biomed. Eng. 51(4):570-581, 2004. (Pubitemid 38375132)
4. Fernando, S., Martinez, J. P., Laguna, P., Van G. B., Cyril, R., and Richard, H., Impact of sampling rate reduction on automatic ECG delineation, Proceedings of the 29th annual international conference of the IEEE EMBS, August 23-26, 2007, pp 2587-2590.
5. Pan, J., and Tompkins, W. J., A real time QRS detection algorithm. IEEE Trans. Biomed. Eng., BME 32(3):230-236, 1985.
6. Mallat, S. G., Matching pursuit with time frequency dictionaries. IEEE Trans. Signal Process. 41(12):3397-3415, 1993.
7. Li, C., Zheng, C., and Tai, C., Detection of ECG characteristic points using wavelet transforms. IEEE Trans. Biomed. Eng. 42 (1):21-29, 1995.
8. Karimifard, S., and Ahmadian, A., Morphological heart arrhythmia classification using Hermitian model of higher-order statistics, Proc. 29th Annual International Conference of the IEEE EMBS Cité Internationale, Lyon, France August, 2007.
9. Chazal, P. D., Automatic classification of heartbeats using ECG morphology and heartbeat interval features. IEEE Trans. Biomed. Eng. 51(7):1196-1206, 2004.
10. Maglaveras, N., Stamkopoulos, T., Pappas, C., and Strintzis, G. M., An adaptive backpropagation neural network for real-time ischemia episodes detection: Development and performance analysis using the European ST-T database. IEEE Trans. Biomed. Eng. 45(7):805-813, 1998. (Pubitemid 28292318)
11. Lagerholm, M., Peterson, C., Braccini, G., Edenbrandt, L., and Sornmo, L., Clustering ECG complexes using Hermite functions and self organizing maps. IEEE Trans. Biomed. Eng. 47(7):838-848, 2000. (Pubitemid 30421818)
12. Yu, S. N., and Chou, K. T., Integration of independent component analysis and neural networks for ECG beat classification. Expert Syst. Appl. 34(4):2841-2846, 2008.
13. Ubeyli, E. D., ECG beats classification using multi-class support vector machines with error correcting output codes. Digit. Signal Process. 17(3):675-684, 2007. (Pubitemid 46551740)
14. Martis, R. J., Chakraborty, C., and Ray, A. K., A two stage mechanism for registration and classification of ECG using Gaussian Mixture Model. Pattern Recognit. 42:2979-2988, 2009.
15. Throne, R. D., Jenkins, J. M., Winston, S. A., and DiCarlo, L. A., A comparison of four new time domain techniques for discriminating nonmorphic ventricular tachycardia from sinus rhythm using ventricular waveform morphology. IEEE Trans. Biomed. Eng. 38:561-570, 1991.
16. Krasteva, V., and Jekova, I., QRS template matching for recognition of ventricular ectopic beats. Ann. Biomed. Eng. 35 (12):2065-2076, 2007. (Pubitemid 350165293)
17. Bishop, C., Neural Networks for pattern recognition. Oxford University Press, 1995.
18. Christianini, N., and Taylor, J. S., An introduction to support vector machines and other kernel based learning methods. Cambridge University Press, 2000.
19. Vaidyanathan, P. P., Multirate systems and filter banks. Pearson education (Asia) Pte. Ltd., 2004.
20. Oppenheim, A. O., and Schaffer, R. A., Discrete time signal processing. Mc-Graw Hill edition, 2003.
21. Strang, G., and Nguyen, T., Wavelets and filter banks. Willesley Cambridge Press, 1996.
22. Duda, R., Hart, P., and Stork, D., Pattern classification, Second Edition, John Wiley & Sons, 2001
23. Gun, A. M., Gupta, M. K., and Dasgupta, B., An outline of statistical theory. Vol I and II. World Press Private Ltd, 4th edition, 2005.
24. Gun, A. M., Gupta, M. K., and Dasgupta B., Fundamentals of statistics. Vol. I and II. World Press Private Ltd, 4th edition, 2008.
25. Banik, S., and Martis, R. J., Code excited linear prediction codec for electrocardiogram, Proc. of Int. Conf. on Engineering in medicine and biology society, September 2004.