Detection and Localization of Myocardial Infarction using K-nearest Neighbor Classifier

Author:

Arif Muhammad,Malagore Ijaz A.,Afsar Fayyaz A.

Publisher

Springer Science and Business Media LLC

Subject

Health Information Management,Health Informatics,Information Systems,Medicine (miscellaneous)

Reference26 articles.

1. Reddy, M. R. S. E., Svensson, L., Haisty, J., and Pahlm, W. K., Neural network versus electrocardiographer and conventional computer criteria in diagnosing anterior infarct from the ECG, Proc of Computers in Cardiology, pp. 667–670, 1992.

2. Zheng, H., Wang, H., Nugent, C. D., and Finlay, D. D., Supervised classification models to detect the presence of old myocardial infarction in Body Surface Potential Maps. Proc of Computers in Cardiology, pp. 265–268, 2006.

3. Yang, H., Malshe, M., Bukkapatnam, S. T. S., and Komanduri, R., Recurrence quantification analysis and principal components in the detection of myocardial infarction from vectorcardiogram signals, Proceedings of the 3rd INFORMS Workshop on Data Mining and Health Informatics (DM-HI 2008), 2008.

4. McDarby, G., Celler, B. G., and Lovell, N. H., Characterising the discrete wavelet transform of an ECG signal with simple parameters for use in automated diagnosis, of the 2nd International Conference on Bioelectromagnetism, pp. 31–32, 1998.

5. Jayachandran, E. S., Joseph, P. K., and Acharya, R. U., Analysis of myocardial infarction using discrete wavelet transform. J. Med. Syst., Online doi: 10.1007/s10916-009-9314-5 , 2009.

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