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Computational and Mathematical Methods in Medicine
Volume 2016 (2016), Article ID 9460375, 8 pages
http://dx.doi.org/10.1155/2016/9460375
Research Article

Comparison of Support-Vector Machine and Sparse Representation Using a Modified Rule-Based Method for Automated Myocardial Ischemia Detection

1Department of Electrical Engineering, Fu Jen Catholic University, New Taipei City 24205, Taiwan
2Institute of Biomedical Engineering, National Taiwan University, Taipei 10617, Taiwan
3Graduate Institute of Communication Engineering, National Taiwan University, Taipei 10617, Taiwan

Received 10 October 2015; Accepted 3 January 2016

Academic Editor: Ezequiel López-Rubio

Copyright © 2016 Yi-Li Tseng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

An automatic method is presented for detecting myocardial ischemia, which can be considered as the early symptom of acute coronary events. Myocardial ischemia commonly manifests as ST- and T-wave changes on ECG signals. The methods in this study are proposed to detect abnormal ECG beats using knowledge-based features and classification methods. A novel classification method, sparse representation-based classification (SRC), is involved to improve the performance of the existing algorithms. A comparison was made between two classification methods, SRC and support-vector machine (SVM), using rule-based vectors as input feature space. The two methods are proposed with quantitative evaluation to validate their performances. The results of SRC method encompassed with rule-based features demonstrate higher sensitivity than that of SVM. However, the specificity and precision are a trade-off. Moreover, SRC method is less dependent on the selection of rule-based features and can achieve high performance using fewer features. The overall performances of the two methods proposed in this study are better than the previous methods.