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


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

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

Abstract Views: 94  |  PDF Views: 2

Authors

Yi-Li Tseng
Department of Electrical Engineering, Fu Jen Catholic University, New Taipei City 24205, Taiwan, Province of China
Keng-Sheng Lin
Graduate Institute of Communication Engineering, National Taiwan University, Taipei 10617, Taiwan, Province of China
Fu-Shan Jaw
Institute of Biomedical Engineering, National Taiwan University, Taipei 10617, Taiwan, Province of China

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.