Tseng, Y.-L.Y.-L.TsengLin, K.-S.K.-S.LinFU-SHAN JAW2020-02-262020-02-262016https://scholars.lib.ntu.edu.tw/handle/123456789/464117An 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.[SDGs]SDG3journal article10.1155/2016/94603752-s2.0-84958190961https://www.scopus.com/inward/record.uri?eid=2-s2.0-84958190961&doi=10.1155%2f2016%2f9460375&partnerID=40&md5=549da862a20f756cb56993141f30d5f6