title: Comparison of Support-Vector Machine and Sparse Representation Using a Modified Rule-Based Method for Automated Myocardial Ischemia Detection.
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.
authors.affiliation: Department of Electrical Engineering, Fu Jen Catholic University, New Taipei City 24205, Taiwan; Institute of Biomedical Engineering, National Taiwan University, Taipei 10617, Taiwan.
authors.forename: Yi-Li
authors.initials: YL
authors.lastname: Tseng
authors.affiliation: Graduate Institute of Communication Engineering, National Taiwan University, Taipei 10617, Taiwan.
authors.forename: Keng-Sheng
authors.initials: KS
authors.lastname: Lin
authors.affiliation: Institute of Biomedical Engineering, National Taiwan University, Taipei 10617, Taiwan.
authors.forename: Fu-Shan
authors.initials: FS
authors.lastname: Jaw
mesh_terms: D000465:Algorithms; D001331:Automation; D004562:Electrocardiography; D005060:Europe; D006801:Humans; D007091:Image Processing, Computer-Assisted; D017202:Myocardial Ischemia / Q000175:diagnosis* / Q000473:pathology*; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D060388:Support Vector Machine; D058067:Wavelet Analysis