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.

ECG features and methods for automatic classification of ventricular premature and ischemic heartbeats: A comprehensive experimental study

“… 5 Acc(linear SVM)=91.5% Acc(RBF SVM)=95.3% NOR, ISCH Morphological SVM Se = 94.8%, Sp = 99.5% Tseng et al . 6 NOR, VPB and 2 others Morphological, RR intervals, higher-order statistics Linear DFA, DFA: Acc(NOR)=88.6% Doquire et al . 10 SVM Acc(VPB)=80.6% SVM: Acc(NOR)=75.9% Acc(VPB)=85.1% NOR, VPB and 1 other AR model coefficients and non-linear features Linear DFA Acc up to 88% Balli et al .…”

“…Commonly used (e.g refs 6 , 10 , 14 and 15 ) and some newly proposed morphological and spectral features were calculated from each selected QRS-T segment.…”

“…The correct choice of classification algorithm and features representing heartbeats is crucial for successful classification. Although many methods have been reported, their direct comparison is questionable due to their differences in: a) types of heartbeats being classified (normal vs ischemic 3 – 6 , normal vs ventricular premature beats – VPBs 7 – 14 etc. ); b) ECG features (morphological 4 , 5 , 7 , 15 , 16 spectral 8 , 14 first-order or higher order statistics 10 , 12 non-linear 9 , heart-vector projection 17 etc.…”

“…); b) ECG features (morphological 4 , 5 , 7 , 15 , 16 spectral 8 , 14 first-order or higher order statistics 10 , 12 non-linear 9 , heart-vector projection 17 etc. ); c) classification models (discriminant function 4 , 9 cluster analysis 14 , artificial neural network 18 , naive Bayes classifier 13 , support vector machine 6 , 7 k-nearest neighbors 5 , 11 etc.).…”

Automated ECG Analysis for Localizing Thrombus in Culprit Artery Using Rule Based Information Fuzzy Network

“…Using K value as 5, KNN classifier was used in [18] to classify the signal using description found in [46]. Support Vector Machines (SVMs) have been widely used for classification of ischemia and MI with linear [17,18,24,33], Radial Basis Function (RBF) [18,21,25,35], polynomial [11] and exponential chi-squared kernel functions [11]. Review in [47] used artificial neural networks, fuzzy logic, rough set theory, decision trees, genetic and hybrid algorithms for classification of various heart diseases.…”