Arrhythmia classification using SVM with selected features

ECG datasets from the University of California at Irvine (UCI) cardiac arrhythmias database containing various arrhythmias including ischemic changes, old inferior myocardial infarction, sinus bradycardy, right bundle branch block, and others

Support vector machine (SVM) classification using One Against All (OAA) algorithm with Principal Component Analysis (PCA) for feature selection

Other SVM algorithms (One Against One, Fuzzy Decision Function, Decision Directed Acyclic Graph) and classification without feature selection

Accuracy rate in percentages for distinguishing between presence and absence of cardiac arrhythmia and classifying them into arrhythmia groupssurrogate