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This study investigates relevant diagnosis information for arrhythmia classification from previously collected cardiac data. Discrimination ability of various time-domain attributes and rules were discussed for automatic diagnosis of arrythmia using electrocardiogram (ECG) signals. Naive Bayes, C4.5, multilayer perceptron (MLP) and support vector machines (SVM) algorithms were tested on a number of the input features selected by correlative feature selection (CFS) method. Hot Spot algorithm was employed to extract a number of rules that is useful in diagnosing cardiac problems from ECG signal. 257 time domain features of 452 cases from a cardiac arrhythmia database [1] were used. Various testing configurations and performance measures such as accuracy, TP and FP rates, precision, recall and AUC were considered. The discrimination ability of selected-features and the extracted-rules were demonstrated.

Discrimination Ability of Time-Domain Features and Rules for Arrhythmia Classification