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Math. Comput. Appl. 2012, 17(2), 111-120; doi:10.3390/mca17020111

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

Boğaziçi University, Dept. of Computer Eng. Bebek, 34342 Istanbul/Turkey
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Published: 1 August 2012
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Abstract

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.
Keywords: Arrhythmia; ECG, Rule extraction; Hot Spot algorithm; Classification; Naive Bayes; C4.5; multilayer perceptron (MLP) and support vector machines (SVM) Arrhythmia; ECG, Rule extraction; Hot Spot algorithm; Classification; Naive Bayes; C4.5; multilayer perceptron (MLP) and support vector machines (SVM)
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Arıkan, U.; Gürgen, F. Discrimination Ability of Time-Domain Features and Rules for Arrhythmia Classification. Math. Comput. Appl. 2012, 17, 111-120.

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