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

Mathematical and Computational Applications

Arıkan, Umut¹ 

关键词: Arrhythmia;    ECG;    Rule extraction;    Hot Spot algorithm;    Classification;    Naive Bayes;    C4.5;    multilayer perceptron (MLP);    support vector machines (SVM);   

DOI  :  10.3390/mca17020111

学科分类：计算数学

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY