Mathematical and Computational Applications | |
Discrimination Ability of Time-Domain Features and Rules for Arrhythmia Classification | |
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
【 预 览 】
Files | Size | Format | View |
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RO201902029300153ZK.pdf | 138KB | download |