4.6 Article

Efficient classification of ventricular arrhythmias using feature selection and C4.5 classifier

期刊

BIOMEDICAL SIGNAL PROCESSING AND CONTROL
卷 44, 期 -, 页码 200-208

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.bspc.2018.04.005

关键词

Sudden cardiac arrest; Ventricular arrhythmias; ECG signals; Ventricular tachycardia (VT); Ventricular fibrillation (VF); C4.5 classifier

向作者/读者索取更多资源

The occurrence of sudden cardiac arrest (SCA) leads to a massive death across the world. Hence the early prediction of ventricular tachycardia (VT) and ventricular fibrillation (VF) becomes vital to prevent from ventricular arrhythmia. In this study, we present a process to detect and classify VT and VF arrhythmias using temporal, spectral, and statistical features. The CU Ventricular Tachyarrhythmia Database (CUDB) and MIT-BIH Malignant Ventricular Ectopy Database (VFDB) databases are used from PhysioNet database for evaluation and comparison of the proposed algorithm. Thirteen time-frequency based features were extracted for a window length of 5 s with an appropriate thresholding to make a feature set. The gain ratio attribute evaluation has been used for potential utilization of the informative features by ranking them according to their individual evaluation weightage. Classification of selected features for VF, VT, and normal sinus rhythm (NSR) is done by using cubic support vector machine (SVM) and the C4.5 classifiers. Assessment of this process is done on 57 records of electrocardiogram (ECG) signals and the result shows that the proposed method achieved a sensitivity of 90.97%, specificity of 97.86% and accuracy of 97.02% in C4.5 classifier, which was better than the obtained results of cubic SVM having an accuracy of 92.23%. This study demonstrated that by using informative features and classifying them with C4.5 algorithms, the system data could be an aid to the clinician for precise detection of ventricular arrhythmias. (C) 2018 Elsevier Ltd. All rights reserved.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据