Detection of cardiac arrhythmia in electrocardiograms using adaptive feature extraction and modified support vector machines
Journal
Expert Systems With Applications
Journal Volume
39
Journal Issue
9
Pages
7845 – 7852
Date Issued
2012-07
Author(s)
Abstract
The electrocardiogram (ECG) analysis is one of the most important approaches to cardiac arrhythmia detection. Many algorithms have been proposed, however, the recognition rate is still unsatisfactory due to unreliable feature extraction in signal characteristic analysis or poor generalization capability of the classifier. In this paper, we propose a system for cardiac arrhythmia detection in ECGs with adaptive feature selection and modified support vector machines (SVMs). Wavelet transform-based coefficients and signal amplitude/interval parameters are first enumerated as candidates, but only a few specific ones are adaptively selected for the classification of each class pair. A new classifier, which integrates k-means clustering, one-against-one SVMs, and a modified majority voting mechanism, is proposed to further improve the recognition rate for extremely similar classes. The experimental results show that the proposed ECG analysis approach can obtain a higher recognition rate than the published approaches. By testing the system with more than 100,000 samples in MIT-BIH arrhythmia database, the average recognition rate is 98.92%, and the recognition rate for each class is kept above 92%. © 2012 Elsevier Ltd. All rights reserved.
Subjects
Adaptive feature extraction; Electrocardiogram (ECG); k-Means clustering; Support vector machines (SVMs)
SDGs
Other Subjects
Adaptive feature selection; Cardiac arrhythmia; ECG analysis; Electrocardiogram (ECG); Generalization capability; K-means clustering; Majority voting mechanism; Recognition rates; Signal amplitude; Signal characteristic; Diseases; Electrocardiography; Feature extraction; Signal detection; Support vector machines
Type
journal article