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http://dx.doi.org/10.5391/IJFIS.2011.11.3.204

The Classification of Electrocardiograph Arrhythmia Patterns using Fuzzy Support Vector Machines  

Lee, Soo-Yong (Dept. of General Education and Teacher Training, College of Humanities & Arts, Yonsei Univ.)
Ahn, Deok-Yong (Dept. of Computer Science, Graduate School of Engineering, Yonsei University)
Song, Mi-Hae (Dept. of Biomedical Engineering, College of Health Sciences, Yonsei University)
Lee, Kyoung-Joung (Dept. of Biomedical Engineering, College of Health Sciences, Yonsei University)
Publication Information
International Journal of Fuzzy Logic and Intelligent Systems / v.11, no.3, 2011 , pp. 204-210 More about this Journal
Abstract
This paper proposes a fuzzy support vector machine ($FSVM_n$) pattern classifier to classify the arrhythmia patterns of an electrocardiograph (ECG). The $FSVM_n$ is a pattern classifier which combines n-dimensional fuzzy membership functions with a slack variable of SVM. To evaluate the performance of the proposed classifier, the MIT/BIH ECG database, which is a standard database for evaluating arrhythmia detection, was used. The pattern classification experiment showed that, when classifying ECG into four patterns - NSR, VT, VF, and NSR, VT, and VF classification rate resulted in 99.42%, 99.00%, and 99.79%, respectively. As a result, the $FSVM_n$ shows better pattern classification performance than the existing SVM and FSVM algorithms.
Keywords
Fuzzy Support Vector Machine; FSVM; ECG pattern classifier; Ventricular Fibrillation; Ventricular Tachycardia;
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Times Cited By KSCI : 8  (Citation Analysis)
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