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SVM Classifier for the Detection of Ventricular Fibrillation  

Song, Mi-Hye (Department of Biomedical Engineering, Yonsei University)
Lee, Jeon (Department of Biomedical Engineering, Yonsei University, Center for Emergency Medical Informatics)
Cho, Sung-Pil (Department of Biomedical Engineering, Yonsei University)
Lee, Kyoung-Joung (Department of Biomedical Engineering, Yonsei University, Center for Emergency Medical Informatics)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SC / v.42, no.5, 2005 , pp. 27-34 More about this Journal
Abstract
Ventricular fibrillation(VF) is generally caused by chaotic behavior of electrical propagation in heart and may result in sudden cardiac death. In this study, we proposed a ventricular fibrillation detection algorithm based on support vector machine classifier, which could offer benefits to reduce the teaming costs as well as good classification performance. Before the extraction of input features, raw ECG signal was applied to preprocessing procedures, as like wavelet transform based bandpass filtering, R peak detection and segment assignment for feature extraction. We selected input features which of some are related to the rhythm information and of others are related to wavelet coefficients that could describe the morphology of ventricular fibrillation well. Parameters for SVM classifier, C and ${\alpha}$, were chosen as 10 and 1 respectively by trial and error experiments. Each average performance for normal sinus rhythm ventricular tachycardia and VF, was 98.39%, 96.92% and 99.88%. And, when the VF detection performance of SVM classifier was compared to that of multi-layer perceptron and fuzzy inference methods, it showed similar or higher values. Consequently, we could find that the proposed input features and SVM classifier would one of the most useful algorithm for VF detection.
Keywords
SVM 분류기;입력 특징;웨이브렛 변환;심실세동;심실빈맥;다층 퍼셉트론;퍼지 추론;
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Times Cited By KSCI : 1  (Citation Analysis)
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