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A Study on the Detection of the Ventricular Fibrillation based on Wavelet Transform and Artificial Neural Network  

Song Mi-Hye (연세대 보건과학대 의공학과)
Park Ho-Dong (연세대 보건과학대 의공학과)
Lee Kyoung-Joung (연세대 보건과학대 의공학과)
Park Kwang-Li (용인송담대 의료정보시스템과)
Publication Information
The Transactions of the Korean Institute of Electrical Engineers D / v.53, no.11, 2004 , pp. 780-785 More about this Journal
Abstract
In this paper, we proposed a ventricular fibrillation detection algorithm based on wavelet transform and artificial neural network. we selected RR intervals, the 6th and 7th wavelet coefficients(D6, D7) as features for classifying ventricular fibrillation. To evaluate the performance of the proposed algorithm, we compared the result of the proposed algorithm with that of fuzzy inference and fuzzy-neural network. MIT-BIH Arrhythmia database, Creighton University Ventricular Tachyarrhythmia database and MIH-BIH Malignant Ventricular Arrhythmia database were used as test and learning data. Among the algorithms, the proposed algorithm showed that the classification rate of normal and abnormal beat was sensitivity(%) of 96.10 and predictive positive value(%) of 99.07, and that of ventricular fibrillation was sensitivity(%) of 99.45. Finally. the proposed algorithm showed good performance compared to two other methods.
Keywords
Ventricular Fibrillation; Wavelet Transform; Artificial Neural Network;
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Times Cited By KSCI : 1  (Citation Analysis)
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