Extracting Fuzzy Rules for Classifying Ventricular Tachycardia/Ventricular Fibrillation Based on NEWFM

Dong-Kun Shin; Sang-Hong Lee; Joon-S. Lim

Extracting Fuzzy Rules for Classifying Ventricular Tachycardia/Ventricular Fibrillation Based on NEWFM

Dong-Kun Shin, Sang-Hong Lee, Joon-S. Lim, Journal of Internet Computing and Services, Vol. 10, No. 2, pp. 179-0, Apr. 2009
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Keywords: Ventricular Tachycardia(VT), Ventricular Fibrillation(VF), Fuzzy Neural Networks, Wavelet Transforms

Abstract

This paper presents an approach to classify normal and Ventricular Tachycardia/Ventricular Fibrillation(VT/VF) from the Creighton University Ventricular Tachyarrhythmia DataBase(CUDB) using the neural network with weighted fuzzy membership functions(NEWFM). In the first step, wavelet transform is used for producing input values which are used in the next step. In the second step, two numbers of input features are extracted by phase space reconstruction method and peak extraction method using coefficients produced by wavelet transform in the previous step. NEWFM classifies normal and VT/VF beats using two numbers of input features, and then the accuracy rate is 90.13%.

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Cite this article

[APA Style]

Shin, D., Lee, S., & Lim, J. (2009). Extracting Fuzzy Rules for Classifying Ventricular Tachycardia/Ventricular Fibrillation Based on NEWFM. Journal of Internet Computing and Services, 10(2), 179-0.

[IEEE Style]

D. Shin, S. Lee, J. Lim, "Extracting Fuzzy Rules for Classifying Ventricular Tachycardia/Ventricular Fibrillation Based on NEWFM," Journal of Internet Computing and Services, vol. 10, no. 2, pp. 179-0, 2009.

[ACM Style]

Dong-Kun Shin, Sang-Hong Lee, and Joon-S. Lim. 2009. Extracting Fuzzy Rules for Classifying Ventricular Tachycardia/Ventricular Fibrillation Based on NEWFM. Journal of Internet Computing and Services, 10, 2, (2009), 179-0.