Extracting Input Features and Fuzzy Rules for Classifying Epilepsy Based on NEWFM

Sang-Hong Lee; Joon-S. Lim

Extracting Input Features and Fuzzy Rules for Classifying Epilepsy Based on NEWFM

Sang-Hong Lee, Joon-S. Lim, Journal of Internet Computing and Services, Vol. 10, No. 5, pp. 127-134, Oct. 2009
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Keywords: Electroencephalogram, Epilepsy, Fuzzy Neural Networks, Wavelet Transforms, feature extraction

Abstract

This paper presents an approach to classify normal and epilepsy from electroencephalogram(EEG) using a neural network with weighted fuzzy membership functions(NEWFM). To extract input features used in NEWFM, wavelet transform is used in the first step. In the second step, the frequency distribution of signal and the amount of changes in frequency distribution are used for extracting twenty-four numbers of input features from coefficients and approximations produced by wavelet transform in the previous step. NEWFM classifies normal and epilepsy using twenty four numbers of input features, and then the accuracy rate is 98%.

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Cite this article

[APA Style]

Lee, S. & Lim, J. (2009). Extracting Input Features and Fuzzy Rules for Classifying Epilepsy Based on NEWFM. Journal of Internet Computing and Services, 10(5), 127-134.

[IEEE Style]

S. Lee and J. Lim, "Extracting Input Features and Fuzzy Rules for Classifying Epilepsy Based on NEWFM," Journal of Internet Computing and Services, vol. 10, no. 5, pp. 127-134, 2009.

[ACM Style]

Sang-Hong Lee and Joon-S. Lim. 2009. Extracting Input Features and Fuzzy Rules for Classifying Epilepsy Based on NEWFM. Journal of Internet Computing and Services, 10, 5, (2009), 127-134.