E-Health Design of EEG Signal Classification for Epilepsy Diagnosis

Epilepsy, which is caused by abnormal discharges in the brain, is one of the most common neurological disorders. To diagnose efficiently epilepsy, it is valuable to classify electroencephalogram signal. In this paper, we proposed a new e-health design of Electroencephalogram (EEG) signal classification for epilepsy diagnosis. The design is based on support vector machine to classify electroencephalogram signal. We first decompose electroencephalogram signal into bands by using discrete wavelet transform and compute the approximate entropy values in the bands. Next, by proposed feature selection method, the feature vectors are selected adaptively from statistical wavelet coefficients and approximate entropy values. Finally, the support vector machine is used to classify the selected features. The experimental results showed the proposed system has great performance and reliability and the total accuracy of classification can achieve 98%.

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