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This paper presents a new method to automate the process of epileptic seizure detection in electroencephalogram (EEG) signals using wavelet transform and an improved version of negative correlation learning (NCL) algorithm. An improved version of NCL is proposed by incorporating the capability of gating network, as a dynamic combining part of the mixture of experts (ME), into the combining outputs of base experts which are trained using negative correlation learning algorithm. The NCL training algorithm encourages the base experts to learn different parts or aspects of data set and the gating network provides the local competence of these base experts. Three types of normal (recorded from five healthy persons with eyes open), seizure-free (recorded from epileptogenic zoon of five patients) and epileptic EEG signals were decomposed into wavelet coefficients using discrete wavelet transform. Then the statistical features of the wavelet coefficients were computed representing them into the classifiers. Experimental results show that our proposed method classifies normal, seizure-free and epileptic EEG signals with the accuracy of 96.92% which is significantly better than previous combining methods.

EPILEPTIC SEIZURE DETECTION USING A NEURAL NETWORK ENSEMBLE METHOD AND WAVELET TRANSFORM