Epileptic seizure identification from electroencephalography signal using DE-RBFNs ensemble

Abstract In this paper, an ensemble of radial basis function neural networks (RBFNs) optimized by differential evolution (DE) (DE- RBFNs) is presented for identification of epileptic seizure by analyzing the electroencephalography (EEG) signal. The ensemble is based on the bagging approach and the base learner is DE-RBFNs. The EEGs are decomposed with wavelet transform into different sub-bands and some statistical information is extracted from the wavelet coefficients to supply as the input to ensemble of DE-RBFNs. A benchmark publicly available dataset is used to evaluate the proposed method. The classification results confirm that the proposed ensemble of DE-RBFNs has greater potentiality to identify the epileptic disorders.

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