Epileptic Detection and Classification Using Convolutional Neural Network with Dual Tree Complex Wavelet Features

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Sreelekha Panda, et. al.

Abstract

Epilepsy is a kind of brain disease that can be diagnosed by observation of EEG signals. Mostly it occurs within the children. However, some of the cases are observed in adults. It is a challenging task for physicians to detect this disease at an early stage. Authors in this work have classified the Epileptic and normal EEG signal by adopting the deep learning approach. For efficient features, dual tree complex wavelet (DTCWT) is considered. The decomposed wavelet features are used as the input to the convolutional neural network (CNN) classifier. Around 97% classification accuracy is observed by using the proposed approach.

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