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Improving convolutional neural network using accelerated proximal gradient method for epilepsy diagnosis

The task of epilepsy diagnosing in medicine by classification of electroencephao-graph (EEG) signals is considered. Since an EEG signal has a large number of dimensions as an input sample vector, many previous classification methods have been proposed as hybrid frameworks, which are structurally complex and computationally expensive. In this paper, convolutional neural network (CNN) is used to realize feature extraction and classification simultaneously. The scheme of CNN is adopted to overcome the curse of dimensionality. Meanwhile, the accelerated proximal gradient method is used to increase the training ratio. Experimental results show that the proposed method achieves ideal accuracy of epilepsy diagnosis and converges faster than CNNs based on traditional gradient back propagation.