Automatic Detection of Epilepsy Based on Entropy Feature Fusion and Convolutional Neural Network

Abstract

Epilepsy is a neurological disorder, caused by various genetic and acquired factors. Electroencephalogram (EEG) is an important means of diagnosis for epilepsy. Aiming at the low efficiency of clinical artificial diagnosis of epilepsy signals, this paper proposes an automatic detection algorithm for epilepsy based on multifeature fusion and convolutional neural network. Firstly, in order to retain the spatial information between multiple adjacent channels, a two-dimensional Eigen matrix is constructed from one-dimensional eigenvectors according to the electrode distribution diagram. According to the feature matrix, sample entropy SE, permutation entropy PE, and fuzzy entropy FE were used for feature extraction. The combined entropy feature is taken as the input information of three-dimensional convolutional neural network, and the automatic detection of epilepsy is realized by convolutional neural network algorithm. Epilepsy detection experiments were performed in CHB-MIT and TUH datasets, respectively. Experimental results show that the performance of the algorithm based on spatial multifeature fusion and convolutional neural network achieves excellent results.