Time-Frequency Domain Deep Convolutional Neural Network for the Classification of Focal and Non-Focal EEG Signals

Abstract

The neurological disease such as the epilepsy is diagnosed using the analysis of electroencephalogram (EEG) recordings. The areas of the brain associated with the consequence of epilepsy are termed as epileptogenic regions. The focal EEG signals are generated from epileptogenic areas, and the nonfocal signals are obtained from other regions of the brain. Thus, the classification of the focal and non-focal EEG signals are necessary for locating the epileptogenic areas during surgery for epilepsy. In this paper, we propose a novel method for the automated classification of focal and non-focal EEG signals. The method is based on the use of the synchrosqueezing transform (SST) and deep convolutional neural network (CNN) for the classification. The time-frequency matrices of EEG signal are evaluated using both Fourier SST (FSST) and wavelet SST (WSST). The two-dimensional (2D) deep CNN is used for the classification using the time-frequency matrix of EEG signals. The experimental results reveal that the proposed method attains the accuracy, sensitivity, and specificity values of more than 99% for the classification of focal and non-focal EEG signals. The method is compared with existing approaches for the discrimination of focal and non-focal categories of EEG signals.