Studying of deep neural networks and delta and alpha sub-bands harmony signals for Prediction of epilepsy

Abstract

Epilepsy, a seizure disorder, is one of the significant diseases in the global community. More than 1% of the world’s population is affected by this disease. It is controlled with medicine in a mild case. Neurologists use Electroencephalography (EEG) to diagnose epilepsy in most medical centers and hospitals. In recent years, researchers have conducted numerous studies to estimate epilepsy attacks using EEG. In this study, a new method is presented to enhance the accuracy, sensitivity, and other necessary parameters for estimating epilepsy attacks. In the proposed algorithm, the processing of brain signals is performed in two stages. In the first stage, the brain signals are decomposed into delta, theta, beta and alpha sub-bands using Discrete Wavelet Transform (DWT). Subsequently, the accuracy of the sub-bands are analyzed using a Long Short-Term Memory Neural Network (LSTM). Sub-bands with an accuracy of over 70% are selected for the second stage. In the second processing stage, selected sub-band harmonic signal images are used as input for a convolutional neural network (CNN) to extract features and make the final decision. The use of the proposed method results in an improvement in all parameters for estimating epilepsy attacks, including accuracy, sensitivity, and AUC. The results of the proposed method show a 45% increase compared to the conventional method.