Classification of Epileptic EEG Signals Using Improved Atomic Search Optimization Algorithm

Abstract

Epilepsy is a neurological disorder that is distinguished by the presence of seizures, which are caused by abnormal brain activity. On average, about 60% of epilepsy cases are due to focal seizures, which affect a specified part of the cerebrum. Epileptic seizures can be cured at the initial stage, but untreated seizure for a long time cause severe effect and sometimes leads to death. Therefore, accurate classification of the epileptic Electroencephalogram (EEG) signal is an essential step in the diagnosis of epilepsy, aiding physicians in making the diagnosis. This research presents an effective classification of epileptic EEG signals using the improved atomic search optimization (IASO) algorithm and the random search strategy (RSS). The IASO is used to select the appropriate features but has a high probability to enhance the convergence rate. However, due to a lack of population variety and exploratory capabilities and this convergence rate may decrease in some circumstances. As a result, the RSS is offered to enhance the solution and improvise the exploration capabilities. The raw data of EEG signals are obtained from TUH, BONN, and BERN datasets. The raw data is pre-processed using an adaptive filtering method. The IASO is utilized in selecting the relevant features which ease the process of classification using LSTM. The results show that IASO attained better classification accuracy of 98.40% for TUH dataset which is comparatively lower than the existing Deep network model with 97%.