ESIMD: Epileptic seizure identification using metaheuristic deep learning technique

Abstract

AbstractThe identification of epileptic seizure from electroencephalogram (EEG) segments is significant for seizure identification and classification. In epilepsy, the patients develop epileptic seizures. These lead to neurological disorders and abnormal brain activities. The manual identification of seizures is not very efficient. As the manual analysis of EEG data requires expert neurologists it is prone to inaccuracies. Therefore, automated methods for seizure identification are required. Deep learning methods have shown efficiency in the seizure classification problem. Many challenges are faced by the existing methods owing to the properties of the EEG signals like transiency, non‐stationary behaviour and presence of noise. Therefore, to overcome these challenges and design a highly efficient method an automated method based on deep learning and spotted hyena optimization (SHO) algorithm is proposed. The SHO algorithm is used to initialize the network as it avoids the local minima problem. Epileptic seizure identification using metaheuristic deep learning (ESIMD) is proposed in this paper which is a hybridization of spotted hyena and deep convolution network. The convolutional neural network used has two blocks, the first block is used for feature extraction and the second block for seizure classification. ESIMD is used for seizure detection and classification. The proposed algorithm can handle the complex and large number of features in EEG signals. The performance of the proposed is evaluated by applying it on EEG Bonn Dataset. The results are compared with 13 other conventional methods. The comparative study shows that ESIMD gives higher accuracy than the existing methods.