Automated Epileptic Seizure Detection using Improved Crystal Structure Algorithm with Stacked Autoencoder

Abstract

Epilepsy can be referred to as a neurological disorder, categorized by intractable seizures with serious consequences. To forecast such seizures, Electroencephalogram (EEG) datasets should be gathered continuously. EEG signals were recorded by using numerous electrodes fixed on the scalp that cannot be worn by patients continuously. Neurostimulators can intervene in advance and ignore the seizure rate. Its productivity is increased by using heuristics such as advanced seizure prediction. In recent times, several authors have deployed various deep learning approaches for predicting epileptic seizures, utilizing EEG signals. In this work, an Automated Epileptic Seizure Detection using Improved Crystal Structure Algorithm with Stacked Auto encoder (AESD-ICSASAE) technique has been developed. The presented AESD-ICSASAE technique executes a three-stage process. At the initial level, the AESD-ICSASAE technique applies min-max normalization approach to normalize the input data. Next, the AESD-ICSASAE technique uses ICSA based feature selection method for optimal choice of features. Finally, the SAE based classification process takes place and the hyperparameter selection process is performed by Arithmetic Optimization Algorithm (AOA). To depict the enhanced classification outcomes of the AESD-ICSASAE technique, series of experiments was made. Furthermore, the proposed method's results have been tested utilizing the CHB-MIT database, with results indicating an accuracy of 98.9%. These results validate the highest level of accuracy in seizure classification across all of the analyzed EEG data. A full set of experiments validated the AESD-ICSASAE method's enhancements.