The term Epilepsy refers to a most commonly occurring brain disorder after a migraine. Early identification of incoming seizures significantly impacts the lives of people with Epilepsy. Automated detection of epileptic seizures (ES) has dramatically improved the life quality of the patients. Recent Electroencephalogram (EEG) related seizure detection mechanisms encountered several difficulties in real-time. The EEGs are the non-stationary signal, and seizure patterns would change with patients and recording sessions. Further, EEG data were disposed to wide noise varieties that adversely moved the recognition accuracy of ESs. Artificial intelligence (AI) methods in the domain of ES analysis use traditional deep learning (DL), and machine learning (ML) approaches. This article introduces an Oppositional Aquila Optimizer-based Feature Selection with Deep Belief Network for Epileptic Seizure Detection (OAOFS-DBNECD) technique using EEG signals. The primary aim of the presented OAOFS-DBNECD system is to categorize and classify the presence of ESs. The suggested OAOFS-DBNECD technique transforms the EEG signals into .csv format at the initial stage. Next, the OAOFS technique selects an optimal subset of features using the preprocessed data. For seizure classification, the presented OAOFS-DBNECD technique applies Artificial Ecosystem Optimizer (AEO) with a deep belief network (DBN) model. An extensive range of simulations was performed on the benchmark dataset to ensure the enhanced performance of the presented OAOFS-DBNECD algorithm. The comparison study shows the significant outcomes of the OAOFS-DBNECD approach over other methodologies. In addition, the result of the suggested approach has been evaluated using the CHB-MIT database, and the findings demonstrate accuracy of 97.81%. These findings confirmed the best seizure categorization accuracy on the EEG data considered.

Approximately 50 million people worldwide suffer from Epileptic Seizure (ES), a persistent neurological disorder that cannot spread from person to person. Electroencephalography (EEG) is a tool that is often used to identify and diagnose epilepsy by observing how the brain works. However, analyzing EEG recordings to identify epileptic activity can be difficult, time-consuming, and requires specialist expertise. However, a precise and early diagnosis of epilepsy is necessary to start anti-seizure medication treatment and reduce the risk of consequences from recurrent episodes. In this paper, a modified Gorilla Troops Optimization with a Deep Learning based ES Prediction model (MGTODL-ESP) using EEG signals is implemented. The proposed MGTODL-ESP model comprises two main processes: feature selection and prediction. The MGTODL-ESP model uses a modified gorilla troops optimization (MGTO) based feature selection algorithm to select the optimal subset of features. The MGTO-based Gated Recurrent Unit (GRU) model predicts different types of ES. Finally, the Grey Wolf Optimizer (GWO) algorithm was used to tune the parameters of the MGTODL model. The outline of the MGTO-ESP-based feature selection and Grey Wolf Optimizer (GWO)-based parameter tuning indicates the novelty of this research. A comprehensive empirical study was conducted using a benchmark CHB-MIT scalp EEG database from IEEE DataPort to investigate the improved prediction performance of the MGTODL-ESP model. A comparison of the different methods showed that the MGTODL-ESP approach was the most accurate, with an accuracy rate of 98.50%.

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.