Epileptic Seizure Detection using an Advanced Deep Learning Model Applied to Epileptic EEG Signals: A Survey and Analysis

The chronic neurological disorder known as epilepsy is characterized by frequent, unprovoked seizures that impacts millions of people globally. In order to effectively treatment and management epilepsy, early and accurate diagnosis is essential. Automated detection and categorization of epilepsy using Electroencephalogram (EEG) signals has been made possible by Deep Learning (DL) models. Several DL models, such as Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and hybrid models, have been used for epilepsy detection. The research also evaluates and contrasts the models according to their structures, data processing methods, and performance indicators like specificity, sensitivity, and accuracy. Furthermore, the survey delves into the pros and cons of each method, highlighting the potential for future improvements in terms of model interpretability and real-time application