Efficient Novel Method for EEG Signal Classification in Epileptic Seizure Identification using Metaheuristic Deep Learning

Using a metaheuristic deep learning strategy, the research focuses on the challenge of epileptic seizure detection. A diagnostic tool known as an electroencephalogram (EEG) may detect improperly functioning brain cells, the underlying cause of seizures. To enhance the precision of seizure detection, the offered approach merges deep learning with a metaheuristic optimization technique. This method involves preprocessing raw EEG data in order to extract fundamental characteristics. A classifier that is part of a deep learning system is subsequently fed these characteristics in stages. When it comes to finding a decent solution with high accuracy dimensions, the metaheuristic component finds the optimal values for the deep learning model. Due to the sluggish convergence and trapping of local minima in standard clustering methods, this approach seeks to improve detection performance. Using industry standard EEG datasets, we evaluate our hybrid technique’s performance to that of other relevant models. These findings demonstrate that the system is a dependable tool for real-time seizure detection, with sensitivity, specificity, and accuracy all surpassing the current standards. This method stands head and shoulders above its predecessors, proving its viability for clinical usage in epilepsy monitoring and diagnosis.