EEG Based Epileptic Seizure Detection Using Binary Dragonfly Algorithm and Deep Neural Networks

Electroencephalogram (EEG) is a widely used method for monitoring brain activity, particularly in the context of seizure detection. EEG signals typically exhibit symmetrical patterns, but during seizures, asymmetrical signals may arise due to sudden spikes in electrical activity in one hemisphere. Spotting these trends can really help doctors figure out if someone has epilepsy and how to handle it. This study presents an advanced automatic seizure detection system that leverages EEG data, utilizing a Deep Neural Network (DNN) combined with the Binary Dragonfly Algorithm (BDFA) for enhanced feature selection.