Enhancing Epileptic Seizure Detection Through Advanced Artificial Intelligence Analysis of EEG Signals

Epilepsy is a chronic neurological disorder characterized by recurrent, unprovoked seizures resulting from abnormal electrical activity in the brain. Seizures are episodes of disturbed brain function caused by brief hypersynchronous neuronal firing. EEG is the quintessential modality for monitoring and identifying these discordant patterns. This study aimed to enhance the diagnostic accuracy of detecting epileptic seizures by applying sophisticated analytical techniques, including machine learning and deep learning, to electroencephalogram (EEG) data. we performed comprehensive preprocessing on a standardized dataset of EEG records from 100 subjects, with 50 diagnosed with epilepsy and 50 classified as non-epileptic controls. The preprocessing encompassed steps including data formatting, labeling, scaling, cleaning, and partitioning into training and test sets. The machine learning models employed were Random Forest Classifier (RFC) and Gradient Boosting (GB), while Long Short-Term Memory (LSTM) networks represented deep learning. Rigorous performance assessment revealed remarkable effectiveness for all three models in classifying EEG data into seizure and non-seizure categories. Specifically, the RFC achieved an accuracy of 99.7%, GB demonstrated 92% accuracy, and the LSTM network exhibited 96.9% accuracy. The robust performance across these computational methods highlights their potential to significantly improve seizure detection and diagnosis in epilepsy care through the analysis of EEG signals.