A Hybrid Model for Epileptic Seizure Prediction Using EEG Data

More than 65 million people’s quality of life is affected by a neurological brain condition epilepsy. When a seizure can be anticipated, therapeutic action can be employed to stop it from happening. The data analysis of epileptic seizures makes use of EEG impulses. In the interim, amplitude integrated Electroencephalography (aEEG) has shown promise in the identification of epileptic episodes. This article describes a method for automatically identifying epileptic episodes in EEG readings. Three steps make up the suggested methodology: preprocessing, feature selection, and classification. The current study proposes a deep learning-based seizure prediction system that includes preprocessing scalp EEG signals, extracting key characteristics implementing convolutional neural networks (CNN), and classifying them with the as distance of vector machines. The put forward framework demonstrates its powerful capability in the automatic the identification of seizures, as evidenced by its competitive hypothetical results on EEG datasets analyzed to state-of-the-art approach with a success rate of 98.57%.