Detection of epileptic seizures in EEG signals using Deep Learning

Abstract

This research project aims to explore the use of deep learning for detecting epilepsy from EEG signals, a rising need since epilepsy is one of the most common neurological disorders. For this study, we propose a methodology that involves several stages such as, data collection, signal preprocessing, feature extraction, model design, training and, also evaluation using performance metrics. Convolutional neural network (CNN) was chosen due to its effectiveness in learning spatial and temporal patterns in EEG data. Each stage of the pipeline was carefully designed to ensure that the system could learn relevant features from the data while minimizing overfitting and ensuring generalizability. The results were promising and incorporated into a user interface as an aid to medical diagnosis.