Robust Seizure Prediction Model Using EEG Signal for Temporal Lobe Epilepsy Leveraging Deep Learning and Continual LearninG

This project focuses on developing a robust seizure prediction model using EEG signals for Temporal Lobe Epilepsy (TLE) by leveraging advanced deep learning and continual learning techniques. The dataset used for this project, available from Kaggle, contains EEG signals that are essential for the accurate classification of seizure events. The model integrates a combination of algorithms, including MobileNet, ResNet-GRU, Swin-LSTM, and ViT-CNN, to improve prediction accuracy. The system's objective is to process EEG data, classify seizures, and predict seizure occurrences based on EEG data, providing valuable insights for medical professionals. The system is implemented using Python (Flask framework) for backend, and HTML, CSS, and JavaScript for the frontend, with features such as user registration, login, and prediction. This model aims to offer an advanced solution for improving seizure prediction and management.

Keywords:

Seizure prediction, EEG signal, Temporal Lobe Epilepsy, deep learning, continual learning, MobileNet, ResNet-GRU, Swin-LSTM, ViT-CNN, medical application.