Epileptic Seizure Detection System

This comprehensive machine learning system is dedicated to the detection and prediction of epileptic seizures using EEG data. The primary goal is to support early medical intervention and improve safety for patients through accurate classification of seizure activity.

The project features an advanced pipeline that includes robust data preprocessing and feature extraction from EEG signals. It utilizes sophisticated deep learning architectures, specifically CNN and LSTM models, to enhance the accuracy and sensitivity of the seizure detection process.

Detailed evaluation metrics are integrated into the training phase to ensure the models generalize well across different datasets. This includes the generation of confusion matrices, ROC curves, and cross-validation results.

For practical application, the system is deployed as a Streamlit-based web app, allowing users to interact with the models and visualize EEG data in real-time.