EEG-based seizure detection system using deep learning

The project focuses on developing an EEG-based seizure detection system using deep learning to improve the accuracy and efficiency of epilepsy diagnosis. It utilizes a one-dimensional Convolutional Neural Network (1D-CNN) to analyze EEG signals and automatically classify them into four categories: Seizure, Low Seizure, Medium Seizure, and High Seizure. The system includes preprocessing steps such as data normalization and reshaping to make the signals suitable for model input, followed by training and prediction. By replacing manual EEG analysis—which is time-consuming and prone to human error—the proposed model offers a faster, more reliable, and computationally efficient solution, enabling clinicians to identify seizure severity quickly and prioritize patient care effectively in real-world medical applications.