Epileptic Seizure Prediction Using a Deep Hybrid CNN-GAN Model on EEG Data

Epileptic seizure detection from EEG signals is essential for the diagnosis and treatment of neurological conditions. However, this task is difficult because of the complex and fluctuating characteristics of EEG data. To overcome these difficulties, this research employs a specialized CNN-GAN hybrid model designed to improve seizure prediction by generating realistic synthetic data. This approach addresses class imbalance and enhances feature learning. The process begins with preprocessing EEG datasets to remove noise and artifacts. Data augmentation follows, expanding the dataset and reducing the risk of overfitting. Segmentation then isolates important features to boost prediction accuracy. Feature extraction is achieved using a custom CNN model, and classification is performed with a GAN model. The model demonstrates high accuracy, with results of 99.95% on the BONN Dataset, 98.93% on the CHB-MIT Dataset and 98.80% on the SWEC-ETHZ Dataset. Overall, the custom CNN-GAN hybrid model significantly improves the accuracy of predicting epileptic seizures from EEG signals across various datasets.