DiffNet-A Diffusion Convolutional Neural Network for Classification of Epileptic Seizure

Epileptic seizure detection using electroencephalogram (EEG) signals remains a challenging problem in neuroscience and biomedical engineering. In this study, we propose DiffNet, a novel Diffusion Convolutional Neural Network (DCNN) designed for accurate and automated classification of epileptic seizures. DiffNet combines spatial and temporal feature extraction capabilities, leveraging graph-based diffusion processes and convolutional layers to enhance classification performance. The model was evaluated on multiple benchmark EEG datasets, including Bonn, ResearchGate, and Mendeley Data, achieving an average accuracy of 99.7% during training and 98.3% during validation. Additionally, DiffNet outperformed existing state-of-theart algorithms across various statistical metrics such as precision (97.2%), sensitivity (96.8%), and F1-score (97.5%). These results highlight the robustness and reliability of DiffNet in addressing variability in EEG signal patterns. The proposed architecture also demonstrates reduced computational complexity, making it suitable for real-time seizure detection applications.