EpilepsyNet: A Self-Calibrating Deep Learning Network for Accurate and Robust Seizure Detection

Epilepsy affects millions of people worldwide and poses significant challenges for diagnosis and treatment. While traditional EEG analysis remains essential for seizure detection, it is often time-intensive and requires specialized expertise. To overcome these limitations, we proposed EpilepsyNet, an innovative deep learning architecture for automatic seizure detection. EpilepsyNet incorporates a self-calibration mechanism that enhances feature extraction by expanding the receptive field and enabling dynamic interactions across multiple EEG channels. Using neonatal EEG data, we validated EpilepsyNet’s effectiveness through five-fold cross-validation, demonstrating high classification accuracy and robustness across various performance metrics. Ablation experiments further underscored the important role of the self-calibrated convolution, as removing this component led to a notable decline in accuracy. These findings suggest that EpilepsyNet provides a scalable and reliable solution for seizure detection, with the potential to significantly improve clinical interventions by automating and optimizing the analysis of EEG data.