Improving Cross-Patient Epilepsy Detection via EEG Decomposition into Canonical Brain Rhythms with Deep Learning

Epilepsy affects more than 50 million people world-wide, and almost 80% of them live in low-income countries with limited access to medical and public services. Beyond these challenges, epileptic patients also face other problems, such as stigma and social exclusion due the misunderstanding of epilepsy. Thus, epilepsy has become a major public health problem with a high social impact. Electroencephalography (EEG) remains the primary tool for diagnosing epilepsy; however, the traditional procedure of reviewing long EEG recordings is time-consuming, error-prone, and highly dependent on the neurologist’s experience. Recent advances in deep learning (DL) have driven the development of new methods for automatic epilepsy detection. Despite these advances, most methods are not generalizable to all patients, limiting their clinical applicability in real-life cases. In this work, we present a cross-patient method capable of improving epilepsy detection by spectral decomposition of EEG signals into canonical brain rhythms. These spectral bands improve the signal significance and the model performance. The proposal was evaluated in a cross-patient validation scheme on the CHB-MIT dataset and proved superior performance using EEG signals from the interictal and ictal epilepsy stages. The model achieved of 100% of sensibility and specificity using the theta band, outperforming the state-of-the-art methods and offering a promising step towards real-world clinical implementation.