A hybrid deep learning framework for epileptic seizure prediction using scalp and intracranial EEG data

Epilepsy is a prevalent neurological disorder affecting over 50 million people globally, often impairing quality of life due to unpredictable and recurrent seizures. Despite advances in seizure prediction, challenges remain due to variability in EEG signals. EEG data exists in two primary modalities: non-invasive scalp EEG (sEEG) and invasive intracranial EEG (iEEG), and current deep learning models often exhibit limited generalizability across these modalities due to signal and structural differences. In this study, we propose a custom hybrid CNN-LSTM model optimized for scalp EEG, and assess its effectiveness across modalities by also evaluating it on invasive EEG datasets. We utilize the CHB-MIT (sEEG) and AES-Kaggle (iEEG) datasets employing a unified pipeline of preprocessing, signal transformation, and deep learning techniques. The model reported an accuracy of 98.84% on sEEG, using a 10-minute seizure anticipation window, outperforming conventional CNN, LSTM and several recent state-of-the-art models. On iEEG data, the hybrid model performed comparably well, achieving an impressive accuracy of 97.18%. The models are further validated using real-world scalp EEG data collected from the General Hospital Lahore, Pakistan. This additional validation demonstrates strong generalizability to clinical settings and confirms the practical applicability of the proposed approach. These findings not only highlight the effectiveness of the proposed approach for seizure prediction but also underscore its readiness for real-world deployment, potentially enabling reliable early warning systems to improve the lives of epilepsy patients.