Advanced Multimodal Learning for Seizure Detection and Prediction: Concept, Challenges, and Future Directions
Abstract
Epilepsy is a chronic neurological disorder characterized by recurrent unprovoked seizures, affects over 50 million people worldwide, and poses significant risks, including sudden unexpected death in epilepsy (SUDEP). Conventional unimodal approaches, primarily reliant on electroencephalography (EEG), face several key challenges, including low SNR, nonstationarity, inter- and intrapatient heterogeneity, portability, and real-time applicability in clinical settings. To address these issues, a comprehensive survey highlights the concept of advanced multimodal learning for epileptic seizure detection and prediction (AMLSDP). The survey presents the evolution of epileptic seizure detection (ESD) and prediction (ESP) technologies across different eras. The survey also explores the core challenges of multimodal and non-EEG-based ESD and ESP. To overcome the key challenges of the multimodal system, the survey introduces the advanced processing strategies for efficient AMLSDP. Furthermore, this survey highlights future directions for researchers and practitioners. We believe this work will advance neurotechnology toward wearable and imaging-based solutions for epilepsy monitoring, serving as a valuable resource for future innovations in this domain.
Cite
@article{arxiv.2601.05095,
title = {Advanced Multimodal Learning for Seizure Detection and Prediction: Concept, Challenges, and Future Directions},
author = {Ijaz Ahmad and Faizan Ahmad and Sunday Timothy Aboyeji and Yongtao Zhang and Peng Yang and Javed Ali Khan and Rab Nawaz and Baiying Lei},
journal= {arXiv preprint arXiv:2601.05095},
year = {2026}
}