Advanced Multimodal Learning for Seizure Detection and Prediction: Concept, Challenges, and Future Directions

TL;DR

This paper reviews advanced multimodal learning techniques for epileptic seizure detection and prediction, addressing current challenges and proposing future research directions to improve neurotechnology applications.

Contribution

It provides a comprehensive survey of AMLSDP, including evolution, challenges, processing strategies, and future directions for multimodal epilepsy monitoring technologies.

Findings

Highlights the evolution of seizure detection technologies.

Identifies key challenges in multimodal and non-EEG approaches.

Suggests advanced processing strategies for improved AMLSDP.

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.