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

2026-01-08

arXiv.org

AI Summary

Traditional unimodal approaches to seizure detection and prediction—such as those based solely on EEG—are limited by low signal-to-noise ratios, non-stationarity, inter-subject variability, and insufficient clinical real-time performance. This work presents a systematic review of recent advances in multimodal learning for epilepsy analysis and, for the first time, establishes a theoretical framework for multimodal seizure monitoring. It proposes a novel paradigm that integrates wearable physiological signals with neuroimaging data, leveraging heterogeneous multimodal fusion, lightweight deep learning architectures, real-time computation, and cross-subject generalization strategies. By shifting from isolated unimodal analysis to synergistic multimodal integration, this study outlines a scalable, wearable, and clinically viable roadmap for next-generation epilepsy monitoring systems.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

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.

Problem

• epileptic seizure detection
• epileptic seizure prediction
• multimodal learning
• EEG
• neurological disorder

Innovation

• multimodal learning
• seizure prediction
• non-EEG modalities
• wearable neurotechnology
• epilepsy monitoring