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

TL;DR

The paper surveys advanced multimodal learning for epileptic seizure detection and prediction (AMLSDP), arguing that EEG alone is limited by noise, nonstationarity, and variability. It reviews a broad set of modalities—physiological signals (ECG, EDA, EMG, PPG), neuroimaging (MEG, fNIRS, PET, fMRI), and video—to enable robust, real-time monitoring, and outlines fusion strategies, neural decoding methods, evaluation metrics, and edge-computing considerations. Key contributions include a structured framework for data-, feature-, and decision-level fusion, transfer and adaptive learning approaches to generalization, and a detailed roadmap for deploying AMLSDP in wearable and clinical settings. The work emphasizes the importance of high-quality multimodal datasets, self-supervised learning, and explainable AI to achieve clinically trustworthy, scalable epilepsy management solutions with tangible impact on patient safety and SUDEP risk reduction.

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.

Figures (6)

• Figure 1: Outline of the survey paper. We explore a comprehensive overview of advanced multimodal learning for ESD and ESP, including the multimodal monitoring system, the core challenges of non-EEG and multimodality-based seizure detection and prediction, advanced strategies, and future directions.
• Figure 2: A typical epileptic seizure detection and prediction pipeline: EEG is recorded and preprocessed, key pre-ictal/ictal biomarkers are extracted, and relevant features are fed into a decision module that identifies seizure-related changes and triggers patient alerts. The typical pipeline presents a feature engineering and decision-making system with diagnosis and treatment management.
• Figure 3: Evolution of epilepsy management technologies, showing seizure detection (1950-2025) and prediction (1975-2025) timelines. The flow highlights the shift in the evaluation of the biomedical signals processing and key AI milestones driving real-time epileptic seizure detection and prediction.
• Figure 4: Advanced multimodal system for seizure detection and prediction integrating physiological, imaging, and video data. The pipeline includes multimodal fusion, feature engineering, neural decoding, and edge-based validation, enabling early prediction, clinical diagnosis, personalized treatment, and real-time alerts.
• Figure 5: Advanced multimodal fusion strategies include (a) early fusion (combining data before processing), (b) intermediate fusion (combining features at an intermediate layer), and (c) late fusion (combining predictions from separate models).