Multimodal EEG Seizure Prediction Method Based on Deep Learning

Siyu Chen

2025

Abstract

Epileptic seizure prediction has become a critical area of research due to its vital role in ensuring patient safety and improving quality of life. Electroencephalography (EEG), as a non-invasive tool with high temporal resolution, is significant in monitoring seizures. However, traditional EEG-based methods are constrained by the complexity of signals and the reliance on manual feature extraction, limiting their accuracy and scalability. The advent of deep learning has introduced automated feature extraction and end-to-end learning, significantly enhancing seizure prediction capabilities. Nonetheless, single-modality EEG approaches often fail to capture the diverse physiological changes associated with seizures. Multimodal methods have emerged to address this limitation. These methods integrate EEG with other physiological signals, such as electrocardiograms (ECG) and electrodermal activity (EDA), offering improved accuracy. This paper provides a systematic review of deep learning-based multimodal seizure prediction methods. It discusses the role of EEG and advances in deep learning, highlights the advantages of multimodal approaches in integrating multiple signals, and examines challenges such as data synchronization, computational efficiency, and practical deployment. The findings demonstrate the transformative potential of multimodal deep learning frameworks in achieving accurate real-time seizure prediction. Through comprehensive analysis, this research provides valuable insights for developing scalable seizure detection systems, thereby advancing both clinical practice and real-world applications.

Paper Citation

in Harvard Style

Chen S. (2025). Multimodal EEG Seizure Prediction Method Based on Deep Learning. In Proceedings of the 1st International Conference on Biomedical Engineering and Food Science - Volume 1: BEFS; ISBN 978-989-758-789-4, SciTePress, pages 10-15. DOI: 10.5220/0014299200004933

in Bibtex Style

@conference{befs25,
author={Siyu Chen},
title={Multimodal EEG Seizure Prediction Method Based on Deep Learning},
booktitle={Proceedings of the 1st International Conference on Biomedical Engineering and Food Science - Volume 1: BEFS},
year={2025},
pages={10-15},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0014299200004933},
isbn={978-989-758-789-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Biomedical Engineering and Food Science - Volume 1: BEFS
TI - Multimodal EEG Seizure Prediction Method Based on Deep Learning
SN - 978-989-758-789-4
AU - Chen S.
PY - 2025
SP - 10
EP - 15
DO - 10.5220/0014299200004933
PB - SciTePress