Deep learning-based real-time seizure detection and multi-seizure classification on pediatric EEG
Our approach proposes robust methods which include EEG preprocessing strategy with real-time detection/classification of multiple seizures, which helps monitor pediatric seizure. The result shows that real-time seizure detection can be effectively applied to real-world clinical datasets from a pediatric epilepsy unit with realistic performance and speed.
Authors
- Kwanhyung Lee (ORCID: https://orcid.org/0000-0002-1191-5535)
- Seyun Kim
- Hyewon Jeong (ORCID: https://orcid.org/0000-0002-6552-5276)
- Hoon-Chul Kang
- Donghwa Yang
Institutions
- Korea Advanced Institute of Science and Technology (KR)
- Severance Hospital (KR)
- Korea University (JP)
- Massachusetts Institute of Technology (US)
- Cooper Union (US)
Publication Details
- Journal
- Frontiers in Neurology
- Published
- 2026-02-23
- DOI
- https://doi.org/10.3389/fneur.2026.1726258
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Korea University Guro Hospital
- Korea University Medicine
- National Research Foundation
- Korea University
- Korea Health Industry Development Institute
- National Research Foundation of Korea