ORIGINAL RESEARCH article
Front. Neurol.
Sec. Pediatric Neurology
This article is part of the Research TopicInnovative Approaches in Pediatric Epilepsy: Early Diagnosis, Biomarkers, and Emerging TherapiesView all 6 articles
Deep Learning-based Real-Time Seizure Detection and Multi-Seizure Classification on Pediatric EEG
Provisionally accepted
- 1Massachusetts Institute of Technology, Cambridge, United States
- 2Korea Advanced Institute of Science and Technology Kim Jaechul Graduate School of Artificial Intelligence, Yuseong-gu, Republic of Korea
- 3The Cooper Union for the Advancement of Science and Art, New York, United States
- 4Korea University Guro Hospital, Seoul, Republic of Korea
- 5Yonsei University College of Medicine, Seodaemun-gu, Republic of Korea
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Abstract Background and Objective: To develop a reliable and accurate seizure detection method using deep learning models capable of detecting and classifying multiple seizure types in real time. Methods: We retrospectively collected electroencephalography (EEG) recordings, which were acquired as part of routine diagnostic tests for patients aged 3 months to ≤18 years of age with childhood absence epilepsy, infantile epileptic spasms syndrome, other generalized epilepsy, and focal epilepsy, between January 2018 and December 2022 at Severance Children's Hospital. We used EEG recordings from both seizure and non-seizure patients, which were downsampled to 200 Hz for real-time seizure detection and multi-classification. Results: Of the 199 patients (620 seizures), 49 (297 seizures) belonged to the childhood absence epilepsy group, 16 (200 seizures) to the infantile epileptic spasms syndrome group, 14 (76 seizures) to other generalized epilepsy group, 19 (47 seizures) to focal epilepsy group, and 101 to the normal group. The results showed the best overall performance of AUROC 0.98 and APROC of 0.73 with ResNet with Long-Short Term Network and a 12 s sliding window on real-time seizure detection task. Furthermore, ResNet50 without the frequency bands feature extractor showed the best overall weighted performance for multi-class seizure detection with 0.99 AUROC and 0.99 APPRC. Discussion: 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.
Keywords: deep learning, EEG, pediatric epilepsy, Real-time, Seizure detection
Received: 16 Oct 2025; Accepted: 31 Jan 2026.
Copyright: © 2026 Jeong, Lee, Kim, Yang and Kang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Donghwa Yang
Hoon-Chul Kang
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