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Original Article

Patient-independent automated pediatric seizure monitoring based on expert-labeled electrographic ictal data as core reference knowledge

Yoon Gi Chung, Jaeso Cho, Anna Cho, Hunmin Kim, Byung Chan Lim

Background: Automated seizure detection using scalp electroencephalography (EEG) is essential to the efficient monitoring of seizures in patients with epilepsy. However, patient-independent seizure detection remains challenging, primarily because of the inherent intersubject variability in EEG characteristics.
Purpose: We proposed a patient-independent seizure detection approach based on 1,604 single-channel electrographic focal-onset ictal EEG segments verified by epileptologists in patients with focal epilepsy. Methods:...

DOI: https://doi.org/10.3345/cep.2026.01011 [Accepted]

Review Article

Neurology

Big data analysis and artificial intelligence in epilepsy – common data model analysis and machine learning-based seizure detection and forecasting

Yoon Gi Chung, Yonghoon Jeon, Sooyoung Yoo, Hunmin Kim, Hee Hwang

Clin Exp Pediatr. 2022;65(6):272-282. Published online November 26, 2021

Web of Science 11 Crossref 12

· Big data analysis, such as common data model and artificial intelligence, can solve relevant questions and improve clinical care.
· Recent deep learning studies achieved 0.887–0.996 areas under the receiver operating characteristic curve for automated interictal epileptiform discharge detection.
· Recent deep learning studies achieved 62.3%–99.0% accuracy for interictal-ictal classification in seizure detection and 75.0%– 87.8% sensitivity with a 0.06–0.21/hr false positive rate in seizure forecasting.

DOI: https://doi.org/10.3345/cep.2021.00766

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Clinical and Experimental Pediatrics is an open access journal. All articles are distributed under the terms of the Creative Commons Attribution NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/)