Clinical and Experimental Pediatrics

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]

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Neurology

Long-term outcome in children with infantile epileptic spasms syndrome: a multicenter retrospective study in Korea

Sun Ah Choi, Minhye Kim, Hye Jin Kim, Woo Joong Kim, Byung Chan Lim, Ji Yeon Han, Hunmin Kim, Min-Jee Kim, Mi-Sun Yum, Jiwon Lee, Jeehun Lee, Hyewon Woo, Jon Soo Kim

Clin Exp Pediatr. 2026;69(5):386-393. Published online February 19, 2026

Web of Science 1 Crossref 1

Question: How have epilepsy and cognitive outcomes of children with infantile epileptic spasms syndrome (IESS) evolved over the past 20 years?
Finding: Approximately 78% of children developed chronic epilepsy, and one-third progressed to drug-resistant epilepsy, while 90% of them exhibited intellectual disabilities.
Meaning: Given the poor outcomes associated with IESS, consensus guidelines tailored to Korean clinical practice are required to ensure timely treatment and improve outcomes.

DOI: https://doi.org/10.3345/cep.2025.02089

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

Magnetoencephalography in pediatric epilepsy

Hunmin Kim, Chun Kee Chung, Hee Hwang

Clin Exp Pediatr. 2013;56(10):431-438. Published online October 31, 2013

Crossref 8

Magnetoencephalography (MEG) records the magnetic field generated by electrical activity of cortical neurons. The signal is not distorted or attenuated, and it is contactless recording that can be performed comfortably even for longer than an hour. It has excellent and decent temporal resolution, especially when it is combined with the patient's own brain magnetic resonance imaging (magnetic source imaging). Data...

DOI: https://doi.org/10.3345/kjp.2013.56.10.431

Case Report

Hypokalemic periodic paralysis; two different genes responsible for similar clinical manifestations

Hunmin Kim, Hee Hwang, Hae Il Cheong, Hye Won Park

Clin Exp Pediatr. 2011;54(11):473-476. Published online November 30, 2011

Crossref 10

Primary hypokalemic periodic paralysis (HOKPP) is an autosomal dominant disorder manifesting as recurrent periodic flaccid paralysis and concomitant hypokalemia. HOKPP is divided into type 1 and type 2 based on the causative gene. Although 2 different ion channels have been identified as the molecular genetic cause of HOKPP, the clinical manifestations between the 2 groups are similar. We report the...

DOI: https://doi.org/10.3345/kjp.2011.54.11.473

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