- Original Article
- Neurology
- Patient-independent automated pediatric seizure monitoring based on expert-labeled electrographic ictal data as core reference knowledge
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Yoon Gi Chung, Jaeso Cho, Anna Cho, Hunmin Kim, Byung Chan Lim
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Clin Exp Pediatr. 2026;69(8):665-675. Published online July 14, 2026
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Question: Can deep learning models trained on expert-verified electroencephalography segments reliably detect seizures and be applied to single-channel wearable monitoring?
Finding: This technique demonstrated 80.1%–100% sensitivity and low false-alarm rates. On 1,604 expert-labeled segments, the model outperformed previous studies in terms of latency and accuracy across unseen datasets.
Meaning: Expert-labeled data are essential for achieving universal seizure detection. This channel-specific approach enabled efficient and continuous monitoring using wearable devices. |
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- Editorial
- Neurology
- Network-based biomarkers in pediatric drug-resistant epilepsy
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Joseph Lim, Jaeso Cho, Hunmin Kim
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Clin Exp Pediatr. 2026;69(8):636-638. Published online July 24, 2026
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Conventional preoperative evaluation in pediatric drug-resistant epilepsy relies on clinical surrogates that quantify seizure exposure but not its impact on brain networks. A newly developed diffusion-weighted imaging connectome biomarker, anchored to intracranial electroencephalography-confirmed seizure onset zones and resolved across 6 neurocognitive domains, addresses this gap. This individualized approach classifies cognitive impairment with high accuracy and may offer a more precise, clinically interpretable tool to inform surgical timing. |
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- Original Article
- Neurology
- Long-term outcome in children with infantile epileptic spasms syndrome: a multicenter retrospective study in Korea
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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
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Clin Exp Pediatr. 2026;69(5):386-393. Published online February 19, 2026
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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. |
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- Review Article
- Neurology
- Big data analysis and artificial intelligence in epilepsy – common data model analysis and machine learning-based seizure detection and forecasting
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Yoon Gi Chung, Yonghoon Jeon, Sooyoung Yoo, Hunmin Kim, Hee Hwang
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Clin Exp Pediatr. 2022;65(6):272-282. Published online November 26, 2021
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· 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. |
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- Magnetoencephalography in pediatric epilepsy
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Hunmin Kim, Chun Kee Chung, Hee Hwang
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Clin Exp Pediatr. 2013;56(10):431-438. Published online October 31, 2013
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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... |
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- Case Report
- Hypokalemic periodic paralysis; two different genes responsible for similar clinical manifestations
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Hunmin Kim, Hee Hwang, Hae Il Cheong, Hye Won Park
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Clin Exp Pediatr. 2011;54(11):473-476. Published online November 30, 2011
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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... |
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