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Original Article

Neurology

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

Clin Exp Pediatr. 2026;69(8):665-675. Published online July 14, 2026

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.

DOI: https://doi.org/10.3345/cep.2026.01011

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