ArticleJournal of clinical neurology (Seoul, Korea)2026
A Deep Learning Approach for Classifying Rapid Eye Movement Sleep Behavior Disorder Using EEGNet.
Article in Journal of clinical neurology (Seoul, Korea), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Artificial Intelligence That Changes Clinical Neurology Practice: Translating Algorithms Into Actionable Care.Journal of clinical neurology (Seoul, Korea) · 2026Review
- Unmasking the Prodrome: A Deep-Learning Bridge to Precision Diagnostics in Rapid Eye Movement Sleep Behavior Disorder.Journal of clinical neurology (Seoul, Korea) · 2026Article
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Authors and funding
7 authors.
Funding
Abstract
background and purposeRapid eye movement (REM) sleep behavior disorder (RBD) is a prodromal marker for α-synucleinopathies, and its early and accurate diagnosis provides invaluable insights into the development of neurodegenerative diseases. This study aimed to propose a deep-learning-based automated framework for RBD classification and to evaluate its performance using real-world, multi-cohort polysomnography (PSG) data.
methodsREM electromyogram (EMG) data of 227 PSG recordings collected across five tertiary hospitals were analyzed. The dataset was divided into RBD (
resultsThe EEGNet-based classifier achieved a receiver operating characteristics-area under the curve of 0.812 (95% confidence interval [CI], 0.744-0.871) using manual staging, which was significantly higher than the 0.777 (95% CI, 0.704-0.843) achieved with the automated U-Sleep approach (
conclusionsOur EEGNet-based RBD classifier, focused solely on EMG data, demonstrated reasonable performance in detecting RBD. This proposed model has the potential to reduce clinicians' workload and support diagnostic decision-making and enabling more accurate RBD classification. This multi-center study highlights the feasibility of an automated and generalizable RBD detection framework, paving the way for broader clinical implementation.
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