ArticleJournal of clinical neurology (Seoul, Korea)2025
Deep-Learning-Based Automated REM Sleep Detection in Patients With REM Sleep Behavior Disorder: Is It Reliable?
Article in Journal of clinical neurology (Seoul, Korea), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Artificial Intelligence That Changes Clinical Neurology Practice: Translating Algorithms Into Actionable Care.Journal of clinical neurology (Seoul, Korea) · 2026Review
- A Deep Learning Approach for Classifying Rapid Eye Movement Sleep Behavior Disorder Using EEGNet.Journal of clinical neurology (Seoul, Korea) · 2026Article
- Automated EEG-based sleep staging in REM sleep behavior disorder using MSIF-Net: epoch-level validation and whole-night clinical agreement.Frontiers in neurologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
background and purposeRapid eye movement (REM) sleep without atonia makes it difficult to detect REM sleep stages using electromyography in patients with REM sleep behavior disorder (RBD). The objectives of this study were to propose an automated REM sleep detector that requires only electroencephalography (EEG) and electrooculography (EOG) data, and to evaluate its performance using real-world polysomnography (PSG) data in RBD patients.
methodsThis multicenter study used 310 PSG datasets obtained from 5 tertiary hospitals. The data were divided into RBD (
resultsThe U-Sleep-based REM sleep-detection algorithm correctly identified REM sleep with an area under the receiver operating characteristic curve (AUC) of 0.90±0.14. The classification performance of the REM sleep detector differed significantly between RBD and non-RBD patients (AUC=0.88±0.13 vs. 0.93±0.14,
conclusionsOur U-Sleep-based REM sleep detector based on only EEG and EOG data showed good performance in detecting REM sleep. However, it performed considerably worse in RBD, especially in PD with RBD. Using transfer learning with fine-tuning by expert review, a high-performance REM sleep-detecting system will be realized.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.