ArticleScientific reports2022
RBDtector: an open-source software to detect REM sleep without atonia according to visual scoring criteria.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Fully-automated sleep staging for Parkinson's disease and isolated REM sleep behavior disorder.NPJ digital medicine · 2026Article
- Case Report: non-monotonic longitudinal RSWA with tonic-phasic dissociation across serial attended video-polysomnography in isolated REM sleep behaviour disorder.Frontiers in sleep · 2026Article
- Validation of RBDtector: An Open-Source Automated Software for Scoring REM Sleep Without Atonia.Journal of sleep research · 2025Article
- The Future of Parasomnias.Journal of sleep research · 2025Review
- Isolated REM Sleep Behaviour Disorder-Is Screening Possible?Journal of sleep research · 2025Review
- Diagnostic performance of actigraphy in Alzheimer's disease using a machine learning classifier - a cross-sectional memory clinic study.Alzheimer's research & therapy · 2025Article
- Semi automatic quantification of REM sleep without atonia in natural sleep environment.NPJ digital medicine · 2024Article
- Clinical subtypes in patients with isolated REM sleep behaviour disorder.NPJ Parkinson's disease · 2023Article
- REM Behavior Disorder: Implications for PD Therapeutics.Current neurology and neuroscience reports · 2023Review
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Authors and funding
16 authors.
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Abstract
REM sleep without atonia (RSWA) is a key feature for the diagnosis of rapid eye movement (REM) sleep behaviour disorder (RBD). We introduce RBDtector, a novel open-source software to score RSWA according to established SINBAR visual scoring criteria. We assessed muscle activity of the mentalis, flexor digitorum superficialis (FDS), and anterior tibialis (AT) muscles. RSWA was scored manually as tonic, phasic, and any activity by human scorers as well as using RBDtector in 20 subjects. Subsequently, 174 subjects (72 without RBD and 102 with RBD) were analysed with RBDtector to show the algorithm's applicability. We additionally compared RBDtector estimates to a previously published dataset. RBDtector showed robust conformity with human scorings. The highest congruency was achieved for phasic and any activity of the FDS. Combining mentalis any and FDS any, RBDtector identified RBD subjects with 100% specificity and 96% sensitivity applying a cut-off of 20.6%. Comparable performance was obtained without manual artefact removal. RBD subjects also showed muscle bouts of higher amplitude and longer duration. RBDtector provides estimates of tonic, phasic, and any activity comparable to human scorings. RBDtector, which is freely available, can help identify RBD subjects and provides reliable RSWA metrics.
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