ArticleNPJ digital medicine2024
Semi automatic quantification of REM sleep without atonia in natural sleep environment.
Article in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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.
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Who cites it
7 citing papers in PubMed.
- Fully-automated sleep staging for Parkinson's disease and isolated REM sleep behavior disorder.NPJ digital medicine · 2026Article
- Research priorities to improve classification of sleep-wake disorders.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2026Review
- Finger joint angle and gesture estimation under natural conditions with a soft printed electrode array.APL bioengineering · 2025Article
- Dry-printed electrodes for transcutaneous electrical stimulation of innervated muscles: Towards wearable and closed-loop stimulation.Journal of neuroengineering and rehabilitation · 2025Article
- The Future of Parasomnias.Journal of sleep research · 2025Review
- Isolated REM Sleep Behaviour Disorder-Is Screening Possible?Journal of sleep research · 2025Review
- Novel technologies for REM sleep behavior disorder detection for home screening in Parkinson's disease and related alpha-synucleinopathies.NPJ Parkinson's disease · 2025Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Polysomnography, the gold standard diagnostic tool in sleep medicine, is performed in an artificial environment. This might alter sleep and may not accurately reflect typical sleep patterns. While macro-structures are sensitive to environmental effects, micro-structures remain more stable. In this study we applied semi-automated algorithms to capture REM sleep without atonia (RSWA) and sleep spindles, comparing lab and home measurements. We analyzed 107 full-night recordings from 55 subjects: 24 healthy adults, 28 Parkinson's disease patients (15 RBD), and three with isolated Rem sleep behavior disorder (RBD). Sessions were manually scored. An automatic algorithm for quantifying RSWA was developed and tested against manual scoring. RSWAi showed a 60% correlation between home and lab. RBD detection achieved 83% sensitivity, 79% specificity, and 81% balanced accuracy. The algorithm accurately quantified RSWA, enabling the detection of RBD patients. These findings could facilitate more accessible sleep testing, and provide a possible alternative for screening RBD.
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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.