ArticleBrain sciences2024
Machine Learning Predicts Phenoconversion from Polysomnography in Isolated REM Sleep Behavior Disorder.
Article in Brain sciences, 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 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.
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Who cites it
7 citing papers in PubMed.
- Article
- Hypnogram and Hypnodensity Analysis of REM Sleep Behaviour Disorder Using Both EEG and HRV-Based Sleep Staging Models.Journal of sleep research · 2025Article
- The Future of Parasomnias.Journal of sleep research · 2025Review
- Machine learning-based brain magnetic resonance imaging radiomics for identifying rapid eye movement sleep behavior disorder in Parkinson's disease patients.BMC medical imaging · 2025Article
- Sleep as the Foundation of Brain Health.Seminars in neurology · 2025Review
- Predicting Phenoconversion in Isolated RBD: Machine Learning and Explainable AI Approach.Clocks & sleep · 2025Article
- Sleep Disturbance in Parkinson's Disease: Consequences for the Brain and Disease Progression - A Narrative Review.Nature and science of sleep · 2025Review
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Isolated rapid eye movement (REM) sleep behavior disorder (iRBD) is a prodromal stage of alpha-synucleinopathies. This study aimed at developing a fully-automated machine learning framework for the prediction of phenoconversion in patients with iRBD by using data recorded during polysomnography (PSG). A total of 66 patients with iRBD were included, of whom 18 converted to an overt alpha-synucleinopathy within 2.7 ± 1.0 years. For each patient, a baseline PSG was available. Sleep stages were scored automatically, and time and frequency domain features were derived from electromyography (EMG) and electroencephalography (EEG) signals in REM and non-REM sleep. Random survival forest was employed to predict the time to phenoconversion, using a four-fold cross-validation scheme and by testing several combinations of features. The best test performances were obtained when considering EEG features in REM sleep only (Harrel's C-index: 0.723 ± 0.113; Uno's C-index: 0.741 ± 0.11; integrated Brier score: 0.174 ± 0.06). Features describing EEG slowing had high importance for the machine learning model. This is the first study employing machine learning applied to PSG to predict phenoconversion in patients with iRBD. If confirmed in larger cohorts, these findings might contribute to improving the design of clinical trials for neuroprotective treatments.
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Registered trials
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