ReviewFrontiers in sleep2023
Overview of the hypnodensity approach to scoring sleep for polysomnography and home sleep testing.
Review in Frontiers in sleep, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Article
- Characterizing transition state in mouse vigilance with electroencephalogram-electromyogram hypnodensity.Sleep advances : a journal of the Sleep Research Society · 2026Article
- Automated sleep stage and event detection algorithms using quality-controlled polysomnography annotations.Sleep advances : a journal of the Sleep Research Society · 2026Article
- An explainable deep learning approach for sleep staging in sleep apnea patients across all age subgroups from pulse oximetry signals.Engineering applications of artificial intelligence · 2025Article
- Article
- Retrospective validation of automatic sleep analysis with grey areas model for human-in-the-loop scoring approach.Journal of sleep research · 2025Article
- Somnotate: A probabilistic sleep stage classifier for studying vigilance state transitions.PLoS computational biology · 2024Article
- Enhanced sleep staging with artificial intelligence: a validation study of new software for sleep scoring.Frontiers in artificial intelligence · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Human experts scoring sleep according to the American Academy of Sleep Medicine (AASM) rules are forced to select, for every 30-second epoch, one out of five stages, even if the characteristics of the neurological signals are ambiguous, a very common occurrence in clinical studies. Moreover, experts cannot score sleep in studies where these signals have not been recorded, such as in home sleep apnea testing (HSAT). In this topic review we describe how artificial intelligence can provide consistent and reliable scoring of sleep stages based on neurological signals recorded in polysomnography (PSG) and on cardiorespiratory signals recorded in HSAT. We also show how estimates of sleep stage probabilities, usually displayed as hypnodensity graph, can be used to quantify sleep stage ambiguity and stability. As an example of the application of hypnodensity in the characterization of sleep disordered breathing (SDB), we compared 49 patients with sleep apnea to healthy controls and revealed a severity-depending increase in ambiguity and decrease in stability during non-rapid eye movement (NREM) sleep. Moreover, using autoscoring of cardiorespiratory signals, we show how HSAT-derived apnea-hypopnea index and hypoxic burden are well correlated with the PSG indices in 80 patients, showing how using this technology can truly enable HSATs as alternatives to PSG to diagnose SDB.
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