ArticleJournal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine2021
Estimating sleep stages using cardiorespiratory signals: validation of a novel algorithm across a wide range of sleep-disordered breathing severity.
Article in Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed, 38 citations in OpenAlex.
- Predicting sleep state from continuous positive airway pressure flow in patients with obstructive sleep apnea.Sleep medicine · 2026Article
- Validation of a deep learning-based system for sleep staging, arousal detection, and respiratory event scoring in atrial fibrillation patients.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2026Article
- Modified scoring criteria to improve the accuracy of the home sleep apnea test.Sleep & breathing = Schlaf & Atmung · 2026Article
- Apnea-hypopnea index estimation using overnight chest-wall accelerometry.Frontiers in sleep · 2026Article
- Sleep stage classification using instantaneous heart and breathing rates with a TCN-BiGRU-SE model.Frontiers in bioengineering and biotechnology · 2026Article
- Longitudinal cardiorespiratory wearable sleep staging in the home.Frontiers in neuroscience · 2026Article
- Beyond the apnea-hypopnea index: a call to action for a multidimensional evaluation in obstructive sleep apnea.Frontiers in sleep · 2026Article
- Unconstrained deep learning-based sleep stage classification using cardiorespiratory and body movement activities in adults with suspected sleep apnea.Proceedings of the Japan Academy. Series B, Physical and biological sciences · 2025Article
- Detecting arousals and sleep from respiratory inductance plethysmography.Sleep & breathing = Schlaf & Atmung · 2025Article
- Overnight Sleep Staging Using Chest-Worn Accelerometry.Sensors (Basel, Switzerland) · 2024Article
- Paediatric sleep diagnostics in the 21st century: the era of "sleep-omics"?European respiratory review : an official journal of the European Respiratory Society · 2024Review
- Performance of cardiorespiratory-based sleep staging in patients using beta blockers.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2024Article
- Diagnosing OSA and Insomnia at Home Based Only on an Actigraphy Total Sleep Time and RIP Belts an Algorithm "Nox Body Sleep™".Nature and science of sleep · 2024Article
- Deep learning-based sleep stage classification with cardiorespiratory and body movement activities in individuals with suspected sleep disorders.Scientific reports · 2023Article
- A computationally efficient algorithm for wearable sleep staging in clinical populations.Scientific reports · 2023Article
- Article
- Autonomic arousal detection and cardio-respiratory sleep staging improve the accuracy of home sleep apnea tests.Frontiers in physiology · 2023Article
- Overview of the hypnodensity approach to scoring sleep for polysomnography and home sleep testing.Frontiers in sleep · 2023Review
- An automated heart rate-based algorithm for sleep stage classification: Validation using conventional polysomnography and an innovative wearable electrocardiogram device.Frontiers in neuroscience · 2022Article
- Automated Scoring of Sleep and Associated Events.Advances in experimental medicine and biology · 2022Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 2 countries.
Funding
Abstract
STUDY
objectivesWe have developed the CardioRespiratory Sleep Staging (CReSS) algorithm for estimating sleep stages using heart rate variability and respiration, allowing for estimation of sleep staging during home sleep apnea tests. Our objective was to undertake an epoch-by-epoch validation of algorithm performance against the gold standard of manual polysomnography sleep staging.
methodsUsing 296 polysomnographs, we created a limited montage of airflow and heart rate and deployed CReSS to identify each 30-second epoch as wake, light sleep (N1 + N2), deep sleep (N3), or rapid eye movement (REM) sleep. We calculated Cohen's kappa and the percentage of accurately identified epochs. We repeated our analyses after stratification by sleep-disordered breathing (SDB) severity, and after adding thoracic respiratory effort as a backup signal for periods of invalid airflow.
resultsCReSS discriminated wake/light sleep/deep sleep/REM sleep with 78% accuracy; the kappa value was 0.643 (95% confidence interval, 0.641-0.645). Discrimination of wake/sleep demonstrated a kappa value of 0.711 and accuracy of 89%, non-REM sleep/REM sleep demonstrated a kappa of 0.790 and accuracy of 94%, and light sleep/deep sleep demonstrated a kappa of 0.469 and accuracy of 87%. Kappa values did not vary by more than 0.07 across subgroups of no SDB, mild SDB, moderate SDB, and severe SDB. Accuracy increased to 80%, with a kappa value of 0.680 (95% confidence interval, 0.678-0.682), when CReSS additionally utilized the thoracic respiratory effort signal.
conclusionsWe observed substantial agreement between CReSS and the gold-standard comparator of manual sleep staging of polysomnographic signals, which was consistent across the full range of SDB severity. Future research should focus on the extent to which CReSS reduces the discrepancy between the apnea-hypopnea index and the respiratory event index, and the ability of CReSS to identify REM sleep-related obstructive sleep apnea.
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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.