Estimating Sleep Stages Using Cardiorespiratory Signals: Validation of a Novel Algorithm Across a Wide Range of Sleep-disordered Breathing Severity

Overview

Journal J Clin Sleep Med

Specialties Neurology
Psychiatry

Date 2021 Mar 4

PMID 33660612

Citations 11

Authors

Jessie P Bakker

Marco Ross

Ray Vasko

Andreas Cerny

Pedro Fonseca

Jeff Jasko

Edmund Shaw

David P White

Peter Anderer

Affiliations

Soon will be listed here.

Abstract

Study Objectives: We 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.

Methods: Using 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.

Results: CReSS 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.

Conclusions: We 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.

Citing Articles

Overnight Sleep Staging Using Chest-Worn Accelerometry.

Schipper F, Grassi A, Ross M, Cerny A, Anderer P, Hermans L Sensors (Basel). 2024; 24(17).

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Paediatric sleep diagnostics in the 21st century: the era of "sleep-omics"?.

Vennard H, Buchan E, Davies P, Gibson N, Lowe D, Langley R Eur Respir Rev. 2024; 33(172).

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Diagnosing OSA and Insomnia at Home Based Only on an Actigraphy Total Sleep Time and RIP Belts an Algorithm "Nox Body Sleep™".

Leger D, Elbaz M Nat Sci Sleep. 2024; 16:833-845.

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Performance of cardiorespiratory-based sleep staging in patients using beta blockers.

Hermans L, van Meulen F, Anderer P, Ross M, Cerny A, van Gilst M J Clin Sleep Med. 2023; 20(4):575-581.

PMID: 38063156 PMC: 10985295. DOI: 10.5664/jcsm.10938.

Deep learning-based sleep stage classification with cardiorespiratory and body movement activities in individuals with suspected sleep disorders.

Morokuma S, Hayashi T, Kanegae M, Mizukami Y, Asano S, Kimura I Sci Rep. 2023; 13(1):17730.

PMID: 37853134 PMC: 10584883. DOI: 10.1038/s41598-023-45020-7.

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