Estimating sleep stages using cardiorespiratory signals: validation of a novel algorithm across a wide range of sleep-disordered breathing severity.

Abstract

STUDY OBJECTIVES\nWe 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 tests. Our objective was to undertake an epoch-by-epoch validation of algorithm performance against the gold-standard of manual polysomnography (PSG) sleep-staging.\n\n\nMETHODS\nUsing 296 PSGs, we created a limited montage of airflow and heart rate and deployed CReSS to identify each 30-second epoch as wake, light sleep (LS; N1+N2), deep sleep (DS; 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 back-up signal for periods of invalid airflow.\n\n\nRESULTS\nCReSS discriminated wake/LS/DS/REM with 78% accuracy; the kappa value was 0.643 (95% CI 0.641-0.645). Discrimination of wake/sleep demonstrated a kappa value of 0.711 and accuracy of 89%; NREM/REM demonstrated a kappa of 0.790 and accuracy of 94%; and LS/DS demonstrated a kappa of 0.469 and accuracy of 87%. Kappa values did not vary by more than 0.07 across sub-groups of no SDB, mild, moderate, and severe SDB. Accuracy increased to 80%, with kappa of 0.680 (95% CI 0.678-0.682), when CReSS additionally utilized the thoracic respiratory effort signal.\n\n\nCONCLUSIONS\nWe observed substantial agreement between CReSS and the gold-standard comparator of manual sleep-staging of PSG 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-related obstructive sleep apnea.