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

Study Objectives: Automatic sleep staging based on cardiorespiratory signals from home sleep monitoring devices holds great clinical potential. Using state-of-the-art machine learning, promising performance has been reached in patients with sleep disorders. However, it is unknown whether performance would hold in individuals with potentially altered autonomic physiology, for example under the influence of medication. Here, we assess an existing sleep staging algorithm in patients with sleep disorders with and without the use of beta blockers. Methods: We analyzed a retrospective dataset of sleep recordings of 57 patients with sleep disorders using beta blockers and 57 age-matched patients with sleep disorders not using beta blockers. Sleep stages were automatically scored based on electrocardiography and respiratory effort from a thoracic belt, using a previously developed machine-learning algorithm (CReSS algorithm). For both patient groups, sleep stages classified by the model were compared to gold standard manual polysomnography scoring using epoch-by-epoch agreement. Additionally, for both groups, overall sleep parameters were calculated and compared between the two scoring methods. Results: Substantial agreement was achieved for four-class sleep staging in both patient groups (beta blockers: kappa = 0.635, accuracy = 78.1%; controls: kappa = 0.660, accuracy = 78.8%). No statistical difference in epoch-by-epoch agreement was found between the two groups. Additionally, the groups did not differ on agreement of derived sleep parameters. Conclusions: We showed that the performance of the CReSS algorithm is not deteriorated in patients using beta blockers. Results do not indicate a fundamental limitation in leveraging autonomic characteristics to obtain a surrogate measure of sleep in this clinically relevant population.