Purpose In this proof of principle study, we evaluated the diagnostic accuracy of the novel Nox BodySleepTM 1.0 algorithm (Nox Medical, Iceland) for the estimation of disease severity and sleep stages based on features extracted from actigraphy and respiratory inductance plethysmography (RIP) belts. Validation was performed against in-lab polysomnography (PSG) in patients with sleep-disordered breathing (SDB). Methods Patients received PSG according to AASM. Sleep stages were manually scored using the AASM criteria and the recording was evaluated by the novel algorithm. The results were analyzed by descriptive statistics methods (IBM SPSS Statistics 25.0). Results We found a strong Pearson correlation (r=0.91) with a bias of 0.2/h for AHI estimation as well as a good correlation (r=0.81) and an overestimation of 14 min for total sleep time (TST). Sleep efficiency (SE) was also valued with a good Pearson correlation (r=0.73) and an overestimation of 2.1%. Wake epochs were estimated with a sensitivity of 0.65 and a specificity of 0.59 while REM and non-REM (NREM) phases were evaluated a sensitivity of 0.72 and 0.74, respectively. Specificity was 0.74 for NREM and 0.68 for REM. Additionally, a Cohen’s kappa of 0.62 was found for this 3-class classification problem. Conclusion The algorithm shows a moderate diagnostic accuracy for the estimation of sleep. In addition, the algorithm determines the AHI with good agreement with the manual scoring and it shows good diagnostic accuracy in estimating wake-sleep transition. The presented algorithm seems to be an appropriate tool to increase the diagnostic accuracy of portable monitoring. The validated diagnostic algorithm promises a more appropriate and cost-effective method if integrated in out-of-center (OOC) testing of patients with suspicion for SDB.

Introduction We tested the diagnostic accuracy of the novel Nox BodySleep™ algorithm (Nox Medical, Iceland) for the estimation of sleep states from polygraphy (PG) sleep recordings based on features extracted from actigraphy and respiratory inductance plethysmography (RIP) belts. The algorithm automatically classifies epochs into three states, Wake, REM sleep and NonREM sleep. Validation was performed against polysomnography (PSG) in a sleep laboratory collective including patients with sleep disordered breathing (SBAS) and sleep related movements disorders. Methods Patients received PSG according to clinical routine. The recording was evaluated by the novel algorithm and the results were evaluated by descriptive statistics methods (IBM SPSS Statistics 25.0). Results We found a good Spearman correlation (r=0.8) and a bias of 11 minutes for the estimation of Total Sleep Time. Sleep Efficiency was also valued with a good Spearman correlation (r=0.7) and a bias of 1.6%. Wake phases were estimated with a F1 score of 0.64 while REM and Non-REM phases were evaluated with a F1 score of 0.73 and 0.82, respectively. Additionally, an overall accuracy of 0.8 and a Cohens kappa of 0.7 were found. Patients with sleep related movement disorders showed a slighly weaker correlation as patients with SBAS. Conclusion The algorithm shows a good diagnostic accuracy for the estimation of sleep states and significant sleep parameters. After validation on a larger patient collective, it could be used in the ambulatory and telemedical field to allow investigations comparable to the accuracy of a PSG. Support No support.