Proof of principle study: diagnostic accuracy of a novel algorithm for the estimation of sleep stages and disease severity in patients with sleep-disordered breathing based on actigraphy and respiratory inductance plethysmography

• Published in: Sleep and Breathing
• Main Authors: Dietz-Terjung, Sarah, Martin, Amelie Ricarda, Finnsson, Eysteinn, Ágústsson, Jón Skínir, Helgason, Snorri, Helgadóttir, Halla, Welsner, Matthias, Taube, Christian, Weinreich, Gerhard, Schöbel, Christoph
• Format: Article in Journal/Newspaper
• Language: English
• Published: Springer Science and Business Media LLC 2021
• Subjects: Neurology (clinical); Otorhinolaryngology; Rip; Iceland
• Online Access: http://dx.doi.org/10.1007/s11325-021-02316-0; https://link.springer.com/content/pdf/10.1007/s11325-021-02316-0.pdf; https://link.springer.com/article/10.1007/s11325-021-02316-0/fulltext.html

Abstract Purpose In this proof of principle study, we evaluated the diagnostic accuracy of the novel Nox BodySleep TM 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.