Abstract
PurposeIn 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).MethodsPatients 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).ResultsWe 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.ConclusionThe 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.
Highlights
Sleep-disordered breathing (SDB) shows a high prevalence in the general population [1]
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
The presented algorithm seems to be an appropriate tool to increase the diagnostic accuracy of portable monitoring
Summary
Sleep-disordered breathing (SDB) shows a high prevalence in the general population [1]. SDB leads to repetitive arousals and activation of the sympathetic nervous system resulting in surges of blood pressure and heart rate and stressing the cardiovascular system. The long-term risk of developing cardiovascular diseases is significantly increased [1]. Affected patients often complain of snoring, non-restorative sleep, or daytime sleepiness. This can increase the risk of road traffic accidents [2]. Current epidemiological studies show a high prevalence of SDB. In Germany, for instance, 29.7% of all men and 13.2% of all women are suspected to suffer from moderate to severe sleep apnea.
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