Study Objectives: The objective of the study is to validate the performance of Belun Ring Platform, a novel home sleep apnea testing system using a patented pulse oximeter sensor and a proprietary cloud-based neural networks algorithm. Methods: The Belun Ring captures oxygen saturation, photoplethysmography, and accelerometer signals. The Belun Ring total sleep time is derived from features extracted from accelerometer, oxygen saturation, and photoplethysmography signals. The Belun Ring respiratory event index is derived from Belun Ring total sleep time and features extracted from heart rate variability and oxygen saturation changes. A total of 50 adults without significant cardiopulmonary or neuromuscular comorbidities and heart rate affecting medications were evaluated. In-lab sleep studies were performed simultaneously with the Ring and the studies were manually scored using the American Academy of Sleep Medicine Scoring Manual 4% desaturation criteria. Results: The Belun Ring respiratory event index correlated well with the polysomnography-apnea-hypopnea index (AHI; r = .894, P < .001). The sensitivity and specificity in categorizing AHI ≥ 15 events/h were 0.85 and 0.87, respectively, and the positive predictive value and negative predictive value were 0.88 and 0.83, respectively. The Belun Ring total sleep time also correlated well with the polysomnography-total sleep time (r = .945, P < .001). Although the Belun Ring Platform has a good overall performance, it tends to overestimate AHI in individuals with AHI under 15 events/h and underestimate AHI in those with AHI over 15 events/h. Conclusions: In this proof-of-concept study, the Belun Ring Platform demonstrated a reasonable accuracy in predicting AHI and total sleep time in patients without significant comorbidities and heart rate-affecting medications.

Many wearables allow physiological data acquisition in sleep and enable clinicians to assess sleep outside of sleep labs. Belun Sleep Platform (BSP) is a novel neural network-based home sleep apnea testing system utilizing a wearable ring device to detect obstructive sleep apnea (OSA). The objective of the study is to assess the performance of BSP for the evaluation of OSA. Subjects who take heart rate-affecting medications and those with non-arrhythmic comorbidities were included in this cohort. Polysomnography (PSG) studies were performed simultaneously with the Belun Ring in individuals who were referred to the sleep lab for an overnight sleep study. The sleep studies were manually scored using the American Academy of Sleep Medicine Scoring Manual (version 2.4) with 4% desaturation hypopnea criteria. A total of 78 subjects were recruited. Of these, 45% had AHI < 5; 18% had AHI 5–15; 19% had AHI 15–30; 18% had AHI ≥ 30. The Belun apnea-hypopnea index (bAHI) correlated well with the PSG-AHI (r = 0.888, P < 0.001). The Belun total sleep time (bTST) and PSG-TST had a high correlation coefficient (r = 0.967, P < 0.001). The accuracy, sensitivity, specificity in categorizing AHI ≥ 15 were 0.808 [95% CI, 0.703–0.888], 0.931 [95% CI, 0.772–0.992], and 0.735 [95% CI, 0.589–0.850], respectively. The use of beta-blocker/calcium-receptor antagonist and the presence of comorbidities did not negatively affect the sensitivity and specificity of BSP in predicting OSA. A diagnostic algorithm combining STOP-Bang cutoff of 5 and bAHI cutoff of 15 events/h demonstrated an accuracy, sensitivity, specificity of 0.938 [95% CI, 0.828–0.987], 0.944 [95% CI, 0.727–0.999], and 0.933 [95% CI, 0.779–0.992], respectively, for the diagnosis of moderate to severe OSA. BSP is a promising testing tool for OSA assessment and can potentially be incorporated into clinical practices for the identification of OSA. Trial registration: ClinicalTrial.org NCT03997916 https://clinicaltrials.gov/ct2/show/NCT03997916?term=belun+ring&draw=2&rank=1

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Introduction There is a substantial need for an accurate and easy-to-use tool for obstructive sleep apnea (OSA) assessment. Belun Ring Platform (BRP), a novel photoplethysmography (PPG)-based home sleep apnea testing system with a proprietary deep learning algorithm, has been shown to have good sensitivity and specificity in predicting OSA in subjects without significant comorbidities and medications known to affect heart rate (HR). In this study, we further tested its performance in subjects referred for in-lab polysomnography (PSG) assessment of sleep disorders without excluding those with non-arrhythmia comorbidities or the subjects on HR-affecting medications. Methods PSG was recorded simultaneously with the Ring in the sleep lab and the studies were manually scored by certified sleep technicians according to the AASM Scoring manual version 2.4. Exclusion criteria include age &lt;18, unstable cardiopulmonary status, recent hospitalization within 30 days, significant arrhythmias, baseline HR &lt;50 or &gt;100, home oxygen use, pacemaker/defibrillator, post-cardiac transplantation or Left ventricular assist device. Results A cohort of 78 individuals (26 males and 52 females, age 50.5) were studied with 26 taking HR-affecting medications. Of these, 35 (45%) had AHI &lt; 5; 14 (18%) had AHI 5-15; 15 (19%) had AHI 15-30; 14 (18%) had AHI &gt; 30. The Ring-REI correlated well with the PSG-AHI (r =0.83, P &lt;0.001). The accuracy, sensitivity, specificity in categorizing AHI &gt;15 were 0.808, 0.931, and 0.735 respectively. The positive predictive value, negative predictive value, positive likelihood ratio, and negative likelihood ratio were 0.675, 0.947, 3.509, and 0.094 respectively. The use of HR-affecting medications did not significantly affect the sensitivity and specificity of BRP in predicting OSA (P =0.16 and 0.44 respectively). Conclusion BRP is promising as a reasonable tool for OSA assessment and can potentially be incorporated into a broad spectrum of clinical practices for identification of patients with OSA. Support This study is supported by a Grant from Belun Technology Company Limited.

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