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., Competing Interests: A. A. C. received grant BL2018/1001 from Belun Technology for conducting this study at University Hospitals Cleveland Medical Center but otherwise has no financial conflicts of interest. I. W. is a professor in Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan, and has received research funding from Belun Technology. W. G. is a PhD student at the Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan, and is also an engineer of Belun Technology. L. L. and C. T. are Belun Technology Company employees. E. Y., E. W., W. Y., K. P. S., R. J. F., and P. C. have no financial conflicts of interest for the submitted work.