Belun Sleep Platform versus in-lab polysomnography for obstructive sleep apnea diagnosis.

Objective: We aimed to compare the Belun Sleep Platform (BSP), an artificial intelligence-driven home sleep testing device, with polysomnography (PSG) for diagnosing obstructive sleep apnea. The BSP analyzes oxygen saturation, heart rate, and accelerometry patterns.

Methods: Participants scheduled for PSG and with no significant cardiovascular or neuromuscular comorbidities were recruited. They underwent simultaneous in-laboratory, full-night PSG with the BSP. We assessed diagnostic properties, including sensitivity, specificity, positive predictive value, negative predictive value, and accuracy.

Results: A total of 40 participants (54.3% male) with a mean age of 49.9 years were enrolled. For an apnea-hypopnea index (AHI) cutoff of ≥ 15 events/h, BSP showed an accuracy of 68.5%, sensitivity of 35.2%, and specificity of 100% under American Academy of Sleep Medicine criteria 1 A and 1B. For AHI thresholds of ≥ 5 and ≥ 30 events/h, sensitivity was 82.1% and 33.3%, respectively, while specificity was 14.2% and 100%, respectively. BSP-AHI correlated moderately with PSG-AHI (intraclass correlation coefficient [ICC] = 0.737). BSP's oxygen desaturation index (ODI) showed a strong correlation with PSG-ODI (ICC = 0.882). Moderate correlations were observed between BSP and PSG for non-rapid eye movement sleep duration (ICC = 0.736), rapid eye movement sleep duration (ICC = 0.664), total sleep time (ICC = 0.617), and sleep efficiency (ICC = 0.719).

Conclusions: The BSP's high specificity but low sensitivity suggests it serves better as a confirmatory tool rather than a primary screening method. Its moderate concordance with PSG underscores its potential in settings where PSG is unavailable. However, further investigation is needed to refine its clinical applications.