Belun Ring (Belun Sleep System BLS-100): Deep learning-facilitated wearable enables obstructive sleep apnea detection, apnea severity categorization, and sleep stage classification in patients suspected of obstructive sleep apnea

Zachary Strumpf, Wenbo Gu, Chih Wei Tsai, Pai Lien Chen, Eric Yeh, Lydia Leung, Cynthia Cheung, I. Chen Wu, Kingman P. Strohl, Tiffany Tsai, Rodney J. Folz, Ambrose A. Chiang*

*此作品的通信作者

研究成果: Article同行評審

3 引文 斯高帕斯(Scopus)

摘要

Goal and aims: Our objective was to evaluate the performance of Belun Ring with second-generation deep learning algorithms in obstructive sleep apnea (OSA) detection, OSA severity categorization, and sleep stage classification. Focus technology: Belun Ring with second-generation deep learning algorithms Reference technology: In-lab polysomnography (PSG) Sample: Eighty-four subjects (M: F = 1:1) referred for an overnight sleep study were eligible. Of these, 26% had PSG-AHI<5; 24% had PSG-AHI 5–15; 23% had PSG-AHI 15–30; 27% had PSG-AHI ≥ 30. Design: Rigorous performance evaluation by comparing Belun Ring to concurrent in-lab PSG using the 4% rule. Core analytics: Pearson's correlation coefficient, Student's paired t-test, diagnostic accuracy, sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio, Cohen's kappa coefficient (kappa), Bland-Altman plots with bias and limits of agreement, receiver operating characteristics curves with area under the curve, and confusion matrix. Core outcomes: The accuracy, sensitivity, specificity, and kappa in categorizing AHI ≥ 5 were 0.85, 0.92, 0.64, and 0.58, respectively. The accuracy, sensitivity, specificity, and Kappa in categorizing AHI ≥ 15 were 0.89, 0.91, 0.88, and 0.79, respectively. The accuracy, sensitivity, specificity, and Kappa in categorizing AHI ≥ 30 were 0.91, 0.83, 0.93, and 0.76, respectively. BSP2 also achieved an accuracy of 0.88 in detecting wake, 0.82 in detecting NREM, and 0.90 in detecting REM sleep. Core conclusion: Belun Ring with second-generation algorithms detected OSA with good accuracy and demonstrated a moderate-to-substantial agreement in categorizing OSA severity and classifying sleep stages.

原文English
頁(從 - 到)430-440
頁數11
期刊Sleep Health
9
發行號4
DOIs
出版狀態Published - 8月 2023

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