Hypothesis: BR's Gen3 DL algorithms, combined with its subxiphoid body sensor, can accurately diagnose OSA, categorize its severity, identify REM OSA and supine OSA, and detect central sleep apnea (CSA). Primary Objective: To rigorously evaluate the overall performance of the BR with Gen3 DL Algorithms and Subxiphoid Body Sensor in assessing SDB in individuals referred to the sleep labs with clinical suspicion of sleep apnea and a STOP-Bang score \> 3, by comparing to the attended in-lab PSG, the gold standard. Secondary Objectives: To determine the accuracy of BR sleep stage parameters using the Gen3 DL algorithms by comparing to the in-lab PSG; To assess the accuracy of the BR arrhythmia detection algorithm; To assess the impact of CPAP on HRV (both time- and frequency-domain), delta HR, hypoxic burden, and PWADI during split night studies; To assess if any of the baseline HRV parameters (both time- and frequency-domain), delta heart rate (referred to as Delta HR), hypoxic burden, and pulse wave amplitude drop index (PWADI) or the change of these parameters may predict CPAP compliance; To evaluate the minimum duration of quality data necessary for BR to achieve OSA diagnosis; To examine the performance of OSA screening tools using OSA predictive AI models formulated by National Taiwan University Hospital (NTUH) and Northeast Ohio Medical University (NEOMED).
Sleep-Disordered Breathing, Sleep Architecture, Arrhythmia
Hypothesis: BR's Gen3 DL algorithms, combined with its subxiphoid body sensor, can accurately diagnose OSA, categorize its severity, identify REM OSA and supine OSA, and detect central sleep apnea (CSA). Primary Objective: To rigorously evaluate the overall performance of the BR with Gen3 DL Algorithms and Subxiphoid Body Sensor in assessing SDB in individuals referred to the sleep labs with clinical suspicion of sleep apnea and a STOP-Bang score \> 3, by comparing to the attended in-lab PSG, the gold standard. Secondary Objectives: To determine the accuracy of BR sleep stage parameters using the Gen3 DL algorithms by comparing to the in-lab PSG; To assess the accuracy of the BR arrhythmia detection algorithm; To assess the impact of CPAP on HRV (both time- and frequency-domain), delta HR, hypoxic burden, and PWADI during split night studies; To assess if any of the baseline HRV parameters (both time- and frequency-domain), delta heart rate (referred to as Delta HR), hypoxic burden, and pulse wave amplitude drop index (PWADI) or the change of these parameters may predict CPAP compliance; To evaluate the minimum duration of quality data necessary for BR to achieve OSA diagnosis; To examine the performance of OSA screening tools using OSA predictive AI models formulated by National Taiwan University Hospital (NTUH) and Northeast Ohio Medical University (NEOMED).
Validation of Belun Ring Gen3 Deep Learning Algorithms With Subxiphoid Body Sensor
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UH Geauga Health Center Services, Chardon, Ohio, United States, 44024
University Hospitals Cleveland Medical Center, Cleveland, Ohio, United States, 44106
Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.
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18 Years to 80 Years
ALL
No
Belun Technology Company Limited,
Ambrose A. Chiang, MD, PRINCIPAL_INVESTIGATOR, University Hospitals Cleveland Medical Center
Susheel P. Patil, MD, PRINCIPAL_INVESTIGATOR, University Hospitals Cleveland Medical Center
Kingman P. Strohl, MD, PRINCIPAL_INVESTIGATOR, University Hospitals Cleveland Medical Center
2027-09-30