Detection of Sleep Stages and Arousals Using Neural Network Classifiers

The objective of this clinical study is to evaluate the accuracy of the Smart Mask V1 System (herein 'Smart Mask') in measuring sleep stages-Stage N1/N2, Stage N3, Rapid Eye Movement (herein 'REM'), and WAKE-arousals, and the Arousal Index in adults diagnosed with sleep-disordered breathing, such as obstructive sleep apnea (herein 'OSA'). The Smart Mask operates in concert with a Wireless Access Module (herein 'WAM'), which is connected to a standard positive air pressure (herein 'PAP') device used in the treatment of OSA. Collectively the Smart Mask and WAM operate neural network classifier algorithms to determine sleep stages, arousals, and Arousal Index. These algorithms are coded into an embedded software system called the Sleep Staging and Arousal Module (herein 'SSAM') that operates directly on the WAM. The SSAM processes the following parameters, collected while the participant is asleep: 1) instantaneous values of pulse rate, determined from embedded optical sensors within the Smart Mask that measure photoplethysmogram waveforms (herein 'PPG'); and 2) full-resolution flow waveforms measured by sensors within the PAP device and retrieved by the WAM.

During the study, volunteer participants (preferably those with OSA) will undergo an overnight sleep study in sleep testing facility located at three separate clinical sites. The test device (comprising the SSAM operating on the WAM) will retrospectively determine sleep stages and arousals, after the participant's sleep session has concluded. To evaluate the accuracy of the test device, its values of sleep stages, arousals, and Arousal Index will be compared to those parameters determined by polysomnography (herein 'PSG', a recognized gold-standard reference) and EnsoSleep (a FDA-cleared predicate device, and specifically a software package that uses artificial intelligence (AI) to determine sleep stages and arousals). Each volunteer participant will wear an FDA-cleared wrist-worn pulse oximeter called the 'Checkme O2' which generates data (specifically PPG waveforms and values of SpO2 and pulse rate) for the EnsoSleep cloud-based software platform.

The main questions this study aims to answer are:

* Can the Smart Mask accurately identify different sleep stages compared to the EnsoSleep device? * Can the Smart Mask accurately identify sleep arousals and calculate the Arousal Index compared to the EnsoSleep device?

Answers to these questions will be derived through comparative statistical analysis involving the test device, the gold-standard PSG reference, and the FDA-cleared predicate device, employing methodologies similar to those used in the validation of the EnsoSleep. The study will include two cohorts. The first cohort will include approximately 75 participants from a single clinical site and will be used for device training purposes. The second cohort will consist of approximately 72 different participants, and will be used to validate the test device. Participants in the second cohort will be distributed roughly evenly across two separate clinical sites.