Detection of Sleep Stages and Arousals Using Neural Network Classifiers

NCT07136272 | Not Yet Recruiting FDA Device

• 1 other identifier: TP-SMS-0005
• Study Type: interventional
• Target: 150
• Locations: 1 country
• Sites: 3

Brief Summary

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.

Conditions

Sleep Disordered Breathing (SDB); Sleep Apnea

Keywords

Sleep Scoring; Sleep Stages; Arousals; Neural Network Classifier

Outcome Measures

Primary Outcomes (1)

• Agreement Between Smart Mask V1 System and EnsoSleep in Detecting Arousals and Arousal Index

  This outcome is also based on a non-inferiority analysis, and assesses the agreement between the test device's measurements arousals and arousal index (number of arousals per hour of sleep) and those from PSG and EnsoSleep, a software system that automatically scores PSG data to determine these parameters. The study will be conducted similarly to that described above, substituting arousals and arousal index for the various sleep stages. The outcome for this comparison will be determined similarly to that described above using NA, PA, OA, and Evals 1 and 2 involving test, predicate, and reference devices. Secondary outcomes will be determined using more quantitative approaches (e.g., Bland-Altman and Deming regression plots) to determine errors between the test device and reference devices. For this analysis, the outcome will evaluate if relative errors for the test device's measurements of arousals and arousal indices are within clinically accepted ranges.

  One overnight sleep study session (6-8 hours per participant)

Study Arms (2)

Phase I - Training

EXPERIMENTAL

Participants in this arm will undergo a one-night, in-lab sleep study while wearing the Smart Mask and WatchPAT while receiving positive airway pressure ('PAP') therapy. Standard polysomnography ('PSG') data will be collected and stored. Three independent registered PSG technologists ('RPSGTs') will manually score the data to estimate sleep stages and arousals, following the guidelines in 'The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications', which is the definitive reference for the scoring of PSG and HSATs. PSG data will also be uploaded to EnsoSleep's cloud-based software system. Data from this phase will be used solely to train and refine the Smart Mask's neural network algorithms for detecting sleep stages and arousals. No algorithm validation will occur in this phase.

Device: SSAM; Diagnostic Test: Polysomnography; Device: EnsoSleep

Phase II - Validation

EXPERIMENTAL

Participants in this arm will undergo a single overnight, in-lab sleep study while wearing the Smart Mask and WatchPAT device, while receiving PAP therapy. Standard PSG data will be collected and stored. Data from the Smart Mask will be processed using trained neural networks to detect sleep stages (N1/N2, N3, REM, Wake), arousals, and calculate the Arousal Index. These outputs will be compared to: 1) manual scoring of PSG data by three independent, blinded RPSGTs; and 2) results from the predicate device, EnsoSleep. Data from this phase will be used validate the Smart Mask's neural network algorithms for detecting sleep stages and arousals.

Device: SSAM; Diagnostic Test: Polysomnography; Device: EnsoSleep

Interventions

SSAM DEVICE

Embedded software featuring neural-network classifiers designed to detect and classify sleep stages (N1/N2, N3, REM, Wake), autonomic arousals, and an Arousal Index based on data (specifically instantaneous pulse rate and full-resolution flow waveforms) collected during PAP therapy.

Also known as: Smart Mask V1 System, Smart Mask, SMV1

Phase I - Training; Phase II - Validation

Polysomnography DIAGNOSTIC_TEST

Polysomnography (PSG) is an overnight, in-laboratory diagnostic procedure that records multiple physiological parameters during sleep. It typically includes monitoring of brain activity (EEG), eye movements (EOG), muscle activity (EMG), heart rhythm (ECG), respiratory effort, airflow, oxygen saturation, and body position. PSG is used to evaluate sleep architecture, identify sleep stages, detect arousals, and diagnose sleep disorders such as sleep apnea. The collected data are analyzed manually by trained sleep technologists following standardized scoring criteria.

Also known as: Sleep Study, PSG

Phase I - Training; Phase II - Validation

EnsoSleep DEVICE

EnsoSleep is software-only medical device intended for use by physicians to assess sleep quality and aid in the diagnosis of sleep disorders. The software analyzes physiological signals and automatically scores sleep study results, including respiratory, sleep staging, arousal and movement events. Automatically scored events and physiological signals are analyzed, displayed, and summarized for review by clinicians.

Phase I - Training; Phase II - Validation

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Subjects must be at least 18 years of age at screening.
• Subjects are individuals diagnosed with sleep-disordered breathing who have been prescribed PAP therapy or referred for a PSG.
• Subjects must be willing and able to comply with the study requirements, which include using test, reference and predicate devices (if needed), completing training, interacting with study personnel and filling out questionnaires.
• Subjects must be fluent in English.
• Subject must be willing to undergo the screening and informed consent process prior to enrollment in the study.
• Subjects must be deemed suitable candidates for this study based on the PI's evaluation of their condition and the features of the investigational device being tested.

You may not qualify if:

• Subjects unable or unwilling to wear a PAP mask as required for the study.
• Subjects currently employed by, previously employed by or in any way affiliated (consulting, etc.) with any manufacturer or provider of PAP equipment and/or services.
• Subjects who are pregnant.
• Subjects with or requiring an implantable device, such as an electronic defibrillator, pacemaker, or other device.
• Subjects considered by the PI to be medically unsuitable for study participation.

Sponsors & Collaborators

• Pathway Medtech, LLC.: lead

Study Sites (3)

Peninsula Sleep Center

Burlingame, California, 94010, United States

Location

Amnova Research

Irvine, California, 92604, United States

Location

ACTRI Center for Clinical Research

La Jolla, California, 92093, United States

Location

MeSH Terms

Conditions

Sleep Apnea Syndromes

Interventions

Polysomnography

Condition Hierarchy (Ancestors)

Apnea; Respiration Disorders; Respiratory Tract Diseases; Sleep Disorders, Intrinsic; Dyssomnias; Sleep Wake Disorders; Nervous System Diseases

Intervention Hierarchy (Ancestors)

Monitoring, Physiologic; Diagnostic Techniques and Procedures; Diagnosis

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NON RANDOMIZED
• Masking: NONE
• Purpose: SUPPORTIVE CARE
• Intervention Model: SINGLE GROUP
• Sponsor Type: INDUSTRY
• Responsible Party: SPONSOR

Model Details: Two-phase clinical study: * Phase I (Training): Used to train and refine neural-network classifiers used in the SSAM * Phase II (Validation): Used to validate the neural-network classifiers used in the SSAM by comparing their outputs to manually scored PSG (gold-standard) and to an FDA-cleared reference device (EnsoSleep)

Study Record Dates

• First Submitted: July 18, 2025
• First Posted: August 22, 2025
• Study Start: September 1, 2025
• Primary Completion: December 1, 2025
• Study Completion: December 1, 2025
• Last Updated: September 16, 2025

Record last verified: 2025-06

Locations

US (3)