Validation Study of Sleep Tracking Devices

Completed

• Conditions: Sleep Disorder; Sleep
• Interventions: Device: Sleep tracking device

• Registration Number: NCT06357039
• Lead Sponsor: PNAPS Health Informatics and Space Technologies Inc.

Brief Summary

In this study, a two-part recursive convolutional neural networks model was developed, extracting features for each epoch window independently from before and after sleep onset (epoch encoder), and then trained in the context of long-term relationships in the sleep process (sequence encoder), using an approach similar to human expert classification based on information from single-channel forehead EEG and PPG (IR, Green, Red). The classification is based on guidelines from the American Academy of Sleep Medicine and calculated six parameters: total sleep duration (TST), wake (W), N1, N2, N3, and REM.

The validation study of the developed model and the device was conducted at the Sleep Disorders Centre of the Istanbul Medical Faculty using concurrent polysomnographic data from 305 male and female patients aged 18 to 65 years.

Detailed Description

Not available

Study Timeline

• Study Start: 2023-03-21
• Primary Completion: 2023-05-31
• Study Completion: 2023-07-17
• Study First Submitted: 2024-02-22
• Status Verified: 2024-04
• Study First Submitted QC: 2024-04-04
• Study First Posted: 2024-04-10
• Last Update Submitted: 2024-04-23
• Last Update Posted: 2024-04-25

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 305

Inclusion Criteria

• Participants suffering from sleep disorders
• Participants sent to PSG test by neurologists, pulmonologists, psychiatrists, and otolaryngologists

Exclusion Criteria

• Anyone who has been diagnosed as having a contagious skin disease
• Participants who do not have consent to have an additional device in their forehead area
• Incomplete of sleep measurement

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
Female	Sleep tracking device	Participants aged 18-65 who identify as female for biological
Male	Sleep tracking device	Participants aged 18-65 who identify as male for biological

Primary Outcome Measures

Name	Time	Method
Sleep Stages Classification Accuracy	4-5 months	The collected EEG data were classified according to Cohen's kappa (\>85), which is considered successful in the literature. Initially the open source codes YASA, tinysleepnet and attentionsleep have been implemented. These codes yielded kappa 0.64, accuracy 0.80, kappa 0.69, accuracy 0.79 and kappa 0.65, accuracy 0.78 respectively. The values obtained do not correspond to those reported in the classification articles. Subsequently, 29 participants from our own dataset were tested in these classifications as a preliminary test, with poor results. On an individual basis, the highest cappa score was 0.51. Development of our own classification system is in progress.
Interoception analysis from PPG data collected from facial skin	4-5 months	According to our preliminary analyses, we found that the intermediary rhythm (0.12-0.18 Hz) associated with interoception is also present in sleep patients. In one participant, for example, a value of 0.19 was obtained as a ratio of total sleep time. In addition, an intermediary rhythm is observed in all stages of sleep, including wakefulness, light sleep, deep sleep and REM.

Trial Locations

Locations (1)

Pnaps Health Informatics and Space Technologies Inc; 🇹🇷 Istanbul, Başıbüyük, Maltepe, Turkey