NCT06357039

Validation Study of Sleep Tracking Devices

Validation Study of an Artificial Intelligence-based Sleep Stage Classification for a Home Sleep Tracking Device

• Status: Completed
• Phase: —
• Study type: Observational
• Enrollment: 305 (actual)
• Sponsor: PNAPS Health Informatics and Space Technologies Inc. · Academic / Other
• Sex: All
• Age: 18 Years – 65 Years
• Healthy volunteers: Accepted

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.

Conditions

• Sleep
• Sleep Disorder

Interventions

Type	Name	Description
DEVICE	Sleep tracking device	In addition to polysomnography, a device containing EEG+PPG sensors for sleep classification was placed on the forehead, and another device containing PPG and accelerometer sensors was placed on the wrist. The wrist to which the device is attached is randomly assigned.

Timeline

• Start date: 2023-03-21
• Primary completion: 2023-05-31
• Completion: 2023-07-17
• First posted: 2024-04-10
• Last updated: 2024-04-25

Locations

1 site across 1 country: Turkey (Türkiye)