Validation Study of Sleep Tracking Devices

Sleep Clinical Trial

Official title:

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

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT06357039
• Other study ID #: 09.2023.422
• Secondary ID: 22201241065371
• Status: Completed
• Start date: March 21, 2023
• Est. completion date: July 17, 2023

Study information

• Verified date: April 2024
• Source: PNAPS Health Informatics and Space Technologies Inc.
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 305
• Est. completion date: July 17, 2023
• Est. primary completion date: May 31, 2023
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years to 65 Years

Eligibility

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

Related Conditions & MeSH terms

• Sleep
• Sleep Disorder
• Sleep Wake Disorders

Intervention

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.

Locations

Country	Name	City	State
Turkey	Pnaps Health Informatics and Space Technologies Inc	Istanbul	Basibüyük, Maltepe

Sponsors (2)

Lead Sponsor	Collaborator
PNAPS Health Informatics and Space Technologies Inc.	Analog Devices

Country where clinical trial is conducted

Turkey, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Sleep Stages Classification Accuracy	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.	4-5 months
Primary	Interoception analysis from PPG data collected from facial skin	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.	4-5 months