Validation Study of Sleep Tracking Devices

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.

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Key information

Conditions

Sleep Mental Disorders Nervous System Diseases Neurologic Manifestations Pathological Conditions, Signs and Symptoms Signs and Symptoms Sleep Disorder Sleep Wake Disorders

Age range

18 year–65 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Pnaps Health Informatics and Space Technologies Inc

Istanbul, Başıbüyük, Maltepe, 34854, Turkey (Türkiye)

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

Sleep tracking device

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.

Primary outcomes

Sleep Stages Classification Accuracy

Time frame: 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

Time frame: 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.

Sponsors and collaborators

Lead sponsor

PNAPS Health Informatics and Space Technologies Inc.

Other

Collaborators

Analog Devices

Registry information

Official study title

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

Important dates

Study start: 2023
Primary completion: 2023
Study completion: 2023
First posted: Apr 10, 2024
Registry last updated: Apr 25, 2024

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This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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Completed