SleepNCT06357039
Validation Study of Sleep Tracking Devices
An observational study of Sleep and Sleep Disorder, run by PNAPS Health Informatics and Space Technologies Inc. at 1 site in Turkey (Türkiye).
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… Read more about this study
- Who is it for?
- Ages 18 to 65 yearsHealthy volunteers can take part
- Conditions the study is for
- Sleep, Sleep Disorder
- What is being tested?
- Sleep tracking device
- Type of treatment
- Medical device
- How many people
- 305 participants
- Where
- 1 siteTurkey (Türkiye)
- Study dates
- 21 Mar 2023to 17 Jul 2023
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 65 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: The study population includes individuals aged between 18 and 65 who have voluntarily agreed to undergo sleep measurements specifically individuals suffering from sleep disorders such as sleep apnea. Participants in the study have been referred for polysomnography (PSG) testing by neurologists, pulmonologists, psychiatrists, and otolaryngologists.
Who may be able to join
- Participants suffering from sleep disorders
- Participants sent to PSG test by neurologists, pulmonologists, psychiatrists, and otolaryngologists
Who cannot join
- 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
Trial locations and contacts
1 site in 1 country.
Turkey (Türkiye)1 site
Pnaps Health Informatics and Space Technologies Inc
Istanbul, Başıbüyük, Maltepe, 34854
About this study
Official title: Validation Study of an Artificial Intelligence-based Sleep Stage Classification for a Home Sleep Tracking Device
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
- Being tested
- Sleep tracking device
- Keywords
- Sleep, Polysomnography, EEG, PPG, Wearable Device
- Medical subject headings
- Sleep Wake Disorders
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Sleep tracking deviceDevice
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.
| Study group | Role | What they receive |
|---|---|---|
| Female | Participants aged 18-65 who identify as female for biological Receives: Device: Sleep tracking device | |
| Male | Participants aged 18-65 who identify as male for biological Receives: Device: Sleep tracking device |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 4-5 months
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.
Measured: 4-5 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted10 Apr 2024
- Study start21 Mar 2023
- Primary completion31 May 2023
- Study completion17 Jul 2023
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for SleepStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Cross-sectional
- Enrolment
- 305 participants (actual)
Sponsor and investigators
- Lead sponsor
- PNAPS Health Informatics and Space Technologies Inc.
- Sponsor type
- University, hospital or other
- Collaborators
- Analog Devices
- Responsible party
- Sponsor
- Investigators
- Asuman Çevik, Master's, Study chair, PNAPS Health Informatics & Space Technologies Inc.Hasan Birol Çotuk, PhD, Study director, Marmara University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06357039
- Sponsor's protocol ID
- 09.2023.422
- Other IDs
- 2220124, 1065371
- Status last verified
- Apr 2024
- First submitted
- 22 Feb 2024
- Last updated
- 25 Apr 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided: The PSG data will be shared, but the main structure is not yet planned.
Study at a glance
From the registry record, last updated by the sponsor on 25 Apr 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Validation Study of an Artificial Intelligence-based Sleep Stage Classification for a Home Sleep Tracking Device
- ClinicalTrials.gov ID
- NCT06357039
- Acronym / sponsor ID
- 09.2023.422
- Conditions
- Sleep, Sleep Disorder
- Keywords
- Sleep, Polysomnography, EEG, PPG, Wearable Device
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 305 participants (actual)
- Lead sponsor
- PNAPS Health Informatics and Space Technologies Inc.University, hospital or other
- Collaborators
- Analog Devices
- Study start
- 21 Mar 2023 (actual)
- Primary completion
- 31 May 2023 (actual)
- Study completion
- 17 Jul 2023 (actual)
- First posted
- 10 Apr 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Other
- Time perspective
- Cross-sectional
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Female | – | Device: Sleep tracking deviceParticipants aged 18-65 who identify as female for biological |
| Male | – | Device: Sleep tracking deviceParticipants aged 18-65 who identify as male for biological |
Who may qualify
- Ages
- 18 to 65 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- The study population includes individuals aged between 18 and 65 who have voluntarily agreed to undergo sleep measurements specifically individuals suffering from sleep disorders such as sleep apnea. Participants in the study have been referred for polysomnography (PSG) testing by neurologists, pulmonologists, psychiatrists, and otolaryngologists.
Key inclusion criteria (2 listed)
- Participants suffering from sleep disorders
- Participants sent to PSG test by neurologists, pulmonologists, psychiatrists, and otolaryngologists
Key exclusion criteria (3 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Sleep tracking deviceIn 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. | Device | Female, Male |
Primary outcome
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.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 25 Apr 2024.
- Individual participant dataSharing planned: Undecided. The PSG data will be shared, but the main structure is not yet planned.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Asuman Çevik, Master's study chair
PNAPS Health Informatics & Space Technologies Inc.
Hasan Birol Çotuk, PhD study director
Marmara University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Sleep and Sleep Disorder, run by PNAPS Health Informatics and Space Technologies Inc. at 1 site in Turkey (Türkiye).
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 25 Apr 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Turkey (Türkiye). Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Sleep Stages Classification Accuracy (time frame: 4-5 months). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 25 Apr 2024.