SleepNCT04755504
The Development of an Algorithm to Detect Sleep Structure With a Wearable EEG Monitor in an Elderly Population
A study testing EEG behind the ear in people with Sleep, run by Universitaire Ziekenhuizen KU Leuven at 1 site in Belgium.
Trial summary
To evaluate whether it is able to perform sleep staging with EEG data recorded from 2 electrodes behind each ear. Read more about this study
- Who is it for?
- Ages 60 years and older
- Conditions the study is for
- Sleep
- What is being tested?
- EEG behind the ear
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 100 participants
- Where
- 1 siteBelgium
- Study dates
- 21 Jan 2021to 30 Jan 2023
- Phase
- Interventional
- 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 60 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Subjects planned to undergo a diagnostic polysomnography
- > 60y old
Who cannot join
- Patients unable to provide informed consent
Trial locations and contacts
1 site in 1 country.
About this study
To evaluate whether it is able to perform sleep staging with EEG data recorded from 2 electrodes behind each ear.
Read the detailed description
The Sensor Dot wearable device measures electroencephalography (EEG). It records from 2 electrodes behind each ear. The device was designed as a wearable for seizure detection in epilepsy patients. The purpose of this study is to test its ability to capture the information necessary for sleep monitoring in elderly patients. Trained electrophysiologists are unable to stage sleep on data from novel wearable devices, since AASM sleep scoring rules are only defined for standardized recording positions on the head. Therefore, we need an automated algorithm to perform sleep staging with data from the Sensor Dot device. We will train this algorithm using manual annotations made with the polysomnography simultaneously acquired with the wearable EEG.
- Conditions
- Sleep
- Being tested
- EEG behind the ear
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
EEG behind the earDiagnostic test
2 additional electrodes behind each ear will record EEG
| Study group | Role | What they receive |
|---|---|---|
| EEG evaluation | Experimental | All patients will be evaluated during 1 night by standard polysomnography and additionally EEG will be evaluated by 2 electrodes behind each ear connected to a recording device (Sensor Dot) Receives: Diagnostic Test: EEG behind the ear |
What is being measured
The primary outcome is the main question the study is designed to answer.
Sleep algorithm
To develop an algorithm to characterize sleep architecture based on EEG measurement by 2 electrodes behind each ear.
To classify the sleep stages, a deep learning algorithm will be used. The algorithm will learn a complex function, transforming an input to an output, based on several examples. In this specific case, the input are 30s EEG epochs and the output are sleep stages. To classify the measured signal in the correct sleep stage, the deep learning algorithm will learn to extract useful features from the data.
Measured: 1 night
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted16 Feb 2021
- Study start21 Jan 2021
- Primary completion30 Jan 2023
- Study completion30 Jan 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.
- More trials run by Universitaire Ziekenhuizen KU LeuvenThe sponsor's other registered studies.
- 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
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Model description
- Each patient will be evaluated with routine polysomnography and additionally 2 EEG signals will be recorded behind each air.
- Masking
- None (open label)
- Enrolment
- 100 participants (actual)
Sponsor and investigators
- Lead sponsor
- Universitaire Ziekenhuizen KU Leuven
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04755504
- Sponsor's protocol ID
- S64190
- Status last verified
- Jul 2024
- First submitted
- 8 Feb 2021
- Last updated
- 3 Jul 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 3 Jul 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- The Development of an Algorithm to Detect Sleep Structure With a Wearable EEG Monitor in an Elderly Population
- ClinicalTrials.gov ID
- NCT04755504
- Acronym / sponsor ID
- S64190
- Conditions
- Sleep
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 100 participants (actual)
- Lead sponsor
- Universitaire Ziekenhuizen KU LeuvenUniversity, hospital or other
- Study start
- 21 Jan 2021 (actual)
- Primary completion
- 30 Jan 2023 (actual)
- Study completion
- 30 Jan 2023 (actual)
- First posted
- 16 Feb 2021 (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.
To evaluate whether it is able to perform sleep staging with EEG data recorded from 2 electrodes behind each ear.
Detailed description
The Sensor Dot wearable device measures electroencephalography (EEG). It records from 2 electrodes behind each ear. The device was designed as a wearable for seizure detection in epilepsy patients. The purpose of this study is to test its ability to capture the information necessary for sleep monitoring in elderly patients. Trained electrophysiologists are unable to stage sleep on data from novel wearable devices, since AASM sleep scoring rules are only defined for standardized recording positions on the head. Therefore, we need an automated algorithm to perform sleep staging with data from the Sensor Dot device. We will train this algorithm using manual annotations made with the polysomnography simultaneously acquired with the wearable EEG.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
- Model description
- Each patient will be evaluated with routine polysomnography and additionally 2 EEG signals will be recorded behind each air.
| Arm | Type | Receives |
|---|---|---|
| EEG evaluation | Experimental | Diagnostic Test: EEG behind the earAll patients will be evaluated during 1 night by standard polysomnography and additionally EEG will be evaluated by 2 electrodes behind each ear connected to a recording device (Sensor Dot) |
Who may qualify
- Ages
- 60 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (2 listed)
- Subjects planned to undergo a diagnostic polysomnography
- > 60y old
Key exclusion criteria (1 listed)
- Patients unable to provide informed consent
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 |
|---|---|---|
| EEG behind the ear2 additional electrodes behind each ear will record EEG | Diagnostic test | EEG evaluation |
Primary outcome
Sleep algorithm
Time frame: 1 night
To develop an algorithm to characterize sleep architecture based on EEG measurement by 2 electrodes behind each ear. To classify the sleep stages, a deep learning algorithm will be used. The algorithm will learn a complex function, transforming an input to an output, based on several examples. In this specific case, the input are 30s EEG epochs and the output are sleep stages. To classify the measured signal in the correct sleep stage, the deep learning algorithm will learn to extract useful features from the data.
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 3 Jul 2024.
- Individual participant dataSharing planned: No
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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.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing EEG behind the ear in people with Sleep, run by Universitaire Ziekenhuizen KU Leuven at 1 site in Belgium.
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 1 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 3 Jul 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 Belgium. 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 algorithm (time frame: 1 night). 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 3 Jul 2024.