The Development of an Algorithm to Detect Sleep Structure With a Wearable EEG Monitor in an Elderly Population
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Detailed Description
Study Type
Study Type
Enrollment (Actual)
Enrollment
Phase
Phase
- Not Applicable
Contacts and Locations
Study Contact
Study Contact
- Name: Bertien Buyse, MD, PhD
- Phone Number: 00 32 16 342522
- Email: bertien.buyse@uzleuven.be
Study Locations
-
-
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Leuven, Belgium, 3000
- UZ Leuven
-
-
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Description
Inclusion Criteria:
- Subjects planned to undergo a diagnostic polysomnography
- > 60y old
Exclusion Criteria:
- Patients unable to provide informed consent
Study Plan
How is the study designed?
Design Details
- Primary Purpose: Diagnostic
- Allocation: N/A
- Interventional Model: Single Group Assignment
- Masking: None (Open Label)
Number of Arms
Arms and Interventions
Participant Group / ArmParticipant Group / Arm |
Intervention / TreatmentIntervention / Treatment |
|---|---|
|
Experimental: EEG evaluation
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)
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2 additional electrodes behind each ear will record EEG
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What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
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. |
1 night
|
Collaborators and Investigators
Sponsor
Sponsor
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Actual)
Primary Completion
Study Completion (Actual)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Other Study ID Numbers
Other Study ID Numbers
- S64190
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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