The Development of an Algorithm to Detect Sleep Structure With a Wearable EEG Monitor in an Elderly Population

Study Overview
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Brief Summary
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To evaluate whether it is able to perform sleep staging with EEG data recorded from 2 electrodes behind each ear.

Detailed Description
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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.

Official Title
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The Development of an Algorithm to Detect Sleep Structure With a Wearable EEG Monitor in an Elderly Population

Conditions
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Sleep

Intervention / Treatment
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* Diagnostic Test: EEG behind the ear


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Subjects planned to undergo a diagnostic polysomnography > 60y old Exclusion Criteria: Patients unable to provide informed consent

Ages Eligible for Study
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Minimum Age: 60 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Diagnostic
Intervention Model: Single Group Assignment
Interventional 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)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: EEG evaluation<br>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) | Diagnostic Test: EEG behind the ear<br>* 2 additional electrodes behind each ear will record EEG<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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. | 1 night |