The Development of an Algorithm to Detect Sleep Structure With a Wearable EEG Monitor in an Elderly Population
Last updated on July 2021Recruitment
- Recruitment Status
- Recruiting
- Estimated Enrollment
- Same as current
Summary
- Conditions
- Sleep
- Type
- Interventional
- Phase
- Not Applicable
- Design
- Allocation: N/AIntervention Model: Single Group AssignmentIntervention 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)Primary Purpose: Diagnostic
Participation Requirements
- Age
- Between 60 years and 125 years
- Gender
- Both males and females
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 monito...
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.
Tracking Information
- NCT #
- NCT04755504
- Collaborators
- Not Provided
- Investigators
- Not Provided
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