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

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

Study Overview

• Status: Completed
• Conditions: Sleep
• Intervention / Treatment: Diagnostic test: EEG behind the 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 Type: Interventional
• Enrollment (Actual): 100
• Phase: Not Applicable

Contacts and Locations

Study Locations

• Belgium
  • Leuven, Belgium, 3000
    • UZ Leuven

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 60 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: No

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

1

Arms and Interventions

Participant Group / Arm	Intervention / 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)	Diagnostic test: EEG behind the ear; 2 additional electrodes behind each ear will record EEG

What is the study measuring?

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

Collaborators and Investigators

• Sponsor: Universitaire Ziekenhuizen KU Leuven

Study record dates

Study Major Dates

• Study Start (Actual): January 21, 2021
• Primary Completion (Actual): January 30, 2023
• Study Completion (Actual): January 30, 2023

Study Registration Dates

• First Submitted: February 8, 2021
• First Submitted That Met QC Criteria: February 15, 2021
• First Posted (Actual): February 16, 2021

Study Record Updates

• Last Update Posted (Actual): July 3, 2024
• Last Update Submitted That Met QC Criteria: July 2, 2024
• Last Verified: July 1, 2024

More Information

Terms related to this study

• Other Study ID Numbers: S64190

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No