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

Sleep Clinical Trial

Official title:

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

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT04755504
• Other study ID #: S64190
• Status: Recruiting
• Phase: N/A
• Start date: January 21, 2021
• Est. completion date: December 2021

Study information

• Verified date: February 2021
• Source: Universitaire Ziekenhuizen Leuven
• Contact: Dries Testelmans, MD, PhD
• Phone: 00 32 16 342522
• Email: dries.testelmans[@]uzleuven.be
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

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

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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 100
• Est. completion date: December 2021
• Est. primary completion date: September 2021
• Accepts healthy volunteers: No
• Gender: All
• Age group: 60 Years and older

Eligibility

Inclusion Criteria:

• Subjects planned to undergo a diagnostic polysomnography

• > 60y old

Exclusion Criteria:

• Patients unable to provide informed consent

Related Conditions & MeSH terms

• Sleep

Intervention

Diagnostic Test:
• EEG behind the ear
2 additional electrodes behind each ear will record EEG

Locations

Country	Name	City	State
Belgium	UZ Leuven	Leuven

Sponsors (1)

Lead Sponsor	Collaborator
Universitaire Ziekenhuizen Leuven

Country where clinical trial is conducted

Belgium, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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