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

NCT04755504 | Completed

• 1 other identifier: S64190
• Study Type: interventional
• Target: 100
• Locations: 1 country
• Sites: 1

Brief Summary

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

Conditions

Sleep

Outcome Measures

Primary Outcomes (1)

• 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

Study Arms (1)

EEG evaluation

EXPERIMENTAL

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

Interventions

EEG behind the ear DIAGNOSTIC_TEST

2 additional electrodes behind each ear will record EEG

EEG evaluation

Eligibility Criteria

• Age: 60 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Subjects planned to undergo a diagnostic polysomnography
• \> 60y old

You may not qualify if:

• Patients unable to provide informed consent

Sponsors & Collaborators

• Universitaire Ziekenhuizen KU Leuven: lead

Study Sites (1)

UZ Leuven

Leuven, 3000, Belgium

Location

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Model Details: Each patient will be evaluated with routine polysomnography and additionally 2 EEG signals will be recorded behind each air.

Study Record Dates

• First Submitted: February 8, 2021
• First Posted: February 16, 2021
• Study Start: January 21, 2021
• Primary Completion: January 30, 2023
• Study Completion: January 30, 2023
• Last Updated: July 3, 2024

Record last verified: 2024-07

Data Sharing

• IPD Sharing: Will not share

Locations

BE (1)