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Completed

NCT Number: NCT04755504

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.

Completed

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Key information

Conditions

Sleep

Age range

60 year and older

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

UZ Leuven

Leuven, 3000, Belgium

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

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

Exclusion criteria

  • Patients unable to provide informed consent

Treatment and study plan

EEG behind the ear

Diagnostic Test

2 additional electrodes behind each ear will record EEG

Primary outcomes

  1. Sleep algorithm

    Time frame: 1 night

    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.

Sponsors and collaborators

Lead sponsor

Universitaire Ziekenhuizen KU Leuven

Other

Registry information

Important dates

Study start
2021
Primary completion
2023
Study completion
2023
First posted
Feb 16, 2021
Registry last updated
Jul 3, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

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This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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