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

Status

Completed

Phases

Study type

Interventional

Source

ClinicalTrials.gov

Registry ID

NCT04755504

Enrollment

100

Registered

2021-02-16

Start date

2021-01-21

Completion date

2023-01-30

Last updated

2024-07-03

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Sleep

Brief summary

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

Interventions

DIAGNOSTIC_TESTEEG behind the ear

2 additional electrodes behind each ear will record EEG

Study design

Allocation

Intervention model

SINGLE_GROUP

Primary purpose

DIAGNOSTIC

Masking

NONE

Intervention model description

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

Eligibility

Sex/Gender

ALL

Age

60 Years to No maximum

Healthy volunteers

Inclusion criteria

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

Exclusion criteria

* Patients unable to provide informed consent

Design outcomes

Primary

Measure	Time frame	Description
Sleep algorithm	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.

Countries

Belgium

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026