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Multi-Modal Home Sleep Monitoring in Older Adults

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Published on: January 26, 2019

Evaluating Electroencephalogram-Based Predictive Model for Drowsiness Measurement to Reduce Accident Risk in Active

Chloé Boitard¹, Zoé Mazurie¹, Khadijeh Sadatnejad¹

¹Université de Bordeaux, SANPSY, UMR 6033, Groupe Hospitalier Pellegrin 13ème Etage - Aile 3 Place Amélie Raba Léon, Bordeaux, 33076, France, 33 05 57 82 01 72.

JMIR Research Protocols

|February 17, 2026

Summary

This study validates electroencephalogram (EEG) measures for detecting drowsiness, aiming to improve safety in high-risk environments. Findings will support proactive drowsiness management strategies for occupational and transportation settings.

Keywords:

EEG Objective Sleepiness Scale cognitive performance drowsiness electroencephalogram prediction sleep deprivation

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Area of Science:

Neuroscience
Occupational Health
Transportation Safety

Background:

Socioeconomic factors like shift work cause sleep disruption and drowsiness.
Drowsiness in occupational and driving contexts impairs alertness and increases accident risk.
Automated drowsiness detection is crucial for managing sleepy individuals and ensuring safety in vigilance-critical environments.

Purpose of the Study:

To validate continuous or predictive drowsiness assessment methods using automated analysis of limited electroencephalogram (EEG) channels.
To evaluate the efficacy of novel EEG-based measures in detecting and predicting drowsiness.

Main Methods:

A single-center, nonrandomized study involving 40 healthy volunteers under simulated sleep deprivation.
Primary outcome: Objective Sleepiness Scale (OSS) automated analysis correlated with Maintenance of Wakefulness Test (MWT).
Secondary outcomes: EEG markers, subjective/objective sleepiness, simulated driving performance, cognitive tests, and sleep quality assessments.

Main Results:

Data analysis is ongoing, with recruitment completed in May 2025.
The study is evaluating the ability of automated EEG analysis to measure objective wakefulness.

Conclusions:

Validation of novel EEG-based drowsiness measures is expected.
Findings will establish foundations for proactive drowsiness management in occupational, transportation, and clinical settings.