Sleep assessment using EEG-based wearables - A systematic review.

Area of Science:

• Biomedical Engineering
• Sleep Medicine
• Wearable Technology

Background:

• Polysomnography (PSG) is the gold standard for sleep measurement but is resource-intensive and burdensome.
• Electroencephalography (EEG)-based wearables are emerging as accessible alternatives for sleep monitoring.
• Selecting appropriate EEG wearables can be challenging for clinicians and researchers.

Purpose of the Study:

• To systematically review and provide an overview of available EEG-based wearables for human sleep measurement.
• To detail the validated populations and measurement properties of various EEG sleep wearables.
• To assess the feasibility and accuracy of EEG wearables as alternatives to PSG.

Main Methods:

• Systematic literature search across OVID MEDLINE, Embase.com, and CINAHL databases.
• Utilized machine learning (ASReview) for efficient screening of titles and abstracts.
• Included 60 papers covering 34 unique EEG-based wearables, with 42 validation studies.

Main Results:

• Feasibility studies reported good participant tolerance, high compliance, and successful implementation.
• Validation studies across diverse populations demonstrated consistently high accuracy in sleep staging detection.
• EEG wearables show significant advancements, offering a viable alternative to PSG for sleep monitoring.

Conclusions:

• EEG-based wearables present a promising, accurate, and feasible solution for at-home sleep monitoring.
• These devices offer a user-friendly alternative to traditional polysomnography.
• Consideration of user-friendliness, comfort, and cost is crucial for selecting suitable EEG wearables.