TY - JOUR
T1 - Sleep assessment using EEG-based wearables – A systematic review
AU - de Gans, Carlijn J.
AU - Burger, P.
AU - van den Ende, E.S.
AU - Hermanides, J
AU - Nanayakkara, K.D.P.W.B.
AU - Gemke, R.J.B.J.
AU - Rutters, F.
AU - Stenvers, D.J.
N1 - Publisher Copyright: © 2024 The Authors
PY - 2024/8
Y1 - 2024/8
N2 - Polysomnography (PSG) is the reference standard of sleep measurement, but is burdensome for the participant and labor intensive. Affordable electroencephalography (EEG)-based wearables are easy to use and are gaining popularity, yet selecting the most suitable device is a challenge for clinicians and researchers. In this systematic review, we aim to provide a comprehensive overview of available EEG-based wearables to measure human sleep. For each wearable, an overview will be provided regarding validated population and reported measurement properties. A systematic search was conducted in the databases OVID MEDLINE, Embase.com and CINAHL. A machine learning algorithm (ASReview) was utilized to screen titles and abstracts for eligibility. In total, 60 papers were selected, covering 34 unique EEG-based wearables. Feasibility studies indicated good tolerance, high compliance, and success rates. The 42 included validation studies were conducted across diverse populations and showed consistently high accuracy in sleep staging detection. Therefore, the recent advancements in EEG-based wearables show great promise as alternative for PSG and for at-home sleep monitoring. Users should consider factors like user-friendliness, comfort, and costs, as these devices vary in features and pricing, impacting their suitability for individual needs.
AB - Polysomnography (PSG) is the reference standard of sleep measurement, but is burdensome for the participant and labor intensive. Affordable electroencephalography (EEG)-based wearables are easy to use and are gaining popularity, yet selecting the most suitable device is a challenge for clinicians and researchers. In this systematic review, we aim to provide a comprehensive overview of available EEG-based wearables to measure human sleep. For each wearable, an overview will be provided regarding validated population and reported measurement properties. A systematic search was conducted in the databases OVID MEDLINE, Embase.com and CINAHL. A machine learning algorithm (ASReview) was utilized to screen titles and abstracts for eligibility. In total, 60 papers were selected, covering 34 unique EEG-based wearables. Feasibility studies indicated good tolerance, high compliance, and success rates. The 42 included validation studies were conducted across diverse populations and showed consistently high accuracy in sleep staging detection. Therefore, the recent advancements in EEG-based wearables show great promise as alternative for PSG and for at-home sleep monitoring. Users should consider factors like user-friendliness, comfort, and costs, as these devices vary in features and pricing, impacting their suitability for individual needs.
KW - EEG
KW - Electro-encephalography
KW - Sleep
KW - Sleep assessment
KW - Systematic review
KW - Wearables
UR - http://www.scopus.com/inward/record.url?scp=85192776798&partnerID=8YFLogxK
U2 - 10.1016/j.smrv.2024.101951
DO - 10.1016/j.smrv.2024.101951
M3 - Review article
C2 - 38754209
SN - 1087-0792
VL - 76
JO - Sleep Medicine Reviews
JF - Sleep Medicine Reviews
IS - 101951
M1 - 101951
ER -