C.J. de Gans , P. Burger , E.S. van den Ende , J. Hermanides , P.W.B. Nanayakkara , R.J.B.J. Gemke , F. Rutters , D.J. Stenvers
{"title":"使用脑电图可穿戴设备进行睡眠评估 - 系统综述","authors":"C.J. de Gans , P. Burger , E.S. van den Ende , J. Hermanides , P.W.B. Nanayakkara , R.J.B.J. Gemke , F. Rutters , D.J. Stenvers","doi":"10.1016/j.smrv.2024.101951","DOIUrl":null,"url":null,"abstract":"<div><p>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<strong>,</strong> 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.</p></div>","PeriodicalId":49513,"journal":{"name":"Sleep Medicine Reviews","volume":null,"pages":null},"PeriodicalIF":11.2000,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1087079224000558/pdfft?md5=5757a5c8010e5bba8fe2f2b638b96be6&pid=1-s2.0-S1087079224000558-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Sleep assessment using EEG-based wearables – A systematic review\",\"authors\":\"C.J. de Gans , P. Burger , E.S. van den Ende , J. Hermanides , P.W.B. Nanayakkara , R.J.B.J. Gemke , F. Rutters , D.J. Stenvers\",\"doi\":\"10.1016/j.smrv.2024.101951\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>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<strong>,</strong> the recent advancements in EEG-based wearables show great promise as alternative for PSG and for at-home sleep monitoring. 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Sleep assessment using EEG-based wearables – A systematic review
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.
期刊介绍:
Sleep Medicine Reviews offers global coverage of sleep disorders, exploring their origins, diagnosis, treatment, and implications for related conditions at both individual and public health levels.
Articles comprehensively review clinical information from peer-reviewed journals across various disciplines in sleep medicine, encompassing pulmonology, psychiatry, psychology, physiology, otolaryngology, pediatrics, geriatrics, cardiology, dentistry, nursing, neurology, and general medicine.
The journal features narrative reviews, systematic reviews, and editorials addressing areas of controversy, debate, and future research within the field.