AbstractThe rise of wearable technology has led to EEG-based sleep monitoring devices that use electrodes placed on the forehead, ear, or neck. These devices offer promising applications in clinical and healthy populations by comparing sleep patterns, monitoring intervention responses, and examining the relationship between sleep and lifestyle factors.

摘要可穿戴技术的兴起导致了基于脑电图的睡眠监测设备，该设备使用放置在额头，耳朵或脖子上的电极。这些设备通过比较睡眠模式，监测干预反应以及检查睡眠与生活方式因素之间的关系，在临床和健康人群中提供了有希望的应用。

Despite their potential, challenges like validation against polysomnography, regulatory hurdles, data privacy, and usability hinder clinical adoption. This review explores these devices, their applications, and integration challenges in clinical practice..

尽管有其潜力，但诸如针对多导睡眠图的验证，监管障碍，数据隐私和可用性等挑战阻碍了临床采用。这篇综述探讨了这些设备，它们的应用以及临床实践中的集成挑战。。

IntroductionSleep plays a crucial role in overall health and well-being, and its quality is strongly linked to numerous health issues, as well as mental health and cognitive performance1,2. Polysomnography (PSG) has been the gold standard for clinical sleep monitoring and diagnostics, capturing a variety of physiological responses, including electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG) activity, as well as breathing effort, airflow, pulse, and blood oxygen saturation3.

引言睡眠在整体健康和福祉中起着至关重要的作用，其质量与许多健康问题以及心理健康和认知表现密切相关1,2。多导睡眠图（PSG）已成为临床睡眠监测和诊断的金标准，可捕获多种生理反应，包括脑电图（EEG），眼电图（EOG）和肌电图（EMG）活动，以及呼吸努力，气流，脉搏和血氧饱和度3。

Despite PSG’s reliability in recording sleep patterns, it has several limitations. PSG studies are expensive and time-consuming, requiring trained professionals for setup and data scoring. Sleep stages (wake, sleep stages 1 (N1), 2 (N2), 3 (N3), and rapid eye movement (REM) sleep) are manually annotated by experts in 30-second epochs according to the American Academy of Sleep Medicine’s guidelines3.

尽管PSG在记录睡眠模式方面很可靠，但它有一些局限性。PSG研究既昂贵又耗时，需要训练有素的专业人员进行设置和数据评分。睡眠阶段（唤醒，睡眠阶段1（N1），2（N2），3（N3）和快速眼动（REM）睡眠）由专家根据美国睡眠医学学会的指南3在30秒的时期内手动注释。

N1 and N2 stages are often combined as light sleep, and N3 is referred to as deep sleep. This manual scoring suffers from low inter-rater reliability, with an average agreement of 82.6%4 or κ = 0.765, decreasing even further in patients with sleep pathologies4. The stage-specific agreement has shown to be even lower, with N1 demonstrating only fair agreement between scorers (κ = 0.24)5.

N1和N2阶段通常组合为轻度睡眠，N3被称为深度睡眠。这种手动评分具有较低的评分者间可靠性，平均一致性为82.6%4或κ=0.765，在睡眠病理患者中进一步下降4。阶段特定的协议显示甚至更低，N1表明得分者之间只有公平的协议（κ=0.24）5。

Furthermore, PSG may not accurately represent a patient’s typical sleep. The unfamiliar clinical settings can cause stress, and one-night recordings do not account for intra-individual night-to-night variabilities.Wearables and nearables are increasingly being used for sleep monitoring6,7,8,9,10,11.

此外，PSG可能无法准确代表患者的典型睡眠。不熟悉的临床环境可能会引起压力，一夜之间的记录并不能解释个体之间的夜间变化。可穿戴设备和近距离设备越来越多地用于睡眠监测6、7、8、9、10、11。

While nearables offer non-contact methods to monitor sleep, wearables provide more detailed and accurate sleep data12. Unlike other sleep-monitoring wearbles, EEG-based devices closely mimic the EEG c.

虽然nearables提供非接触式睡眠监测方法，但可穿戴设备提供更详细和准确的睡眠数据12。与其他睡眠监测穿戴设备不同，基于EEG的设备与EEG c非常相似。

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Download referencesAuthor informationAuthor notesThese authors contributed equally: Karmen Markov, Mohamed Elgendi.Authors and AffiliationsBiomedical and Mobile Health Technology Lab, Department of Health Sciences and Technology, ETH Zurich, Zurich, SwitzerlandKarmen Markov, Mohamed Elgendi & Carlo MenonDepartment of Biomedical Engineering and Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, United Arab EmiratesMohamed ElgendiHealthcare Engineering Innovation Group (HEIG), Khalifa University of Science and Technology, Abu Dhabi, United Arab EmiratesMohamed ElgendiAuthorsKarmen MarkovView author publicationsYou can also search for this author in.

下载参考文献作者信息作者注意到这些作者做出了同样的贡献：Karmen Markov，Mohamed Elgendi。作者和附属机构苏黎世联邦理工大学健康科学与技术系医学与移动健康技术实验室，苏黎世苏黎世，瑞士人马尔可夫，穆罕默德·埃尔根迪和卡罗·梅农生物医学工程与生物技术系，哈利法理工大学，阿布扎比，阿拉伯联合酋长国哈米德·埃尔根迪健康护理工程创新小组（HEIG），哈利法科技大学，阿布扎比，阿拉伯联合酋长国哈米德·埃尔根迪作者卡门·马尔科夫维尤作者出版物你也可以在中搜索这位作者。

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PubMed Google ScholarContributionsK.M. and M.E. researched data for the article, made substantial contributions to discussions of the content, wrote the article, and reviewed and/or edited the manuscript before submission. M.E. led and designed the study. K.M., M.E., and C.M.

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Reprints and permissionsAbout this articleCite this articleMarkov, K., Elgendi, M. & Menon, C. EEG-based headset sleep wearable devices.

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npj Biosensing 1, 12 (2024). https://doi.org/10.1038/s44328-024-00013-yDownload citationReceived: 19 January 2024Accepted: 16 September 2024Published: 01 October 2024DOI: https://doi.org/10.1038/s44328-024-00013-yShare this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy to clipboard.

npj生物传感1,12（2024）。https://doi.org/10.1038/s44328-024-00013-yDownload引文接收日期：2024年1月19日接受日期：2024年9月16日发布日期：2024年10月1日OI：https://doi.org/10.1038/s44328-024-00013-yShare本文与您共享以下链接的任何人都可以阅读此内容：获取可共享链接对不起，本文目前没有可共享的链接。复制到剪贴板。

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