无创技术监测睡眠的不同方面:一个全面的回顾

Z. Hussain, Quan Z. Sheng, W. Zhang, Jorge Ortiz, Seyedamin Pouriyeh
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引用次数: 16

摘要

高质量的睡眠对健康的生活非常重要。如今,世界上许多人睡眠不足,这对他们的生活方式产生了负面影响。人们正在进行睡眠监测和更好地理解睡眠行为的研究。睡眠分析的金标准方法是在临床环境中进行的多导睡眠图,但这种方法既昂贵又复杂,无法长期使用。随着传感器领域的进步和现成技术的引入,不引人注目的解决方案正成为家庭睡眠监测的替代方案。已经提出了各种解决方案,使用可穿戴和非可穿戴方法,这些方法便宜且易于用于家庭睡眠监测。在本文中,我们对2015年及以后在睡眠监测的各个领域的最新研究工作进行了全面的综述,包括睡眠阶段分类、睡眠姿势识别、睡眠障碍检测和生命体征监测。我们回顾了使用非侵入性方法的最新研究成果,涵盖了可穿戴和非可穿戴方法。我们讨论了所提出的工作的设计方法和关键属性,并基于十个关键因素进行了广泛的分析,目的是对所有四类睡眠监测的最新发展和趋势进行全面概述。我们还收集了不同类别睡眠监测的公开可用数据集。最后讨论了睡眠监测领域的几个开放性问题和未来的研究方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Non-invasive Techniques for Monitoring Different Aspects of Sleep: A Comprehensive Review
Quality sleep is very important for a healthy life. Nowadays, many people around the world are not getting enough sleep, which has negative impacts on their lifestyles. Studies are being conducted for sleep monitoring and better understanding sleep behaviors. The gold standard method for sleep analysis is polysomnography conducted in a clinical environment, but this method is both expensive and complex for long-term use. With the advancements in the field of sensors and the introduction of off-the-shelf technologies, unobtrusive solutions are becoming common as alternatives for in-home sleep monitoring. Various solutions have been proposed using both wearable and non-wearable methods, which are cheap and easy to use for in-home sleep monitoring. In this article, we present a comprehensive survey of the latest research works (2015 and after) conducted in various categories of sleep monitoring, including sleep stage classification, sleep posture recognition, sleep disorders detection, and vital signs monitoring. We review the latest research efforts using the non-invasive approach and cover both wearable and non-wearable methods. We discuss the design approaches and key attributes of the work presented and provide an extensive analysis based on ten key factors, with the goal to give a comprehensive overview of the recent developments and trends in all four categories of sleep monitoring. We also collect publicly available datasets for different categories of sleep monitoring. We finally discuss several open issues and future research directions in the area of sleep monitoring.
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