一篇关于通过生物信号记录自动睡眠分期的当前趋势及未来挑战的综述。
A review on current trends in automatic sleep staging through bio-signal recordings and future challenges.
机构信息
Laboratory of Medical Physics, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Laboratory of Medical Physics, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece; Greek Aerospace Medical Association and Space Research (GASMA-SR), Thessaloniki, Greece.
出版信息
Sleep Med Rev. 2021 Feb;55:101377. doi: 10.1016/j.smrv.2020.101377. Epub 2020 Sep 9.
Sleep staging is a vital process conducted in order to analyze polysomnographic data. To facilitate prompt interpretation of these recordings, many automatic sleep staging methods have been proposed. These methods rely on bio-signal recordings, which include electroencephalography, electrocardiography, electromyography, electrooculography, respiratory, pulse oximetry and others. However, advanced, uncomplicated and swift sleep-staging-evaluation is still needed in order to improve the existing polysomnographic data interpretation. The present review focuses on automatic sleep staging methods through bio-signal recording including current and future challenges.
睡眠分期是分析多导睡眠图数据的重要过程。为了方便这些记录的快速解读，已经提出了许多自动睡眠分期方法。这些方法依赖于生物信号记录，包括脑电图、心电图、肌电图、眼电图、呼吸、脉搏血氧饱和度等。然而，仍然需要先进、简单和快速的睡眠分期评估，以改善现有的多导睡眠图数据解读。本综述重点介绍了通过生物信号记录进行自动睡眠分期的方法，包括当前和未来的挑战。
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