Abstract：
Rapid eye movement sleep is associated with distinct changes in various biomedical signals that can be easily captured during sleep, lending themselves to automated sleep staging using machine learning systems. Here, we provide a perspective on the critical characteristics of biomedical signals associated with rapid eye movement sleep and how they can be exploited for automated sleep assessment. We summarise key historical developments in automated sleep staging systems, having now achieved classification accuracy on par with human expert scorers and their role in the clinical setting. We also discuss rapid eye movement sleep assessment with consumer sleep trackers and its potential for unprecedented sleep assessment on a global scale. We conclude by providing a future outlook of computerised rapid eye movement sleep assessment and the role AI systems may play.
摘要：
快速眼动睡眠与各种生物医学信号的明显变化有关，这些变化在睡眠期间很容易被捕获。让自己使用机器学习系统进行自动睡眠分期。这里,我们提供了与快速眼动睡眠相关的生物医学信号的关键特征，以及如何将其用于自动睡眠评估。我们总结了自动睡眠分期系统的关键历史发展，现在已经实现了与人类专家评分者相当的分类准确性及其在临床环境中的作用。我们还讨论了消费者睡眠跟踪器的快速眼动睡眠评估及其在全球范围内进行前所未有的睡眠评估的潜力。最后，我们提供了计算机快速眼动睡眠评估的未来前景以及AI系统可能扮演的角色。
