Xu S R, Peng C, Wang Y
Department of Nursing, Affiliated Hospital of Guizhou Medical University, Guiyang 550001, China.
Department of Respiratory and Critical Care Medicine, Tianjin Medical University General Hospital, Tianjin 300052, China.
Zhonghua Jie He He Hu Xi Za Zhi. 2024 Jun 12;47(6):554-559. doi: 10.3760/cma.j.cn112147-20231215-00372.
Obstructive sleep apnea (OSA) is primarily characterized by intermittent nocturnal hypoxia and sleep fragmentation. Arousals interrupt sleep continuity and lead to sleep fragmentation, which can lead to cognitive dysfunction, excessive daytime sleepiness, and adverse cardiovascular outcome events, making arousals important for diagnosing OSA and reducing the risk of complications, including heart disease and cognitive impairment. Traditional arousal interpretation requires sleep specialists to manually score PSG recordings throughout the night, which is time consuming and has low inter-specialist agreement, so the search for simple, efficient, and reliable arousal detection methods can be a powerful tool to clinicians. In this paper, we systematically reviewed different methods for recognizing arousal in OSA patients, including autonomic markers (pulse conduction time, pulse wave amplitude, peripheral arterial tone, heart rate, ) and machine learning-based automated arousal detection systems, and found that autonomic markers may be more beneficial in certain subgroups, and that deep artificial networks will remain the main research method for automated arousal detection in the future.
阻塞性睡眠呼吸暂停（OSA）主要特征为间歇性夜间低氧血症和睡眠片段化。觉醒会中断睡眠连续性并导致睡眠片段化，进而可能导致认知功能障碍、日间过度嗜睡以及不良心血管结局事件，这使得觉醒对于诊断OSA和降低包括心脏病和认知障碍在内的并发症风险具有重要意义。传统的觉醒解读需要睡眠专家在整个夜间手动对多导睡眠图（PSG）记录进行评分，这既耗时又在专家之间一致性较低，因此寻找简单、高效且可靠的觉醒检测方法对临床医生而言可能是一个有力工具。在本文中，我们系统回顾了识别OSA患者觉醒的不同方法，包括自主神经标志物（脉搏传导时间、脉搏波振幅、外周动脉张力、心率等）以及基于机器学习的自动觉醒检测系统，发现自主神经标志物在某些亚组中可能更有益，并且深度人工网络在未来仍将是自动觉醒检测的主要研究方法。
