The State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing, 210096, People's Republic of China.
Air Force Medical Center, PLA. Beijing, 100142, People's Republic of China.
Physiol Meas. 2021 May 11;42(4). doi: 10.1088/1361-6579/abf1b2.
. This study aimed to prove that there is a sudden change in the human physiology system when switching from one sleep stage to another and physical threshold-based sample entropy (SampEn) is able to capture this transition in an RR interval time series from patients with disorders such as sleep apnea.. Physical threshold-based SampEn was used to analyze different sleep-stage RR segments from sleep apnea subjects in the St. Vincents University Hospital/University College Dublin Sleep Apnea Database, and SampEn differences were compared between two consecutive sleep stages. Additionally, other standard heart rate variability (HRV) measures were also analyzed to make comparisons.. The findings suggested that the sleep-to-wake transitions presented a SampEn decrease significantly larger than intra-sleep ones ( < 0.01), which outperformed other standard HRV measures. Moreover, significant entropy differences between sleep and subsequent wakefulness appeared when the previous sleep stage was either S1 ( < 0.05), S2 ( < 0.01) or S4 ( < 0.05).. The results demonstrated that physical threshold-based SampEn has the capability of depicting physiological changes in the cardiovascular system during the sleep-to-wake transition in sleep apnea patients and it is more reliable than the other analyzed HRV measures. This noninvasive HRV measure is a potential tool for further evaluation of sleep physiological time series.
本研究旨在证明，当从一个睡眠阶段切换到另一个睡眠阶段时，人体生理系统会发生突然变化，基于物理阈值的样本熵（SampEn）能够捕捉到睡眠呼吸暂停患者的 RR 间期时间序列中的这种转变。基于物理阈值的 SampEn 用于分析来自圣文森特大学医院/都柏林大学学院睡眠呼吸暂停数据库中睡眠呼吸暂停患者不同睡眠阶段的 RR 段，并比较两个连续睡眠阶段之间的 SampEn 差异。此外，还分析了其他标准心率变异性（HRV）措施进行比较。研究结果表明，睡眠到清醒的过渡呈现出显著大于睡眠内的 SampEn 下降（<0.01），优于其他标准 HRV 措施。此外，当前一个睡眠阶段为 S1（<0.05）、S2（<0.01）或 S4（<0.05）时，睡眠和随后的清醒之间会出现显著的熵差异。研究结果表明，基于物理阈值的 SampEn 能够描述睡眠呼吸暂停患者睡眠到清醒过渡期间心血管系统的生理变化，并且比其他分析的 HRV 措施更可靠。这种非侵入性 HRV 测量方法是进一步评估睡眠生理时间序列的潜在工具。
