简介:为了测量重症监护病房(ICU)的睡眠,完整的多导睡眠监测是不切实际的,而活动监测和主观评估严重混淆。然而,睡眠是一种高度网络化的状态,并反映在许多信号中。这里,我们探索使用人工智能方法在ICU中使用心率变异性(HRV)和呼吸信号估计常规睡眠指数的可行性方法:我们使用深度学习模型对接受外科和内科ICU的危重成年患者的HRV(通过心电图)和呼吸努力(通过可穿戴皮带)信号进行睡眠阶段。以及年龄和性别匹配的睡眠实验室患者结果:我们研究了ICU中的102名成年患者,和220名临床睡眠实验室的病人。我们发现,基于HRV和呼吸的模型预测的睡眠阶段在60%的ICU数据和81%的睡眠实验室数据中显示出一致性。在ICU,深度NREM(N2+N3)占总睡眠持续时间的比例降低(ICU39%,睡眠实验室57%,p<0.01),REM比例呈重尾分布,并且每小时睡眠的觉醒转变次数(中位数3.6)与睡眠呼吸紊乱的睡眠实验室患者(中位数3.9)相当.ICU的睡眠也是支离破碎的,38%的睡眠发生在白天。最后,与睡眠实验室患者相比,ICU患者的呼吸模式更快,变化更少。结论:心血管和呼吸网络编码睡眠状态信息,which,结合人工智能方法,可用于测量ICU中的睡眠状态。
Introduction: To measure sleep in the intensive care unit (ICU), full polysomnography is impractical, while activity monitoring and subjective assessments are severely confounded. However, sleep is an intensely networked state, and reflected in numerous signals. Here, we explore the feasibility of estimating conventional sleep indices in the ICU with heart rate variability (HRV) and respiration signals using artificial intelligence methods Methods: We used deep learning models to stage sleep with HRV (through electrocardiogram) and respiratory effort (through a wearable belt) signals in critically ill adult patients admitted to surgical and medical ICUs, and in age and sex-matched sleep laboratory patients Results: We studied 102 adult patients in the ICU across multiple days and nights, and 220 patients in a clinical sleep laboratory. We found that sleep stages predicted by HRV- and breathing-based models showed agreement in 60% of the ICU data and in 81% of the sleep laboratory data. In the ICU, deep NREM (N2 + N3) proportion of total sleep duration was reduced (ICU 39%, sleep laboratory 57%, p < 0.01), REM proportion showed heavy-tailed distribution, and the number of wake transitions per hour of sleep (median 3.6) was comparable to sleep laboratory patients with sleep-disordered breathing (median 3.9). Sleep in the ICU was also fragmented, with 38% of sleep occurring during daytime hours. Finally, patients in the ICU showed faster and less variable breathing patterns compared to sleep laboratory patients Conclusion: The cardiovascular and respiratory networks encode sleep state information, which, together with artificial intelligence methods, can be utilized to measure sleep state in the ICU.