Philips Research, Eindhoven, The Netherlands.
Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
Sleep. 2020 Sep 14;43(9). doi: 10.1093/sleep/zsaa048.
To validate a previously developed sleep staging algorithm using heart rate variability (HRV) and body movements in an independent broad cohort of unselected sleep disordered patients.
We applied a previously designed algorithm for automatic sleep staging using long short-term memory recurrent neural networks to model sleep architecture. The classifier uses 132 HRV features computed from electrocardiography and activity counts from accelerometry. We retrained our algorithm using two public datasets containing both healthy sleepers and sleep disordered patients. We then tested the performance of the algorithm on an independent hold-out validation set of sleep recordings from a wide range of sleep disorders collected in a tertiary sleep medicine center.
The classifier achieved substantial agreement on four-class sleep staging (wake/N1-N2/N3/rapid eye movement [REM]), with an average κ of 0.60 and accuracy of 75.9%. The performance of the sleep staging algorithm was significantly higher in insomnia patients (κ = 0.62, accuracy = 77.3%). Only in REM parasomnias, the performance was significantly lower (κ = 0.47, accuracy = 70.5%). For two-class wake/sleep classification, the classifier achieved a κ of 0.65, with a sensitivity (to wake) of 72.9% and specificity of 94.0%.
This study shows that the combination of HRV, body movements, and a state-of-the-art deep neural network can reach substantial agreement in automatic sleep staging compared with polysomnography, even in patients suffering from a multitude of sleep disorders. The physiological signals required can be obtained in various ways, including non-obtrusive wrist-worn sensors, opening up new avenues for clinical diagnostics.
使用心率变异性（HRV）和身体运动在一个独立的、广泛的未选择的睡眠障碍患者群体中验证先前开发的睡眠分期算法。
我们应用了先前设计的使用长短期记忆递归神经网络对睡眠结构进行建模的自动睡眠分期算法。该分类器使用从心电图计算的 132 个 HRV 特征和来自加速度计的活动计数。我们使用包含健康睡眠者和睡眠障碍患者的两个公共数据集重新训练我们的算法。然后，我们在一个由三级睡眠医学中心收集的广泛的睡眠障碍睡眠记录的独立保留验证集中测试算法的性能。
分类器在四分类睡眠分期（觉醒/N1-N2/N3/快速眼动[REM]）方面达到了实质性的一致性，平均κ值为 0.60，准确率为 75.9%。在失眠患者中，睡眠分期算法的性能明显更高（κ=0.62，准确率=77.3%）。只有在 REM 睡眠障碍中，性能明显较低（κ=0.47，准确率=70.5%）。对于两分类觉醒/睡眠分类，分类器的κ值为 0.65，对觉醒的敏感性为 72.9%，特异性为 94.0%。
这项研究表明，HRV、身体运动和最先进的深度神经网络的组合可以在自动睡眠分期方面与多导睡眠图达到实质性的一致性，即使在患有多种睡眠障碍的患者中也是如此。所需的生理信号可以通过多种方式获得，包括非侵入性的腕戴式传感器，为临床诊断开辟了新的途径。
