Werth Jan, Long Xi, Zwartkruis-Pelgrim Elly, Niemarkt Hendrik, Chen Wei, Aarts Ronald M, Andriessen Peter
Department of Electrical Engineering, University of Technology Eindhoven, De Zaale, 5612 AJ, Eindhoven, The Netherlands; Philips Research, High Tech Campus 34, 5656 AE, Eindhoven, The Netherlands.
Department of Electrical Engineering, University of Technology Eindhoven, De Zaale, 5612 AJ, Eindhoven, The Netherlands; Philips Research, High Tech Campus 34, 5656 AE, Eindhoven, The Netherlands.
Early Hum Dev. 2017 Oct;113:104-113. doi: 10.1016/j.earlhumdev.2017.07.004. Epub 2017 Jul 18.
As an approach of unobtrusive assessment of neonatal sleep state we aimed at an automated sleep state coding based only on heart rate variability obtained from electrocardiography used for regular patient monitoring. We analyzed active and quiet sleep states of preterm infants between 30 and 37weeks postmenstrual age. To determine the sleep states we used a nonlinear kernel support vector machine for sleep state separation based on known heart rate variability features. We used unweighted and weighted misclassification penalties for the imbalanced distribution between sleep states. The validation was performed with leave-one-out-cross-validation based on the annotations of three independent observers. We analyzed the classifier performance with receiver operating curves leading to a maximum mean value for the area under the curve of 0.87. Using this sleep state separation methods, we show that automated active and quiet sleep state separation based on heart rate variability in preterm infants is feasible.
作为一种对新生儿睡眠状态进行无干扰评估的方法，我们旨在仅基于从用于常规患者监测的心电图获得的心率变异性进行自动睡眠状态编码。我们分析了孕龄30至37周的早产儿的活跃睡眠和安静睡眠状态。为了确定睡眠状态，我们使用非线性核支持向量机，基于已知的心率变异性特征进行睡眠状态分离。针对睡眠状态之间的不平衡分布，我们使用了未加权和加权误分类惩罚。基于三位独立观察者的注释，采用留一法交叉验证进行验证。我们通过接收器操作曲线分析分类器性能，得出曲线下面积的最大平均值为0.87。使用这种睡眠状态分离方法，我们表明基于早产儿心率变异性的自动活跃和安静睡眠状态分离是可行的。
