Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
Department of Applied Physics and Science Education, Eindhoven University of Technology, Eindhoven, The Netherlands.
Acta Paediatr. 2024 Jun;113(6):1236-1245. doi: 10.1111/apa.17211. Epub 2024 Mar 19.
This study aimed to classify quiet sleep, active sleep and wake states in preterm infants by analysing cardiorespiratory signals obtained from routine patient monitors.
We studied eight preterm infants, with an average postmenstrual age of 32.3 ± 2.4 weeks, in a neonatal intensive care unit in the Netherlands. Electrocardiography and chest impedance respiratory signals were recorded. After filtering and R-peak detection, cardiorespiratory features and motion and cardiorespiratory interaction features were extracted, based on previous research. An extremely randomised trees algorithm was used for classification and performance was evaluated using leave-one-patient-out cross-validation and Cohen's kappa coefficient.
A sleep expert annotated 4731 30-second epochs (39.4 h) and active sleep, quiet sleep and wake accounted for 73.3%, 12.6% and 14.1% respectively. Using all features, and the extremely randomised trees algorithm, the binary discrimination between active and quiet sleep was better than between other states. Incorporating motion and cardiorespiratory interaction features improved the classification of all sleep states (kappa 0.38 ± 0.09) than analyses without these features (kappa 0.31 ± 0.11).
Cardiorespiratory interactions contributed to detecting quiet sleep and motion features contributed to detecting wake states. This combination improved the automated classifications of sleep states.
本研究旨在通过分析来自常规患者监护仪的心肺信号，对早产儿的安静睡眠、活跃睡眠和清醒状态进行分类。
我们在荷兰的一家新生儿重症监护病房研究了 8 名早产儿，平均胎龄为 32.3±2.4 周。记录心电图和胸部阻抗呼吸信号。在滤波和 R 波检测后，基于先前的研究提取心肺特征以及运动和心肺交互特征。使用极端随机树算法进行分类，并使用留一患者交叉验证和 Cohen's kappa 系数评估性能。
一位睡眠专家对 4731 个 30 秒的时段（39.4 小时）进行了注释，活跃睡眠、安静睡眠和清醒状态分别占 73.3%、12.6%和 14.1%。使用所有特征和极端随机树算法，活跃睡眠和安静睡眠之间的二进制判别优于其他状态之间的判别。纳入运动和心肺交互特征可改善所有睡眠状态的分类（kappa 0.38±0.09），优于不包括这些特征的分析（kappa 0.31±0.11）。
心肺相互作用有助于检测安静睡眠，运动特征有助于检测清醒状态。这种组合提高了睡眠状态的自动分类。
