IEEE J Biomed Health Inform. 2021 May;25(5):1441-1449. doi: 10.1109/JBHI.2021.3073632. Epub 2021 May 11.
Sleep is a natural phenomenon controlled by the central nervous system. The sleep-wake pattern, which functions as an essential indicator of neurophysiological organization in the neonatal period, has profound meaning in the prediction of cognitive diseases and brain maturity. In recent years, unobtrusive sleep monitoring and automatic sleep staging have been intensively studied for adults, but much less for neonates. This work aims to investigate a novel video-based unobtrusive method for neonatal sleep-wake classification by analyzing the behavioral changes in the neonatal facial region. A hybrid model is proposed to monitor the sleep-wake patterns of human neonates. The model combines two algorithms: deep convolutional neural network (DCNN) and support vector machine (SVM), where DCNN works as a trainable feature extractor and SVM as a classifier. Data was collected from nineteen Chinese neonates at the Children's Hospital of Fudan University, Shanghai, China. The classification results are compared with the gold standard of video-electroencephalography scored by pediatric neurologists. Validations indicate that the proposed hybrid DCNN-SVM model achieved reliable performances in classifying neonatal sleep and wake states in RGB video frames (with the face region detected), with an accuracy of 93.8 ± 2.2% and an F1-score 0.93 ± 0.3.
睡眠是由中枢神经系统控制的自然现象。睡眠-觉醒模式作为新生儿期神经生理组织的重要指标，对认知疾病和大脑成熟度的预测具有深远意义。近年来，人们对成人非侵入性睡眠监测和自动睡眠分期进行了深入研究，但对新生儿的研究则少得多。本研究旨在通过分析新生儿面部区域的行为变化，探索一种基于视频的新型非侵入性新生儿睡眠-觉醒分类方法。提出了一种混合模型来监测人类新生儿的睡眠-觉醒模式。该模型结合了两种算法：深度卷积神经网络（DCNN）和支持向量机（SVM），其中 DCNN 用作可训练的特征提取器，SVM 用作分类器。数据来自中国上海复旦大学附属儿科医院的 19 名中国新生儿。将分类结果与儿科神经学家通过视频脑电图评分的金标准进行比较。验证结果表明，所提出的混合 DCNN-SVM 模型在分类 RGB 视频帧（检测到面部区域）中的新生儿睡眠和觉醒状态方面表现出可靠的性能，准确率为 93.8±2.2%，F1 得分为 0.93±0.3。
