许力, 吴云肖, 肖冰, 许志飞, 张远. 基于一维卷积神经网络的儿童睡眠分期[J]. 工程科学学报, 2021, 43(9): 1253-1260. DOI: 10.13374/j.issn2095-9389.2021.01.13.011
引用本文: 许力, 吴云肖, 肖冰, 许志飞, 张远. 基于一维卷积神经网络的儿童睡眠分期[J]. 工程科学学报, 2021, 43(9): 1253-1260. DOI: 10.13374/j.issn2095-9389.2021.01.13.011
XU Li, WU Yun-xiao, XIAO Bing, XU Zhi-fei, ZHANG Yuan. One-dimensional convolutional neural network for children’s sleep staging[J]. Chinese Journal of Engineering, 2021, 43(9): 1253-1260. DOI: 10.13374/j.issn2095-9389.2021.01.13.011
Citation: XU Li, WU Yun-xiao, XIAO Bing, XU Zhi-fei, ZHANG Yuan. One-dimensional convolutional neural network for children’s sleep staging[J]. Chinese Journal of Engineering, 2021, 43(9): 1253-1260. DOI: 10.13374/j.issn2095-9389.2021.01.13.011

基于一维卷积神经网络的儿童睡眠分期

One-dimensional convolutional neural network for children’s sleep staging

  • 摘要: 高质量睡眠与儿童的身体发育、认知功能、学习和注意力密切相关,由于儿童睡眠障碍的早期症状不明显,需要进行长期监测,因此急需找到一种适用于儿童睡眠监测,且能够提前预防和诊断此类疾病的方法。多导睡眠图(Polysomnography,PSG)是临床指南推荐的睡眠障碍基本检测方法,通过观察PSG各睡眠期间的变化和规律,对睡眠质量评估和睡眠障碍识别具有基础作用。本文对儿童睡眠分期进行了研究,利用多导睡眠图记录的单通道脑电信号,在Alexnet的基础上,用一维卷积代替二维卷积,提出一种1D-CNN结构,由5个卷积层、3个池化层和3个全连接层组成,并在1D-CNN中添加了批量归一化层(Batch normalization layer),保持卷积核的大小保持不变。针对数据集少的情况,采用了重叠的方法对数据集进行了扩充。实验结果表明,该模型儿童睡眠分期的准确率为84.3%。通过北京市儿童医院的PSG数据获得的归一化混淆矩阵,可以看出,Wake、N2、N3和REM期睡眠的分类性能很好。对于N1期睡眠,存在将N1期睡眠被误分类为Wake、N2和REM期睡眠的情况,因此以后的工作应重点提升N1期睡眠的准确性。总体而言,对于基于带有睡眠阶段标记的单通道EEG的自动睡眠分期,本文提出的1D-CNN模型可以实现针对于儿童的自动睡眠分期。在未来的工作中,仍需要研究开发更适合于儿童的睡眠分期策略,在更大数据量的基础上进行实验。

     

    Abstract: High-quality sleep is linked with physical development, cognitive function, learning, and attention in children. Since early symptoms of sleep disorders in children are not obvious and require long-term monitoring, there is an urgent need to develop a method for monitoring children’s sleep that can prevent and diagnose these disorders in advance. Polysomnography (PSG) is the basic test for sleep disorders recommended by clinical guidelines. Sleep quality can be assessed and sleep disorders can be identified by observing the changes in patterns of PSG during each sleep period. Sleep staging in children was researched and single-channel electroencephalogram (EEG) signals recorded by PSG was used in this study. On the basis of Alexnet, we use a one-dimensional convolutional neural network (1D-CNN) model instead of a two-dimensional model to propose a 1D-CNN structure composed of five convolutional layers, three pooling layers, and three fully connected layers, as well as a batch normalization layer to 1D-CNN while keeping the size of the convolutional kernel constant. Moreover, the dataset was augmented with an overlapping method to address its small size. The experimental results showed that the accuracy of this model for children’s sleep staging was 84.3%. According to the normalized confusion matrix obtained from the PSG data of Beijing Children’s Hospital, the classification performance of wake, N2, N3, and REM stages of sleep was very good. Because stage N1 sleep was misclassified as wake, N2, and REM sleep in some cases, future research should focus on improving the accuracy of stage N1 sleep. Overall, the 1D-CNN model proposed in this paper can realize automatic sleep staging for children based on single-channel EEG with sleep stage markers. In the future, more research is needed to develop a more suitable sleep staging strategy for children and to conduct experiments with a larger amount of data.

     

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