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摘要: 高質量睡眠與兒童的身體發育、認知功能、學習和注意力密切相關，由于兒童睡眠障礙的早期癥狀不明顯，需要進行長期監測，因此急需找到一種適用于兒童睡眠監測，且能夠提前預防和診斷此類疾病的方法。多導睡眠圖(Polysomnography，PSG)是臨床指南推薦的睡眠障礙基本檢測方法，通過觀察PSG各睡眠期間的變化和規律，對睡眠質量評估和睡眠障礙識別具有基礎作用。本文對兒童睡眠分期進行了研究，利用多導睡眠圖記錄的單通道腦電信號，在Alexnet的基礎上，用一維卷積代替二維卷積，提出一種1D-CNN結構，由5個卷積層、3個池化層和3個全連接層組成，并在1D-CNN中添加了批量歸一化層（Batch normalization layer），保持卷積核的大小保持不變。針對數據集少的情況，采用了重疊的方法對數據集進行了擴充。實驗結果表明，該模型兒童睡眠分期的準確率為84.3%。通過北京市兒童醫院的PSG數據獲得的歸一化混淆矩陣，可以看出，Wake、N2、N3和REM期睡眠的分類性能很好。對于N1期睡眠，存在將N1期睡眠被誤分類為Wake、N2和REM期睡眠的情況，因此以后的工作應重點提升N1期睡眠的準確性。總體而言，對于基于帶有睡眠階段標記的單通道EEG的自動睡眠分期，本文提出的1D-CNN模型可以實現針對于兒童的自動睡眠分期。在未來的工作中，仍需要研究開發更適合于兒童的睡眠分期策略，在更大數據量的基礎上進行實驗。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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表 1 超參數表
Table 1. Model hyperparameters
Parameter Value Optimizer Adam Learning rate 0.00001 Loss function Cross-entropy Batch size 256 L2 regularization 0.001 
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表 2 實驗結果
Table 2. Experimental results
Method Accuracy / % Precision Recall F1-score K Our method 85.57 0.847 0.866 0.855 0.820 DeepSleepNet 69.58 0.687 0.659 0.632 0.581
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