Studies from the literature show that the prevalence of sleep disorder in children is far higher than that in adults. Although much research effort has been made on sleep stage classification for adults, children have significantly different characteristics of sleep stages. Therefore, there is an urgent need for sleep stage classification targeting children in particular. Our method focuses on two issues: The first is timestamp-based segmentation (TSS) to deal with the fine-grained annotation of sleep stage labels for each timestamp. Compared to this, popular sliding window approaches unnecessarily aggregate such labels into coarse-grained ones. We utilize DeConvolutional Neural Network (DCNN) that inversely maps features of a hidden layer back to the input space to predict the sleep stage label at each timestamp. Thus, our DCNN can yield better classification performances by considering labels at numerous timestamps. The second issue is the necessity of multiple channels. Different clinical signs, symptoms or other auxiliary examinations could be represented by different Polysomnography (PSG) recordings, so all of them should be analyzed comprehensively. We therefor exploit multivariate time-series of PSG recordings, including 6 electroencephalograms (EEGs) channels, 2 electrooculograms (EOGs) channels (left and right), 1 electromyogram (chin EMG) channel and two leg electromyogram channels. Our DCNN-based method is tested on our SDCP dataset collected from child patients aged from 5 to 10 years old. The results show that our method yields the overall classification accuracy of 84.27% and macro F1-score of 72.51% which are higher than those of existing sliding window-based methods. One of the biggest advantages of our DCNN-based method is that it processes raw PSG recordings and internally extracts features useful for accurate sleep stage classification. We examine whether this is applicable for sleep data of adult patients by testing our method on a well-known public dataset Sleep-EDFX. Our method achieves the average overall accuracy of 90.89% which is comparable to those of state-of-the-art methods without using any hand-crafted features. This result indicates the great potential of our method because it can be generally used for timestamp-level classification on multivariate time-series in various medical fields. Additionally, we provide source codes so that researchers can reproduce the results in this paper and extend our method.

文献研究表明，儿童睡眠障碍的患病率远高于成人。尽管对成人的睡眠阶段分类进行了大量研究，但儿童的睡眠阶段具有显着不同的特征。因此，迫切需要特别针对儿童的睡眠阶段分类。我们的方法侧重于两个问题：第一个是基于时间戳的分段 (TSS)，用于处理每个时间戳的睡眠阶段标签的细粒度注释。与此相比，流行的滑动窗口方法不必要地将此类标签聚合为粗粒度的标签。我们利用反卷积神经网络(DCNN) 将隐藏层的特征反向映射回输入空间，以预测每个时间戳的睡眠阶段标签。因此，通过考虑多个时间戳的标签，我们的 DCNN 可以产生更好的分类性能。第二个问题是多渠道的必要性。不同的多导睡眠图可以代表不同的临床体征、症状或其他辅助检查(PSG) 录音，因此应全面分析所有这些录音。因此，我们利用 PSG 记录的多元时间序列，包括 6 个脑电图 (EEG) 通道、2 个眼电图 (EOG) 通道（左右）、1 个肌电图（下巴 EMG）通道和两个腿部肌电图通道。我们基于 DCNN 的方法在从 5 至 10 岁儿童患者收集的 SDCP 数据集上进行了测试。结果表明，我们的方法产生了 84.27% 的整体分类准确率和 72.51% 的宏观 F1-score，高于现有的基于滑动窗口的方法。我们基于 DCNN 的方法的最大优势之一是它处理原始 PSG 记录并在内部提取对准确睡眠阶段分类有用的特征。我们通过在众所周知的公共数据集 Sleep-EDFX 上测试我们的方法来检查这是否适用于成年患者的睡眠数据。我们的方法实现了 90.89% 的平均总体准确率，与最先进的方法相媲美，而无需使用任何手工制作的特征。该结果表明我们方法的巨大潜力，因为它通常可用于各种医学领域的多元时间序列的时间戳级分类。此外，我们提供源代码，以便研究人员可以重现本文中的结果并扩展我们的方法。该结果表明我们的方法具有巨大的潜力，因为它通常可用于各种医学领域的多元时间序列的时间戳级分类。此外，我们提供源代码，以便研究人员可以重现本文中的结果并扩展我们的方法。该结果表明我们的方法具有巨大的潜力，因为它通常可用于各种医学领域的多元时间序列的时间戳级分类。此外，我们提供源代码，以便研究人员可以重现本文中的结果并扩展我们的方法。