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首页> 外文期刊>Computer Methods and Programs in Biomedicine: An International Journal Devoted to the Development, Implementation and Exchange of Computing Methodology and Software Systems in Biomedical Research and Medical Practice >Complex-valued unsupervised convolutional neural networks for sleep stage classification
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Complex-valued unsupervised convolutional neural networks for sleep stage classification

机译：复杂的无监督卷积神经网络，用于睡眠阶段分类

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摘要

Background and objectiveDespite numerous deep learning methods being developed for automatic sleep stage classification, almost all the models need labeled data. However, obtaining labeled data is a subjective process. Therefore, the labels will be different between two experts. At the same time, obtaining labeled data also is a time-consuming task. Even an experienced expert requires hours to annotate the sleep stage patterns. More important, as the development of wearable sleep devices, it is very difficult to obtain labeled sleep data. Therefore, unsupervised training algorithm is very important for sleep stage classification. Hence, a new sleep stage classification method named complex-valued unsupervised convolutional neural networks (CUCNN) is proposed in this study. MethodsThe CUCNN operates with complex-valued inputs, outputs, and weights, and its training strategy is greedy layer-wise training. It is composed of three phases: phase encoder, unsupervised training and complex-valued classification. Phase encoder is used to translate real-valued inputs into complex numbers. In the unsupervised training phase, the complex-valued K-means is used to learn filters which will be used in the convolution. ResultsThe classification performances of handcrafted features are compared with those of learned features via CUCNN. The total accuracy (TAC) and kappa coefficient of the sleep stage from UCD dataset are 87% and 0.8, respectively. Moreover, the comparison experiments indicate that the TACs of the CUCNN from UCD and MIT-BIH datasets outperform these of unsupervised convolutional neural networks (UCNN) by 12.9% and 13%, respectively. Additionally, the convergence of CUCNN is much faster than that of UCNN in most cases. ConclusionsThe proposed method is fully automated and can learn features in an unsupervised fashion. Results show that unsupervised training and automatic feature extraction on sleep data are possible, which are very important for home sleep monitoring.
机译：背景和ObjectiveMesEpite为自动睡眠阶段分类开发了众多深度学习方法，几乎​​所有模型都需要标记数据。但是，获得标记数据是一个主观过程。因此，两个专家之间的标签将不同。同时，获得标记的数据也是一个耗时的任务。即使是经验丰富的专家也需要几个小时来注释睡眠阶段模式。更重要的是，作为可穿戴睡眠设备的开发，很难获得标记的睡眠数据。因此，无监督的训练算法对于睡眠阶段分类非常重要。因此，在本研究中提出了一种名为复合值无监督卷积神经网络（CUCNN）的新的睡眠阶段分类方法。方法使用复合输入，输出和权重进行了CUCnn，其培训策略是贪婪的层展培训。它由三个阶段组成：阶段编码器，无监督培训和复杂的分类。相位编码器用于将实值输入转换为复数。在无监督的训练阶段，复合值的K-means用于学习将在卷积中使用的过滤器。结果将手工特征的分类表演与通过CUCNN的学习功能进行比较。来自UCD数据集的睡眠阶段的总精度（TAC）和Kappa系数分别为87％和0.8。此外，比较实验表明，来自UCD和MIT-BIH数据集的CUCNN的TAC分别优于这些无监督的卷积神经网络（UCNN）分别为12.9％和13％。另外，在大多数情况下，CUCnn的收敛比UCNN的收敛性快得多。结论拟议方法完全自动化，可以以无人监督的方式学习特征。结果表明，可以对睡眠数据进行无监督培训和自动特征提取，这对于家庭睡眠监测非常重要。

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