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首页> 外文期刊>IEEE/ACM transactions on computational biology and bioinformatics >Deep Learning for Automated Feature Discovery and Classification of Sleep Stages
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Deep Learning for Automated Feature Discovery and Classification of Sleep Stages

机译：深度学习自动特征发现和睡眠阶段的分类

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摘要

Convolutional neural networks (CNN) have demonstrated state-of-the-art classification results in image categorization, but have received comparatively little attention for classification of one-dimensional physiological signals. We design a deep CNN architecture for automated sleep stage classiffication of human sleep EEG and EOG signals. The CNN proposed in this paper amply outperforms recent work that uses a different CNN architecture over a single-EEG-channel version of the same dataset. We show that the performance gains achieved by our network rely mainly on network depth, and not on the use of several signal channels. Performance of our approach is on par with human expert inter-scorer agreement. By examining the internal activation levels of our CNN, we find that it spontaneously discovers signal features such as sleep spindles and slow waves that figure prominently in sleep stage categorization as performed by human experts.
机译：卷积神经网络（CNN）已经证明了最先进的分类导致图像分类，但是对一维生理信号的分类进行了相对较少的关注。我们设计了一个深入的CNN架构，用于人类睡眠脑电图和EOG信号的自动睡眠阶段分类。本文提出的CNN最近的工作表现优于使用不同的CNN架构在同一数据集的单个EEG频道版本中使用不同的CNN架构。我们表明，我们的网络实现的性能增益主要依赖于网络深度，而不是使用多个信号通道。我们的方法的表现与人类专家间的帧间协议相提并论。通过检查我们的CNN的内部激活级别，我们发现它自发地发现了睡眠主轴和慢波的信号特征，如人专家所执行的睡眠阶段分类中突出的困扰。

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