基于无监督特征学习的单通道睡眠分期

Yutong Wang, Yikun Wang, Li Yao, Xiao-jie Zhao
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引用次数: 1

摘要

基于脑电图(EEG)信号的睡眠分期作为睡眠诊断研究的重要基础之一,一直受到广泛关注。随着近年来深度学习的兴起,将深度学习结构与自动睡眠分期相结合的想法成为了一个很有吸引力的话题。然而,睡眠阶段的标记需要专业知识和大量的时间,这增加了评估这个想法的障碍。本研究提出了基于大量未标记数据和少量标记数据的无监督特征学习方法来完成睡眠分期。基于一对对称卷积神经网络构建无监督特征学习结构,借助于浅层神经网络分类器对睡眠阶段进行分类。结果表明,在标记数据很少的情况下,基于无监督特征学习的睡眠分期可以达到与有监督特征学习相似的准确率,这为深度学习方法在处理难以标记或缺乏先验知识的数据方面的应用提供了新的方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Single Channel Sleep Staging Based on Unsupervised Feature Learning
Sleep staging based on electroencephalogram (EEG) signal, as one of the vital bases of study on sleep diagnosis, has been under massive attention. With the spring up of deep learning these years, the idea of combining deep learning structure with automatic sleep staging has been an attractive topic. However, the labeling of sleep stages requires professional knowledge as well as plenty of time, which raise the barrier to evaluate this idea. In this study, the method of unsupervised feature learning based on a mass of unlabeled data and a small number of labeled data was proposed to accomplish sleep staging. The unsupervised feature learning structure was built based on a pair of symmetric convolutional neural networks, with the help of a shallow neural network classifier to classify sleep stages. The results showed that under the condition of the very few labeled data, sleep staging based on unsupervised feature learning can achieve similar accuracy to supervised feature learning, which provides a new direction for the application of deep learning method in dealing with data that is difficult to label or lack of prior knowledge.
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