论文标题
使用关节频率和无监督功能的睡眠阶段评分
Sleep Stage Scoring Using Joint Frequency-Temporal and Unsupervised Features
论文作者
论文摘要
如果患有睡眠障碍的患者知道自己的特殊情况，可以更好地管理自己的生活方式。通常可以通过分析从患者收集的许多重要信号来检测这种睡眠障碍。为了简化此任务，已经提出了许多自动睡眠阶段识别（ASSR）方法。这些方法中的大多数都使用了从重要信号提取的时间频率特征。但是，由于睡眠信号的非平稳性，此类方案并不是可接受的准确性。最近，已经提出了一些ASSR方法，该方法使用深度神经网络进行无监督的特征提取。在本文中，我们建议将两个想法结合在一起，同时使用时间频率和无监督的功能。为了增加时间分辨率，每个标准时期都分为5个子类别。此外，为了提高准确性，我们采用了三个具有不同属性的分类器，然后使用集合方法作为最终分类器。仿真结果表明，所提出的方法增强了常规ASSR方法的准确性。
Patients with sleep disorders can better manage their lifestyle if they know about their special situations. Detection of such sleep disorders is usually possible by analyzing a number of vital signals that have been collected from the patients. To simplify this task, a number of Automatic Sleep Stage Recognition (ASSR) methods have been proposed. Most of these methods use temporal-frequency features that have been extracted from the vital signals. However, due to the non-stationary nature of sleep signals, such schemes are not leading an acceptable accuracy. Recently, some ASSR methods have been proposed which use deep neural networks for unsupervised feature extraction. In this paper, we proposed to combine the two ideas and use both temporal-frequency and unsupervised features at the same time. To augment the time resolution, each standard epoch is segmented into 5 sub-epochs. Additionally, to enhance the accuracy, we employ three classifiers with different properties and then use an ensemble method as the ultimate classifier. The simulation results show that the proposed method enhances the accuracy of conventional ASSR methods.
