Wang Wenshuai, Liao Pan, Sun Yi, Su Guiping, Ye Shiwei, Liu Yan
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:625-628. doi: 10.1109/EMBC44109.2020.9175460.
In this paper, we propose a novel method of automatic sleep stage classification based on single-channel electroencephalography (EEG). First, we use marginal Hilbert spectrum (MHS) to depict time-frequency domain features of five sleep stages of 30-second (30s) EEG epochs. Second, the extracted MHSs features are input to a convolutional neural network (CNN) as multi-channel sequences for the sleep stage classification task. Third, a focal loss function is introduced into the CNN classifier to alleviate the classes imbalance problem of sleep data. Experimental results show that the proposed method can obtain an overall accuracy of 86.14% on the public Sleep-EDF dataset, which is competitive and worth exploring among a series of deep learning methods for the automatic sleep stage classification task.
在本文中，我们提出了一种基于单通道脑电图（EEG）的自动睡眠阶段分类新方法。首先，我们使用边际希尔伯特谱（MHS）来描绘30秒脑电图时段五个睡眠阶段的时频域特征。其次，将提取的MHS特征作为多通道序列输入到卷积神经网络（CNN）中，用于睡眠阶段分类任务。第三，在CNN分类器中引入焦点损失函数，以缓解睡眠数据的类别不平衡问题。实验结果表明，该方法在公开的Sleep-EDF数据集上可获得86.14%的总体准确率，在一系列用于自动睡眠阶段分类任务的深度学习方法中具有竞争力且值得探索。
