Zhang Junming, Wu Yan
College of Electronics and Information Engineering, Tongji University, Shanghai, 201804, China.
Biomed Tech (Berl). 2018 Mar 28;63(2):177-190. doi: 10.1515/bmt-2016-0156.
Many systems are developed for automatic sleep stage classification. However, nearly all models are based on handcrafted features. Because of the large feature space, there are so many features that feature selection should be used. Meanwhile, designing handcrafted features is a difficult and time-consuming task because the feature designing needs domain knowledge of experienced experts. Results vary when different sets of features are chosen to identify sleep stages. Additionally, many features that we may be unaware of exist. However, these features may be important for sleep stage classification. Therefore, a new sleep stage classification system, which is based on the complex-valued convolutional neural network (CCNN), is proposed in this study. Unlike the existing sleep stage methods, our method can automatically extract features from raw electroencephalography data and then classify sleep stage based on the learned features. Additionally, we also prove that the decision boundaries for the real and imaginary parts of a complex-valued convolutional neuron intersect orthogonally. The classification performances of handcrafted features are compared with those of learned features via CCNN. Experimental results show that the proposed method is comparable to the existing methods. CCNN obtains a better classification performance and considerably faster convergence speed than convolutional neural network. Experimental results also show that the proposed method is a useful decision-support tool for automatic sleep stage classification.
许多系统被开发用于自动睡眠阶段分类。然而，几乎所有模型都是基于手工制作的特征。由于特征空间很大，存在如此多的特征以至于应该使用特征选择。同时，设计手工制作的特征是一项困难且耗时的任务，因为特征设计需要经验丰富的专家的领域知识。当选择不同的特征集来识别睡眠阶段时，结果会有所不同。此外，还存在许多我们可能未意识到的特征。然而，这些特征可能对睡眠阶段分类很重要。因此，本研究提出了一种基于复值卷积神经网络（CCNN）的新睡眠阶段分类系统。与现有的睡眠阶段方法不同，我们的方法可以从原始脑电图数据中自动提取特征，然后基于学习到的特征对睡眠阶段进行分类。此外，我们还证明了复值卷积神经元的实部和虚部的决策边界正交相交。通过CCNN将手工制作特征的分类性能与学习到的特征的分类性能进行了比较。实验结果表明，所提出的方法与现有方法相当。CCNN比卷积神经网络获得了更好的分类性能和显著更快的收敛速度。实验结果还表明，所提出的方法是用于自动睡眠阶段分类的有用决策支持工具。
