Phan Huy, Andreotti Fernando, Cooray Navin, Chen Oliver Y, Vos Maarten De
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:1452-1455. doi: 10.1109/EMBC.2018.8512480.
We propose in this work a feature learning approach using deep bidirectional recurrent neural networks (RNNs) with attention mechanism for single-channel automatic sleep stage classification. We firstly decompose an EEG epoch into multiple small frames and subsequently transform them into a sequence of frame-wise feature vectors. Given the training sequences, the attention-based RNN is trained in a sequence-to-label fashion for sleep stage classification. Due to discriminative training, the network is expected to encode information of an input sequence into a high-level feature vector after the attention layer. We, therefore, treat the trained network as a feature extractor and extract these feature vectors for classification which is accomplished by a linear SVM classifier. We also propose a discriminative method to learn a filter bank with a DNN for preprocessing purpose. Filtering the frame-wise feature vectors with the learned filter bank beforehand leads to further improvement on the classification performance. The proposed approach demonstrates good performance on the Sleep-EDF dataset.
在这项工作中，我们提出了一种特征学习方法，该方法使用带有注意力机制的深度双向循环神经网络（RNN）进行单通道自动睡眠阶段分类。我们首先将一个脑电图时段分解为多个小帧，随后将它们转换为逐帧特征向量序列。给定训练序列，基于注意力的RNN以序列到标签的方式进行训练以进行睡眠阶段分类。由于采用了判别式训练，预计网络在注意力层之后将输入序列的信息编码为高级特征向量。因此，我们将训练好的网络视为特征提取器，并提取这些特征向量用于由线性支持向量机分类器完成的分类。我们还提出了一种判别方法，用于使用深度神经网络学习一个滤波器组以用于预处理目的。预先使用学习到的滤波器组过滤逐帧特征向量会进一步提高分类性能。所提出的方法在Sleep-EDF数据集上表现出良好的性能。
