基于深度卷积神经网络的睡眠分期模型

针对现阶段数据和特征决定睡眠分期模型的分类精度上限的问题，提出深度卷积神经网络模型. 在模型主体构建方面，并行卷积网络可以自动学习原始信号的时域特征和频域特征，特征融合网络通过空洞卷积和残差连接进行多特征融合，分类网络基于融合后的特征进行睡眠分期. 利用生成少数类过采样技术（SMOTE）减少类别不平衡对分类效果的影响，结合两步训练法对模型进行优化. 实验使用Sleep-EDF数据集的原始单导脑电信号（Fpz-Cz通道）对模型进行20折交叉验证，得到总体精度和宏F₁分别为86.73%和81.70%. 提出的深度卷积模型在没有任何先验知识的情况下，对脑电信号进行端到端的学习，分类准确率优于传统的深度学习模型.

Sleep stage classification model based ondeep convolutional neural network

A deep convolutional neural network model was proposed aiming at the problem that the current data and features determine the upper limit of the classification accuracy of the sleep staging model. The parallel convolutional neural network automatically learns the time-domain and frequency-domain features of the original signals in terms of model construction. The feature fusion neural network fuses multi-features through dilated convolution and residual connection. The classification neural network recognizes the sleep stages based on fused features. Synthetic minority oversampling technique (SMOTE) method was applied to enhance data in order to reduce the effect of classification imbalance on classification effect, and two-step training method was applied to optimize the model. The original single-channel electroencephalogram (Fpz-Cz channel) of the Sleep-EDF data set was used to evaluate the proposed model by the 20-fold cross-validate scheme. The overall accuracy and macro-averaging F₁-score were 86.73% and 81.70% respectively. The proposed deep convolution neural network was an end-to-end deep learning model without any prior knowledge. The experimental results showed that the classification accuracy of the proposed model was better than traditional deep learning models.