基于混合注意力时序网络的睡眠分期算法研究

A hybrid attention temporal sequential network for sleep stage classification

睡眠分期是研究睡眠疾病的重要途径,近年来受到了广泛关注。传统手工标记方法与传统机器学习算法存在效率低下、泛化性不足的问题,虽然近期流行的深度学习网络模型依靠其学习复杂特征的能力改善了睡眠分期结果,但仍存在着忽略片段内时序信息与通道相关性的问题。本文提出了一种混合注意力时序网络,利用循环神经网络取代较为传统的卷积神经网络,从时间角度提取多导睡眠图的时序特征;然后采用片段内时序注意力与通道注意力机制,实现信号片段内时序特征融合和通道相关性特征融合;再基于循环神经网络与片段间时序注意力机制,进一步实现信号片段间时序上下文特征融合;最终根据上述混合特征完成端到端自动睡眠分期。本文采用开源数据网站上包含多个多导睡眠图的睡眠数据集进行对比实验,实验结果表明本文模型能够优于10种典型基线模型,睡眠分期准确率分别可达到0.801、0.801、0.717,平均F1分数可达到0.752、0.728、0.700,验证了本文模型的有效性。

Sleep stage classification is a necessary fundamental method for the diagnosis of sleep diseases,which has attracted extensive attention in recent years.Traditional methods for sleep stage classification,such as manual marking methods and machine learning algorithms,have the limitations of low efficiency and defective generalization.Recently,deep neural networks have shown improved results by the capability of learning complex pattern in the sleep data.However,these models ignore the intra-temporal sequential information and the correlation among all channels in each segment of the sleep data.To solve these problems,a hybrid attention temporal sequential network model is proposed in this paper,choosing recurrent neural network to replace traditional convolutional neural network,and extracting temporal features of polysomnography from the perspective of time.Furthermore,intra-temporal attention mechanism and channel attention mechanism are adopted to achieve the fusion of the intra-temporal representation and the fusion of channel-correlated representation.And then,based on recurrent neural network and inter-temporal attention mechanism,this model further realized the fusion of inter-temporal contextual representation.Finally,the endto-end automatic sleep stage classification is accomplished according to the above hybrid representation.This paper evaluates the proposed model based on two public benchmark sleep datasets downloaded from open-source website,which include a number of polysomnography.Experimental results show that the proposed model could achieve better performance compared with ten state-of-the-art baselines.The overall accuracy of sleep stage classification could reach 0.801,0.801 and 0.717,respectively.Meanwhile,the macro average F1-scores of the proposed model could reach 0.752,0.728 and 0.700.All experimental results could demonstrate the effectiveness of the proposed model.