一种用于多阶段睡眠阶段分类的混合自我关注深度学习框架。,BMC Bioinformatics

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一种用于多阶段睡眠阶段分类的混合自我关注深度学习框架。
BMC Bioinformatics ( IF 2.9 ) Pub Date : 2019-12-02 , DOI: 10.1186/s12859-019-3075-z
Ye Yuan _{1,

2,

3} , Kebin Jia _{1,

2,

3} , Fenglong Ma ₄ , Guangxu Xun ₅ , Yaqing Wang ₄ , Lu Su ₄ , Aidong Zhang ₅

Affiliation

背景技术睡眠是一个以不同的睡眠方式为特征的复杂而动态的生物学过程。使用多元多导睡眠图（PSG）记录进行的全面睡眠监测和分析已为预防睡眠相关疾病做出了巨大努力。为了减轻PSG人工目视检查所造成的时间消耗，自动多元睡眠阶段分类已成为医学和生物信息学中的重要研究课题。结果我们提出了一个统一的混合自我注意深度学习框架，即HybridAtt，该框架通过从多变量PSG记录中捕获通道和时间相关性来自动对睡眠阶段进行分类。我们构造了一个新的多视图卷积表示模块，以从异构PSG输入中学习特定于通道和全局视图的功能。混合注意力机制旨在通过推断多视图功能的依赖性来进一步融合多视图功能，而无需任何其他监督。随后将学习到的注意力表示馈送到softmax层，以训练端到端深度学习模型。结论我们在两个特征域（称为时域和频域）中，在基准PSG数据集上经验性地评估了我们提出的HybridAtt模型。实验结果表明，HybridAtt在两个特征空间中始终优于十种基线方法，证明了HybridAtt在睡眠阶段分类任务中的有效性。随后将学习到的注意力表示通过softmax层进行馈送，以训练端到端深度学习模型。结论我们在两个特征域（称为时域和频域）中，在基准PSG数据集上经验性地评估了我们提出的HybridAtt模型。实验结果表明，HybridAtt在两个特征空间中始终优于十种基线方法，证明了HybridAtt在睡眠阶段分类任务中的有效性。随后将学习到的注意力表示通过softmax层进行馈送，以训练端到端深度学习模型。结论我们在两个特征域（称为时域和频域）中，在基准PSG数据集上经验性地评估了我们提出的HybridAtt模型。实验结果表明，HybridAtt在两个特征空间中始终优于十种基线方法，证明了HybridAtt在睡眠阶段分类任务中的有效性。

"点击查看英文标题和摘要"

A hybrid self-attention deep learning framework for multivariate sleep stage classification.

BACKGROUND Sleep is a complex and dynamic biological process characterized by different sleep patterns. Comprehensive sleep monitoring and analysis using multivariate polysomnography (PSG) records has achieved significant efforts to prevent sleep-related disorders. To alleviate the time consumption caused by manual visual inspection of PSG, automatic multivariate sleep stage classification has become an important research topic in medical and bioinformatics. RESULTS We present a unified hybrid self-attention deep learning framework, namely HybridAtt, to automatically classify sleep stages by capturing channel and temporal correlations from multivariate PSG records. We construct a new multi-view convolutional representation module to learn channel-specific and global view features from the heterogeneous PSG inputs. The hybrid attention mechanism is designed to further fuse the multi-view features by inferring their dependencies without any additional supervision. The learned attentional representation is subsequently fed through a softmax layer to train an end-to-end deep learning model. CONCLUSIONS We empirically evaluate our proposed HybridAtt model on a benchmark PSG dataset in two feature domains, referred to as the time and frequency domains. Experimental results show that HybridAtt consistently outperforms ten baseline methods in both feature spaces, demonstrating the effectiveness of HybridAtt in the task of sleep stage classification.

更新日期：2019-12-02

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