Atrial fibrillation (AF) is one of the most common abnormal heart rhythms, which is caused by the fast contraction of the two upper atria. Despite of the fact that convolutional neural network (CNN) has been applied to electrocardiogram analysis for AF rhythm, it cannot achieve the expected performance due to the lack of consideration for temporal features and the imbalance problem. In order to make the network concentrate on the learning of AF temporal features, we propose a residual-based temporal attention block (RTA-block). The RTA-block utilizes residual learning to generate temporal attention weights, which enhance informative features related to AF. Powered by the RTA-block, a residual-based temporal attention convolutional neural network (RTA-CNN) is further proposed for AF detection. The network can automatically focus on the parts with more sematic information to achieve better performance. In addition, we propose a novel exponential nonlinearity loss (EN-Loss), which addresses the imbalance problem by changing the nonlinearity of the loss function. We evaluated our framework on the single lead ECG classification dataset of The PhysioNet Computing in Cardiology Challenge 2017. The experimental results show that the proposed RTA-CNN with EN-Loss can obtain competitive results over the state-of-the-arts classification networks, which proves the method’s effectiveness.

心房颤动（AF）是最常见的异常心律之一，其由两个上心房的快速收缩引起。尽管已将卷积神经网络（CNN）应用于心律性心律的心电图分析，但由于缺乏对时间特征和不平衡问题的考虑，因此无法实现预期的性能。为了使网络专注于AF时间特征的学习，我们提出了一个基于残差的时间注意块（RTA-block）。RTA块利用残差学习来生成时间注意力权重，从而增强与AF相关的信息功能。在RTA块的支持下，进一步提出了基于残差的时间注意力卷积神经网络（RTA-CNN）用于AF检测。网络可以自动关注具有更多语义信息的零件，以实现更好的性能。此外，我们提出了一种新颖的指数非线性损失（EN-Loss），它通过更改损失函数的非线性来解决不平衡问题。我们在《 PhysioNet Computing in Cardiology Challenge 2017》的单导ECG分类数据集上评估了我们的框架。实验结果表明，带有EN-Loss的RTA-CNN可以在最新的分类网络上获得竞争性结果，证明了该方法的有效性。