近年来, 随着人工智能的发展, 深度学习模型已在ECG数据分析(尤其是房颤的检测)中得到广泛应用. 本文提出了一种基于多头注意力机制的算法来实现房颤的分类, 并通过PhysioNet 2017年挑战赛的公开数据集对其进行训练和验证. 该算法首先采用深度残差网络提取心电信号的局部特征, 随后采用双向长短期记忆网络在此基础上提取全局特征, 最后传入多头注意力机制层对特征进行重点提取, 通过级联的方式将多个模块相连接并发挥各自模块的作用, 整体模型的性能有了很大的提升. 实验结果表明, 本文所提出的heads-8模型可以达到精度0.861, 召回率0.862, F1得分0.861和准确率0.860, 这优于目前针对心电信号的房颤分类的最新方法.
In recent years, driven by the progress in artificial intelligence, deep learning models have been widely applied to ECG data analysis (especially the detection of atrial fibrillation). This study proposes an algorithm based on the multi-head attention mechanism to classify atrial fibrillation, which is trained and validated through the public data set of the PhysioNet 2017 Challenge. Firstly, the local features of the ECG signal are extracted through the deep residual network. Then, the bidirectional long short-term memory network is built to extract the global features on this basis. Finally, the multi-head attention mechanism layer is used to extract the key features, and cascade modules greatly improve the performance of the overall model. The experimental results show that the proposed heads-8 model can achieve precision of 0.861, recall of 0.862, F1 score of 0.861, and accuracy of 0.860, which is better than the latest methods based on ECG signals for classifying atrial fibrillation.