This paper proposes a novel hybrid network named multiple-feature-branch convolutional bidirectional recurrent neural network (MFB-CBRNN) for myocardial infarction (MI) detection using 12-lead ECGs. The model efficiently combines convolutional neural network-based and recurrent neural network-based structures. Each feature branch consists of several one-dimensional convolutional and pooling layers, corresponding to a certain lead. All the feature branches are independent from each other, which are utilized to learn the diverse features from different leads. Moreover, a bidirectional long short term memory network is employed to summarize all the feature branches. Its good ability of feature aggregation has been proved by the experiments. Furthermore, the paper develops a novel optimization method, lead random mask (LRM), to alleviate overfitting and implement an implicit ensemble like dropout. The model with LRM can achieve a more accurate MI detection. Class-based and subject-based fivefold cross validations are both carried out using Physikalisch-Technische Bundesanstalt diagnostic database. Totally, there are 148 MI and 52 healthy control subjects involved in the experiments. The MFB-CBRNN achieves an overall accuracy of 99.90% in class-based experiments, and an overall accuracy of 93.08% in subject-based experiments. Compared with other related studies, our algorithm achieves a comparable or even better result on MI detection. Therefore, the MFB-CBRNN has a good generalization capacity and is suitable for MI detection using 12-lead ECGs. It has a potential to assist the real-world MI diagnostics and reduce the burden of cardiologists.

中文翻译：

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MFB-CBRNN：使用12导联心电图进行MI检测的混合网络

本文提出了一种新颖的混合网络，称为多特征分支卷积双向递归神经网络（MFB-CBRNN），用于使用12导联心电图检测心肌梗塞（MI）。该模型有效地结合了基于卷积神经网络和基于递归神经网络的结构。每个要素分支都包含对应于特定引线的几个一维卷积和池化层。所有特征分支都彼此独立，可用来从不同的线索中学习各种特征。此外，采用双向长期短期存储网络来总结所有特征分支。实验证明了其良好的特征聚合能力。此外，本文还开发了一种新颖的优化方法，即引线随机掩码（LRM），减轻过度拟合并实现像dropout这样的隐式集成。具有LRM的模型可以实现更准确的MI检测。基于类别和基于主题的五重交叉验证均使用Physikalisch-Technische Bundesanstalt诊断数据库进行。总共有148名MI和52名健康对照受试者参与了实验。MFB-CBRNN在基于班级的实验中达到99.90％的整体准确度，在基于受试者的实验中达到93.08％的整体准确度。与其他相关研究相比，我们的算法在MI检测方面取得了可比甚至更好的结果。因此，MFB-CBRNN具有良好的泛化能力，适合使用12导联ECG进行MI检测。它有可能协助现实世界中的MI诊断并减轻心脏病专家的负担。