MFB-CBRNN: A Hybrid Network for MI Detection Using 12-Lead ECGs

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.