Mobi-Trans: A Hybrid Network with Attention Mechanism for Myocardial Infarction Localization

Myocardial infarction (MI) can cause serious harm to the human body. For patients with acute MI, coronary intervention is the treatment of choice, and electrocardiogram (ECG) is a useful tool for diagnosing the type and location of MI. With the rapid development of the 5th Generation (5G) newtork technology, remote interventional surgery will be the future development trend. In this paper, a 12-lead ECG signal acquisition device that can be used for wireless communication is designed. We propose a multi-branch Mobi-Trans model for MI localization, taking each lead of the ECG signal as the input of each corresponding branch. For each branch, we first extract features using lightweight depthwise convolution and channel-based attention mechanism and then use self-attention with relative position representations in the improved Transformer module to make the model pay more attention to more important positions in the ECG signal, and finally we use the branch attention module to enable the model to focus more on the branches that contribute more to the localization. The experimental results on the PTB database show that our proposed model achieves an overall accuracy of 99.91%, so the Mobi-Trans model can be used in conjunction with our ECG signal acquisition device to assist in the localization of lesions in remote interventional cardiac surgery in the future, and perform real-time ECG monitoring during the surgery.