Spa-Tem MI: A Spatial-Temporal Network for Detecting and Locating Myocardial Infarction

Worldwide, the automatic diagnosis (i.e., detection and localization) of myocardial infarction (MI) remains a challenging problem. Clinically, MI is mainly detected and located according to the changes and differences between each electrocardiogram (ECG) lead. Therefore, to capture the features of different MI locations with their corresponding leads, we propose an end-to-end spatial-temporal MI (Spa-Tem MI) network for detecting and locating MI. To facilitate the extraction of spatial and temporal features, we proposed a width-concat method to convert 1-D multilead ECG signals into a 2-D ECG matrix. To learn the spatial and temporal features of MI, the Spa-Tem MI consists of a spatial feature extraction (SFE) module and a temporal feature extraction (TFE) module. The SFE module aims to obtain the spatial differences between leads via dense blocks. Through the self-attention mechanism, the TFE module can capture the global temporal changes in leads. The proposed method was validated with the real MI dataset HeMI, and the dataset was divided by the interpatient paradigm. The precision, recall, F1 score, and Hamming loss of the results were 94.07%, 95.45%, 93.81%, and 0.03%, respectively. In addition, the model was also verified with the public dataset PTB-XL. Compared with that of the state-of-the-art methods, the proposed method achieved superior performance.