Localization of myocardial infarction using a multi-branch weight sharing network based on 2-D vectorcardiogram

Abstract

Early diagnosis and localization of myocardial infarction (MI) assist clinicians in saving numerous lives through the timely treatment for patients with MI. Vectorcardiogram (VCG) can reflect the characteristic changes of cardiac electrical activity in MI in detail. In this context, the present study reports a multi-branch weight sharing network model based on 2-D VCG constructed to realize the automatic localization of MI. The three-branch network extracted the spatial morphological features of the three planes of the 2-D VCG, respectively, and the weight-sharing part of the network obtained the spatial correlation information among the three planes. Subsequently, the Softmax classifier was employed to classify normal individuals and MI patients (11 class infarct sites). To evaluate the performance of the proposed method for MI localization, PTB(Physikalisch-Technische Bundesanstalt) diagnostic ECG database was employed. The localization accuracy, sensitivity, and specificity achieved using the proposed method were 99.87%, 99.92%, and 99.99%, respectively. Thus, the proposed scheme is expected to be useful in assisting cardiologists in interpreting VCG for clinical diagnosis.