Myocardial Infarction Detection Based On Multi-Lead Ensemble Neural Network

Automatic myocardial infarction (MI) detection using an electrocardiogram (ECG) is of great significance for improving the survival rate of patients. In this study, we propose a multi-lead ensemble neural network (MENN) to distinguish anterior myocardial infarction (AMI) and inferior myocardial infarction (IMI) from healthy control (HC) respectively. In the study, three kinds of sub-networks and multi-lead ECG signals are combined, which fully explores the information of ECG signals and improves the classification performance. The algorithm is evaluated on the PTB database by 5-fold inter-subject cross-validation and the sensitivity (Se), specificity (Sp) and area under the curve (AUC) of AMI detection are 98.35%, 97.49%, 97.92%; The Se, Sp, and AUC of IMI detection are 93.17%, 92.02%, 92.60%. The proposed method achieves the state of the art results on both tasks and outperforms the baseline methods. Hence, the proposed method is potential for automatic MI diagnosis.