Myocardial Infarction Detection Based on Multi-lead Ensemble Neural Network
作者： H.M. Wang , W. Zhao , D.Y. Jia , J. Hu , Z.Q. Li
DOI: 10.1109/EMBC.2019.8856392
关键词:
摘要: Automatic myocardial infarction (MI) detection using an electrocardiogram (ECG) is of great significance for improving the survival rate patients. In this study, we propose a multi-lead ensemble neural network (MENN) to distinguish anterior (AMI) and inferior (IMI) from healthy control (HC) respectively. three kinds sub-networks ECG signals are combined, which fully explores information improves classification performance. The algorithm evaluated on PTB database by 5-fold inter-subject cross-validation sensitivity (Se), specificity (Sp) area under curve (AUC) AMI 98.35%, 97.49%, 97.92%; Se, Sp, AUC IMI 93.17%, 92.02%, 92.60%. proposed method achieves state art results both tasks outperforms baseline methods. Hence, potential automatic MI diagnosis.
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