A novel myocardial infarction localization method using multi-branch DenseNet and spatial matching-based active semi-supervised learning

Abstract

Individual differences among patients and the high cost of manual labeling are major challenges for electrocardiogram (ECG) diagnosis algorithms. To tackle this problem, we develop a novel deep active semi-supervised learning framework for myocardial infarction localization based on 12-lead ECG signals. First, a new deep learning model named multi-branch densely connected convolutional network (MB-DenseNet) is designed to automatically extract and fuse heartbeat features from 12-lead ECG signals. Moreover, to improve the classification results for new patients, we propose a novel active semi-supervised learning (ASSL) mechanism to update the model. Active learning (AL) is employed to improve the classification ability of the initial model firstly, and then a new semi-supervised learning method named self-training with spatial matching (STSM) is designed to update the model further. Based on the artificial knowledge from AL, STSM combines spatial matching algorithm and the trained model to label valuable unlabeled samples automatically. We conduct experiments based on intra-patient and patient-specific schemes using the PTB database. The MB-DenseNet yields an accuracy of 99.87% under the intra-patient scheme. For the patient-specific scheme, the updated model achieves an accuracy of 96.09%. Compared with state-of-the-art methods, our method can effectively reduce manual labeling while achieving comparable performance.