Multi-scale SE-residual network with transformer encoder for myocardial infarction classification

Abstract

In recent years, there has been an increase in the number of deaths caused by heart disease. The design of computer-aided systems for the diagnosis of heart diseases, especially myocardial infarction (MI), has become a hot topic. The diagnosis of MI is a classification task. Electrocardiogram (ECG) is one of the most important tools for diagnosing heart disease. ECG signals are classified into corresponding MI categories based on their extracted features. Thus, the diagnosis of MI depends on the feature extraction of the ECG signal. Because signal processing methods, such as wavelet transform, may not effectively extract hidden features, researchers nowadays turn to using convolutional neural networks to solve the problem of hidden feature extraction. However, this approach ignores the role of temporal information. we proposed a new network called Multi-scale SE-Residual Network with Transformer encoder (MRTNet) to extract hidden features and temporal information features better. The data processed are localized to heartbeat units based on the way cardiologists diagnose myocardial infarction. Inspired by multi-scale learning, the sampling module was designed to acquire heartbeat units at different scales. We provided a processing model for multi-scale data, called the feature extraction module. The residual network with SE block and Customized Pooling Component (CPC) was used to extract global and local features of the heartbeat units. A Transformer encoder was used to extract the common features of the previous module’s output. The common features at different scales were fused to provide the basis for the classification task. The experiment was conducted on the public PTB-XL dataset. MRTNet achieved a higher accuracy and F1 score than other models by approximately 2%. Furthermore, MRTNet demonstrated a superior AUC of approximately 0.77. It is a superior classifier when compared to other models. The results demonstrate the effectiveness of the designed multi-scale architecture. And it provides a way into exploring further potential information from ECG signals.