A structure for extracting features of electrocardiogram signals

Cardiovascular disease, especially coronary artery disease, is always a threat to human health. Myocardial infarction is a form of coronary artery disease. Cardiologists frequently use electrocardiogram (ECG) to diagnose this condition and ensure the health of their patients. Therefore, studying ECG signal classification can aid doctors in accurately identifying the disease and providing appropriate treatment. We develop structure to extract feature from ECG signals, which achieves excellent performance in classification tasks. A single lead ECG signal typically consists of P, QRS, T, and U waves, which collectively form an ECG signal beat. We utilize R-peak detection technology to obtain ECG signal beats, and extract beat features using a residual network. To avoid the loss of global information, we employ a simple onedimensional convolutional neural network (CNN) to obtain global signal features. The fully connected layer is then used to fuse the features obtained from both beats and global signal features. The classification task is completed based on the fused features. Our designed structure improves performance metrics by at least 2% when compared to the performance of a one-dimensional convolutional neural network and a residual network individually. Additionally, we also introduce the SE block into the residual network, which provides an attention mechanism to effectively suppress unnecessary features and enhance important ones. By comparing the performance of our structure with and without SE block, we prove that SE block can enhance our structure's ability to extract ECG signal characteristics.