SE-ECGNet: Multi-scale SE-Net for Multi-lead ECG Data

Cardiovascular disease is a life-threatening condition, and more than 20 million people die from heart disease. Therefore, developing an objective and efficient computeraided tool for diagnosis of heart disease has become a promising research topic. In this paper, we design a multiscale shared convolution kernel model. In this model, two paths are designed to extract the features of electrocardiogram (ECG). The two paths have different convolution kernel sizes, which are 31 and 51 , respectively. Such multi-scale design enables the network to obtain different receptive fields and capture information at different scales, which significantly improves the classification effect. And squeeze-and-excitation networks (SE-Net) are added to every path of the model. The attention mechanism of SE-Net learns feature weights according to loss, which makes the effective feature maps have large weights and the ineffective or low-effect feature maps have small weights. Our team name is CQUPT_ECG. Our approach achieved a challenge validation score of 0.640, and full test score of 0.411, placing us 8 out of 41 in the official ranking.