GFR: An Effective Plugin for Enhancing the ECG Classification Capability of Models

Electrocardiogram ECG serves as a crucial non-invasive diagnostic tool for monitoring clinical cardiac conditions. Remarkable progress has been achieved in deep learning-based ECG classification research. Generally, its overall architecture can be divided into three parts: the feature extraction layer, the feature fusion methods typically concatenation and summation , and the multi-layer perceptron MLP classification layer.In this paper, we propose a plugin Global Feature Refinement GFR module to enhance the performance of multi-branch models for ECG classification.The GFR plugin assigns weights to different branching features in a dynamic disease-aware manner to capture critical global information while emphasizing important features. Specifically, these dynamic weights are obtained through the integration, mapping, and scaling of global features. Finally, the weighted features are summed for ECG classification. Extensive experiments on three large-scale imbalanced datasets demonstrate that the GFR plugin, with less 6.2k additional parameters,improves the performance of eight models of different sizes to varying degrees. Specifically, the maximum improvement in F1 score and accuracy was 8.27 and 6.41 , respectively.