  Electrocardiograms (ECG), which record the electrophysiological activity of
the heart, have become a crucial tool for diagnosing these diseases. In recent
years, the application of deep learning techniques has significantly improved
the performance of ECG signal classification. Multi-resolution feature
analysis, which captures and processes information at different time scales,
can extract subtle changes and overall trends in ECG signals, showing unique
advantages. However, common multi-resolution analysis methods based on simple
feature addition or concatenation may lead to the neglect of low-resolution
features, affecting model performance. To address this issue, this paper
proposes the Multi-Resolution Mutual Learning Network (MRM-Net). MRM-Net
includes a dual-resolution attention architecture and a feature complementary
mechanism. The dual-resolution attention architecture processes high-resolution
and low-resolution features in parallel. Through the attention mechanism, the
high-resolution and low-resolution branches can focus on subtle waveform
changes and overall rhythm patterns, enhancing the ability to capture critical
features in ECG signals. Meanwhile, the feature complementary mechanism
introduces mutual feature learning after each layer of the feature extractor.
This allows features at different resolutions to reinforce each other, thereby
reducing information loss and improving model performance and robustness.
Experiments on the PTB-XL and CPSC2018 datasets demonstrate that MRM-Net
significantly outperforms existing methods in multi-label ECG classification
performance. The code for our framework will be publicly available at
https://github.com/wxhdf/MRM.
