# A Multi-Resolution Mutual Learning Network for Multi-Label ECG Classification

arXiv: https://arxiv.org/abs/2406.16928 · HTML: https://mlsanity.com/paper/2406.16928/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 27 references that are in the map.
- First posted on arXiv 12 Jun 2024.

## Builds on (most cited first, 14 of 14)

- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [Deep Mutual Learning](https://mlsanity.com/paper/1706.00384/) (2017)
- [Neuralangelo: High-Fidelity Neural Surface Reconstruction](https://mlsanity.com/paper/2306.03092/) (2023)
- [Bag of Tricks for Image Classification with Convolutional Neural Networks](https://mlsanity.com/paper/1812.01187/) (2018)
- [InceptionTime: Finding AlexNet for Time Series Classification](https://mlsanity.com/paper/1909.04939/) (2019)
- [Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline](https://mlsanity.com/paper/1611.06455/) (2016)
- [StylizedNeRF: Consistent 3D Scene Stylization as Stylized NeRF via 2D-3D Mutual Learning](https://mlsanity.com/paper/2205.12183/) (2022)
- [An Empirical Study of Spatial Attention Mechanisms in Deep Networks](https://mlsanity.com/paper/1904.05873/) (2019)
- [MutualNet: Adaptive ConvNet via Mutual Learning from Network Width and Resolution](https://mlsanity.com/paper/1909.12978/) (2019)
- [SE-ECGNet: A Multi-scale Deep Residual Network with Squeeze-and-Excitation Module for ECG Signal Classification](https://mlsanity.com/paper/2012.05510/) (2020)
- [Arrhythmia Classifier Based on Ultra-Lightweight Binary Neural Network](https://mlsanity.com/paper/2304.01568/) (2023)
- [ECG-CL: A Comprehensive Electrocardiogram Interpretation Method Based on Continual Learning](https://mlsanity.com/paper/2304.04646/) (2023)

## Built on by (most cited first, 4 of 4)

- [ECG-Soup: Harnessing Multi-Layer Synergy for ECG Foundation Models](https://mlsanity.com/paper/2509.00102/) (2025)
- [Reasoning Before Diagnosis: Physician-Inspired Structured Thinking for ECG Classification](https://mlsanity.com/paper/2605.17308/) (2026)
- [Information-theoretic Multimodal Representation Learning for Electrocardiogram Signals](https://mlsanity.com/paper/2605.27583/) (2026)
- [ECG-LENS: Lead-Aware Clinical Context Enriched ECG Report Generation and Evaluation](https://mlsanity.com/paper/2608.05893/) (2026)

## Abstract (arXiv)

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.