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
this https URL

本论文提出了一种多分辨率相互学习网络（MRM-Net），通过双重分辨率注意力架构和特征互补机制处理心电图（ECG）信号，以提高多标签 ECG 分类性能。

多分辨率互学习网络用于多标签心电图分类

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

Representation learning has become a crucial area of research in machine
learning, as it aims to discover efficient ways of representing raw data with
useful features to increase the effectiveness, scope and applicability of
downstream tasks such as classification and prediction. In this paper, we
propose a novel method to generate representations for time series-type data.
This method relies on ideas from theoretical physics to construct a compact
representation in a data-driven way, and it can capture both the underlying
structure of the data and task-specific information while still remaining
intuitive, interpretable and verifiable. This novel methodology aims to
identify linear laws that can effectively capture a shared characteristic among
samples belonging to a specific class. By subsequently utilizing these laws to
generate a classifier-agnostic representation in a forward manner, they become
applicable in a generalized setting. We demonstrate the effectiveness of our
approach on the task of ECG signal classification, achieving state-of-the-art
performance.

本文提出一种基于理论物理思想生成时序型数据表示的新方法，该方法旨在识别可以有效捕捉特定类别样本的共享特征的线性规律，并利用这些规律构建了一个具有良好解释性和可验证性的分类器不可知表示方法，该表示方法能够在广泛的场景下应用，以实现心电图信号分类等任务的最先进性能。