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

The adoption of deep learning in ECG diagnosis is often hindered by the
scarcity of large, well-labeled datasets in real-world scenarios, leading to
the use of transfer learning to leverage features learned from larger datasets.
Yet the prevailing assumption that transfer learning consistently outperforms
training from scratch has never been systematically validated. In this study,
we conduct the first extensive empirical study on the effectiveness of transfer
learning in multi-label ECG classification, by investigating comparing the
fine-tuning performance with that of training from scratch, covering a variety
of ECG datasets and deep neural networks. We confirm that fine-tuning is the
preferable choice for small downstream datasets; however, when the dataset is
sufficiently large, training from scratch can achieve comparable performance,
albeit requiring a longer training time to catch up. Furthermore, we find that
transfer learning exhibits better compatibility with convolutional neural
networks than with recurrent neural networks, which are the two most prevalent
architectures for time-series ECG applications. Our results underscore the
importance of transfer learning in ECG diagnosis, yet depending on the amount
of available data, researchers may opt not to use it, considering the
non-negligible cost associated with pre-training.

深度学习在心电图诊断中的应用受限于现实场景中大规模标记数据集的稀缺，因此使用迁移学习来利用从更大数据集中学到的特征。然而，迁移学习能够始终优于从头开始训练的普遍假设尚未得到系统验证。本研究通过调查不同心电图数据集和深度神经网络的微调性能与从头开始训练的性能的比较，首次进行了关于迁移学习在多标签心电图分类中效果的大规模实证研究，结果显示对于小规模下游数据集，微调是一种较好的选择；然而，当数据集足够大时，从头开始训练也能够取得可比较的性能，尽管需要更长的训练时间来迎头赶上。此外，我们发现迁移学习与卷积神经网络相比较适用于时序心电图应用中最常见的两种结构：卷积神经网络和循环神经网络。我们的结果强调了迁移学习在心电图诊断中的重要性，但根据可用数据量，研究人员可能选择不使用迁移学习，考虑到与预训练相关的不可忽视的成本。

心电图诊断中的迁移学习：有效吗？

Transfer Learning in ECG Diagnosis: Is It Effective?

We introduce MELEP, which stands for Muti-label Expected Log of Empirical
Predictions, a novel measure to estimate how effective it is to transfer
knowledge from a pre-trained model to a downstream task in a multi-label
settings. The measure is generic to work with new target data having a
different label set from source data. It is also computationally efficient,
only requires forward passing the downstream dataset through the pre-trained
model once. To the best of our knowledge, we are the first to develop such a
transferability metric for multi-label ECG classification problems. Our
experiments show that MELEP can predict the performance of pre-trained
convolutional and recurrent deep neural networks, on small and imbalanced ECG
data. Specifically, strong correlation coefficients, with absolute values
exceeding 0.6 in most cases, were observed between MELEP and the actual average
F1 scores of the fine-tuned models.

MELEP 是一种用于多标签 ECG 分类问题的转移度量标准，可以估计从预训练模型到下游任务的知识转移效果，并且与微调模型的实际平均 F1 分数之间存在较强的相关性。