Automatic classification of electrocardiogram (ECG) signals plays a crucial
role in the early prevention and diagnosis of cardiovascular diseases. While
ECG signals can be used for the diagnosis of various diseases, their
pathological characteristics exhibit minimal variations, posing a challenge to
automatic classification models. Existing methods primarily utilize
convolutional neural networks to extract ECG signal features for
classification, which may not fully capture the pathological feature
differences of different diseases. Transformer networks have advantages in
feature extraction for sequence data, but the complete network is complex and
relies on large-scale datasets. To address these challenges, we propose a
single-layer Transformer network called Multi-Scale Shifted Windows Transformer
Networks (MSW-Transformer), which uses a multi-window sliding attention
mechanism at different scales to capture features in different dimensions. The
self-attention is restricted to non-overlapping local windows via shifted
windows, and different window scales have different receptive fields. A
learnable feature fusion method is then proposed to integrate features from
different windows to further enhance model performance. Furthermore, we
visualize the attention mechanism of the multi-window shifted mechanism to
achieve better clinical interpretation in the ECG classification task. The
proposed model achieves state-of-the-art performance on five classification
tasks of the PTBXL-2020 12-lead ECG dataset, which includes 5 diagnostic
superclasses, 23 diagnostic subclasses, 12 rhythm classes, 17 morphology
classes, and 44 diagnosis classes, with average macro-F1 scores of 77.85%,
47.57%, 66.13%, 34.60%, and 34.29%, and average sample-F1 scores of 81.26%,
68.27%, 91.32%, 50.07%, and 63.19%, respectively.

提出了一种称为 MSW-Transformer 的单层 Transformer 网络，它使用多窗口滑动注意机制在不同尺度上捕获不同维度的信号特征，进而进行心电图分类诊断，该模型在 5 项分类任务中均取得了最先进的表现。

MSW-Transformer: 多尺度平移窗口变换网络用于 12 导联心电图分类

MSW-Transformer: Multi-Scale Shifted Windows Transformer Networks for  12-Lead ECG Classification

Patients who suffer an acute coronary syndrome are at elevated risk for
adverse cardiovascular events such as myocardial infarction and cardiovascular
death. Accurate assessment of this risk is crucial to their course of care. We
focus on estimating a patient's risk of cardiovascular death after an acute
coronary syndrome based on a patient's raw electrocardiogram (ECG) signal.
Learning from this signal is challenging for two reasons: 1) positive examples
signifying a downstream cardiovascular event are scarce, causing drastic class
imbalance, and 2) each patient's ECG signal consists of thousands of
heartbeats, accompanied by a single label for the downstream outcome. Machine
learning has been previously applied to this task, but most approaches rely on
hand-crafted features and domain knowledge. We propose a method that learns a
representation from the raw ECG signal by using a multiple instance learning
framework. We present a learned risk score for cardiovascular death that
outperforms existing risk metrics in predicting cardiovascular death within 30,
60, 90, and 365 days on a dataset of 5000 patients.

本文提出一种使用机器学习从原始心电图 (ECG) 信号学习患者心血管死亡风险的方法，该方法通过采用多元实例学习框架从原始 ECG 信号中学习表征，并在 5000 名患者数据集上证明其可以准确地预测患者在 30、60、90 和 365 天内心血管死亡风险，优于现有的风险因子评估方案。