MSW-Transformer: Multi-Scale Shifted Windows Transformer Networks for
  12-Lead ECG Classification
Renjie Cheng Zhemin Zhuang Shuxin Zhuang Lei Xie Jingfeng Guo
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.