Multi-label ECG classification algorithm based on the XResT network
ZHANG Yulong
HAN Shuze
LI Yuwen
LIU Chengyu
Abstract：Aiming at the problem of insufficient use of hierarchical feature information in the feature extraction process of traditional convolutional neural networks due to scale limitations,we proposed a multi-label classification model XResT for electrocardiogram(ECG)signals based on deep feature fusion using residual convolutional network and Transformer encoder.Firstly,the XResT em-ployed an improved ResNet101 backbone network to achieve deep mining of multi-level local features of ECG signals.Subsequently,the Transformer encoder was adopted for global feature enhancement and a cross-layer coordinate attention was designed to achieve dy-namic fusion of local and global features.Comparative experiments on the CPSC-2018 dataset demonstrated that while the accuracy rate of this model was 96.43％,the average F1 score of multi-label classification reached 82.87％,a 3％improvement over existing bench-mark models.The ablation experiment verified the effectiveness of the cross-layer attention mechanism for complex heart rhythm char-acteristics such as ventricular premature beats.The feature fusion framework proposed in this study provides new insights for multi-level feature extraction of ECG signals,significantly improving the clinical applicability of automatic arrhythmia diagnosis systems.
Keywords：ECG classificationMulti-labelFeature fusionMulti-level featureAttention mechanism
Publication Date：2025-04-30
Online Publishing Date：2026-09-11(First online date of this platform, not the publication date of the document)
Pages：8( 67-74 )