摘要： 心血管疾病的全球蔓延使得心电图（electrocardiogram，ECG）信号分析成为临床诊断的关键工具。然而，ECG信号的多标签分类多依赖完整的12导联，且面临导联间的时空特征融合不充分、类别不平衡等挑战。为此，提出了一种基于少数导联的端对端深度学习模型，通过轻量化多尺度倒残差特征提取模块提取ECG信号的跨尺度时域特征，结合时序卷积网络与双向门控循环单元捕捉信号中的时序依赖，提升模型对复杂时空特征的建模能力。为优化特征融合过程，设计了一种双向的时域-时序交叉注意力模块，自适应融合多导联时空信息。针对类别不平衡问题，设计动态加权焦点损失函数，该损失函数通过动态调整样本权重增强少数类识别能力。在CPSC-2018数据集上的实验结果表明，在仅使用I、II和V1导联信号的情况下，该模型平均F1-score达到0.841，其中房颤、左/右束支传导阻滞的F1-score分别为0.942、0.906和0.951。在PTB-XL数据集上的实验结果同样表现优异，验证了其在资源受限环境中的应用潜力，为精简导联下的ECG多标签分类提供了新思路。

Abstract: The global spread of cardiovascular disease has made electrocardiogram (ECG) signal analysis a key tool for clinical diagnosis. However, the multi-label classification of ECG signals relies on the complete 12 leads, and faces challenges such as insufficient fusion of spatio-temporal features between leads and category imbalance.. To this end, an end-to-end deep learning model based on a few leads is proposed. The time domain features of ECG signals are extracted by a lightweight multi-scale inverse residual feature extraction module, and the time sequence dependence in the signals is captured by a sequential convolutional network and a bidirectional gated loop unit to improve the modeling ability of the model for complex spatio-temporal features. In order to optimize the feature fusion process, a bidirectional time-temporal cross-attention module is designed, which adaptively fuses multi-lead spatio-temporal information. To solve the problem of class imbalance, a dynamic weighted focus loss function is designed to enhance the ability of minority class recognition by dynamically adjusting sample weights. Experimental results on the CPSC-2018 dataset showed that the mean F1-score of the model reached 0.841 when only I, II and V1 lead signals were used, among which F1-score for atrial fibrillation and left/right bundle branch block were 0.942, 0.906 and 0.951, respectively. The experimental results on the PTB-XL dataset also perform well, confirming its application potential in resource-constrained environments and providing new ideas for ECG multi-label classification under reduced leads.