| 摘要: |
| 临床12导联心电图(ECG)是测试心脏活动最常用的信号源, 其自动分类及可解释性对心血管疾病的早期筛查和诊断至关重要. 现有的ECG分类研究多集中于单标记分类, 即每条心电记录仅对应一种心功能障碍, 而在临床中, 心血管疾病患者常伴有多种并发心脏疾病, 因此多标记ECG分类任务更符合现实需求. 现有基于深度网络的多标记ECG分类方法主要聚焦于标记相关性分析或神经网络架构的改进, 而忽略了多标记学习中的本质问题, 即天然存在的正负标记不平衡. 为此, 提出一种策略, 即每次仅推开一对标记, 使得正负标记在训练过程中维持平衡. 具体而言, 最大化正负标记之间的间隔并由此导出一个新的损失函数, 以缓解正负标记不平衡问题. 此外, 针对现有ECG方法可解释性不足, 难以辅助诊断的问题, 引入时域显著性重缩放方法来对提出方法的实验结果进行可视化展示, 以辅助定位并解释不同的疾病. 在PhysioNet Challenge 2021 ECG标准数据集上(包含8个子集)进行实验, 结果表明与最先进的多标记ECG分类方法相比, 所提方法取得了更优的性能. |
| 关键词: 心电图分类 多标记 不平衡 可解释性 |
| DOI：10.13328/j.cnki.jos.007492 |
| 分类号:TP391 |
| 基金项目:国家自然科学基金(62376126); 江苏省教育强省建设专项资金-2025年优势学科-人才启动费(1083142501007) |
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| Electrocardiogram Classification and Interpretability Based on Imbalanced Multi-label Learning |
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LI Xiang1, CAO Si-Yuan2, CHEN Song-Can2
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1.School of Computer Science/School of Cyber Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China;2.College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
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| Abstract: |
| The 12-lead electrocardiogram (ECG) is the most commonly used signal source for testing cardiac activity, and its automatic classification and interpretability are crucial for the early screening and diagnosis of cardiovascular diseases. Most ECG classification studies focus on single-label classification, where each ECG record corresponds to only one type of cardiac dysfunction. However, in clinical practice, patients with cardiovascular diseases often have multiple concurrent heart diseases, making multi-label ECG classification more aligned with real-world needs. Existing deep learning-based multi-label ECG classification methods have mostly concentrated on label correlation analyses or neural network modifications, neglecting the fundamental issue in multi-label learning: the inherent imbalance between positive and negative labels. To address this issue, this study proposes a novel strategy that balances positive and negative labels during training by pushing away only one pair of labels each time. Specifically, it maximizes the margin between positive and negative labels and derives a new loss function to mitigate the imbalance issue. Furthermore, to address the insufficiency of interpretability in existing ECG methods, which hinders diagnostic assistance, the study introduces a temporal saliency rescaling method to visualize the experimental results of the proposed method, aiding in the localization and interpretation of different diseases. Experiments conducted on the PhysioNet Challenge 2021 ECG dataset, which includes 8 subsets, demonstrate that the proposed method outperforms state-of-the-art multi-label ECG classification methods. |
| Key words: ECG classification multi-label imbalance interpretability |
