发表机构
School of Biomedical Engineering, Shanghai Jiao Tong University; Department of Neurology and Neurological Rehabilitation, Shanghai Yangzhi Rehabilitation Hospital, School of Medicine, Tongji University（上海交通大学生物医学工程学院; 同济大学医学院附属上海养志康复医院神经科与神经康复科）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对长尾心电图心律失常诊断问题，提出角高斯监督对比学习（AG-SCL），集成角高斯对比分支、自适应对数调整和尾部感知增强三个组件，在两个数据集上取得最佳宏观性能，增强对罕见心律失常敏感性并保持特异性。
AI 中文摘要
长尾标签分布降低了深度学习用于心电图（ECG）心律失常诊断的可靠性，尤其是对临床重要但罕见的异常情况。现有再平衡和对数调整方法主要解决类别频率问题，而忽略了ECG类别间方向相关的形态变异性。本研究提出用于长尾多标签ECG诊断的角高斯监督对比学习（AG-SCL）。AG-SCL将三个组件集成到统一框架中：一个角高斯对比分支，对单位归一化嵌入上的全协方差类别不确定性进行建模；自适应对数调整，学习有界的特定标签状态先验校正而非固定频率边际；尾部感知增强，生成保留形态的视图同时保护7-25Hz QRS主导频段。该方法在公共PTB-XL基准和包含141名受试者1317小时记录的夜间ECG数据集上进行评估。AG-SCL在两个数据集上均取得了最佳宏观性能。在PTB-XL上，其平衡准确率为0.838，灵敏度为0.709，特异性为0.968，平均平均精度为0.495，5%误报率下的真阳性率为0.778。在Noc-ECG上，相应值为0.918、0.889、0.947、0.488和0.900。最大增益出现在罕见或形态不稳定的节律类别中，消融研究证实了全协方差建模、自适应对数调整和尾部感知增强的贡献。AG-SCL通过将先验校准与各向异性表示学习相结合，提高了长尾ECG诊断，增强了对罕见心律失常的敏感性，同时保持了临床相关的特异性。
英文摘要
Long-tailed label distributions reduce the reliability of deep learning for electrocardiogram (ECG) arrhythmia diagnosis, particularly for clinically important but rare abnormalities. Existing rebalancing and logit adjustment methods mainly address class frequency while overlooking direction-dependent morphological variability across ECG classes. This study proposes Angular Gaussian Supervised Contrastive Learning (AG-SCL) for long-tailed multi-label ECG diagnosis. AG-SCL integrates three components into a unified framework: an Angular Gaussian contrastive branch that models full-covariance class uncertainty on unit-normalized embeddings, Adaptive Logit Adjustment that learns bounded label-state-specific prior corrections instead of fixed frequency-based margins, and tail-aware augmentation that generates morphology-preserving views while protecting the 7-25 Hz QRS-dominant band. The method was evaluated on the public PTB-XL benchmark and a nocturnal ECG dataset comprising 1317 hours of recordings from 141 subjects. AG-SCL achieved the best macro-level performance on both datasets. On PTB-XL, it obtained a balanced accuracy of 0.838, sensitivity of 0.709, specificity of 0.968, mean average precision of 0.495, and TPR at 5% FPR of 0.778. On Noc-ECG, the corresponding values were 0.918, 0.889, 0.947, 0.488, and 0.900. The largest gains occurred in rare or morphologically unstable rhythm classes, while ablation studies confirmed the contributions of full-covariance modelling, Adaptive Logit Adjustment, and tail-aware augmentation. AG-SCL improves long-tailed ECG diagnosis by combining prior calibration with anisotropic representation learning, enhancing sensitivity to rare arrhythmias while maintaining clinically relevant specificity. Our code is available at: https://github.com/Open-EXG/AG-SCL-for-Long-Tailed-ECG.
