针对心电图罕见心律失常诊断难题，提出新方法提升模型对稀有类别的识别能力。
Angular Gaussian Supervised Contrastive Learning for Long-Tailed Electrocardiogram Arrhythmia Diagnosis
- 通过角度高斯对比学习建模类别不确定性，捕捉心电图形态变异特征。
- 在PTB-XL和夜间心电数据集上，罕见心律失常敏感性提升至0.709和0.889。
- 适合临床需高灵敏度检测罕见心律失常的场景，如睡眠心电监测。
长尾标签分布降低了深度学习在心电图心律失常诊断中的可靠性，尤其对临床重要的罕见异常。现有重平衡与逻辑调整方法主要关注类别频率，忽视了心电图类别间方向依赖的形态变异性。本文提出角高斯监督对比学习（AG-SCL）用于长尾多标签心电图诊断。AG-SCL将三个组件统一整合：角度高斯对比分支，在单位归一化嵌入上建模全协方差类别不确定性；自适应逻辑调整，学习有界标签状态特定先验修正，而非固定频率边缘；尾部感知增强，生成保持形态且保护7-25 Hz QRS主导频段的视图。在公开的PTB-XL基准和包含141名受试者共1317小时记录的夜间心电数据集上评估。AG-SCL在两个数据集上均取得最佳宏观性能。在PTB-XL上，平衡准确率0.838，敏感性0.709，特异性0.968，平均精度0.495，5%误报率下的真阳性率0.778；在Noc-ECG上分别为0.918、0.889、0.947、0.488、0.900。罕见或形态不稳定的节律类别获最大提升，消融实验证实全协方差建模、自适应逻辑调整和尾部感知增强的贡献。该方法通过先验校准与各向异性表示学习结合，提升了对罕见心律失常的敏感性，同时维持临床相关特异性。代码已开源。
原文摘要 · Abstract (English)
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.
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