HeartBeatAI提升心电图多标签分类的鲁棒性与可解释性。
HeartBeatAI: An Interpretable and Robust Deep Learning Framework for Multi-Label ECG Arrhythmia Detection
- 融合多尺度特征与领域泛化，识别诊断导联和心律异常
- 在单一数据集上达98%宏平均F1分数，跨机构部署时罕见异常检测下降
- 适合临床部署，强调可解释性与真实场景适应性
深度学习虽提升了心电图自动化分析能力，但临床应用受限于类别不平衡和泛化差距。本文提出HeartBeatAI框架，结合领域泛化、多尺度特征聚合与临床可解释性，实现12导联心电图的鲁棒分类。不同于图像范式，HeartBeatAI采用塞壬-激励残差网络（SE ResNet）识别关键诊断导联，并通过多层浓缩管道捕捉宏观心律与微观形态异常。为缓解领域偏移，引入MixStyle正则化与标签平滑。在四个大规模数据集上进行基准测试，使用内部源与留一域排除（LODO）协议评估，内部源条件下表现优异（宏平均F1得分98%），但LODO评估显示罕见异常检测性能显著下降，凸显跨机构部署的持续挑战。
原文摘要 · Abstract (English)
While Deep Learning (DL) enhances automated electrocardiogram (ECG) analysis, clinical deployment is hindered by class imbalance and the generalization gap. This paper presents HeartBeatAI, a deep learning framework combining domain generalization, multi-scale feature aggregation, and clinical explainability for robust 12-lead ECG classification. Moving beyond image-based paradigms, HeartBeatAI integrates a Squeeze-and-Excitation (SE) ResNet to isolate diagnostic leads alongside a Multi-Layer Concentration Pipeline to capture macro-rhythm and micro-morphological anomalies. To mitigate domain shift, the framework employs MixStyle regularization and Label Smoothing. Rigorous benchmarking across four large-scale datasets using intra-source and Leave-One-Domain-Out (LODO) protocols demonstrates high performance (98% Macro F1-score) under intra-source conditions. However, LODO evaluations reveal significant degradation in detecting rare anomalies, highlighting a persistent challenge in cross-institutional deployment.
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