ExECG框架让心电图模型诊断结果可解释，提升临床可信度。
ExECG: An Explainable AI Framework for ECG models
- 三阶段流程：格式统一、解释方法集成、可视化对比
- 支持多种解释方法在统一界面比较，结果可复现
- 适合医疗AI研究者和临床开发者使用
深度学习已使心电图（ECG）诊断模型在心律失常分类和异常检测等任务中表现优异。然而，仅靠准确率不足以支撑临床应用，因缺乏对特定输出原因的解释，限制了结果验证、错误分析与医生信任。尽管已有大量关于ECG可解释AI（XAI）的研究并持续改进，但实际工具链和报告规范不统一，阻碍了方法复用与结果重现。为此，我们提出面向心电图模型的可解释AI框架ExECG，采用三阶段流水线：Wrapper统一异构的ECG数据格式与中间表示；Explainer将多种XAI方法纳入共享执行协议；Visualizer提供统一界面实现跨方法一致性对比。通过简洁示例与两个案例研究，展示了端到端可解释性流程的互操作性与可复现性。
原文摘要 · Abstract (English)
Deep learning has enabled ECG diagnostic models with strong performance in tasks such as arrhythmia classification and abnormality detection. However, accuracy alone is insufficient for clinical deployment because it does not explain why a specific output was produced, limiting justification, error analysis, and trust. Although ECG XAI has been extensively investigated and steadily improved, practical pipelines and reporting conventions vary across studies, hindering reuse and reproducibility. To address these issues, we present Explainable AI framework for ECG models (ExECG), a Python framework that provides a three-stage pipeline: Wrapper standardizes access across heterogeneous ECG formats and intermediate representations, Explainer unifies diverse XAI methods under a shared execution protocol, and Visualizer supports consistent cross-method comparison within a unified interface. We demonstrate end-to-end usage with concise examples and two case studies, highlighting interoperable and reproducible ECG explainability.
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