标题： 从原型到稀疏心电图解释：多变量时间序列多类分类的SHAP驱动反事实方法 显示英文标题

标题： From Prototypes to Sparse ECG Explanations: SHAP-Driven Counterfactuals for Multivariate Time-Series Multi-class Classification

摘要： 在可解释的人工智能（XAI）中，基于实例的时间序列解释由于其在医疗保健等领域的可操作和可解释的见解潜力，正获得越来越多的关注。 为了解决先进模型的可解释性挑战，我们提出了一种原型驱动的框架，用于生成针对12导联心电图分类模型的稀疏反事实解释。 我们的方法使用基于SHAP的阈值来识别关键信号段，并将其转换为区间规则，使用动态时间规整（DTW）和中位数聚类来提取代表性原型，并将这些原型与查询R波对齐，以与被解释的样本保持一致。 该框架生成的反事实仅修改原始信号的78%，同时在所有类别中保持81.3%的有效性，并在时间稳定性方面实现了43%的改进。 我们评估了我们方法的三种变体，Original、Sparse和Aligned Sparse，其类别特定性能从心肌梗死（MI）的98.9%有效性到肥厚（HYP）检测的挑战（13.2%）。 这种方法支持近实时生成（<1秒）的临床有效反事实，并为交互式解释平台提供了基础。 我们的研究结果确立了基于生理学意识的反事实解释在基于人工智能的诊断系统中的设计原则，并概述了向临床部署用户控制的解释界面的路径。 显示英文摘要

摘要： In eXplainable Artificial Intelligence (XAI), instance-based explanations for time series have gained increasing attention due to their potential for actionable and interpretable insights in domains such as healthcare. Addressing the challenges of explainability of state-of-the-art models, we propose a prototype-driven framework for generating sparse counterfactual explanations tailored to 12-lead ECG classification models. Our method employs SHAP-based thresholds to identify critical signal segments and convert them into interval rules, uses Dynamic Time Warping (DTW) and medoid clustering to extract representative prototypes, and aligns these prototypes to query R-peaks for coherence with the sample being explained. The framework generates counterfactuals that modify only 78% of the original signal while maintaining 81.3% validity across all classes and achieving 43% improvement in temporal stability. We evaluate three variants of our approach, Original, Sparse, and Aligned Sparse, with class-specific performance ranging from 98.9% validity for myocardial infarction (MI) to challenges with hypertrophy (HYP) detection (13.2%). This approach supports near realtime generation (< 1 second) of clinically valid counterfactuals and provides a foundation for interactive explanation platforms. Our findings establish design principles for physiologically-aware counterfactual explanations in AI-based diagnosis systems and outline pathways toward user-controlled explanation interfaces for clinical deployment.