UniCoMTE：一种用于解释ECG时间序列分类器的通用反事实框架
UniCoMTE: A Universal Counterfactual Framework for Explaining Time-Series Classifiers on ECG Data
AI总结：
UniCoMTE提出了一种通用反事实框架，用于生成ECG时间序列分类器的可解释性，通过分析时间特征提升模型可解释性。
摘要：
机器学习模型，特别是深度神经网络，在分类复杂时间序列数据方面表现出强大的性能。然而，其黑盒性质限制了信任和采用，尤其是在医疗等高风险领域。为了解决这一挑战，我们引入了UniCoMTE，一种通用的生成反事实解释框架，用于多变量时间序列分类器。该框架通过修改输入样本并评估其对模型预测的影响，识别对模型预测影响最大的时间特征。UniCoMTE兼容多种模型架构，并直接在原始时间序列输入上操作。在本研究中，我们评估了UniCoMTE在时间序列ECG分类器上的解释效果。我们通过将我们的解释的可理解性与已建立的技术（LIME和SHAP）的可理解性进行比较，并评估其在类似样本上的泛化能力来量化解释质量。此外，通过医学专家完成的问卷调查评估了临床效用，这些专家在查看反事实解释的同时审查原始ECG样本。结果表明，我们的方法生成了简洁、稳定且与人类一致的解释，其在清晰度和适用性方面均优于现有方法。通过将模型预测与有意义的信号模式联系起来，该框架推进了深度学习模型在现实时间序列应用中的可解释性。
英文摘要：
Machine learning models, particularly deep neural networks, have demonstrated strong performance in classifying complex time series data. However, their black-box nature limits trust and adoption, especially in high-stakes domains such as healthcare. To address this challenge, we introduce UniCoMTE, a model-agnostic framework for generating counterfactual explanations for multivariate time series classifiers. The framework identifies temporal features that most heavily influence a model's prediction by modifying the input sample and assessing its impact on the model's prediction. UniCoMTE is compatible with a wide range of model architectures and operates directly on raw time series inputs. In this study, we evaluate UniCoMTE's explanations on a time series ECG classifier. We quantify explanation quality by comparing our explanations' comprehensibility to comprehensibility of established techniques (LIME and SHAP) and assessing their generalizability to similar samples. Furthermore, clinical utility is assessed through a questionnaire completed by medical experts who review counterfactual explanations presented alongside original ECG samples. Results show that our approach produces concise, stable, and human-aligned explanations that outperform existing methods in both clarity and applicability. By linking model predictions to meaningful signal patterns, the framework advances the interpretability of deep learning models for real-world time series applications.
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