通过生成反事实心电图，解释AI如何基于波形特征做出诊断决策。
CoFE: A Framework Generating Counterfactual ECG for Explainable Cardiac AI-Diagnostics
- 基于输入心电图生成反事实信号，模拟特征变化对预测的影响。
- 在房颤分类与钾离子水平回归任务中，结果符合临床已知规律。
- 适合关注AI诊断可解释性的临床医生与医疗AI研发者。
为推动基于AI的心电图预测模型（AI-ECG）在临床中的应用，我们提出一种生成反事实心电图（CoFE）的框架，用于展示特定特征（如振幅、间期）如何影响模型的预测决策。通过两个案例研究——房颤分类和钾离子水平回归——验证了该框架的有效性。CoFE生成的信号变化与临床已有知识一致，能够清晰揭示模型决策中有效特征的位置及其作用机制。本工作有望提升AI-ECG模型的可解释性，支持更可靠的临床决策。演示视频见：https://www.youtube.com/watch?v=YoW0bNBPglQ。
原文摘要 · Abstract (English)
Recognizing the need for explainable AI (XAI) approaches to enable the successful integration of AI-based ECG prediction models (AI-ECG) into clinical practice, we introduce a framework generating \textbf{Co}unter\textbf{F}actual \textbf{E}CGs (i,e., named CoFE) to illustrate how specific features, such as amplitudes and intervals, influence the model's predictive decisions. To demonstrate the applicability of the CoFE, we present two case studies: atrial fibrillation classification and potassium level regression models. The CoFE reveals feature changes in ECG signals that align with the established clinical knowledge. By clarifying both \textbf{where valid features appear} in the ECG and \textbf{how they influence the model's predictions}, we anticipate that our framework will enhance the interpretability of AI-ECG models and support more effective clinical decision-making. Our demonstration video is available at: https://www.youtube.com/watch?v=YoW0bNBPglQ.
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