CoFE: A Framework Generating Counterfactual ECG for Explainable Cardiac AI-Diagnostics
This work addresses the need for interpretable AI in clinical practice to support effective decision-making for healthcare professionals, though it appears incremental as it applies existing counterfactual methods to ECG data.
The paper tackles the problem of explainable AI for ECG diagnostics by introducing CoFE, a framework that generates counterfactual ECGs to show how features like amplitudes and intervals influence model predictions, with case studies on atrial fibrillation classification and potassium level regression demonstrating alignment with clinical knowledge.
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.