CoFE: A Framework Generating Counterfactual ECG for Explainable Cardiac AI-Diagnostics

TL;DR

The paper introduces CoFE, a framework that generates counterfactual ECGs to explain AI-based ECG predictions, improving interpretability for clinical use.

Contribution

It presents a novel framework for creating counterfactual ECGs that clarify feature influence on AI predictions, aiding explainability in cardiac diagnostics.

Findings

CoFE produces clinically consistent counterfactual ECGs.

It enhances understanding of feature importance in AI-ECG models.

Demonstrated effectiveness in atrial fibrillation and potassium level prediction.

Abstract

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.