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

arXiv: https://arxiv.org/abs/2508.16033 · HTML: https://mlsanity.com/paper/2508.16033/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 22 references that are in the map.
- First posted on arXiv 22 Aug 2025.

## Builds on (most cited first, 6 of 6)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR](https://mlsanity.com/paper/1711.00399/) (2017)
- [GANSpace: Discovering Interpretable GAN Controls](https://mlsanity.com/paper/2004.02546/) (2020)
- [Designing an Encoder for StyleGAN Image Manipulation](https://mlsanity.com/paper/2102.02766/) (2021)
- [Explanation by Progressive Exaggeration](https://mlsanity.com/paper/1911.00483/) (2019)

## Built on by (most cited first, 2 of 2)

- [Signal or Noise? Understanding Generative Models for Real-World Sensor Time Series](https://mlsanity.com/paper/2607.04245/) (2026)
- [ExECG: An Explainable AI Framework for ECG models](https://mlsanity.com/paper/2605.19258/) (2026)

## Abstract (arXiv)

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 CounterFactual ECGs (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 where valid features appear in the ECG and 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.