  Despite the surge in deep learning for ECG signal analysis, its black-box
nature remains a barrier in clinical decision-making. Interpretable machine
learning models are less resource-intensive and more transparent for
clinicians, to enhance diagnostic processes. The primary aim of this study is
to explore the usability and potential benefits of counterfactual explanations
in interpreting machine learning models trained on ECG signals, particularly
for distinguishing between control group subjects and myocardial infarction
patients. To achieve our goal, we leveraged the PTB-XL dataset and developed a
systematic feature extraction methodology. Subsequently, our method entailed
identifying the most significant ECG features via a thorough ranking and
elimination process, refining the feature set for further counterfactual
evaluation. Furthermore, we introduced the Visualizing Counterfactual Clues on
Electrocardiograms (VCCE) method to enhance the practical usability of
counterfactual explanations. The authenticity of the generated counterfactuals
was validated through custom metrics, including cognitive assessments of
diagnostic clues present in the ECG report, with the clinical expertise of two
cardiologists. Our research indicates great potential for future studies in
identifying patient outcomes for cardiac conditions using ECGs, with high and
moderate-quality interpretation validity scores of 23.29 $\pm$ 1.04 and 20.28
$\pm$ 0.99 out of 25, respectively. Clinical alignment scores of 0.83 $\pm$
0.12 for high-quality and 0.57 $\pm$ 0.10 for moderate-quality interpretations
further validate the strong link between our analysis and clinical evaluations.
The source code for this study can be accessed at
https://github.com/tanyelai/vcce.
