Key result
Classifier-guided diffusion models generate synthetic inferior MI ECGs that match real data classification performance.
Why the study?
High-quality ECG data with accurate clinical annotations are scarce due to privacy concerns and resource limits, and existing disease-specific synthetic ECG generation relies on costly labeled datasets.
A classifier-guided diffusion model can generate high-quality, disease-specific synthetic ECG data that performs comparably to real data in training classification models, potentially overcoming data scarcity.
May augment scarce IMI ECG datasets for model training; hypothesis-generating pilot requiring prospective validation.
As a non-invasive diagnostic tool, electrocardiography (ECG) is one of the most widely used cardiac screening methods. However, due to privacy concerns and limitations in medical resources, the scarcity of high-quality ECG data with clear signals and accurate clinical annotations presents a common challenge for researchers. Although synthetic ECG generation offers a solution, existing disease-specific methods rely on costly labeled datasets. To reduce this cost, we propose a method that employs a classifier-guided diffusion model to generate ECG data for a specific disease without requiring extensive annotations. We applied our proposed method to the public PTB-XL dataset and generated ECG data for inferior myocardial infarction (IMI). To evaluate the quality of the generated data, we conducted two analyses: classification performance evaluation using a classifier trained on the synthetic dataset and a statistical analysis of the generated IMI ECGs. In the first analysis, we trained classifiers to distinguish between IMI and normal ECGs. The only difference between these classifiers was the training dataset: one was trained on fully real data, while the others were trained on merged datasets containing both real and synthetic data. The classifiers trained on the merged datasets achieved performance comparable to that of the classifier trained solely on real data, indicating that synthetic data can partially replace real data without degrading model performance. In the second analysis, we conducted a statistical distribution analysis of a key IMI ECG feature: Q wave depth. This analysis revealed that the distribution of the generated IMI ECGs closely resembled that of the real IMI ECGs. These results validate the high quality and reliability of the generated data and confirm the effectiveness of our proposed method.
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Chen et al. (2025) studied Inferior myocardial infarction. Classifier-guided diffusion model for generating synthetic ECG data vs. Fully real data was evaluated on Classification performance and statistical distribution of Q wave depth. A classifier-guided diffusion model generated synthetic inferior myocardial infarction ECGs that closely resembled real ECGs and achieved comparable classification performance when augmenting training data.
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