Invertible Conditional Generative Adversarial Networks to Effectively Generate Myocardial Infarction from Normal ECG

Style transfer techniques based on Deep Learning have shown significant promise in biomedical signal processing, particularly in generating synthetic physiological signals from real ones. In this study, we explored the use of Invertible Conditional Generative Adversarial Networks (IcGANs) for style transfer, specifically transforming 12-lead ECG heartbeats from normal sinus rhythm to myocardial infarction (inferior and antero-septal myocardial infarction). Unlike CycleGAN, another style transfer technique which requires multiple models for each class transformation, Ic-Gan only requires training a single conditional GAN and an encoder, offering a more efficient and flexible framework. We trained both IcGAN and CycleGAN on ECG heartbeats extracted from the PTB-XL dataset available on Physionet. We assessed the quality of the generated ECG signals using visual inspection, GAN-train and GAN-test scores, and quantitative metrics such as ST-segment amplitude comparisons. The results showed that the IcGAN effectively captured the relevant features affected by myocardial infarction while preserving the original ECG ones, generating clinically meaningful variations. The comparison between IcGAN and CycleGAN with similar model architectures demonstrated the advantages of the former in terms of efficiency and performance. In conclusion, the potential of IcGAN for controlled ECG feature modification may find applications in domain adaptation, synthetic data generation for rare conditions, and enhancing model generalization for personalized treatment.