Investigating Transformer-Based GANs for Realistic ECG Time-Series Data Generation

Deep learning (DL) electrocardiogram (ECG) predictions are highly valuable for advancing early diagnosis, risk assessment, and treatment planning for cardiovascular diseases. For developing effective predictive models, ECG synthetic data generation is a promising solution for enhancing training datasets while ensuring patient privacy. This work investigates the use of Transformer-based Generative Adversarial Networks (GANs), specifically the TTS-CGAN model, to generate realistic synthetic ECG time-series data. The TTS-CGAN model architecture relies on Transformer encoders for both the generator and discriminator, incorporating state-of-art self-attention mechanisms to enhance time-series data modeling. The research contributions include (i) the analysis of TTS-CGAN for ECG synthesis, (ii) the assessment of synthetic data quality using both MIMICIV and MIT-BIH datasets, (iii) the key introduction of hybrid datasets combining real and synthetic ECG signals to assess model training performance, and (iv) the evaluation of generated data using qualitative and quantitative metrics. Experiments demonstrate that synthetic ECG signals maintain statistical properties similar to real data, with high Cosine similarity (up to 0.99) and low divergence (Jensen-Shannon distance 0.2). Hybrid dataset experiments reveal that models trained on a mix of real and synthetic data (up to 60 % synthetic) retain classification performance while improving dataset diversity.