TransDiffECG: Semantically controllable ECG synthesis via transformer-based diffusion modeling

Existing generative models for electrocardiogram (ECG) synthesis often lack fine-grained, interpretable control, limiting their utility for addressing data scarcity and imbalance. This study aims to develop a model capable of producing diverse and semantically controllable synthetic ECGs to fill this critical gap.

We propose TransDiffECG, a novel Transformer-based diffusion model that integrates semantic information injection and global temporal modeling to enable fine-grained control over ECG synthesis. The model allows user-controllable generation of ECG signals with customized physiological details. We establish a comprehensive evaluation protocol, including downstream segmentation and classification tasks, to rigorously assess the authenticity and utility of the generated signals. Extensive experiments are conducted on both single-lead (QTDB) and multi-lead (LUDB) ECG datasets.

TransDiffECG significantly outperforms state-of-the-art baselines. On the multi-lead LUDB dataset, it achieved superior signal quality (MMD: 3.21×10^-2; Pearson Correlation: 0.6177). The utility of the synthetic data was confirmed in downstream tasks, where data augmentation improved atrial fibrillation classification to an AUROC of 0.9451. Moreover, a segmentation model trained solely on our synthetic data rivaled one trained on real data (e.g., ∼98% precision/recall on QTDB).

TransDiffECG represents a significant advancement in synthetic medical signal generation by bridging the gap between clinical interpretability and generative flexibility. Its ability to generate semantically controllable and clinically valid ECGs greatly expands the application potential of generative models in healthcare research and practice.