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Related Experiment Video

Updated: Jan 13, 2026

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations
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TransDiffECG: Semantically controllable ECG synthesis via transformer-based diffusion modeling.

Yuxin Lin1, Jing Ma1, Wei Wang2

  • 1School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518055, China.

Journal of Biomedical Informatics
|October 29, 2025
PubMed
Summary
This summary is machine-generated.

TransDiffECG, a Transformer-based diffusion model, generates controllable synthetic electrocardiograms (ECGs) for improved healthcare data. This advanced model enhances ECG data scarcity and imbalance issues effectively.

Keywords:
Data generationElectrocardiographyGenerative modelsTime series

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Area of Science:

  • Artificial Intelligence
  • Biomedical Engineering
  • Medical Signal Processing

Background:

  • Existing generative models for electrocardiogram (ECG) synthesis lack fine-grained control.
  • This limits their utility in addressing data scarcity and imbalance in healthcare.

Purpose of the Study:

  • To develop a model for producing diverse and semantically controllable synthetic ECGs.
  • To fill the critical gap in interpretable ECG data generation.

Main Methods:

  • Proposed TransDiffECG, a novel Transformer-based diffusion model.
  • Integrated semantic information injection and global temporal modeling for controllable ECG synthesis.
  • Evaluated on single-lead (QTDB) and multi-lead (LUDB) ECG datasets using downstream tasks.

Main Results:

  • TransDiffECG outperformed state-of-the-art baselines in signal quality on the LUDB dataset.
  • Achieved superior signal quality (MMD: 3.21×10-2; Pearson Correlation: 0.6177).
  • Data augmentation with synthetic ECGs improved atrial fibrillation classification (AUROC: 0.9451) and segmentation tasks.

Conclusions:

  • TransDiffECG advances synthetic medical signal generation by combining clinical interpretability and generative flexibility.
  • Enables generation of semantically controllable and clinically valid ECGs.
  • Expands the application potential of generative models in healthcare research and practice.