Uncertainty-Guided Diffusion Model for High-Fidelity ECG Synthesis and Classification

Electrocardiogram (ECG) synthesis plays a crucial role in medical research, education and device development. However, achieving high-fidelity ECG signal synthesis remains challenging, particularly in accurately reproducing specific waveform patterns at the sample level. In this paper, we propose an uncertainty-guided diffusion model that integrates uncertainty estimation into the ECG synthesis process. The uncertainty guidance preserves meaningful waveform characteristics. The model combines diffusion models, known for generating high-quality samples from complex distributions, with uncertainty guidance that captures and propagates uncertainty throughout the pipeline. Extensive experiments demonstrate that our approach outperforms existing methods in terms of both distribution-level and sample-level evaluation.