Uncertainty‐Guided Diffusion Model for High‐Fidelity <scp>ECG</scp> Synthesis and Classification

ABSTRACT 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.