首页|Uncertainty-Guided Diffusion Model for High-Fidelity ECG Synthesis and Classification
Uncertainty-Guided Diffusion Model for High-Fidelity ECG Synthesis and Classification
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NETL
NSTL
Wiley
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.
School of Intelligence Science and Technology, Peking University, Beijing, China||State Key Laboratory of General Artificial Intelligence, Peking University, Beijing, China
NETL
NSTL
Wiley
