  In this work, we propose a denoising diffusion generative model (DDGM)
trained with healthy electrocardiogram (ECG) data that focuses on ECG
morphology and inter-lead dependence. Our results show that this innovative
generative model can successfully generate realistic ECG signals. Furthermore,
we explore the application of recent breakthroughs in solving linear inverse
Bayesian problems using DDGM. This approach enables the development of several
important clinical tools. These include the calculation of corrected QT
intervals (QTc), effective noise suppression of ECG signals, recovery of
missing ECG leads, and identification of anomalous readings, enabling
significant advances in cardiac health monitoring and diagnosis.
