  Within cardiovascular disease detection using deep learning applied to ECG
signals, the complexities of handling physiological signals have sparked
growing interest in leveraging deep generative models for effective data
augmentation. In this paper, we introduce a novel versatile approach based on
denoising diffusion probabilistic models for ECG synthesis, addressing three
scenarios: (i) heartbeat generation, (ii) partial signal imputation, and (iii)
full heartbeat forecasting. Our approach presents the first generalized
conditional approach for ECG synthesis, and our experimental results
demonstrate its effectiveness for various ECG-related tasks. Moreover, we show
that our approach outperforms other state-of-the-art ECG generative models and
can enhance the performance of state-of-the-art classifiers.
