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.

翻译：在基于深度学习的心电图信号心血管疾病检测中，处理生理信号的复杂性引发了对利用深度生成模型进行有效数据增强的日益关注。本文提出了一种基于去噪扩散概率模型的心电图合成新通用方法，涵盖三种场景：（i）心搏生成，（ii）部分信号填补，以及（iii）完整心搏预测。该方法首次实现了心电图合成的通用条件化方法，实验结果证明了其在不同心电图相关任务中的有效性。此外，我们表明该方法优于其他最先进的心电图生成模型，并能提升最先进分类器的性能。