Deep learning image processing models have had remarkable success in recent years in generating high quality images. Particularly, the Improved Denoising Diffusion Probabilistic Models (DDPM) have shown superiority in image quality to the state-of-the-art generative models, which motivated us to investigate its capability in generation of the synthetic electrocardiogram (ECG) signals. In this work, synthetic ECG signals are generated by the Improved DDPM and by the Wasserstein GAN with Gradient Penalty (WGAN-GP) models and then compared. To this end, we devise a pipeline to utilize DDPM in its original $2D$ form. First, the $1D$ ECG time series data are embedded into the $2D$ space, for which we employed the Gramian Angular Summation/Difference Fields (GASF/GADF) as well as Markov Transition Fields (MTF) to generate three $2D$ matrices from each ECG time series that, which when put together, form a $3$-channel $2D$ datum. Then $2D$ DDPM is used to generate $2D$ $3$-channel synthetic ECG images. The $1$D ECG signals are created by de-embedding the $2D$ generated image files back into the $1D$ space. This work focuses on unconditional models and the generation of only \emph{Normal} ECG signals, where the Normal class from the MIT BIH Arrhythmia dataset is used as the training phase. The \emph{quality}, \emph{distribution}, and the \emph{authenticity} of the generated ECG signals by each model are compared. Our results show that, in the proposed pipeline, the WGAN-GP model is superior to DDPM by far in all the considered metrics consistently.

翻译：近年来，深度学习图像处理模型在生成高质量图像方面取得了显著成功。特别是，改进的去噪扩散概率模型（DDPM）在图像质量上已超越最先进的生成模型，这促使我们探究其在生成合成心电图（ECG）信号方面的能力。本研究通过改进的DDPM和带梯度惩罚的Wasserstein生成对抗网络（WGAN-GP）模型生成合成心电图信号，并对其进行比较。为此，我们设计了一个流程，以利用DDPM的原始二维形式。首先，将一维心电图时间序列数据嵌入二维空间，我们采用格拉姆角求和/差分场（GASF/GADF）以及马尔可夫转移场（MTF），从每个心电图时间序列中生成三个二维矩阵，组合后形成三通道的二维数据。随后，使用二维DDPM生成二维三通道的合成心电图图像。通过将生成的二维图像文件反嵌入回一维空间，创建一维心电图信号。本研究聚焦于无条件模型，仅生成“正常”心电图信号，并使用MIT-BIH心律失常数据集中的正常类别作为训练阶段。我们比较了各模型生成的心电图信号的“质量”、“分布”和“真实性”。结果表明，在所提出的流程中，WGAN-GP模型在所有评估指标上均显著优于DDPM。