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Deep learning image processing models have had remarkable success in recentyears in generating high quality images. Particularly, the Improved DenoisingDiffusion Probabilistic Models (DDPM) have shown superiority in image qualityto the state-of-the-art generative models, which motivated us to investigatetheir capability in the generation of the synthetic electrocardiogram (ECG)signals. In this work, synthetic ECG signals are generated by the Improved DDPMand by the Wasserstein GAN with Gradient Penalty (WGAN-GP) models and thencompared. 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, which when put together, form a $3$-channel$2D$ datum. Then $2D$ DDPM is used to generate $2D$ $3$-channel synthetic ECGimages. The $1D$ ECG signals are created by de-embedding the $2D$ generatedimage files back into the $1D$ space. This work focuses on unconditional modelsand the generation of \emph{Normal Sinus Beat} ECG signals exclusively, wherethe Normal Sinus Beat class from the MIT-BIH Arrhythmia dataset is used in thetraining phase. The \emph{quality}, \emph{distribution}, and the\emph{authenticity} of the generated ECG signals by each model arequantitatively evaluated and compared. Our results show that in the proposedpipeline and in the particular setting of this paper, the WGAN-GP model isconsistently superior to DDPM in all the considered metrics.

Synthetic ECG Signal Generation using Probabilistic Diffusion Models