  Electrocardiogram (ECG) data collection during emergency situations is
challenging, making ECG data generation an efficient solution for dealing with
highly imbalanced ECG training datasets. In this paper, we propose a novel
approach for ECG signal generation using Generative Adversarial Networks (GANs)
and statistical ECG data modeling. Our approach leverages prior knowledge about
ECG dynamics to synthesize realistic signals, addressing the complex dynamics
of ECG signals. To validate our approach, we conducted experiments using ECG
signals from the MIT-BIH arrhythmia database. Our results demonstrate that our
approach, which models temporal and amplitude variations of ECG signals as 2-D
shapes, generates more realistic signals compared to state-of-the-art GAN based
generation baselines. Our proposed approach has significant implications for
improving the quality of ECG training datasets, which can ultimately lead to
better performance of ECG classification algorithms. This research contributes
to the development of more efficient and accurate methods for ECG analysis,
which can aid in the diagnosis and treatment of cardiac diseases.
