Due to the difficulty of collecting electrocardiogram (ECG) data during
emergency situations, ECG data generation is an efficient solution for dealing
with highly imbalanced ECG training datasets. However, due to the complex
dynamics of ECG signals, the synthesis of such signals is a challenging task.
In this paper, we present a novel approach for ECG signal generation based on
Generative Adversarial Networks (GANs). Our approach combines GANs with
statistical ECG data modeling to leverage prior knowledge about ECG dynamics in
the generation process. To validate the proposed approach, we present
experiments using ECG signals from the MIT-BIH arrhythmia database. The
obtained results show the benefits of modeling temporal and amplitude
variations of ECG signals as 2-D shapes in generating realistic signals and
also improving the performance of state-of-the-art arrhythmia classification
baselines.

本文提出一种基于生成式对抗网络（GANs）和统计的心电图（ECG）数据建模的新方法，以在 ECG 信号生成任务中利用先前对 ECG 信号动态特性的理解，在 MIT-BIH 心律失常数据库中进行实验验证，结果表明以 2-D 格式建模 ECG 信号的时间和幅度变化可以生成更真实的信号并提高最新的心律失常分类基线的性能。