Leveraging Statistical Shape Priors in GAN-based ECG Synthesis
This work addresses the challenge of limited ECG data for medical diagnosis by improving synthetic data quality, though it is incremental as it builds on existing GAN methods with a novel modeling twist.
The paper tackles the problem of generating realistic electrocardiogram (ECG) signals to address imbalanced training datasets, by proposing a GAN-based method that incorporates statistical shape priors, resulting in more realistic signals compared to state-of-the-art baselines as validated on the MIT-BIH arrhythmia database.
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.