Key result
Novel shape-prior GANs outperform state-of-the-art baselines in synthetic ECG generation and downstream classification.
Why the study?
ECG data collection during emergency situations is challenging, making ECG data generation an efficient solution for highly imbalanced training datasets.
Does a GAN architecture leveraging statistical 2-D shape models generate more realistic ECG signals compared to state-of-the-art GAN baselines?
Does a GAN architecture leveraging statistical 2-D shape models generate more realistic ECG signals compared to state-of-the-art GAN baselines?
Incorporating statistical 2-D shape priors into GANs improves the realism of synthetic ECG signals, which can help address data scarcity in training ECG classification algorithms.
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.
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Neifar et al. (2024) studied Arrhythmia (ECG synthesis) (n=47). Generative Adversarial Network (GAN) with statistical shape priors vs. Standard GANs and state-of-the-art GAN baselines was evaluated on ECG signal generation quality (RMSE, MAE, MSE, EMD, DTW) and downstream classification performance. A novel Generative Adversarial Network approach leveraging statistical shape priors generated highly realistic synthetic ECG signals, outperforming state-of-the-art baselines in signal quality metrics and downstream classification performance.
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