Key result
Generative adversarial networks synthesize atrial fibrillation ECG signals to address data imbalance for classification networks.
Why the study?
Data imbalance challenges limit the performance of automatic heart disease detection models using ECG signals.
A progressive training strategy using GANs can generate synthetic atrial fibrillation ECG signals to address data imbalance in automatic detection models.
Electrocardiogram (ECG) signals, as a tool for recording cardiac activity, are widely used for the detection and diagnosis of heart diseases. However, challenges in data imbalance limit the performance of automatic detection models. Generative Adversarial Networks (GANs), as a data augmentation technique, can effectively address these issues. In this paper, we propose a progressive training strategy aimed at preserving the details of ECG signals while ensuring the stability of model training. This strategy helps progressively generate signals by gradually increasing the resolution of template signals, from coarse to fine. Additionally, we employ GANs with the proposed strategy to generate atrial fibrillation (AF) signals and evaluate the similarity between the generated signals and real signals. We further build an ECG signal classification network and evaluate the impact of the generated signals on the performance of the classification network.
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Chen et al. (2025) studied Atrial fibrillation. Progressive training strategy using Generative Adversarial Networks (GANs) was evaluated on Similarity between generated and real signals and impact on classification network performance. A progressive training strategy using Generative Adversarial Networks generated atrial fibrillation ECG signals to address data imbalance and evaluate classification network performance.
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