Key result
WGAN-GP beats standard GANs in generating synthetic ECG spectrograms, improving data quality and intraclass diversity.
Why the study?
Analyzing long-duration ECG recordings is time-consuming, and class imbalance in medical datasets challenges machine learning diagnostic tools.
WGAN-GP models effectively generate high-quality synthetic ECG spectrograms, offering a promising solution to class imbalance in ECG machine learning datasets.
WGAN-GP may improve synthetic ECG data quality for ML; leaves open downstream clinical impact.
According to the World Health Organization, cardiovascular diseases are the leading cause of death worldwide. The electrocardiogram (ECG) is a widely used noninvasive method for detecting these conditions. However, analyzing long‐duration ECG signal recordings can be highly time‐consuming for medical professionals. Machine learning and deep learning techniques have emerged as valuable tools to assist in diagnosis. However, class imbalance in medical datasets poses a significant challenge. This work presents a comparative analysis of three generative adversarial network (GAN)‐based models—deep convolutional GAN, conditional GAN, and Wasserstein GAN with gradient penalty (WGAN‐GP)—to generate synthetic ECG spectrograms. The proposed models are evaluated using the Fréchet inception distance and structural similarity index measure. The results indicate that WGAN‐GP models outperform the other two models in terms of intraclass diversity and data quality. These findings suggest that GAN‐generated spectrograms can help mitigate data imbalance issues and improve ECG classification models.
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Barbosa‐Casanova et al. (2025) studied Cardiovascular diseases. Wasserstein GAN with gradient penalty (WGAN-GP) vs. Deep convolutional GAN and conditional GAN was evaluated on Fréchet inception distance and structural similarity index measure. Wasserstein GAN with gradient penalty (WGAN-GP) outperformed deep convolutional GAN and conditional GAN in generating synthetic ECG spectrograms, improving intraclass diversity and data quality.
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