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Synthetic Electrocardiogram Spectrogram Generation Using Generative Adversarial Network-Based Models: A Comparative Study
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DOI：10.1002/aisy.202500705.png)
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Abstract
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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.
Keywords:
data augmentation
electrocardiogram
generative adversarial networks
spectrograms
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