Synthetic electrocardiogram spectrogram generation using generative adversarial network-based models: a comparative study
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2026-02
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John Wiley and Sons Inc
Resumen
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.
Palabras clave
Data augmentation
Electrocardiogram
Generative adversarial networks
Spectrograms
Electrocardiogram
Generative adversarial networks
Spectrograms
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Barbosa-Casanova, G., Schettini, N., Percybrooks, W., & García-Zapirain, B. (2026). Synthetic electrocardiogram spectrogram generation using generative adversarial network-based models: a comparative study. Advanced Intelligent Systems, 8(2). https://doi.org/10.1002/AISY.202500705