Barbosa Casanova, GiovannySchettini, NorelliPercybrooks Bolívar, Winston SpencerGarcía-Zapirain, Begoña2026-03-132026-03-132026-02Barbosa-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.20250070510.1002/AISY.202500705https://hdl.handle.net/20.500.14454/5433According 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.eng© 2025 The Author(s)Data augmentationElectrocardiogramGenerative adversarial networksSpectrogramsjournal article2026-03-132640-4567