Synthetic electrocardiogram spectrogram generation using generative adversarial network-based models: a comparative study

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dc.contributor.authorBarbosa Casanova, Giovanny
dc.contributor.authorSchettini, Norelli
dc.contributor.authorPercybrooks Bolívar, Winston Spencer
dc.contributor.authorGarcía-Zapirain, Begoña
dc.date.accessioned2026-03-13T10:44:12Z
dc.date.available2026-03-13T10:44:12Z
dc.date.issued2026-02
dc.date.updated2026-03-13T10:44:12Z
dc.description.abstractAccording 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.en
dc.description.sponsorshipThis work was supported by the Bicentenary Grant for doctoral studies from the Ministerio de Ciencia, Tecnolog\u00EDa e Innovaci\u00F3n de la Rep\u00FAblica de Colombia, BPIN2019000100005.en
dc.identifier.citationBarbosa-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
dc.identifier.doi10.1002/AISY.202500705
dc.identifier.eissn2640-4567
dc.identifier.urihttps://hdl.handle.net/20.500.14454/5433
dc.language.isoeng
dc.publisherJohn Wiley and Sons Inc
dc.rights© 2025 The Author(s)
dc.subject.otherData augmentation
dc.subject.otherElectrocardiogram
dc.subject.otherGenerative adversarial networks
dc.subject.otherSpectrograms
dc.titleSynthetic electrocardiogram spectrogram generation using generative adversarial network-based models: a comparative studyen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.issue2
oaire.citation.titleAdvanced Intelligent Systems
oaire.citation.volume8
oaire.licenseConditionhttps://creativecommons.org/licenses/by/4.0/
oaire.versionVoR
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