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Multivariate generative adversarial networks and their loss functions for synthesis of multichannel ECGs

Brophy, Eoin, De Vos, Maarten orcid logoORCID: 0000-0002-3482-5145, Boylan, Geraldine orcid logoORCID: 0000-0003-0920-5291 and Ward, Tomás E. orcid logoORCID: 0000-0002-6173-6607 (2021) Multivariate generative adversarial networks and their loss functions for synthesis of multichannel ECGs. IEEE Access, 9 . pp. 158936-158945. ISSN 2169-3536

Abstract
Access to medical data is highly regulated due to its sensitive nature, which can constrain communities’ ability to utilize these data for research or clinical purposes. Common de-identification techniques to enable the sharing of data may not provide adequate privacy in every circumstance. We investigate the ability of Generative Adversarial Networks (GANs) to generate synthetic, and more significantly, multichannel electrocardiogram signals that are representative of waveforms observed in patients to address these privacy concerns. Successful generation of high-quality synthetic time series data has the potential to act as an effective substitute for actual patient data. For the first time, we demonstrate a range of novel loss functions using our multivariate GAN architecture and analyse their effect on data quality and privacy. We also present the application of multivariate dynamic time warping as a means of evaluating generated time series. Quantitative evidence demonstrates that the inclusion of a penalisation coefficient (Dynamic Time Warping) in the loss function enables our GAN to outperform the other generative models and loss functions explored by 4.9% according to our metrics. This allows for the generation of data that is more representative of the training set and diverse across generated samples, all whilst ensuring sufficient privacy.
Metadata
Item Type:Article (Published)
Refereed:Yes
Uncontrolled Keywords:Generative adversarial networks; ECG; time series
Subjects:Computer Science > Computer networks
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > ADAPT
Publisher:IEEE
Official URL:https://dx.doi.org/10.1109/ACCESS.2021.3130421
Copyright Information:© 2021 The Authors. Open Access (CC-BY 4.0)
Funders:Science Foundation Ireland under Grant 17/RC-PhD/3482 and Grant SFI/12/RC/2289_P2, Flemish Government (AI Research Program)
ID Code:27536
Deposited On:11 Aug 2022 13:53 by Thomas Murtagh . Last Modified 11 Aug 2022 15:32
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