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Authors: Nour Neifar 1 ; Achraf Ben-Hamadou 2 ; Afef Mdhaffar 1 ; Mohamed Jmaiel 1 and Bernd Freisleben 3

Affiliations: 1 ReDCAD Lab, ENIS, University of Sfax, Tunisia ; 2 Centre de Recherche en Numérique de Sfax, Laboratory of Signals, Systems, Artificial Intelligence and Networks, Technopˆole de Sfax, Sfax, Tunisia ; 3 Department of Mathematics and Computer Science, Philipps-Universität Marburg, Germany

Keyword(s): GAN, Time Series, ECG, PPG, Physiological Signals.

Abstract: Due to medical data scarcity and complex dynamics of physiological signals, different solutions based on generative adversarial networks (GANs) have been proposed to generate physiological signals, such as electrocardiograms (ECG) and photoplethysmograms (PPG). In this paper, we present a comparative study of existing methods for ECG and PPG signal generation. The competing methods are evaluated on the MIT-BIH arrhythmia and the PPG-BP datasets. Experimental results demonstrate the benefits of incorporating prior knowledge in the generation process and the robustness of these methods for the synthesis of realistic ECG and PPG signals.

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Paper citation in several formats:
Neifar, N.; Ben-Hamadou, A.; Mdhaffar, A.; Jmaiel, M. and Freisleben, B. (2023). A Comparative Study of GAN Methods for Physiological Signal Generation. In Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-626-2; ISSN 2184-4313, SciTePress, pages 707-714. DOI: 10.5220/0011794200003411

@conference{icpram23,
author={Nour Neifar. and Achraf Ben{-}Hamadou. and Afef Mdhaffar. and Mohamed Jmaiel. and Bernd Freisleben.},
title={A Comparative Study of GAN Methods for Physiological Signal Generation},
booktitle={Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2023},
pages={707-714},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011794200003411},
isbn={978-989-758-626-2},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - A Comparative Study of GAN Methods for Physiological Signal Generation
SN - 978-989-758-626-2
IS - 2184-4313
AU - Neifar, N.
AU - Ben-Hamadou, A.
AU - Mdhaffar, A.
AU - Jmaiel, M.
AU - Freisleben, B.
PY - 2023
SP - 707
EP - 714
DO - 10.5220/0011794200003411
PB - SciTePress

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