A Comparative Study of GAN Methods for Physiological Signal Generation

Nour Neifar, Achraf Ben-Hamadou, Afef Mdhaffar, Mohamed Jmaiel, Bernd Freisleben

2023

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.

Paper Citation

in Harvard Style

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 - Volume 1: ICPRAM, ISBN 978-989-758-626-2, pages 707-714. DOI: 10.5220/0011794200003411

in Bibtex Style

@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 - Volume 1: ICPRAM,},
year={2023},
pages={707-714},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011794200003411},
isbn={978-989-758-626-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - A Comparative Study of GAN Methods for Physiological Signal Generation
SN - 978-989-758-626-2
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