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Log-Spectral Matching GAN: PPG-based Atrial Fibrillation Detection can be Enhanced by GAN-based Data Augmentation with Integration of Spectral Loss.

Publication ,  Journal Article
Ding, C; Xiao, R; Do, D; Lee, DS; Lee, RJ; Kalantarian, S; Hu, X
Published in: IEEE journal of biomedical and health informatics
January 2023

Photoplethysmography (PPG) is a ubiquitous physiological measurement that detects beat-to-beat pulsatile blood volume changes and hence has a potential for monitoring cardiovascular conditions, particularly in ambulatory settings. A PPG dataset that is created for a particular use case is often imbalanced, due to a low prevalence of the pathological condition it targets to predict and the paroxysmal nature of the condition as well. To tackle this problem, we propose log-spectral matching GAN (LSM-GAN), a generative model that can be used as a data augmentation technique to alleviate the class imbalance in a PPG dataset to train a classifier. LSM-GAN utilizes a novel generator that generates a synthetic signal without a up-sampling process of input white noises, as well as adds the mismatch between real and synthetic signals in frequency domain to the conventional adversarial loss. In this study, experiments are designed focusing on examining how the influence of LSM-GAN as a data augmentation technique on one specific classification task - atrial fibrillation (AF) detection using PPG. We show that by taking spectral information into consideration, LSM-GAN as a data augmentation solution can generate more realistic PPG signals.

Duke Scholars

Published In

IEEE journal of biomedical and health informatics

DOI

EISSN

2168-2208

ISSN

2168-2194

Publication Date

January 2023

Volume

PP
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Ding, C., Xiao, R., Do, D., Lee, D. S., Lee, R. J., Kalantarian, S., & Hu, X. (2023). Log-Spectral Matching GAN: PPG-based Atrial Fibrillation Detection can be Enhanced by GAN-based Data Augmentation with Integration of Spectral Loss. IEEE Journal of Biomedical and Health Informatics, PP. https://doi.org/10.1109/jbhi.2023.3234557
Ding, Cheng, Ran Xiao, Duc Do, David Scott Lee, Randall J. Lee, Shadi Kalantarian, and Xiao Hu. “Log-Spectral Matching GAN: PPG-based Atrial Fibrillation Detection can be Enhanced by GAN-based Data Augmentation with Integration of Spectral Loss.IEEE Journal of Biomedical and Health Informatics PP (January 2023). https://doi.org/10.1109/jbhi.2023.3234557.
Ding C, Xiao R, Do D, Lee DS, Lee RJ, Kalantarian S, et al. Log-Spectral Matching GAN: PPG-based Atrial Fibrillation Detection can be Enhanced by GAN-based Data Augmentation with Integration of Spectral Loss. IEEE journal of biomedical and health informatics. 2023 Jan;PP.
Ding, Cheng, et al. “Log-Spectral Matching GAN: PPG-based Atrial Fibrillation Detection can be Enhanced by GAN-based Data Augmentation with Integration of Spectral Loss.IEEE Journal of Biomedical and Health Informatics, vol. PP, Jan. 2023. Epmc, doi:10.1109/jbhi.2023.3234557.
Ding C, Xiao R, Do D, Lee DS, Lee RJ, Kalantarian S, Hu X. Log-Spectral Matching GAN: PPG-based Atrial Fibrillation Detection can be Enhanced by GAN-based Data Augmentation with Integration of Spectral Loss. IEEE journal of biomedical and health informatics. 2023 Jan;PP.

Published In

IEEE journal of biomedical and health informatics

DOI

EISSN

2168-2208

ISSN

2168-2194

Publication Date

January 2023

Volume

PP