  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. The code of LSM-GAN is available at
https://github.com/chengding0713/Log-Spectral-matching-GAN.
