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Photoplethysmography (PPG) is a ubiquitous physiological measurement thatdetects beat-to-beat pulsatile blood volume changes and hence has a potentialfor 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 andthe paroxysmal nature of the condition as well. To tackle this problem, wepropose log-spectral matching GAN (LSM-GAN), a generative model that can beused as a data augmentation technique to alleviate the class imbalance in a PPGdataset to train a classifier. LSM-GAN utilizes a novel generator thatgenerates a synthetic signal without a up-sampling process of input whitenoises, as well as adds the mismatch between real and synthetic signals infrequency domain to the conventional adversarial loss. In this study,experiments are designed focusing on examining how the influence of LSM-GAN asa data augmentation technique on one specific classification task - atrialfibrillation (AF) detection using PPG. We show that by taking spectralinformation into consideration, LSM-GAN as a data augmentation solution cangenerate more realistic PPG signals. The code of LSM-GAN is available athttps://github.com/chengding0713/Log-Spectral-matching-GAN.

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