Key result
Log-Spectral Matching GAN data augmentation improved photoplethysmography-based atrial fibrillation detection, achieving a 99% F1 score in high-quality signals compared to 91% with original data.
Why the study?
PPG datasets are often imbalanced due to low prevalence and the paroxysmal nature of pathological conditions such as atrial fibrillation, hindering classifier training.
Does LSM-GAN data augmentation improve PPG-based atrial fibrillation detection in patient datasets?
Does LSM-GAN data augmentation improve PPG-based atrial fibrillation detection in patient datasets?
LSM-GAN provides a novel data augmentation technique incorporating spectral loss to improve the training of deep learning models for PPG-based atrial fibrillation detection.
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.
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Ding et al. (2023) studied Atrial Fibrillation (n=139). Log-Spectral Matching GAN (LSM-GAN) data augmentation vs. Original dataset (no augmentation) and traditional augmentation methods was evaluated on Atrial fibrillation detection F1 score in excellent quality signals (0% artifacts). Log-Spectral Matching GAN data augmentation improved photoplethysmography-based atrial fibrillation detection, achieving a 99% F1 score in high-quality signals compared to 91% with original data.
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