ArXiv Preprint
We present CardiacGen, a Deep Learning framework for generating synthetic but
physiologically plausible cardiac signals like ECG. Based on the physiology of
cardiovascular system function, we propose a modular hierarchical generative
model and impose explicit regularizing constraints for training each module
using multi-objective loss functions. The model comprises 2 modules, an HRV
module focused on producing realistic Heart-Rate-Variability characteristics
and a Morphology module focused on generating realistic signal morphologies for
different modalities. We empirically show that in addition to having realistic
physiological features, the synthetic data from CardiacGen can be used for data
augmentation to improve the performance of Deep Learning based classifiers.
CardiacGen code is available at
https://github.com/SENSE-Lab-OSU/cardiac_gen_model.
Tushar Agarwal, Emre Ertin
2022-11-15
