CardiacGen: A Hierarchical Deep Generative Model for Cardiac Signals
This work addresses the need for realistic synthetic cardiac data for medical AI applications, but it is incremental as it builds on existing generative modeling approaches.
The authors tackled the problem of generating synthetic cardiac signals by proposing CardiacGen, a hierarchical deep generative model that produces physiologically plausible ECG data, and demonstrated its utility in data augmentation to improve classifier performance.
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.