CardiacGen: A Hierarchical Deep Generative Model for Cardiac Signals

TL;DR

CardiacGen is a hierarchical deep learning model that generates physiologically realistic synthetic cardiac signals, improving data augmentation for cardiac signal classification tasks.

Contribution

It introduces a modular hierarchical generative framework with explicit physiological constraints for realistic cardiac signal synthesis.

Findings

Synthetic signals exhibit realistic physiological features.

Data augmentation with CardiacGen improves classifier performance.

Model effectively captures HRV and morphology characteristics.

Abstract

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.