Synthetic Data Generation Through Statistical Explosion: Improving Classification Accuracy of Coronary Artery Disease Using PPG

Synthetic data generation has recently emerged as a substitution technique for handling the problem of bulk data needed in training machine learning algorithms. Healthcare, primarily cardiovascular domain is a major area where synthetic physiological data can be used improve accuracy of machine learning algorithm. This paper presents a novel approach of generating synthetic Photoplethysmogram (PPG) data using statistical explosion. Synthetic data is subsequently used to classify Coronary Artery Disease (CAD) using a two stage cascaded classifier. Proposed classifier along with synthetic data removes class bias and provides better accuracy compared to state of art. The proposed data generation and cascaded classifier is generic enough to be used to improve machine learning algorithm on any time series signal.