A cardiovascular digital twin model can generate synthetic PPG signals with pathophysiological interpretability for healthy and atherosclerosis conditions, potentially aiding machine learning algorithm training.
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 like Photoplethysmogram (PPG), Electrocardiogram (ECG), Phonocardiogram (PCG), etc. are being used to improve accuracy of machine learning algorithm. Conventional synthetic data generation approach using mathematical formulations lack interpretability. Hence, aim of this paper is to generate synthetic PPG signal from a Digital twin platform replicating cardiovascular system. Such system can serve the dual purpose of replicating the physical system, so as to simulate specific `what if' scenarios as well as to generate large scale synthetic data with patho-physiological interpretability. Cardio-vascular Digital twin is modeled with a two chambered heart, haemodynamic equations and a baroreflex based pressure control mechanism to generate blood pressure and flow variations. Synthetic PPG signal is generated from the model for healthy and Atherosclerosis condition. Initial validation of the platform has been made on the basis of efficiency of the platform in clustering Coronary Artery Disease (CAD) and non CAD PPG data by extracting features from the synthetically generated PPG and comparing that with PPG obtained from Physionet data.
Mazumder et al. (2019) studied this question.