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In this paper, we investigate spatio-temporal progression of Myocardial ischemia (MI) and propose a metric for quantifying ischemic manifestation using cardiac activation time. Spatio-temporal spread is separately analyzed and compared for two different types of ischemia, namely 'Demand' and 'Supply' ischemia. This is done for both surface progression, along the epicardial surface as well as volume progression, along the three sub-myocardial layers. Cardiac activation time or depolarization time is computed from cardiac surface potential using a combined spatio-temporal derivative function. Ischemic zones in the cardiac surface is computed using Principal Component Analysis (PCA) and eigen vector projection of the depolarization time. Spatio-temporal ischemic spread analysis revealed different ischemic initiation and manifestation pattern for Demand and Supply ischemia, both in surface and volume progression.Clinical relevance Activation time based ischemic progression metric can serve as an alternate marker for ischemia detection and can provide more intuitive understanding on the pathological progression, and in turn assist in developing methods to prevent cell damage due to ischemic progression.

In this paper, we present a computational fluid dynamic (CFD) analysis to capture the effect of physical stress and stenosis severity in coronary arteries leading to changes in coronary supply demand oxygen equilibrium. We propose a coupled Od-3d coronary vessel model to predict the variation in flow dynamics of coronary as well as arterial system, modeled using an in-silico model replicating cardiovascular hemodynamics. CFD simulation were solved using subject specific CT scan for coronary and arterial flow and pressure along with metrics related to arterial wall shear stress. Simulations were performed for three heart rates (75, 90 and 120 bpm) and four stenosis states representing different stages of Coronary artery disease (CAD) namely healthy, 50%, 75%, 90% blockage in left anterior descending artery (LAD). Myocardial oxygen supply demand equilibrium were calculated for each cases using hemodynamic surrogate markers naming Diastolic pressure time index for supply and Tension time index for demand. The proposed 0d-3d coupled hemodynamic model of the coronary vessel bed along with supply-demand equilibrium estimated for different stress level and stenosis severity may provide useful insights on the dynamics of CAD manifestation and predict vulnerable regions in coronary bed for early screening and interventions.

In this paper, we present a computational fluid dynamic (CFD) model of left atrium (LA) to analyze the manifestation and progression of atrial fibrillation (AF) in terms of hemodynamic metrics. We propose a coupled lumped-CFD (0d-3d) pipeline to model and predict the pulsatile flow and pressure fields of three-dimensional cardiac chamber under the influence of sinus rhythm, high frequency AF (HF-AF) and LA remodeled AF, considering the interactions between the heart and the arterial system through a separately modeled 0d lumped hemodynamic cardiac model. A novel rhythm generator is modeled to generate modulated cardiac chamber compliance and decoupled auricular and ventricular contraction rate to synthesize variation in sinus rhythm and subsequent AF generation. CFD simulation were solved using subject specific CT scan. Systemic and pulmonary flow and pressure along with metrics related to wall shear stress in LA were derived. Left ventricular (LV) hemodynamic parameters associated with global cardio vascular evaluation like ejection fraction, stroke volume, cardiac output, etc. were also generated for all the rhythmic disturbance under consideration. The proposed 0d-3d coupled hemodynamic model of the LA can provide useful insights on the dynamics of AF manifestation and predict vulnerable regions in the cardiac chambers as well as arterial vasculature for probable thrombogenic plaque formation that leads to stroke and infraction, leading to heart failure.

In this paper, we present a cardiac computational framework aimed at simulating the effects of ischemia on cardiac potentials and hemodynamics. Proposed cardiac model uses an image based pipeline for modeling and analysis of the ischemic condition in-silico. We compute epicardial potential as well as body surface potential (BSP) for acute ischemic conditions based on data from animal model while varying both local coronary supply and global metabolic demand. Single lead ECG equivalent signal processed from computed BSP is used to drive a lumped hemodynamic model and derive left ventricular dynamics. Computational framework combining 3d structural information from image data and integrating electrophysiology and hemodynamics functionality is aimed to evaluate additional cardiac markers along with conventional electrical markers visible during acute ischemia and give a broader understanding of ischemic manifestation leading to pathophysiological changes. Simulation of epicardial to bodysurface potential followed by estimation of hemodynamic parameters like ejection fraction, contractility, blood pressure, etc, would help to infer subtle changes detectable beyond conventional ST segment changes.

Synthesis of accurate, personalize photoplethysmogram (PPG) signal is important to interpret, analyze and predict cardiovascular disease progression. Generative models like Generative Adversarial Networks (GANs) can be used for signal synthesis, however, they are difficult to map to the underlying pathophysiological conditions. Hence, we propose a PPG synthesis strategy that has been designed using a cardiovascular system, modeled through the hemodynamic principle. The modeled architecture is composed of a two-chambered heart along with the systemic-pulmonic blood circulation and a baroreflex auto-regulation mechanism to control the arterial blood pressure. The comprehensive PPG signal is synthesized from the cardiac pressure-flow dynamics. In order to tune the modeled cardiac parameters with respect to a measured PPG data, a novel feature extraction strategy has been employed along with the particle swarm optimization heuristics. Our results demonstrate that the synthesized PPG is accurately followed the morphological changes of the ground truth (GT) signal with an RMSE of 0.003 occurring due to the Coronary Artery Disease (CAD) which is caused by an obstruction in the artery.

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.