Heart disease remains the No. 1 leading cause of death in U.S. and in the world. To improve cardiac care services, there is an urgent need of developing early diagnosis of heart diseases and optimal intervention strategies. As such, it calls upon a better understanding of the pathology of heart diseases. Computer simulation and modeling have been widely applied to overcome many practical and ethical limitations in in-vivo, ex-vivo, and whole-animal experiments. Computer experiments provide physiologists and cardiologists an indispensable tool to characterize, model and analyze cardiac function both in healthy and in diseased heart. Most importantly, simulation modeling empowers the analysis of causal relationships of cardiac dysfunction from ion channels to the whole heart, which physical experiments alone cannot achieve. Growing evidences show that aberrant glycosylation have dramatic influence on cardiac and neuronal function. Variable but modest reduction in glycosylation among congenital disorders of glycosylation (CDG) subtypes has multi-system effects leading to a high infant mortality rate. In addition, CDG in all young patients tends to cause Atrial Fibrillation (AF), i.e., the most common sustained cardiac arrhythmia. The mortality rate from AF has been increasing in the past two decades. Due to the increasing healthcare burden of AF, studying the AF mechanisms and developing optimal ablation strategies are now urgently needed. Very little is known about how glycosylation modulates cardiac electrical signaling. It is also a significant challenge to experimentally connect the changes at one organizational level (e.g., electrical conduction among cardiac tissue) to measured changes at another organizational level (e.g., ion channels). In this study, we integrate the data from in vitro experiments with in-silico models to simulate the effects of reduced glycosylation on the gating kinetics of cardiac ion channel.

The rapid development in sensing and information technology facilitate the effective modeling, monitoring, and control of complex systems. Advanced sensing and imaging have brought a data-rich environment and provided unprecedented opportunities to investigate system dynamics and further optimize decision making for smart health and advanced manufacturing. However, the sensing data is generally with high-dimensionality and complex structures. Realizing full potentials of sensing data depends to a great extent on novel analytical methods and tools with effective information-processing capabilities.The objective of this dissertation is to advance the knowledge on sensor-based system monitoring, modeling, and optimization by developing innovative physical-statisticalmethods for smart health and advanced manufacturing. This research will enable and assist in 1) handling high-dimensional spatiotemporal data; 2) extracting pertinent information about system dynamics; 3) optimizing decision making under uncertainty. My research accomplishments include:Energy-efficient mobile ECG sensing: In Chapter 2, an energy-efficient framework is proposed for mobile ECG sensing through the constrained Markov decision process, where the sensing policy is optimized by maximizing the detection accuracy of cardiac events under the constraint of energy budget.Physical-statistical modeling of space-time complex systems: In Chapter 3, a physics-driven spatiotemporal regularization method is developed for high-dimensional predictive modeling. This model not only captures the physics-based interrelationship between time-varying explanatory and response variables that are distributed in the space, but also addresses the spatial and temporal regularizations to improve the prediction performance.Spatiotemporal inverse ECG modeling: In Chapter 4, a robust inverse ECG model with spatiotemporal regularization is developed to reconstruct the heart-surface electric potentials from body-surface sensor measurements. Furthermore, a wavelet-clustering method is proposed to investigate the cardiac pathological behaviors from the reconstructed heart signals and characterize the location and extent of myocardial infarctions on the heart surface.Multifractal analysis for nonlinear pattern characterization: In Chapter 5, a multifractal approach is developed to quantify the nonlinear and nonhomogeneous patterns in image profiles for defects identification and characterization in additive manufacturing (AM).Sequential optimization and real-time control of additive manufacturing processes: In Chapter 6, a sequential decision-making framework through the Markov decision process is proposed to optimize the AM build quality layer-by-layer. This framework enables on-the-fly assessment of AM build quality and real-time defect mitigation.

This book reviews the development of physics-based modeling and sensor-based data fusion for optimizing medical decision making in connection with spatiotemporal cardiovascular disease processes. To improve cardiac care services and patients’ quality of life, it is very important to detect heart diseases early and optimize medical decision making. This book introduces recent research advances in machine learning, physics-based modeling, and simulation optimization to fully exploit medical data and promote the data-driven and simulation-guided diagnosis and treatment of heart disease. Specifically, it focuses on three major topics: computer modeling of cardiovascular systems, physiological signal processing for disease diagnostics and prognostics, and simulation optimization in medical decision making. It provides a comprehensive overview of recent advances in personalized cardiac modeling by integrating physics-based knowledge of the cardiovascular system with machine learning and multi-source medical data. It also discusses the state-of-the-art in electrocardiogram (ECG) signal processing for the identification of disease-altered cardiac dynamics. Lastly, it introduces readers to the early steps of optimal decision making based on the integration of sensor-based learning and simulation optimization in the context of cardiac surgeries. This book will be of interest to researchers and scholars in the fields of biomedical engineering, systems engineering and operations research, as well as professionals working in the medical sciences.

Addresses the mathematical and numerical modelling of the human cardiovascular system, from patient data to clinical applications.

The book comprises contributions by some of the most respected scientists in the field of mathematical modeling and numerical simulation of the human cardiocirculatory system. It covers a wide range of topics, from the assimilation of clinical data to the development of mathematical and computational models, including with parameters, as well as their efficient numerical solution, and both in-vivo and in-vitro validation. It also considers applications of relevant clinical interest. This book is intended for graduate students and researchers in the field of bioengineering, applied mathematics, computer, computational and data science, and medicine wishing to become involved in the highly fascinating task of modeling the cardiovascular system.

Mathematical models and numerical simulations can aid the understanding of physiological and pathological processes. This book offers a mathematically sound and up-to-date foundation to the training of researchers and serves as a useful reference for the development of mathematical models and numerical simulation codes.