Cardiovascular diseases (CVD) including heart diseases, peripheral vascular disease and heart failure, account for one-third of deaths throughout the world. CVD risk factors include systolic blood pressure, total cholesterol, high-density lipoprotein cholesterol, and diabetic status. Clinical trials have demonstrated that when modifiable risk factors are treated and corrected, the chances of CVD occurring can be reduced. This illustrates the importance of this book's elaborate coverage of cardiovascular physiology by the application of mathematical and computational methods.This book has literally transformed Cardiovascular Physiology into a STEM discipline, involving (i) quantitative formulations of heart anatomy and physiology, (ii) technologies for imaging the heart and blood vessels, (iii) coronary stenosis hemodynamics measure by means of fractional flow reserve and intervention by grafting and stenting, (iv) fluid mechanics and computational analysis of blood flow in the heart, aorta and coronary arteries, and (v) design of heart valves, percutaneous valve stents, and ventricular assist devices.So how is this mathematically and computationally configured landscape going to impact cardiology and even cardiac surgery? We are now entering a new era of mathematical formulations of anatomy and physiology, leading to technological formulations of medical and surgical procedures towards more precise medicine and surgery. This will entail reformatting of (i) the medical MD curriculum and courses, so as to educate and train a new generation of physicians who are conversant with medical technologies for applying into clinical care, as well as (ii) structuring of MD-PhD (Computational Medicine and Surgery) Program, to train competent medical and surgical specialists in precision medical care and patient-specific surgical care.This book provides a gateway for this new emerging scenario of (i) science and engineering based medical educational curriculum, and (ii) technologically oriented medical and surgical procedures. As such, this book can be usefully employed as a textbook for courses in (i) cardiovascular physiology in both the schools of engineering and medicine of universities, as well as (ii) cardiovascular engineering in biomedical engineering departments worldwide.

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

Mathematical and Computational Methods in Physiology discusses the importance of quantitative description of physiological phenomena and for quantitative comparison of experimental data. An article explains the homeostasis of the body with a focus on the controlling aspects. This section evaluates the concepts of modern physiology and biocybernetics. The canal-ocular reflex and the otolith-ocular reflex in man stimulates eye rotations compensatory for head angular and linear displacements. The book enumerates some modelling and simulation to observe the visual-vestibular interaction during angular and linear body acceleration. A section on the determination of cardiovascular control is given. The text reviews the mathematical models of the biological age of the rat. A numerical simulation of water transport in epithelial junctions is explained comprehensively. A chapter analyzing the computer simulation of drug-receptor interaction is presented. The book will provide useful information to zoologists, doctors, ophthalmologists, students and researchers in the field of medicine.

Biomedical Engineering Modeling of Pancreatic, Respiratory, and Renal Regulatory Systems, and their Medical Assessments addresses the need for biomedical engineering to provide physiological analysis of organ systems and their medical applications to help enable quantitative formulation of physiological systems and defining their functions and dysfunctions, leading to precision diagnostics of diabetes, lung diseases, and kidney failure, often in the form of non-dimensional indices. The book chapters also deal with treatment systems, namely automated insulin infusion systems, hemodialysis, and peritoneal dialysis. The book is formulated to solve many physiological, bioengineering, and medical problems. - Covers foundational concepts of the emerging fields of quantitative physiology and computational medicine, developing the biomedical engineering modeling of three important organ systems: pancreas, lungs, and kidneys - Provides readers with detailed understanding of novel biomedical engineering strategies in key areas, such as pancreatic system engineering, glucose-insulin regulatory system engineering, pulmonary system engineering, and renal system engineering - Provides in-depth technical coverage of computational modeling techniques and applied mathematics for these important physiological systems, including differential equations and the associated MATLAB datasets for all applied diagnostic and treatment examples

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.

This volume synthesizes theoretical and practical aspects of both the mathematical and life science viewpoints needed for modeling of the cardiovascular-respiratory system specifically and physiological systems generally. Theoretical points include model design, model complexity and validation in the light of available data, as well as control theory approaches to feedback delay and Kalman filter applications to parameter identification. State of the art approaches using parameter sensitivity are discussed for enhancing model identifiability through joint analysis of model structure and data. Practical examples illustrate model development at various levels of complexity based on given physiological information. The sensitivity-based approaches for examining model identifiability are illustrated by means of specific modeling examples. The themes presented address the current problem of patient-specific model adaptation in the clinical setting, where data is typically limited.