The study of nonlinearities in physiology has been hindered by the lack of effective ways to obtain nonlinear dynamic models from stimulus-response data in a practical context. A considerable body of knowledge has accumulated over the last thirty years in this area of research. This book summarizes that progress, and details the most recent methodologies that offer practical solutions to this daunting problem. Implementation and application are discussed, and examples are provided using both synthetic and actual experimental data. This essential study of nonlinearities in physiology apprises researchers and students of the latest findings and techniques in the field.
A practical approach to obtaining nonlinear dynamic models from stimulus-response dataNonlinear modeling of physiological systems from stimulus-response data is a long-standing problem that has substantial implications for many scientific fields and associated technologies. These disciplines include biomedical engineering, signal processing, neural networks, medical imaging, and robotics and automation. Addressing the needs of a broad spectrum of scientific and engineering researchers, this book presents practicable, yet mathematically rigorous methodologies for constructing dynamic models of physiological systems. Nonlinear Dynamic Modeling of Physiological Systems provides the most comprehensive treatment of the subject to date. Starting with the mathematical background upon which these methodologies are built, the book presents the methodologies that have been developed and used over the past thirty years. The text discusses implementation and computational issues and gives illustrative examples using both synthetic and experimental data. The author discusses the various modeling approaches-nonparametric, including the Volterra and Wiener models; parametric; modular; and connectionist-and clearly identifies their comparative advantages and disadvantages along with the key criteria that must guide successful practical application. Selected applications covered include neural and sensory systems, cardiovascular and renal systems, and endocrine and metabolic systems. This lucid and comprehensive text is a valuable reference and guide for the community of scientists and engineers who wish to develop and apply the skills of nonlinear modeling to physiological systems.
Significant advances have been made in the field since the previous classic texts were written. This text brings the available knowledge up to date. * Enables the reader to use a wide variety of nonlinear system identification techniques. * Offers a thorough treatment of the underlying theory. * Provides a MATLAB toolbox containing implementation of the latest identification methods together with an extensive set of problems using realistic data sets.
This volume is the third in a series entitled" Advanced Methods of Physiological System Modeling" and the fifth in a series of research volumes published by Plenum under the sponsorship of the Biomedical Simulations Resource (BMSR) at the Uni versity of Southern California in the context of dissemination activities supported by the Biomedical Research Technology Program of the National Center for Research Resources at the National Institutes of Health under Grant No. P41 RR-OI861. These volumes are edited by BMSR principal scientists and report on recent research de velopments in the area of physiological systems modeling, as well as on advanced methods for analysis of physiological signals and data. As in the previous two volumes of this series, the work reported herein is con cerned with the development of advanced modeling methodologies and their novel application to problems of biomedical interest, with emphasis on nonlinear aspects of physiological function. The term "advanced methodologies" is used to indicate that the scope of this work extends beyond the ordinary type of analysis, which is confined traditionally to the linear domain. As the importance of nonlinearities in understanding the complex mechanisms of physiological function is increasingly recognized, the need for effective and practical modeling methodologies that address the issue of nonlinear dynamics in life sciences becomes more and more pressing.
Introduces concepts from nonlinear dynamics using an almost exclusively biological setting for motivation, and includes examples of how these concepts are used in experimental investigations of biological and physiological systems. One novel feature of the book is the inclusion of classroom-tested computer exercises. This book will appeal to students and researchers working in the natural and physical sciences wanting to learn about physiological systems from a mathematical perspective.
Over the last century,medicine has come out of theblack bag and emerged as one of the most dynamic and advanced fields of development in science and technology. Today, biomedical engineering plays a critical role in patient diagnosis, care, and rehabilitation. As such, the field encompasses a wide range of disciplines, from biology and physiolog
A guide to common control principles and how they are used to characterize a variety of physiological mechanisms The second edition of Physiological Control Systems offers an updated and comprehensive resource that reviews the fundamental concepts of classical control theory and how engineering methodology can be applied to obtain a quantitative understanding of physiological systems. The revised text also contains more advanced topics that feature applications to physiology of nonlinear dynamics, parameter estimation methods, and adaptive estimation and control. The author—a noted expert in the field—includes a wealth of worked examples that illustrate key concepts and methodology and offers in-depth analyses of selected physiological control models that highlight the topics presented. The author discusses the most noteworthy developments in system identification, optimal control, and nonlinear dynamical analysis and targets recent bioengineering advances. Designed to be a practical resource, the text includes guided experiments with simulation models (using Simulink/Matlab). Physiological Control Systems focuses on common control principles that can be used to characterize a broad variety of physiological mechanisms. This revised resource: Offers new sections that explore identification of nonlinear and time-varying systems, and provide the background for understanding the link between continuous-time and discrete-time dynamic models Presents helpful, hands-on experimentation with computer simulation models Contains fully updated problems and exercises at the end of each chapter Written for biomedical engineering students and biomedical scientists, Physiological Control Systems, offers an updated edition of this key resource for understanding classical control theory and its application to physiological systems. It also contains contemporary topics and methodologies that shape bioengineering research today.
Block-oriented Nonlinear System Identification deals with an area of research that has been very active since the turn of the millennium. The book makes a pedagogical and cohesive presentation of the methods developed in that time. These include: iterative and over-parameterization techniques; stochastic and frequency approaches; support-vector-machine, subspace, and separable-least-squares methods; blind identification method; bounded-error method; and decoupling inputs approach. The identification methods are presented by authors who have either invented them or contributed significantly to their development. All the important issues e.g., input design, persistent excitation, and consistency analysis, are discussed. The practical relevance of block-oriented models is illustrated through biomedical/physiological system modelling. The book will be of major interest to all those who are concerned with nonlinear system identification whatever their activity areas. This is particularly the case for educators in electrical, mechanical, chemical and biomedical engineering and for practising engineers in process, aeronautic, aerospace, robotics and vehicles control. Block-oriented Nonlinear System Identification serves as a reference for active researchers, new comers, industrial and education practitioners and graduate students alike.
With cardiovascular diseases being one of the main causes of death in the world, quantitative modeling, assessment and monitoring of the cardiovascular control system plays a critical role in bringing important breakthroughs to cardiovascular care. Quantification of cardiovascular physiology and its control dynamics from physiological recordings and by use of mathematical models and algorithms has been proved to be of important value in understanding the causes of cardiovascular diseases and assisting the prognostic or diagnostic process. Nowadays, development of new recording technologies (e.g., electrophysiology, imaging, ultrasound, etc) has enabled us to improve and expand acquisition of a wide spectrum of physiological measures related to cardiovascular control. An emerging challenge is to process and interpret such increasing amount of information by using state-of-the-art approaches in systems modeling, estimation and control, and signal processing, which would lead to further insightful scientific findings. In particular, multi-disciplinary engineering-empowered approaches of studying cardiovascular systems would greatly deepen our understanding of cardiovascular functions (e.g., heart rate variability, baroreflex sensitivity) and autonomic control, as it would also improve the knowledge about heart pathology, cardiovascular rehabilitation and therapy. Meanwhile, developing cardiovascular biomedical devices or heart-machine interface for either clinical monitoring or rehabilitation purpose is of greater and greater interest for both scientific advancement and potential medical benefits. This Research Topic will bring together established experts whose areas of research cover a wide range of studies and applications. Contributions include but are not limited to state-of-the-art modeling methodologies, algorithmic development in signal processing and estimation, as well as applications in cardiovascular rehabilitation, and clinical monitoring. The Research Topic will consider both invited reviews and original research.
