Nonlinear Dynamic Modeling Of Physiological Systems

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.

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.

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.

This book provides a compilation of mathematical-computational tools that are used to analyze experimental data. The techniques presented are those that have been most widely and successfully applied to the analysis of physiological systems, and address issues such as randomness, determinism, dimension, and nonlinearity. In addition to bringing together the most useful methods, sufficient mathematical background is provided to enable non-specialists to understand and apply the computational techniques. Thus, the material will be useful to life-science investigators on several levels, from physiologists to bioengineer.Initial chapters present background material on dynamic systems, statistics, and linear system analysis. Each computational technique is demonstrated with examples drawn from physiology, and several chapters present case studies from oculomotor control, neuroscience, cardiology, psychology, and epidemiology. Throughout the text, historical notes give a sense of the development of the field and provide a perspective on how the techniques were developed and where they might lead. The overall approach is based largely on the analysis of trajectories in the state space, with emphasis on time-delay reconstruction of state-space trajectories. The goal of the book is to enable readers to apply these methods to their own research.

This volume is the second in a series of publications sponsored by the Biomedical Simulations Resource (BMSR) at the University of Southern California that report on recent research developments in the area of physiological systems modeling and anal ysis of physiological signals. As in the first volume of this series, the work reported herein is concerned with the development of advanced 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 used by most investigators in this area, which is confined primarily in the linear domain. As the im portance of nonlinearities in understanding the complex mechanisms of physiological function is increasingly recognized, the need for effective and practical methodolo gies that address the issue of nonlinear dynamics in life sciences becomes more and more pressing. The publication of these volumes and the workshops, organized by the BMSR on the same subject, are two key activities in our efforts to promote and intensify research in this area, foster interaction and collaboration among interested investigators, and disseminate recent results throughout the biomedical community.