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Tuesday, April 21, 2020 | History

4 edition of Modeling and parameter estimation in respiratory control found in the catalog.

Modeling and parameter estimation in respiratory control

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  • 10 Currently reading

Published by Plenum Press in New York .
Written in English

    Subjects:
  • Respiration -- Regulation -- Mathematical models -- Congresses.,
  • Parameter estimation -- Congresses.,
  • Models, Biological -- congresses.,
  • Respiration -- physiology -- congresses.

  • Edition Notes

    Statementedited by Michael C.K. Khoo.
    ContributionsKhoo, Michael C. K.
    Classifications
    LC ClassificationsQP123 .B56 1989
    The Physical Object
    Paginationx, 208 p. :
    Number of Pages208
    ID Numbers
    Open LibraryOL1855446M
    ISBN 100306435306
    LC Control Number90007301

    The respiratory rate in humans is measured by counting the number of breaths for one minute through counting how many times the chest rises. A fibre-optic breath rate sensor can be used for monitoring patients during a magnetic resonance imaging scan. Respiration rates may increase with fever, illness, or other medical conditions.. Inaccuracies in respiratory measurement have been reported in.


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Modeling and parameter estimation in respiratory control by Biomedical Simulations Resource Short Course on Modeling and Parameter Estimation in Respiratory Control (4th 1989 Marina del Rey, Calif.) Download PDF EPUB FB2

Respiratory control represents one area in which this kind of cross-pollination has proven particularly fruitful. While earlier modeling ef­ forts were directed primarily at the chemical control of ventilation, more recent studies have extended the scope of modeling to include the neural and mechanical aspects pertinent to respiratory control.

Modeling and parameter estimation in respiratory control. New York: Plenum Press, © (OCoLC) Online version: Biomedical Simulations Resource Short Course on Modeling and Parameter Estimation in Respiratory Control (4th: Marina del Rey, Calif.).

Modeling and parameter estimation in respiratory control. New York: Plenum. ISBN: OCLC Number: Description: 1 online resource ( pages) Contents: I: Modeling of Respiratory Control During Exercise --Why and How One Models Exercise on a Computer (A Tutorial) --Analysis of the Exercise Hyperpnea Using Dynamic Work-Rate Forcing --Optimal Regulation of Ventilation During Exercise.

He has published widely in the field of cardiorespiratory and sleep research, and is the editor of two books: Bioengineering Approaches to Pulmonary Physiology and Medicine (Plenum, ) and Modeling and Parameter Estimation in Respiratory Control (Plenum, ), in addition to over 85 journal articles, book chapters, and conference papers.

He has published widely in the field of cardiorespiratory and sleep research, and is the editor of two books: Bioengineering Approaches to Pulmonary Physiology and Medicine (Plenum, ) and Modeling and Parameter Estimation in Respiratory Control (Plenum, ), in addition to over 85 journal articles, book chapters, and conference by: He has published widely in the field of cardiorespiratory and sleep research, and is the editor of two books: Bioengineering Approaches to Pulmonary Physiology and Medicine (Plenum, ) and Modeling and Parameter Estimation in Respiratory Control (Plenum, ), in addition to over 85 journal articles, book chapters, and conference papers/5(13).

Parameter estimation is a traditional modelling task. Generally, a model contains input variable(s), parameter(s), and output variable(s).

It also describes, in a mathematical form for our purposes, how the output is related to the input and the parameters. 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.

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. INTRODUCTION.

The evaluation of respiratory system with regards to electrical analogue can resolve many problems in behavior of lungs and chest that presented by Otis et al. and others (4–14).Many authors assessed the resistive, inertial and elastic characteristics of respiratory system comparable to RLC electrical model (series format of resistance-inductance-capacitance) or to a resistance Author: Pardis Ghafarian, Hamidreza Jamaati, Seyed Mohammadreza Hashemian.

A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. This E-Book is derived from the Frontiers in Computational Physiology and Medicine Research Topic entitled “Engineering Approaches to Study Cardiovascular Physiology: Modeling, Estimation and Signal Processing.” Its goal is to bring established experts together in order to present a sample of state-of-the-art studies in cardiovascular Cited by: 1.

Chapter Title: Dynamic models and parameter estimation: The hypoxic ventilatory response Book Title: Modeling and Parameter Estimation in Respiratory Control Author List: Ward, DS Edited By: ed M Khoo Published By: Plenum Press in New York.

Chapter Title: Asymmetry in the ventilatory response to a bout of hypoxia in human beings Book. Cardiovascular and Respiratory Systems: Modeling, Analysis, and Control uses a principle-based modeling approach and analysis of feedback control regulation to elucidate the physiological relationships.

Models are arranged around specific questions or conditions, such as exercise or sleep transition, and are generally based on physiological. 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. book [9] is an important landmark), so that a tremendous variety of models have now been formulated, mathematically analyzed and applied to infec-tious diseases.

Reviews of the literature [12, 24, 44, 48, 50, 78, 82, 83, ] show the rapid growth of epidemiology modeling. Recent models have involved aspects such as passive immunity, gradual File Size: KB. The large numbers of parameters and the difficulties of their laboratory estimation, however, made these models impractical for correlating respiratory control loop gain with observed breathing.

In this paper we give a survey on modeling efforts concerning the CVRS. The material we discuss is organized in accordance with modeling goals and stresses control and transport issues. We also address basic modeling approaches and discuss some of the challenges for mathematical modeling methodologies in the context of parameter estimation and Cited by: 6.

Problems in Parameter Estimation: Identifiability and Input Design Closed-Loop Identificationofthe Respiratory Control System Bibliography Problems Physiological control modeling alsohasbeen critical, directly or indirectly, for the development of many improved medical diagnostic File Size: 8MB.

Approaches to Pulmonary Physiology and Medicine (Plenum, ) and Modeling and Parameter Estimation in Respiratory Control (Plenum, ), in addition to over 85 journal articles, book chapters, and conference papers.

I have been using this text for a class in Physiological Control Systems, but have been largely disappointed. Identification and System Parameter Estimation covers the proceedings of the Sixth International Federation of Automatic Control (IFAC) Symposium.

The book also serves as a tribute to Dr. Naum S. Edition: 1. Rideout gives a careful introduction to biomodeling in general, in which he explains why even computer modeling requires some experimentation on animals. The section on tools of modeling includes physiological and biochemical equations, instrumentation sources for values, systems of units (he uses mostly SI), and allometric scaling.

Editorial: eng ineering approaches to study cardiovascular physiology: modeling, estimation, and signal processing The Harvard community has made this article openly available.

Please share how this access benefits you. Your story matters Citation Chen, Zhe, and Riccardo Barbieri. Editorial: engineeringCited by: 1. 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.

Modeling cerebral blood flow control during posture change from Sitting to standing, J Cardiovasc Eng 4(1):Olufsen MS, Nadim A. On deriving lumped models for blood flow and pressure in the systemic arteries, Math Biosci Eng 1(1):The papers explore modeling and control of biotechnological processes such as fermentation and biological wastewater treatment.

This book consists of 37 chapters divided into 11 sections and begins with a discussion on the control of fermentation processes; modeling of biotechnical processes; and application of measurement and estimation. Modeling And Parameter Estimation In Respiratory Control really liked it avg rating — 1 rating — published Want to Read saving 4/5.

Many recently improved medical diagnostic techniques and therapeutic innovations have resulted from physiological systems modeling. This comprehensive book will help undergraduate and graduate students and biomedical scientists to gain a better understanding of how the principles of control theory, systems analysis, and model identification are used in physiological regulation/5(3).

The Modeling Cycle: Dealing with Different Errors 6 Parameter Estimation Introduction: Forward and Inverse Problems General Theory Introduction The Minimization Problem Sensitivity Analysis Correlated Parameters Damping Essential Directions This book presents a technique for applying optimal control theory and parameter estimation to the analysis of regulation processes in the cardiovascular and respiratory systems.

The book is organized into five chapters and three appendices. Each chapter begins with an introduction to the key physiological concepts related to the topics in the chapter. Next, a mathematical model representing Author: David Angeli.

Editorial: engineering approaches to study cardiovascular physiology: modeling, estimation, and signal processing. Zhe Chen. 1,2 * and Riccardo Barbieri. 1,2. Neuroscience Statistics Research Lab, Department of Anesthesia Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.

Kalman Filtering to Parameter Estimation.- 5 Inte-grative and Reductionist Approaches to Modeling of Control of Breathing.- 6 Parameter Identifica-tion in a Respiratory Control System Model with Delay.- 7 Experimental Studies of Respiration and Apnea.- 8 Model Validation and Control Issues in the Respiratory System.- 9 Experimental Studies.

ECE Modeling and Control of Physiological Systems. Spring Credits 3. MW am am. Science and Technology I, Rm Instructor: Siddhartha Sikdar, PhD Assistant Professor Department of Electrical and Computer Engineering Volgenau School of IT&E. It presents the electrical analog of the respiratory mechanics model and the mechanical analog of the muscle model.

A distributed‐parameter model can be viewed as a network of many infinitesimally small lumped‐parameter submodels. The superposition principle is frequently used as a test to determine whether a given system is linear. Comment from the Stata technical group.

This text unifies the principles behind latent variable modeling, which includes multilevel, longitudinal, and structural equation models, as well as generalized mixed models, random coefficient models, item response models, factor models, panel models, repeated-measures models, latent-class models, and frailty models.

New Mplus Book. Regression And Mediation Analysis Using Mplus. Bengt O. Muthén, Linda K. Muthén, Tihomir Asparouhov. The inspiration to write this book came from many years of teaching about Mplus and answering questions on Mplus Discussion and Mplus support.

Parameter identification problems for delay systems motivated by examples from aerody- namics and biochemistry are considered. The problem of estimation of the delays is included. Using approximati Cited by: KEY TOPICS: Explores techniques used to construct mathematical models of systems based on knowledge from physics, chemistry, biology, etc.

(e.g., techniques with so called bond-graphs, as well those which use computer algebra for the modeling work). Explains system identification techniques used to infer knowledge about the behavior of dynamic. @inproceedings{GrMaAbRo19, author={Jan Graßhoff and Georg Männel and Hossam S. Abbas and Philipp Rostalski}, booktitle={ IEEE Conference on Decision and Control (CDC)}, title={Model Predictive Control using Efficient Gaussian Processes for.

Insights into Using the GLIMMIX Procedure to Model Categorical Outcomes with Random Effects Kathleen Kiernan, SAS Institute Inc. ABSTRACT Modeling categorical outcomes with random effects is a major use of the GLIMMIX procedure.

Building, evaluating, and using the resulting model for inference, prediction, or both requires many considerations. Ralph Smith is a Distinguished University Professor of Mathematics in the North Carolina State University Department of Mathematics, Associate Director of the Center for Research in Scientific Computing (CRSC), and a member of the Operations Research Program.

Description Of Research: Mathematical modeling of smart materials, numerical analysis and numerical methods for physical systems.List of Publications and Manuscripts Presented in this Thesis McBryde, E. S., Pettitt, A.N., McElwain, D. L.S., c.A stochastic mathematical model of methicillin resistant Staphylococcus aureus trans- mission in an intensive care unit: Predicting the impact of interven-File Size: 1MB.Batzel, JJ Parameter Estimation for Cardio-Respiratory and Metabolic Control Models.

Third Summer School on Mathematics in Biomedical Engineering: Model Validation in Biomedical Engineering and Biophysics ; July ,; Warwick, Great Britian.