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E-raamat: Intelligent Systems Modeling and Decision Support in Bioengineering

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  • Formaat: 356 pages
  • Ilmumisaeg: 31-Jan-2006
  • Kirjastus: Artech House Publishers
  • ISBN-13: 9781580539999
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Intelligent Systems Modeling and Decision Support in BioengineeringSuurem pilt
  • Formaat: 356 pages
  • Ilmumisaeg: 31-Jan-2006
  • Kirjastus: Artech House Publishers
  • ISBN-13: 9781580539999
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Intelligent systems try to achieve, through the use of computers, what we associate with intelligence - flexible, learning and adaptive activity like we find in the human brain. For the first time, this groundbreaking resource provides a detailed understanding of the analysis, design, and application of new intelligent systems in the biomedical industry. The book covers the three major areas of application in biomedicine, including the modeling and control in human anaesthesia, decision support for critically ill patients in intensive care units, and modeling of humans who are subjected to physiological stress. The culmination of more than 18 years of research, this cutting-edge reference offers practical modeling and control guidance by presenting a combination of simulations, real-time experiments, and actual patient data.
Preface xi
Introduction
1(8)
Fuzzy Logic
2(2)
Artificial Neural Networks
4(1)
Evolutionary Computing
5(1)
Book Organization
5(4)
References
7(2)
A Survey on the Utilization of Fuzzy Logic--Based Technologies in Medicine and Healthcare
9(36)
Introduction
9(2)
Fuzzy Technology in the Identified Fields
11(15)
Conservative Disciplines
11(4)
Invasive Medicine
15(1)
Regionally Defined Medical Disciplines
16(3)
Neuromedicine
19(1)
Image and Signal Processing
20(2)
Laboratory
22(1)
Basic Science
22(2)
Nursing
24(1)
Public Health and Health Policy and Management
25(1)
Eastern Medicine
25(1)
Bibliographic Papers and Books
25(1)
Discussion
26(19)
References
30(15)
Feedback Control of Muscle Relaxation and Unconsciousness Using Predictive Control
45(40)
Introduction
45(2)
The Muscle Relaxation Process and Its Physiological Background
47(1)
Mathematical Modeling of a Muscle Relaxant---Atracurium
48(3)
Pharmacokinetics
48(1)
Pharmacodynamics
49(2)
SISO Adaptive Generalized Predictive Control in Theater
51(17)
Theory of SISO GPC
51(4)
Simulation Results
55(1)
Performance of SISO GPC in the Operating Theater During Surgery
56(12)
Review of the Multivariable Anesthesia Control System
68(13)
Identification of the Multivariable Anesthesia Model
69(3)
Extension of GPC to the Multivariable Case
72(2)
Simulation Results
74(2)
Real-Time Experiments
76(5)
Conclusions
81(4)
References
82(3)
A New Generic Approach to Model Reduction for Complex Physiologically Based Drug Models
85(44)
Introduction
85(4)
Drug Movement Through Membranes
88(1)
Blood Flow
88(1)
Models Associated with Capillary-Tissue Exchange
89(8)
Krogh's Cylinder Model
89(1)
Crone and Renkin's Idea
89(2)
Kety's Model
91(1)
The Concept of the In Vivo Approach to Membrane Transport
92(3)
Discussions
95(2)
The Mapleson-Higgins Flow-Limited Model for Fentanyl
97(4)
Fentanyl Pharmacokinetics
97(1)
Structure of the Model
97(1)
Quantification of the Model
97(4)
A Dynamic Representation of the Mapleson-Higgins Model for Fentanyl
101(4)
Individual Organs' Model Fitting
102(3)
Simulation of the Overall System
105(1)
Model Parameters' Sensitivity Study
105(6)
Model Parameters' Sensitivity with Respect to Body-Weight Variations
106(2)
Model Parameters' Sensitivity with Respect to Cardiac Output Variations
108(1)
Model Parameters' Sensitivity with Respect to Simultaneous Variations of Cardiac Output and Body Weight
109(2)
Model Fitting for Drug Concentrations in Tissues and Blood Pools
111(1)
Concentrations in Tissues
111(1)
Concentrations in Blood Pools
112(1)
Model Reduction Analysis
112(3)
Wada's Model
115(2)
Model-Based Predictive Control Design Using the New Dynamic Model
117(7)
Nonlinear Generalized Predictive Control
120(4)
Simulation Results
124(1)
Conclusions
124(5)
References
126(3)
A Hybrid System's Approach to Modeling and Control of Unconsciousness
129(44)
Introduction
129(2)
The Mean Arterial Pressure Physiological Model
131(2)
Constrained Model-Based Predictive Control Using the Quadratic Programming Approach
133(2)
A Review of Faults Associated with the Anesthesia Control System
135(1)
Sensor Failures
136(1)
Actuator Failures
136(1)
Algorithmic Failures
136(1)
The Hierarchical Supervisory Level: Structure and Algorithm
136(6)
Detection
136(2)
Isolation (Diagnosis)
138(1)
Accommodation (Compensation)
139(3)
Results of Simulation Experiments
142(7)
Identification of Linear and Fuzzy Logic-Based Anesthesia Models
142(3)
Closed-Loop Control Experiments
145(4)
Real-Time Closed-Loop Control Experiments in the Operating Theater
149(16)
Clinical Preparation of Patients Before Surgery
153(2)
Results and Discussions
155(10)
Analyses of the Data
165(4)
Conclusions
169(4)
References
171(2)
Neural-Fuzzy Modeling and Feedback Control in Anesthesia
173(42)
Introduction
173(2)
Alternative Assessment Tools of DOA
175(1)
Mid-Latency Auditory Evoked Potential
176(5)
Evoked Potentials
176(1)
Data Acquisition and Feature Extraction
177(4)
Development of a New Fuzzy Relational Classifier for DOA
181(5)
Surgical Data
181(1)
The Classification Algorithm
182(4)
Development of a Patient Model
186(8)
Pharmacokinetic Models
187(1)
Pharmacodynamic Models
187(4)
Surgical Stimuli Model
191(3)
Exploitation of the Patient Model for Closed-Loop Drug Administration
194(9)
Open-Loop Simulation Results Using the Patient Model
194(2)
Closed-Loop Control Structure
196(3)
SISO Fuzzy Proportional Integral Controller for Propofol
199(1)
Simulation Results
200(3)
Discussions and Conclusions
203(12)
References
205(2)
Appendix 6A: Fuzzy Clustering---The Fuzzy C-Means Algorithm
207(3)
Appendix 6B: Genetic Algorithms
210(2)
Appendix 6C: The ANFIS Architecture
212(3)
Intelligent Modeling and Decision Support in General Intensive Care Unit
215(44)
Introduction
215(3)
Description of the Original SOPAVENT Model
218(10)
Oxygen Transport Equations
220(1)
The Oxygen Gas Dissociation Function (GDF) and Its Inverse
221(1)
Carbon Dioxide Transport Equations
222(1)
The Carbon Dioxide Gas Dissociation Function and Its Inverse
223(3)
Model Implementation and Exploitation
226(2)
Noninvasive Estimation of Shunt
228(3)
Method
228(1)
Estimation of Shunt Using the Respiratory Index
229(1)
Results
230(1)
The Sheffield Intelligent Ventilator Advisor (SIVA): Design Concepts
231(3)
Design of the Knowledge-Based Levels
234(12)
Top-Level FiO2/PEEP Subunit
234(1)
Top-Level Pinsp/Vrate Subunit
235(1)
Parameters Assigned to the Input Membership Functions
236(4)
Derivation of the Initial Rule Base
240(1)
Validation of the Initial Rule Bases
241(2)
Further Tuning of the Initial Rule Bases
243(2)
Assessment of the Final Fuzzy Rule Bases by an Independent Clinician
245(1)
Integration of SOPAVENT with the Knowledge-Based Levels
246(4)
Control of FiO2
247(1)
Control of Pinsp and Vrate
248(1)
Setting the PaO2 and PaCO2 Targets
249(1)
Implementation and Validation of SIVA
250(3)
Conclusions
253(6)
References
256(3)
Hybrid Modeling of Healthy Subjects Experiencing Physical Workload
259(34)
Introduction
259(3)
Experimental Setup
262(5)
The Logistics
262(4)
Experimental Design
266(1)
Modification of the Original Luczak/Raschke Physiological Model
267(24)
Direct Model Identification for Heart Rate and Blood Pressure Under Stress Conditions
268(5)
A New Gray-Box Physiological Closed-Loop Model Describing Stress
273(18)
Conclusions
291(2)
References
291(2)
Physiological Model Extension and Model Exploitation Via Real-Time Fuzzy Control
293(32)
Introduction
293(1)
A Model to Describe Thermoregulation
294(5)
Model Analysis
294(2)
Model Validation
296(3)
Representation of the Brain Centers
299(5)
The Cardiac Center
300(1)
The Vasomotor Center
300(2)
The Respiratory Center
302(1)
The Hypothalamus
302(2)
Modeling the Brain Via EEG Measurements
304(7)
Phase Locking
306(3)
Validation of the Overall Extended Closed-Loop Model
309(2)
A Generic Model
311(3)
Model Exploitation Via Feedback Control
314(6)
Control Structures
317(1)
Closed-Loop Control Simulation Results
318(2)
Conclusions
320(5)
References
322(3)
Conclusion
325(10)
Introduction
325(1)
Summary of the Book's Main Contributions
325(4)
Future Trends
329(6)
References
334(1)
About the Author 335(2)
Index 337


Mahdi Mahfouf is a professor of intelligent systems engineering in the Department of Automatic Control and Systems Engineering at The University of Sheffield. He earned both his M.Phil. and Ph.D. in control systems from that same university.
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