Control Applications for Biomedical Engineering Systems

Cover
Control Applications for
Biomedical Engineering
Systems
Copyright
Contributors
Foreword
Preface
	About the book
	Objectives of the book
	Organization of the book
	Book features
	Audience
	Acknowledgments
1
Neuro-fuzzy inverse optimal control incorporating a multistep predictor as applied to T1DM patients
	Introduction
	Related work
	Fundamentals
		Online discrete-time neural network
		Inverse optimal control
	The Uva/Padova T1DM simulator
	Neuro-fuzzy inverse optimal control using multistep prediction
	Simulation results
	Discussion
	Conclusions
	Acknowledgments
	References
2
Blood glucose regulation in patients with type 1 diabetes by means of output-feedback sliding mode control
	Introduction
		Motivation
		State of the art of the control algorithms for blood glucose regulation
		Dual-hormone strategy
		Contributions
		Chapter outline
	Mathematical model
	Methodology and control objectives
		Glycemic curve
	Food ingestion as input disturbances
	Bihormonal actuator
	FOSMC: Design and stability analysis
		Boundary layer for chattering alleviation
	Terminal sliding mode control: Design and stability analysis
		Continuous nonsingular terminal sliding mode control for chattering alleviation
	HOSM exact differentiators for output feedback
	Numerical examples
		Discontinuous FOSMC with estimate of sliding variable using exact differentiator
		Continuous FOSMC with estimate of sliding variable using exact differentiator and boundary layer
		Discontinuous nonsingular terminal sliding mode control
		Continuous nonsingular terminal sliding mode control
	Conclusions
	References
	Further readings
3
Impulsive MPC schemes for biomedical processes: Application to type 1 diabetes
	Introduction
	Dynamic systems with short-duration inputs
		Underlying discrete-time subsystem
		Extended equilibrium of the impulsive system
		Pulse input scheme
	MPC formulation for impulsive systems
	Case study: Type 1 diabetes mellitus
		An enhanced model for type 1 diabetes patients
			Glucose dynamics
			Insulin and digestion dynamics
		Affine state space model
			Equilibrium and controllability characterization of the model
		Identification
		Impulsive scheme
		State observation schemes
		Impulsive model predictive control (iZMPC)
			Results of the observer/control scheme
	Discussion
	Conclusions
	References
4
Robust control applications in biomedical engineering: Control of depth of hypnosis
	Introduction
	Measurement of depth of hypnosis
		Bispectral analysis
		Wavelet analysis
	Dynamic model of hypnosis
		PK model of propofol
		PD model of propofol
		PKPD model and its uncertainty
	Control of depth of hypnosis
		Linearization
		Nominal model
		Evaluation indices
		Robust PID control scheme
		Robust H control scheme
	Conclusion
	References
5
Robust control strategy for HBV treatment: Considering parametric and nonparametric uncertainties
	Introduction
	HBV mathematical model
	Robust controller design
	Lyapunov stability
	Numerical results
		Untreated HBV infection
		Treated HBV infection using the proposed robust strategy
			Desired scenarios
			Results of the first desired scenario
			Effect of reduction rate on desired treatment scenario
			Discussion and interpretation of the results (comparison of untreated and treated HBV using two scenarios)
		Limitation of the study
	Conclusion
		Future directions of research
	References
6
A closed loop robust control system for electrosurgical generators
	Introduction
	Working and design specifications of electrosurgical unit
	Mathematical modeling of electro surgical unit
	Controller formulation for electro surgical unit
	Results and discussion
	Conclusion
	References
7
Application of a T-S unknown input observer for studying sitting control for people living with spinal cord injury
	Introduction
	Modeling
		Model description
		Euler Lagrange formulation
		Remarks
			Unstable model
			Descriptor form justification
			Unmeasured premise variables
			Notations
	Stabilization
		Discrete-time and Takagi-Sugeno framework
		Design of the control law
	Observation
		Takagi-Sugeno formalism
		Design of the unknown input observer with uncertainties
		Proof of convergence
	Validation results
		Numerical simulations
			Simulation protocol
			Simulation results
		Experimental protocol
			Protocol
			Results
	Conclusions and future works
	References
8
Epidemic modeling and control of HIV/AIDS dynamics in populations under external interactions: A worldwide cha ...
	Introduction
	Related works
	The single society mathematical model
	Stability analysis
		Equilibrium points
		Stability analysis
	Equilibria and stability analysis under constant inputs
		Analysis of the case u1(t) = u1 = const, u2(t) = u3(t) = 0
			Computation of the equilibrium points for u10
			Stability analysis for u10
		Analysis of the case u1(t) = u3(t) = 0 and u2(t) = u2
			Equilibria computation for u20
			Stability analysis for the equilibrium xi1e(u2)
			Stability analysis for the equilibrium xi2ei(u2)
	The interactions between populations
		Equilibria under external interactions
			Equilibrium points and stability properties for the case of healthy population migration
			Equilibrium points and stability properties for the case of infected population migration
	The effects of migration parameters on the individuals evolutions
	Discussion of the results
	Conclusions and future developments
	References
9
Reinforcement learning-based control of drug dosing with applications to anesthesia and cancer therapy
	Introduction
		Motivation
		Literature review
			Drug-dosing control for anesthesia administration
			Drug-dosing control for cancer chemotherapy
			RL-based algorithms
	Control of BIS by accounting for MAP
		Problem formulation
		Learning an optimal policy
		Pharmacokinetic and pharmacodynamic patient model
		Closed-loop control of BIS and MAP using RL
		Details of the simulation
		Results and discussion
	Control of BIS by accounting for synergistic drug interaction
		Training the RL agent
		Simulated patient
		Results and discussion
	Control of cancer chemotherapy treatment
		Mathematical model of cancer chemotherapy
		RL-based optimal control for chemotherapic drug dosing
		Results and discussion
	Summary
	Acknowledgments
	References
10
Control strategies in general anesthesia administration
	Introduction
		Anesthesia delivery today
		Open-loop or closed-loop anesthesia?
		Classic feedback vs model-predictive control of anesthesia
	Case study: Model-predictive control of anesthesia with propofol and remifentanil
		Propofol
		Remifentanil
		Classic and physiologically based pharmacokinetic-pharmacodynamic models
		Model predictive control principles
		Simulation of surgical operations in different patients
	Ethical concerns and clinical outcomes of closed-loop controlled anesthesia
		Guarantee of the safety of new technology and management of timing and process for implementation
		Patient´s informed consent
		Training and credentialing physicians in new technology or technique
		Track and assessment of new technology outcomes
		Balancing responsibilities to patients and society
		Clinical impact and risks
	Conclusions
	References
11
Computational modeling of the control mechanisms involved in the respiratory system
	Introduction
	Control mechanisms in the respiratory system
		Gas exchange at the pulmonary and capillary levels
		Control of ventilation
	Computational modeling as a tool for diagnosis and therapy
	Computational models for different control mechanisms of the respiratory system
	Other research lines in computer modeling
	Conclusion
	Acknowledgments
	References
	Further reading
12
Intelligent decision support for lung ventilation
	Introduction
	General structure of CDSSs in medicine
		Type I. Advisory (open-loop) systems
		Type II. CDSSs for use as both advisory and closed-loop systems
	CDSSs for mechanical ventilation
	Design methodologies
	A model-based CDSS for mechanical ventilation
	Application of a model-based CDSS in differential lung ventilation
		Methods
			Description of the plant
		Equations of the plant
		Application of ILV
		An example of the application of the system
	Examples of CDSSs used in commercial ventilators
	An overview of a CDSS used in closed-loop control of mechanical ventilation
	Conclusion and future directions
	References
13
Customized modeling and optimal control of superovulation stage in in vitro fertilization (IVF) treatment
	Introduction
	Modeling of in vitro fertilization
		Data organization and moment calculation
		Model equations
		FSD evaluation
		Follicle number prediction algorithm
		Model validation
		Results from parameter estimation
	Optimal control for customized optimal dosage determination
		Mathematical formulation
		Solution by maximum principle
		Results from optimal control
	Overall approach for customized medicine
		Clinical trial using the software
	Summary and future work
	References
14
Models based on cellular automata for the analysis of biomedical systems
	Introduction
	Basic concepts of cellular automata
	Historical review
	Applications of cellular automata
		Epidemiology
		Oncology
		Heart electrical conduction system
	Software techniques
	Conclusions
	Acknowledgments
	References
Index
	A
	B
	C
	D
	E
	F
	G
	H
	I
	J
	K
	L
	M
	N
	O
	P
	Q
	R
	S
	T
	U
	V
	W
	Z
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