Automated Drug Delivery in Anesthesia

Contents
List of Contributors
About the Editor
1 Introduction
	1.1 Introduction
2 An overview of computer-guided total intravenous anesthesia and monitoring devices-drug infusion control strategies and analgesia assessment in clinical use and research
	2.1 Introduction
	2.2 Early history of anesthesia delivery control
	2.3 Principles of anesthesia regulation
	2.4 Overview of closed-loop control strategies
	2.5 Overview of current analgesia monitors
	2.6 Prototype ANSPEC-PRO: noninvasive pain monitor
		2.6.1 Device methodology
		2.6.2 Measurements protocols and instructions
		2.6.3 Experimental results and statistical analysis
			Bioelectrical-impedance as function of frequency
			Variability within individual/s
	Acknowledgment
	References
3 A non-Newtonian impedance measurement experimental framework: modeling and control inside blood-like environments-fractional-order modeling and control of a targeted drug delivery prototype with impedance measurement capabilities
	3.1 Introduction
		3.1.1 Blood as a non-Newtonian fluid
		3.1.2 Bridging the gap towards targeted drug delivery for anesthesia
		3.1.3 The suitability of fractional calculus for non-Newtonian impedance measurement
	3.2 Experimental setup
		3.2.1 Circulatory system replica
		3.2.2 Submerged prototype
		3.2.3 Software development
			3.2.3.1 Server functionality and implementation
			3.2.3.2 Submersible's implementation
		3.2.4 Platform's versatility in education
	3.3 Experimental measurements
		3.3.1 Impedance measurement
		3.3.2 Manual mode experimental test results
	3.4 Modeling the submersible's dynamics
		3.4.1 Development of a model based on ship propulsion models
		3.4.2 Fractional-order modeling of the submersible
	3.5 Fractional-order control of the submersible
		3.5.1 Fractional-order tuning methodology
		3.5.2 Experimental validation of the control strategy
	Acknowledgments
	References
4 A multiscale pathway paradigm for pain characterization
	4.1 Introduction
	4.2 Physiological background
		4.2.1 Molecular basis
		4.2.2 Potassium channels activated pain signaling
	4.3 The role of fractional calculus
	4.4 Multiscale modeling approach
		4.4.1 From stimulus to nociception receptor model
		4.4.2 The nanoparticle electrochemical impedance model
		4.4.3 The signaling pathway model
		4.4.4 Pain perception model
		4.4.5 The multiscale lumped model
	4.5 Discussion
		4.5.1 Model evaluation
		4.5.2 Enabling characterizing analgesia levels
		4.5.3 On model specificity
		4.5.4 On drug trapping
	4.6 Conclusions
	Acknowledgments
	References
5 Models for control of intravenous anesthesia
	5.1 Introduction
		5.1.1 Scope
		5.1.2 Disposition
	5.2 Models from clinical pharmacology
		5.2.1 The purpose of modeling
		5.2.2 Pharmacokinetics
		5.2.3 Pharmacodynamics
			5.2.3.1 Effect dynamics
			5.2.3.2 The Hill sigmoid
		5.2.4 The PKPD model structure
		5.2.5 Pharmacodynamic interaction
		5.2.6 Parameter identification
	5.3 Models for control
		5.3.1 The purpose of modeling
		5.3.2 Clinical data
			5.3.2.1 Data quality
			5.3.2.2 Identifiability
		5.3.3 Models for closed-loop anesthesia
			5.3.3.1 Models from clinical pharmacology
			5.3.3.2 Models from clinical pharmacology with identified nonlinearity
			5.3.3.3 Population average PK with identified PD model
			5.3.3.4 First-order models
			5.3.3.5 Application-specific reduced-order model structures
			5.3.3.6 Online identification and adaptive methods
		5.3.4 Patient variability
			5.3.4.1 Limitations due to uncertainty
			5.3.4.2 Reducing variability
		5.3.5 Addressing the PD nonlinearity
		5.3.6 Equipment and disturbance models
	Acknowledgment
	References
6 Modeling and control of neuromuscular blockade level in general anesthesia
	6.1 Introduction
	6.2 Drug effect models
	6.3 Parameter identification
	6.4 Control of the NMB level
		6.4.1 Open-loop methods
		6.4.2 Closed-loop scheme based on total mass control
	6.5 GALENO-Integrated design system for monitoring, digital processing, and control in anesthesia
	6.6 Conclusions
	Acknowledgment
	6.A Realistic database P of patients Pi = (αi, γi), i=1, ..., 50. The parameters αi and γi were obtained by the prediction error method
	References
7 Computer-guided control of the complete anesthesia paradigm
	7.1 Introduction
	7.2 Clinical context
		7.2.1 Pain measurement during consciousness
		7.2.2 Pain measurement during unconsciousness (e.g., general anesthesia)
		7.2.3 Commercial devices
		7.2.4 Challenges to be tackled towards a complete anesthesia control
	7.3 Closed-loop control of the full anesthesia paradigm
	7.4 Preliminary results and discussion
		7.4.1 On models
		7.4.2 On control algorithms
		7.4.3 On stability and safety
		7.4.4 On units and model parameter values
		7.4.5 On additional signals
		7.4.6 On integrated cyber-medical assisting devices
	7.5 Conclusions and perspectives
	References
8 Optimization-based design of closed-loop control of anesthesia
	8.1 Introduction
	8.2 Problem formulation
		8.2.1 General control scheme
		8.2.2 Control specifications
		8.2.3 Performance indices
	8.3 State of the art
	8.4 PKPD model
		8.4.1 Model for propofol administration
		8.4.2 Model for propofol and remifentanil coadministration
	8.5 Optimization-based approach
	8.6 PID control for propofol administration
	8.7 Model-based control for propofol administration
	8.8 Event-based control for propofol administration
	8.9 Control for propofol and remifentanil coadministration
	8.10 Simulation results
	8.11 Discussion
	8.12 Conclusions
	References
9 Integrative cybermedical systems for computer-based drug delivery
	9.1 Introduction
	9.2 Robust optimal control of tumor growth under angiogenic inhibition
		9.2.1 Minimal model of angiogenic inhibiton
		9.2.2 Impulsive control using direct multiple shooting
		9.2.3 Continuous time RFPT method
	9.3 Linear parameter varying method in biorelated controller design
		9.3.1 The linear parameter varying framework
		9.3.2 qLPV model development
			9.3.2.1 Model-A
			9.3.2.2 Model-B
		9.3.3 Controller design
			9.3.3.1 Tensor product model transformation-based control
			9.3.3.2 Linear matrix inequality-based optimization
		9.3.4 Extended Kalman filter design
		9.3.5 Final control structure
		9.3.6 Results
	9.4 Tumor modeling and control
		9.4.1 The tumor growth model
		9.4.2 Parameter estimation
		9.4.3 Results
	9.5 Biostatistics
		9.5.1 Large sample investigations with public health relevance
			9.5.1.1 Hungarian myocardial infarction registry (HUMIR)
				The effect of gender on the prognosis of myocardial infarction
				Comparing traditional statistical and machine learning methods in mortality prediction
			9.5.1.2 The analysis of administrative/financial data in healthcare
		9.5.2 Biostatistical support of tumor growth modeling
	9.6 Outlook to general anesthesia
	Acknowledgment
	References
Index