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CONTROL SYSTEM. Introduction Clinical Setting - Glucoregulatory system - Patients and Data Assessment Proc. - Glucose sensors - Glycemia control system Control System - Black-Box model - Grey-Box model - Controller Conclusions. Ph.D. Defense

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CONTROL SYSTEM

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Control system

CONTROL SYSTEM

Introduction

Clinical Setting

- Glucoregulatory

system

- Patients and Data

Assessment Proc.

- Glucose sensors

- Glycemia control

system

Control System

- Black-Box model

- Grey-Box model

- Controller

Conclusions

Ph.D. Defense

Tom Van Herpe

April 15, 2008


Patient model

Patient model ?

Introduction

Clinical Setting

- Glucoregulatory

system

- Patients and Data

Assessment Proc.

- Glucose sensors

- Glycemia control

system

Control System

- Black-Box model

- Grey-Box model

- Controller

Conclusions

Ph.D. Defense

Tom Van Herpe

April 15, 2008

A = B + (C * D)

C = E + F(B+G)

E = …

White-Box

Grey-Box

Black-Box


Black box model structure

Black-Box model structure

Introduction

Clinical Setting

- Glucoregulatory

system

- Patients and Data

Assessment Proc.

- Glucose sensors

- Glycemia control

system

Control System

- Black-Box model

- Grey-Box model

- Controller

Conclusions

Ph.D. Defense

Tom Van Herpe

April 15, 2008

Black-Box

Data are used for

1. Model structure

2. Model estimation


Black box model structure1

Black-Box model structure

Introduction

Clinical Setting

- Glucoregulatory

system

- Patients and Data

Assessment Proc.

- Glucose sensors

- Glycemia control

system

Control System

- Black-Box model

- Grey-Box model

- Controller

Conclusions

Ph.D. Defense

Tom Van Herpe

April 15, 2008


Initial and adaptive input output modelling

Initial and adaptive input-output modelling

Introduction

Clinical Setting

- Glucoregulatory

system

- Patients and Data

Assessment Proc.

- Glucose sensors

- Glycemia control

system

Control System

- Black-Box model

- Grey-Box model

- Controller

Conclusions

Ph.D. Defense

Tom Van Herpe

April 15, 2008


Black box model structure2

Black-Box model structure

Introduction

Clinical Setting

- Glucoregulatory

system

- Patients and Data

Assessment Proc.

- Glucose sensors

- Glycemia control

system

Control System

- Black-Box model

- Grey-Box model

- Controller

Conclusions

?!!!

Grey-Box

Ph.D. Defense

Tom Van Herpe

April 15, 2008

Data are used for

1. Model structure

2. Model estimation

Black-Box

Acceptable model prediction performance

BUT RESERVATIONS:

Not to be used for CONTROL purposes in clinical real-life due to underestimation of input (insulin) coefficients

==> CLOSED-LOOP DATA ~ “perfect control”


Intensive care unit minimal model icu mm

Intensive Care Unit - Minimal Model (ICU-MM)

Introduction

Clinical Setting

- Glucoregulatory

system

- Patients and Data

Assessment Proc.

- Glucose sensors

- Glycemia control

system

Control System

- Black-Box model

- Grey-Box model

- Controller

Conclusions

Ph.D. Defense

Tom Van Herpe

April 15, 2008

Model structure features:

  • Endogenous (I2) and Exogenous (FI) insulin

  • 2 input variables:

    Exogenous insulin (FI) + Carbohydrate calories (FG)

  • 7 patient parameters to be estimated


Intensive care unit minimal model icu mm1

Intensive Care Unit - Minimal Model (ICU-MM)

Introduction

Clinical Setting

- Glucoregulatory

system

- Patients and Data

Assessment Proc.

- Glucose sensors

- Glycemia control

system

Control System

- Black-Box model

- Grey-Box model

- Controller

Conclusions

glucose effectiveness (fractional clearance) (P1 < 0)

fractional rate of net remote insulin disappearance (P2 < 0)

meal glucose disturbance

plasma glucose

effect of insulin on net glucose disappearance

exogenous insulin

fractional rate of insulin dependent increase (P3 > 0)

plasma insulin

effect of endogenous insulin

Ph.D. Defense

Tom Van Herpe

April 15, 2008

Model structure based on:

  • Minimal model [Bergman et al., 1981]

  • Type I diabetes minimal model [Furler et al., 1985]


Adaptive icu mm

Adaptive ICU-MM

Introduction

Clinical Setting

- Glucoregulatory

system

- Patients and Data

Assessment Proc.

- Glucose sensors

- Glycemia control

system

Control System

- Black-Box model

- Grey-Box model

- Controller

Conclusions

Ph.D. Defense

Tom Van Herpe

April 15, 2008


Patient case study patient no 10

Patient case study (patient no. 10)

Introduction

Clinical Setting

- Glucoregulatory

system

- Patients and Data

Assessment Proc.

- Glucose sensors

- Glycemia control

system

Control System

- Black-Box model

- Grey-Box model

- Controller

Conclusions

Ph.D. Defense

Tom Van Herpe

April 15, 2008

P = 4 hrs

P = 1 hr


Model evaluation

Model evaluation

Introduction

Clinical Setting

- Glucoregulatory

system

- Patients and Data

Assessment Proc.

- Glucose sensors

- Glycemia control

system

Control System

- Black-Box model

- Grey-Box model

- Controller

Conclusions

Average model prediction performance (per patient):

RMSnE ≤1

clinically acceptable

(ISO)

Ph.D. Defense

Tom Van Herpe

April 15, 2008

“Optimal” re-estimation procedure:

  • Model updates every 4 hours based on last 4 hours - data

  • Model updates every hour based on last 5 hours - data


Control system1

CONTROL SYSTEM

Introduction

Clinical Setting

- Glucoregulatory

system

- Patients and Data

Assessment Proc.

- Glucose sensors

- Glycemia control

system

Control System

- Black-Box model

- Grey-Box model

- Controller

Conclusions

Ph.D. Defense

Tom Van Herpe

April 15, 2008


Controller example how to navigate a ship

Controller example: how to navigate a ship?

Introduction

Clinical Setting

- Glucoregulatory

system

- Patients and Data

Assessment Proc.

- Glucose sensors

- Glycemia control

system

Control System

- Black-Box model

- Grey-Box model

- Controller

Conclusions

Feedback control

Error…?! oooops

I need my MODEL !!!

A = B + (C * D)

C = E + F(B+G)

E = …

Ph.D. Defense

Tom Van Herpe

April 15, 2008

Model based Predictive Control


Model based predictive control mpc

Model based Predictive Control (MPC)

Introduction

Clinical Setting

- Glucoregulatory

system

- Patients and Data

Assessment Proc.

- Glucose sensors

- Glycemia control

system

Control System

- Black-Box model

- Grey-Box model

- Controller

Conclusions

Ph.D. Defense

Tom Van Herpe

April 15, 2008

MPC is a control paradigm which, based on a dynamic model of the system to be controlled, solves a mathematical optimization problem in order to find the optimal sequence of input signals within a finite future time window of length N, after which only the first input signal is applied to the system.

  • Constraints in the optimization problem (e.g., 0 ≤FI ≤ max insulin flow)

  • Flexibility with “adaptive” models (to capture varying patient dynamics)

  • Future known disturbances (prevention of deviations from normoglycemia)


Mpc simulation study

MPC simulation study

Introduction

Clinical Setting

- Glucoregulatory

system

- Patients and Data

Assessment Proc.

- Glucose sensors

- Glycemia control

system

Control System

- Black-Box model

- Grey-Box model

- Controller

Conclusions

Ph.D. Defense

Tom Van Herpe

April 15, 2008

Design settings:

  • Cost function

  • Prediction horizon = N = 4 hours

  • Known disturbance input = FG = carbohydrate calories

  • Unknown disturbance inputs (medication + 15% meas. error)

Simulation results (19 critically ill patients):

GPI ≤ 23 ==> “clinically acceptable”


Patient case study patient no 11

Patient case study (patient no. 11)

Introduction

Clinical Setting

- Glucoregulatory

system

- Patients and Data

Assessment Proc.

- Glucose sensors

- Glycemia control

system

Control System

- Black-Box model

- Grey-Box model

- Controller

Conclusions

Ph.D. Defense

Tom Van Herpe

April 15, 2008


Conclusion for objective 3

CONCLUSION FOR OBJECTIVE 3

Introduction

Clinical Setting

- Glucoregulatory

system

- Patients and Data

Assessment Proc.

- Glucose sensors

- Glycemia control

system

Control System

- Black-Box model

- Grey-Box model

- Controller

Conclusions

Ph.D. Defense

Tom Van Herpe

April 15, 2008

Black-Box

Black-Box model

  • Acceptable model prediction performance

  • Closed-loop data affect model structure and model estimation

  • NOT for use in a clinical real-life control system

Publications:

  • T. Van Herpe, M. Espinoza, B. Pluymers, I. Goethals, P. Wouters, G. Van den Berghe, and B. De Moor. An adaptive input-output modeling approach for predicting the glycemia of critically ill patients. Physiol. Meas., 27(11):1057–1069, 2006.

  • T. Van Herpe, M. Espinoza, B. Pluymers, P. Wouters, F. De Smet, G. Van den Berghe, and B. De Moor. Development of a critically ill patient input-output model. In Proceedings of the 14th IFAC Symposium on System Identification (SYSID 2006), Newcastle, Australia, pages 481-486, 2006.

  • T. Van Herpe, I. Goethals, B. Pluymers, F. De Smet, P. Wouters, G. Van den Berghe, and B. De Moor. Challenges in data-based patient modeling for glycemia control in ICU-patients. In Proceedings of the Third IASTED International Conference on Biomedical Engineering, Innsbrück, Austria, pages 685-690, 2005.


Conclusion for objective 31

CONCLUSION FOR OBJECTIVE 3

Introduction

Clinical Setting

- Glucoregulatory

system

- Patients and Data

Assessment Proc.

- Glucose sensors

- Glycemia control

system

Control System

- Black-Box model

- Grey-Box model

- Controller

Conclusions

Ph.D. Defense

Tom Van Herpe

April 15, 2008

Grey-Box

Grey-Box model

  • New model structure based on physiological insight: ICU-MM

  • Closed-loop data only used for model estimation

  • Adaptive modelling strategy

  • Acceptable model prediction performance

  • Potential use in a clinical real-life control system

Publications:

  • T. Van Herpe, M. Espinoza, N. Haverbeke, B. De Moor, and G. Van den Berghe. Glycemia prediction in critically ill patients using an adaptive modeling approach. J. Diabetes. Sci. Technol., 1(3):348–356, 2007.

  • T. Van Herpe, B. Pluymers, M. Espinoza, G. Van den Berghe, and B. De Moor. A minimal model for glycemia control in critically ill patients. In Proceedings of the 28th IEEE EMBS Annual International Conference (EMBC 06), New York, United States, pages 5432-5435, 2006.

  • T. Van Herpe, N. Haverbeke, M. Espinoza, G. Van den Berghe, and B. De Moor. Adaptive modeling for control of glycemia in critically ill patients. In Proceedings of the 10th International IFAC Symposium on Computer Applications in Biotechnology (CAB 07), Cancún, Mexico, Vol. I, pages 159–164, 2007.


Conclusion for objective 32

CONCLUSION FOR OBJECTIVE 3

Introduction

Clinical Setting

- Glucoregulatory

system

- Patients and Data

Assessment Proc.

- Glucose sensors

- Glycemia control

system

Control System

- Black-Box model

- Grey-Box model

- Controller

Conclusions

Ph.D. Defense

Tom Van Herpe

April 15, 2008

Controller

  • Critical review of currently available blood glucose algorithms

  • Design of MPC

  • MPC performance increases if insulin infusion rate can be

    adapted more frequently

Publications:

  • T. Van Herpe, N. Haverbeke, B. Pluymers, G. Van den Berghe, and B. De Moor. The application of Model Predictive Control to normalize glycemia of critically ill patients. In Proceedings of the European Control Conference 2007 (ECC 07), Kos, Greece, pages 3116–3123, 2007.

  • N. Haverbeke, T. Van Herpe, M. Diehl, G. Van den Berghe, B. De Moor. Nonlinear model predictive control with moving horizon state and disturbance estimation - Application to the normalization of blood glucose in the critically ill. Accepted for publication in Proceedings of the 17th IFAC World Congress (IFAC WC 08), Seoul, Korea, 2008.


General conclusions

GENERAL CONCLUSIONS

Introduction

Clinical Setting

- Glucoregulatory

system

- Patients and Data

Assessment Proc.

- Glucose sensors

- Glycemia control

system

Control System

- Black-Box model

- Grey-Box model

- Controller

Conclusions

Ph.D. Defense

Tom Van Herpe

April 15, 2008

Three main objectives:

1. Design of evaluation tool for glucose sensors:

GLYCENSIT procedure

2. Design of evaluation tool for blood glucose control

algorithms used in the ICU:

Glycemic Penalty Index

3. Design of (semi-)automatic control system for normalizing

blood glucose in the ICU:

ICU-MM & MPC


Future research

FUTURE RESEARCH

Introduction

Clinical Setting

- Glucoregulatory

system

- Patients and Data

Assessment Proc.

- Glucose sensors

- Glycemia control

system

Control System

- Black-Box model

- Grey-Box model

- Controller

Conclusions

Ph.D. Defense

Tom Van Herpe

April 15, 2008

New inspiration

  • Patient Database Management System (PDMS)


Future research1

FUTURE RESEARCH

Introduction

Clinical Setting

- Glucoregulatory

system

- Patients and Data

Assessment Proc.

- Glucose sensors

- Glycemia control

system

Control System

- Black-Box model

- Grey-Box model

- Controller

Conclusions

Ph.D. Defense

Tom Van Herpe

April 15, 2008

New inspiration

  • Near-continuous glucose sensor


Future research2

FUTURE RESEARCH

Introduction

Clinical Setting

- Glucoregulatory

system

- Patients and Data

Assessment Proc.

- Glucose sensors

- Glycemia control

system

Control System

- Black-Box model

- Grey-Box model

- Controller

Conclusions

Ph.D. Defense

Tom Van Herpe

April 15, 2008

Five future research topics:


Future research3

FUTURE RESEARCH

Introduction

Clinical Setting

- Glucoregulatory

system

- Patients and Data

Assessment Proc.

- Glucose sensors

- Glycemia control

system

Control System

- Black-Box model

- Grey-Box model

- Controller

Conclusions

Ph.D. Defense

Tom Van Herpe

April 15, 2008

Five future research topics:

1. Optimization of GPI

Time (min)


Future research4

FUTURE RESEARCH

Introduction

Clinical Setting

- Glucoregulatory

system

- Patients and Data

Assessment Proc.

- Glucose sensors

- Glycemia control

system

Control System

- Black-Box model

- Grey-Box model

- Controller

Conclusions

Optimization

of GPI

Frequency

Ph.D. Defense

Tom Van Herpe

April 15, 2008

Time (min)

Five future research topics:

1. Optimization of GPI

Time (min)


Future research5

FUTURE RESEARCH

Introduction

Clinical Setting

- Glucoregulatory

system

- Patients and Data

Assessment Proc.

- Glucose sensors

- Glycemia control

system

Control System

- Black-Box model

- Grey-Box model

- Controller

Conclusions

3. Modelling of glycemia

  • Critically ill rabbits ==> improved dynamic behaviour

  • PDMS ==> patient clustering

    ==> initial model per cluster

Ph.D. Defense

Tom Van Herpe

April 15, 2008

Five future research topics:

2. Assessment of near-continuous glucose sensors

  • Quality requirements for individual measurement

    can be lower

  • GLYCENSIT version 2


Future research6

FUTURE RESEARCH

Introduction

Clinical Setting

- Glucoregulatory

system

- Patients and Data

Assessment Proc.

- Glucose sensors

- Glycemia control

system

Control System

- Black-Box model

- Grey-Box model

- Controller

Conclusions

Ph.D. Defense

Tom Van Herpe

April 15, 2008

Five future research topics:

4. Control of glycemia

  • Recognition of glucose sensor failings

    (introduction of tolerance intervals of GLYCENSIT phase 3)

  • Robustness analysis of the developed glycemia control

    system

5. Clinical validation of a glycemia control system

  • Testing the semi- or fully-closed-loop control system on

    group of critically ill rabbits

  • Testing the semi-closed-loop control system on critically

    ill patients: advising system

  • Testing the fully-closed-loop control system on critically

    ill patients


Control system

The End


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