Active Insulin Infusion Control of the Blood Active Insulin Infusion Control of the Blood Glucose DerivativeGlucose Derivative

J G Chase, Z-H Lam, J-Y Lee and K-S Hwang

University of Canterbury

Dept of Mechanical Engineering

Christchurch

New Zealand

ICARCV 2002, Singapore

Silicon + Biology?Silicon + Biology?

• Many biological and/or medical processes are effectively feedback control systems or can have their function replaced by feedback systems

• New technology creating new possibilities:– “BioMEMS” such as “wet sensors” and “gene chips”, are opening the path

to real time physiological monitoring/sensing and actuation.

– Wireless technology (LAN and PAN) for communication between active elements and/or monitoring technology (increased information flow).

– Advanced embedded computers (DSP’s) and real time operating systems (RTOS’s) can now handle extensive calculations and operations required.

Converging technologies enables the ability to monitor, control and/or replace dysfunctional physiological behaviour(s).

Can the increased information from real-time sensing coupled with feedback control outperform the foreknowledge and intuition of an

experienced diabetic??

3 Elements of Control Systems3 Elements of Control Systems

• Sensing– Real-time sensing from GlucoWatch or

similar technology at BW = 20 minutes or greater

• Computation– Modern embedded DSP’s are far more than

adequate

• Actuation– Insulin pump

• All are existing and near-term technologies

• Must account for limitations of existing tech and determine the limits where practicality and feasibility occur together.

DiabetesDiabetes

• Current Treatment = Manual Monitoring + Injection = Error Prone

Diabetes is reaching epidemic proportions, treatment is dependent on unreliable individuals and has not changed significantly in 30+ years

Ideal curve is flat! 2-3 hours Back to Fasting Level

Normal

Time

Blood Glucose

Level over Basal

Type I

IGT, Type II

GOALS:• Automate the “5:95” (1 day every 3 weeks is “bad”)

• Account for variations in patient response, insulin employed, sensor bandwidth and actuator dynamics/limits.

System ModelSystem Model

• System model is constructed in MATLAB/Simulink.

• Three parts: one part for each equation in model and controller.

• Controller: input - G and dG/dt (GlucoWatch) and output u(t) (Insulin Pump)

ControllersControllers

bb

nVIuG

Gutu

00 ,1

• Relative proportional controller (RPC).

dt

dGkGkutu dp10

• PD controller – controls slopes of incresing/decreasing blood sugar level rather than actual glucose concentration

Two controllers, one proportional based and the other derivative weighted where Kp << Kd create two different approaches to control

Shape control or Magnitude control

Why “Heavy Derivative” Control?Why “Heavy Derivative” Control?

Slope (derivative) is negative

Glucose levelis still positive.

The slope is the highest here –Derivative control most active here

Peak glucose level – Proportional control is most active here

• Derivative control - negative slope prevents further insulin injection when the glucose level is dropping and faster reaction to positive surge.

Control of Glucose Tolerance TestControl of Glucose Tolerance Test

• As sampling rate increases, the more effective the controllers become.

• Optimal control: G is very nearly flat as desired

RPC – BW = 20 min

Insulin Infusion Rates for GTTInsulin Infusion Rates for GTT

• PD controller minics what a diabetic would usally do, a routine optimised over 70 years of clinical treatment.

• Insulin rates are sharper and nearer injections as sensor BW drops.

RPC – sensor BW = 20 min

A More Difficult TestA More Difficult Test

• 1000 calories in 4 hours over five “meal” inputs of glucose which is rapidly absorbed

• Inputs vary in magnitude from 50 – 400 calories

• Inputs occur in two groups of rapid succession at t = 0, 10, 30 minutes and at t = 210 and 300 minutes– The last meal is 40 calories from 980 – 1020 calories so the full

absorption of about 1000 calories occurs by 4 hours quite easily.

• Controller has no knowledge of glucose input except in optimal case– Input knowledge is not currently practicable in any way for this system in

general

The goal is to “hammer” the system and see if it breaks!

Control of Glucose InputsControl of Glucose Inputs

• Glucose excursions shrink with sensor BW• Optimal control very nearly flat as desired• Simple PD control emphasizes derivative over proportional inputs by 100

Normal and Diabetic Glucose ResponseNormal and Diabetic Glucose Response

• Response of a normal subject to Glucose Input (orange) • PD controller developed is slightly better than normal subject by 7-25% on peak value and 1+ hour in return to basal glucose level

Insulin Infusion Rates for Glucose InputsInsulin Infusion Rates for Glucose Inputs

• Insulin rates are sharper and nearer injections expected as sensor BW drops• Lower insulin rates less effective control as might be expected.

u(t)=Uo(1+Kp(G/Gb))

Relative Proportional Control ComparisonRelative Proportional Control Comparison

• Relative proportional control more robust to Hypoglycemic behaviour

Danger @ -1.5

Death @ -3

PD Controller against Sensor LagPD Controller against Sensor Lag

• GlucoWatch™ (glucose sensor) has 20 minute sensor lag• PD Controller ROBUST against 20 minute sensor lag• The peak is slightly increased, but less hypoglycemic response

(RPC)

PD Controller against Sensor FailurePD Controller against Sensor Failure

• Sampling bandwidth = 20 minutes• PD controller ROBUST against 20 minute failure • Hypoglycemia induced for 60 minute failure

Summary & ConclusionsSummary & Conclusions

• Bergman equations found to very suitable for control systems approach

• Feasibility of automated insulin infusion is shown in simulation

• Basic tradeoffs between sensor BW and control efficacy delineated

• Derivative control or “control of slopes” seen to be the most effective form of feedback so far versus proporational dominated or relative proportional.

• Insulin inputs with derivative control trending towards matching those of “optimized” insulin injection regimes followed by diabetics.

Ongoing Future Work = First Known TrialsOngoing Future Work = First Known Trials

GlucoCard error = 7%

• Kidney Failure

• Dialysis Machine

• 67 year old Female

• High fluid levels

• 3rd day in ICU

• Hyper-insulinemic and Hyper-glycemic