Method and apparatus for real-time control of physiological parameters

• US 6,572,545 B2
• Filed: 09/21/2001
• Issued: 06/03/2003
• Est. Priority Date: 09/22/2000
• Status: Expired due to Fees

• Current Assignee: Knobbe LIM & Buckingham
• Original Assignee: Knobbe LIM & Buckingham
• Inventors: Knobbe, Edward J., Buckingham, Bruce A., Lim, Wah L.
• Primary Examiner(s): Nasser, Robert L.
• Assistant Examiner(s): Mallari, Patricia C
• Application Number:
  US09/960,855
• Publication Number:
  US 20020111547A1
• Time in Patent Office: 620 Days
• Field of Search: 600/316, 600/345-347, 600/365, 600/300-301, 604/65-67, 128/905, 623/11.11, 623/23.64
• US Class Current: 600/365
• CPC Class Codes: A61B 2560/0242 adapted to measure environmental factors, e.g. temperature, pollution
• CPC Class Codes: A61B 5/0002 Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network A61B5/07 takes precedence; transmission systems for measured values G08C; transmission H04B; transmission of digital information H04L; wireless communication networks H04W
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/725 using specific filters therefor, e.g. Kalman or adaptive filters A61B5/316 take precedence
• CPC Class Codes: A61M 2230/201 Glucose concentration
• CPC Class Codes: A61M 5/142 Pressure infusion, e.g. using pumps
• CPC Class Codes: G16H 10/40 for data related to laboratory analysis, e.g. patient specimen analysis
• CPC Class Codes: G16H 20/13 delivered from dispensers
• CPC Class Codes: G16H 20/30 relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders
• CPC Class Codes: Y02A 90/10 Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
• CPC Class Codes: Y10S 128/923 by comparison of patient data to other data
• CPC Class Codes: Y10S 128/924 using artificial intelligence

Abstract

A real-time controller operating as an artificial pancreas uses a Kalman control algorithm to control glucose level of a patient in real time. The real-time controller receives an estimate of the patient glucose level and a reference glucose level. The estimate of the patient glucose level can be provided by an optimal estimator implemented using a linearized Kalman filter. The estimated glucose level and the reference glucose level are processed by the Kalman control algorithm to determine a control command in real time. The Kalman control algorithm has a dynamic process forced by the control command a cost function determining a relative level of control. The control command is provided to a dispenser which secretes insulin or glucagon in response to the control command to correct a relatively high glucose level or a relatively low glucose level.

Claims

1. A method for controlling glucose level in real time comprising the acts of:

receiving an estimated glucose level;

receiving a reference signal indicative of a desired glucose level;

providing the estimated glucose level and the reference signal to a Kalman control algorithm in real time;

determining a control command in real time using the Kalman control algorithm; and

providing the control command to a dispenser which outputs medication in response to the control command.

2. The method of claim 1, wherein the estimated glucose level is provided by an optimal estimator implemented using an extended Kalman filter.

3. The method of claim 1, wherein the reference signal is provided by a patient health monitor which accepts inputs from a user.

4. The method of claim 1, wherein the reference signal varies with time.

5. The method of claim 1, wherein the Kalman control algorithm has a dynamic process model forced by the control command and a cost function determining a relative level of control.

6. The method of claim 5, wherein determination of the control command in real time comprises the acts of:

computing a Kalman control gain to minimize the cost function; and

adjusting the control command based on the Kalman control gain and a difference between the estimated glucose level and the desired glucose level.

7. The method of claim 1, wherein the dispenser secretes insulin or glucagon in response to the control command to correct a relatively high or a relatively low estimated glucose level.

8. A method for close-loop control of a physiological parameter comprising the acts of:

obtaining a measurement of the physiological parameter from a patient;

providing the measurement to an optimal estimator in real time, wherein the optimal estimator outputs a best estimate of the physiological parameter in real time based on the measurement;

providing the best estimate of the physiological parameter to an optimal controller in real time, wherein the optimal controller outputs a control command in real time based on the best estimate of the physiological parameter and a control reference, and wherein the optimal estimator and the optimal controller are implemented as a joint Kalman algorithm; and

providing the control command to an actuator, wherein the actuator provides an output to adjust the physiological parameter.

9. A method for close-loop control of a physiological parameter comprising the acts of:

obtaining a measurement of the physiological parameter from a patient;

providing the measurement to an optimal estimator in real time, wherein the optimal estimator outputs a best estimate of the physiological parameter in real time based on the measurement;

providing the best estimate of the physiological parameter to an optimal controller in real time, wherein the optimal controller outputs a control command in real time based on the best estimate of the physiological parameter and a control reference; and

providing the control command to an actuator, wherein the actuator provides an output to adjust the physiological parameter, and wherein the optimal estimator outputs best estimate of additional physiological parameters to the optimal controller in real time, and the optimal controller controls the additional physiological parameters by outputting additional control commands.

10. A real-time optimal glucose controller comprising:

a first input configured to receive an estimated glucose level in real time;

a second input configured to receive a reference glucose level;

a Kalman control algorithm configured to determine a control command based on the estimated glucose level and the reference glucose level, wherein the Kalman control algorithm has a dynamic process model forced by the control command and a cost function defining a desired level of control; and

an output configured to provide the control command to a pump, wherein the pump provides medication in response to the control command to minimize a difference between the estimated glucose level and the reference glucose level.

11. A method for close-loop control of a physiological parameter comprising the acts of:

obtaining a measurement of the physiological parameter from a patient;

providing the measurement to an optimal estimator in real time, wherein the optimal estimator outputs a best estimate of the physiological parameter in real time based on the measurement, and wherein the optimal estimator is implemented using a linearized Kalman algorithm;

providing the best estimate of the physiological parameter to an optimal controller in real time, wherein the optimal controller outputs a control command in real time based on the best estimate of the physiological parameter and a control reference, and wherein the optimal controller is implemented using a Kalman control algorithm; and

providing the control command to an actuator, wherein the actuator provides an output to adjust the physiological parameter.

12. The method of claim 11, wherein the optimal estimator and the optimal controller have substantially identical dynamic process models forced by the control command.

13. The method of claim 12, wherein the optimal estimator provides a best estimate state vector to the optimal controller, and the best estimate of the physiological parameter is an element of the best estimate state vector.

14. The method of claim 12, wherein the optimal controller provides the control command to the optimal estimator.

15. An artificial pancreas for controlling glucose level in real time comprising:

a glucose sensor to provide a measurement of the glucose level;

an optimal glucose estimator, wherein the optimal glucose estimator uses a stochastic model to describe a physiological process relating to the glucose level and uses a linearized Kalman filter to estimate the glucose level in real time based on the measurement from the glucose sensor;

an optimal glucose controller, wherein the optimal glucose controller uses a substantially identical stochastic model as the optimal glucose estimator and uses a Kalman control algorithm to determine a control command to adjust the glucose level in real time; and

a medical dispenser to provide medication to a patient in response to the control command.

16. The artificial pancreas of claim 15, wherein the artificial pancreas is a portable device.

17. The artificial pancreas of claim 15 further comprising one or more additional sensors of different types which operate independently to provide respective glucose measurements.

18. The artificial pancreas of claim 15, wherein the medical dispenser secretes insulin and glucagon to control a relatively high glucose level and a relatively low glucose level respectively.

19. The artificial pancreas of claim 15 further comprising a patient health monitor with an input/output interface to receive inputs from a user and to display status of the artificial pancreas.

20. The artificial pancreas of claim 15, wherein the optimal glucose estimator and the optimal glucose controller are implemented using a software algorithm.