Method and apparatus for real-time control of physiological parameters

• US 20030195404A1
• Filed: 04/16/2003
• Published: 10/16/2003
• Est. Priority Date: 09/22/2000
• Status: Abandoned Application

• Current Assignee: Bruce A. Buckingham, Edward J. Knobbe, Wah L. Lim
• Original Assignee: Bruce A. Buckingham, Edward J. Knobbe, Wah L. Lim
• Inventors: Knobbe, Edward J., Buckingham, Bruce A., Lim, Wah L.
• Application Number:
  US10/417,438
• Publication Number:
  US 20030195404A1
• Time in Patent Office: Days
• 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 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.

2. The method of claim 1, wherein the measurement is obtained using a sensor.

3. The method of claim 1, wherein the optimal estimator is implemented using a linearized Kalman algorithm.

4. The method of claim 1, wherein the control reference is provided by a patient health monitor.