Method and device for predicting physiological values

• US 6,546,269 B1
• Filed: 01/05/2001
• Issued: 04/08/2003
• Est. Priority Date: 05/13/1998
• Status: Expired due to Term

• Current Assignee: LifeScan IP Holdings, LLC
• Original Assignee: Cygnus, Inc. (Johnson & Johnson)
• Inventors: Kurnik, Ronald T.
• Primary Examiner(s): Nasser, Robert L.
• Application Number:
  US09/755,528
• Time in Patent Office: 823 Days
• Field of Search: 600/345, 600/347, 600/365, 600/518, 436/525, 436/149-511, 436/806, 422/82-1-, 204/400, 204/403, 435/176, 435/287.1, 435/14, 435/25, 435/817
• US Class Current: 600/345
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement

Abstract

A method and device are provided for measuring the concentration of target chemical analytes present in a biological system, and then predicting a future or past concentration of an analyte using a series of such measurements. One important application of the invention involves predicting future or past blood glucose concentrations using a series of measured blood glucose values.

Claims

1. One or more microprocessors, comprising programming to

control a sensing device to obtain a series of raw signals at selected time intervals, wherein each raw signal is specifically related to a concentration or amount of blood glucose,

perform a calibration step which correlates each raw signal with a measurement value indicative of the concentration or amount of blood glucose present in a subject thus providing a series of measurement values, and

predict a measurement value at a further time interval which occurs either one time interval before or one time interval after the series of measurement values is obtained, wherein said series of measurement values comprises three or more discrete values, and said predicting is carried out using said series of three or more measurement values in a series function represented by:

$\begin{array}{cc}{y}_{n+1}=y_n+\mathrm{\alpha <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>\left(y_n-y_{n-1}\right)+\frac{{\mathrm{\alpha <br><br>}}^2}{2}<br><br>\left(y_n-2<br><br>y_{n-1}+y_{n-2}\right)& \left(7\right)\end{array}$

wherein y is the measurement value of the analyte, n is the time interval between measurement values, and α

is a real number between 0 and 1.

2. The one or more microprocessors of claim 1, wherein said sensing device comprises a biosensor having an electrochemical sensing element.

3. The one or more microprocessors of claim 2, wherein said biosensor comprises an enzyme that reacts with the glucose to produce an electrochemically detectable signal.

4. The one or more microprocessors of claim 3, wherein the enzyme comprises glucose oxidase.

5. The one or more microprocessors of claim 1, wherein said sensing device comprises a near-IR spectrometer.

6. The one or more microprocessors of claim 1, wherein the selected time intervals are evenly spaced.

7. The one or more microprocessors of claim 1, wherein the series function is used to predict the value of y_n+1 and the time interval n+1 occurs one time interval after the series of measurement values is obtained.

8. The one or more microprocessors of claim 1, wherein the series function is used to predict the value of y_n+1 and the time interval n+1 occurs one time interval before the series of measurement values is obtained.

9. A The one or more microprocessors of claim 8, wherein a reference blood glucose measurement is compared against the predicted value of y_n+1 and used for calibration.

10. The one or more microprocessors of claim 1, wherein the predicted value of y_n+1 is used to control administration of insulin to the biological system from an associated insulin pump.

11. A monitoring system for measuring a concentration or amount of glucose in a subject, said system comprising, the one or more microprocessors of claim 1, and the sensing device.

12. The monitoring system of claim 11, wherein said sensing device comprises a biosensor having an electrochemical sensing element.

13. The monitoring system of claim 12, wherein said biosensor comprises an enzyme that reacts with the glucose to produce an electrochemically detectable signal.

14. The monitoring system of claim 13, wherein the enzyme comprises glucose oxidase.

15. The monitoring system of claim 11, wherein said sensing device comprises a near-IR spectrometer.

16. One or more microprocessors, comprising programming to

control a sensing device to obtain a series of raw signals at selected time intervals, wherein each raw signal is specifically related to an analyte,

perform a calibration step which correlates each raw signal with a measurement value indicative of a concentration or amount of analyte present in a subject thus providing a series of measurement values, and

predict a measurement value at a further time interval which occurs either one time interval before or one time interval after the series of measurement values is obtained, wherein said series of measurement values comprises three or more discrete values, and said predicting is carried out using said series of three or more measurement values in a series function represented by:

$\begin{array}{cc}{y}_{n+1}=y_n+\mathrm{\alpha <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>\left(y_n-y_{n-1}\right)+\frac{{\mathrm{\alpha <br><br>}}^2}{2}<br><br>\left(y_n-2<br><br>y_{n-1}+y_{n-2}\right)& \left(7\right)\end{array}$

wherein y is the measurement value of the analyte, n is the time interval between measurement values, and α

is a real number between 0 and 1.

17. The one or more microprocessors of claim 16, wherein said sensing device comprises a biosensor having an electrochemical sensing element.

18. The one or more microprocessors of claim 17, wherein said biosensor comprises an enzyme that reacts with the analyte to produce an electrochemically detectable signal.

19. The one or more microprocessors of claim 16, wherein said sensing device comprises a near-IR spectrometer.

20. The one or more microprocessors of claim 16, wherein the selected time intervals are evenly spaced.

21. The one or more microprocessors of claim 16, wherein the series function is used to predict the value of y_n+1 and the time interval n+1 occurs one time interval after the series of measurement values is obtained.

22. The one or more microprocessors of claim 16, wherein the series function is used to predict the value of y_n+1 and the time interval n+1 occurs one time interval before the series of measurement values is obtained.

23. The one or more microprocessors of claim 22, wherein a reference analyte measurement is compared against the predicted value of y_n+1 and used for calibration.

24. A monitoring system for measuring an analyte in a subject, said system comprising, the one or more microprocessors of claim 16, and the sensing device.