SYSTEM AND/OR METHOD FOR GLUCOSE SENSOR CALIBRATION

• US 20100162786A1
• Filed: 12/29/2008
• Published: 07/01/2010
• Est. Priority Date: 12/29/2008
• Status: Active Grant

• Current Assignee: Medtronic Minimed Incorporated (Medtronic Plc)
• Original Assignee: Medtronic Minimed Incorporated (Medtronic Plc)
• Inventors: Keenan, Desmond Barry, Mastrototaro, John J.
• Granted Patent: US 9,289,168 B2
• Time in Patent Office: Days
• US Class Current: 73/1.20
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/1495 Calibrating or testing of in-vivo probes
• CPC Class Codes: A61B 5/6849 in combination with a needle set

Abstract

The subject matter disclosed herein relates to systems, methods and/or devices for calibrating sensor data to be used in estimating a blood glucose concentration. A relationship between sensor measurements and reference readings may be used to estimate a relationship between sensor measurements and blood glucose concentration. Such sensor measurements may be weighted according to a decreasing function of uncertainty associated with sensor values.

Claims

1. A method comprising:

obtaining samples of an electrical signal generated by a sensor, said samples having sample values associated with measurements of a blood-glucose concentration;

individually weighting at least some of said sample values according to a function of blood glucose reference samples associated with said sample values; and

estimating a relationship of sample values with said blood-glucose concentration based, at least in part, on said individually weighted samples.

2. The method of claim 1, wherein said estimating said relationship comprises estimating a linear relationship between said sample values and said blood-glucose concentration based, at least in part, on a linear regression of said weighted samples and associated blood-glucose reference values.

3. The method of claim 2, wherein said estimating said linear relationship further comprises:

calculating a linear regression sensitivity ratio based, at least in part, on said weighted samples and associated blood-glucose reference values;

selecting an offset based, at least in part, on said calculated linear regression sensitivity ratio; and

calculating a modified linear regression sensitivity ratio based, at least in part, on said selected offset, said weighted samples and said associated blood-glucose reference values.

4. The method of claim 1, wherein said function of blood glucose reference samples is based, at least in part, on a measure of statistical dispersion of said sample values as function of associated blood glucose reference samples.

5. The method of claim 4, wherein said measure of statistical dispersion comprise a variance and/or approximation of a variance of said sample values as a function of said associated blood glucose reference samples.

6. The method of claim 4, wherein said function comprises an inverse of said measure of statistical dispersion of said sample values.

7. The method of claim 4, and further comprising:

estimating a linear relationship of said measure of statistical dispersion of said sample values versus blood glucose concentration; and

deriving said function based, at least in part, on said linear relationship.

8. The method of claim 1, wherein individually weighting said at least some of said sample values further comprises further weighting said samples based on how recently said samples are obtained.

9. The method of claim 1, and further comprising detecting a failure of said sensor based, at least in part, on a change in said estimated relationship.

10. The method of claim 1, and further comprising calibrating measurements from said sensor for measuring a blood-glucose concentration based, at least in part, on said estimated relationship.

11. The method of claim 1, wherein said individually weighting said at least some of said sample values comprises weighting said at least some of said sample values according to a decreasing function of blood glucose reference values associated with said weighted samples.

12. An apparatus comprising:

means for obtaining samples of an electrical signal generated by a sensor, said samples having sample values associated with measurements of a blood-glucose concentration;

means for individually weighting at least some of said sample values according to a function of blood glucose reference samples associated with said sample values; and

means for estimating a relationship of sample values with said blood-glucose concentration based, at least in part, on said individually weighted samples.

13. The apparatus of claim 12, wherein said means for estimating said relationship comprises means for estimating a linear relationship between said sample values and said blood-glucose concentration based, at least in part, on a linear regression of said weighted samples and associated blood-glucose reference values.

14. The apparatus of claim 13, wherein said means for estimating said linear relationship further comprises:

means for calculating a linear regression sensitivity ratio based, at least in part, on said weighted samples and associated blood-glucose reference values;

means for selecting an offset based, at least in part, on said calculated linear regression sensitivity ratio; and

means for calculating a modified linear regression sensitivity ratio based, at least in part, on said selected offset, said weighted samples and said associated blood-glucose reference values.

15. The apparatus of claim 12, wherein said function of blood glucose reference samples is based, at least in part, on a measure of statistical dispersion of said sample values as function of associated blood glucose reference samples.

16. The apparatus of claim 15, wherein said measure of statistical dispersion comprise a variance and/or approximation of a variance of said sample values as a function of said associated blood glucose reference samples.

17. The apparatus of claim 15, wherein said function comprises an inverse of said measure of statistical dispersion of said sample values.

18. The apparatus of claim 15, and further comprising:

means for estimating a linear relationship of said measure of statistical dispersion of said sample values versus blood glucose concentration; and

means for deriving said function based, at least in part, on said linear relationship.

19. The apparatus of claim 12, wherein said means for individually weighting said at least some of said sample values further comprises means for further weighting said samples based on how recently said samples are obtained.

20. The apparatus of claim 12, and further comprising means for detecting a failure of said sensor based, at least in part, on a change in said estimated relationship.

21. The apparatus of claim 12, and further comprising means for calibrating measurements from said sensor for measuring a blood-glucose concentration based, at least in part, on said estimated relationship.

22. The apparatus of claim 12, wherein said means for individually weighting said at least some of said sample values comprises means for weighting said at least some of said sample values according to a decreasing function of blood glucose reference values associated with said weighted samples.

23. An article comprising:

a storage medium comprising machine-readable instructions stored thereon which, if executed by a computing platform are adapted to direct said computing platform to;

obtain samples of an electrical signal generated by a sensor, said samples having sample values associated with measurements of a blood-glucose concentration;

individually weight at least some of said sample values according to a function of blood glucose reference samples associated with said sample values; and

estimate a relationship of sample values with said blood-glucose concentration based, at least in part, on said individually weighted samples.

24. The article of claim 23, wherein said instructions, if executed by said computing platform, are further adapted to direct said computing platform to estimating said relationship by estimating a linear relationship between said sample values and said blood-glucose concentration based, at least in part, on a linear regression of said weighted samples and associated blood-glucose reference values.

25. The article of claim 24, wherein said instructions, if executed by said computing platform, are further adapted to direct said computing platform to:

calculate a linear regression sensitivity ratio based, at least in part, on said weighted samples and associated blood-glucose reference values;

select an offset based, at least in part, on said calculated linear regression sensitivity ratio; and

calculate a modified linear regression sensitivity ratio based, at least in part, on said selected offset, said weighted samples and said associated blood-glucose reference values.

26. The article of claim 24, wherein said function of blood glucose reference samples is based, at least in part, on a measure of statistical dispersion of said sample values as function of associated blood glucose reference samples.

27. The article of claim 26, wherein said measure of statistical dispersion comprise a variance and/or approximation of a variance of said sample values as a function of said associated blood glucose reference samples.

28. The article of claim 26, wherein said function comprises an inverse of said measure of statistical dispersion of said sample values.

29. The article of claim 26, wherein said instructions, if executed by said computing platform, are further adapted to direct said computing platform to:

estimate a linear relationship of said measure of statistical dispersion of said sample values versus blood glucose concentration; and

derive said function based, at least in part, on said linear relationship.

30. The article of claim 23, wherein instructions, if executed by said computing platform, are further adapted to direct said computing platform to individually weight said samples based on how recently said samples are obtained.

31. The article of claim 23, wherein said instructions, if executed by said computing platform, are further adapted to direct said computing platform to detect a failure of said sensor based, at least in part, on a change in said estimated relationship.

32. The article of claim 23, wherein said instructions, if executed by said computing platform, are further adapted to direct said computing platform to calibrate measurements from said sensor for measuring a blood-glucose concentration based, at least in part, on said estimated relationship.

33. The article of claim 23, wherein said instructions, if executed by said computing platform, are adapted to direct said computing platform to individually weight said at least some of said sample values according to a decreasing function of blood glucose reference values associated with said weighted samples.

34. An apparatus comprising:

a sensor adapted generate one or more signals responsive to a blood glucose concentration in a body;

a computing platform, said computing platform being adapted to;

obtaining samples of said one or more signals, said samples having sample values associated with measurements of said blood-glucose concentration;

individually weight at least some of said sample values according to a function of blood glucose reference samples associated with said sample values; and

estimate a relationship of sample values with said blood-glucose concentration based, at least in part, on said individually weighted samples.

35. The apparatus of claim 34, wherein said computing platform is further adapted to estimate said relationship by estimating a linear relationship between said sample values and said blood-glucose concentration based, at least in part, on a linear regression of said weighted samples and associated blood-glucose reference values.

36. The apparatus of claim 35, wherein said computing platform is further adapted to:

calculate a linear regression sensitivity ratio based, at least in part, on said weighted samples and associated blood-glucose reference values;

select an offset based, at least in part, on said calculated linear regression sensitivity ratio; and

calculate a modified linear regression sensitivity ratio based, at least in part, on said selected offset, said weighted samples and said associated blood-glucose reference values.

37. The apparatus of claim 34, wherein said function of blood glucose reference samples is based, at least in part, on a measure of statistical dispersion of said sample values as function of associated blood glucose reference samples.

38. The apparatus of claim 37, wherein said measure of statistical dispersion comprises a variance and/or approximation of a variance of said sample values as a function of said associated blood glucose reference samples.

39. The apparatus of claim 37, wherein said function comprises an inverse of said measure of statistical dispersion of said sample values.

40. The apparatus of claim 37, wherein said computing platform is further adapted to:

estimate a linear relationship of said measure of statistical dispersion of said sample values versus blood glucose concentration; and

derive said function based, at least in part, on said linear relationship.

41. The apparatus of claim 34, wherein said computing platform is further adapted to further individually weight said samples based on how recently said samples are obtained from said sensor.

42. The apparatus of claim 34, wherein said computing platform is further adapted to detect a failure of said sensor based, at least in part, on a change in said estimated relationship.

43. The apparatus of claim 34, wherein said computing platform is further adapted to calibrate measurements from said sensor for measuring a blood-glucose concentration based, at least in part, on said estimated relationship.

44. The apparatus of claim 34, wherein said computing platform is further adapted to individually weight said at least some of said sample values by weighting said at least some of said sample values according to a decreasing function of blood glucose reference values associated with said weighted samples.