CALIBRATION OF GLUCOSE MONITORING SENSOR AND/OR INSULIN DELIVERY SYSTEM

• US 20110237917A1
• Filed: 03/26/2010
• Published: 09/29/2011
• Est. Priority Date: 03/26/2010
• Status: Active Grant

• Current Assignee: Medtronic Minimed Incorporated (Medtronic Plc)
• Original Assignee: Medtronic Minimed Incorporated (Medtronic Plc)
• Inventors: Roy, Anirban, Keenan, Barry, Kremliovsky, Michael
• Granted Patent: US 9,089,292 B2
• Time in Patent Office: Days
• US Class Current: 600/365
• CPC Class Codes: A61B 2560/0223 of calibration, e.g. protocols for calibrating sensors
• CPC Class Codes: A61B 5/1451 for interstitial fluid
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/14865 invasive, e.g. introduced into the body by a catheter or needle or using implanted sensors
• CPC Class Codes: A61B 5/1495 Calibrating or testing of in-vivo probes
• CPC Class Codes: A61B 5/4839 combined with drug delivery
• CPC Class Codes: A61B 5/6849 in combination with a needle set
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/7221 Determining signal validity, reliability or quality A61B5/7207 and A61B5/7217 take precedence
• CPC Class Codes: A61B 5/725 using specific filters therefor, e.g. Kalman or adaptive filters A61B5/316 take precedence
• CPC Class Codes: A61B 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: A61M 2005/1726 the body parameters being measured at, or proximate to, the infusion site
• CPC Class Codes: A61M 5/1723 using feedback of body parameters, e.g. blood-sugar, pressure measurement of body parameters A61B5/00

Abstract

Disclosed are methods, apparatuses, etc. for calibrating glucose monitoring sensors and/or insulin delivery systems. In certain example embodiments, blood glucose reference samples may be correlated with sensor measurements with regard to a delay associated with the sensor measurements. In certain other example embodiments, one or more parameters of a probability model may be estimated based on blood glucose reference sample-sensor measurement pairs. Based on such information, function(s) for estimating a blood-glucose concentration in a patient may be determined.

Claims

1. A method comprising:

correlating blood glucose reference samples with sensor measurements to provide at least one output signal responsive to a delay associated with said sensor measurements; and

determining a function for estimating a blood-glucose concentration in a patient from sensor measurements based, at least in part, on said at least one output signal.

2. The method of claim 1, wherein said correlating comprises:

applying said blood glucose reference samples and said sensor measurements to a matched filter at a plurality of time shift delays to ascertain said delay.

3. The method of claim 1, wherein said correlating comprises:

correlating said sensor measurements with said blood glucose reference samples at a plurality of different time delays to ascertain said delay.

4. The method of claim 1, wherein said determining comprises:

applying said blood glucose reference samples and said sensor measurements to a Wiener filter in conjunction with said delay to determine multiple filter coefficients.

5. The method of claim 1, wherein said determining further comprises:

determining a function for estimating a blood-glucose concentration in the patient from sensor measurements based, at least in part, on a noise signal that is associated with said sensor measurements.

6. The method of claim 1, wherein said sensor measurements comprise current sensor measurements taken from interstitial fluid of the patient.

7. The method of claim 1, wherein said determining further comprises:

determining a function for estimating a blood-glucose concentration in the patient, said function to account for said delay;

said delay representing, at least partially, an approximated delay associated with blood glucose diffusion between one or more blood vessels and interstitial fluid of the patient.

8. The method of claim 1, wherein said determining further comprises:

determining a slope and an offset for said function for estimating a blood-glucose concentration in a patient.

9. The method of claim 8, wherein said determining further comprises:

determining said slope and said offset for said function using a Bayesian technique in which a parameter vector includes a calfactor variable and an offset variable and in which an independent variable includes a current signal corresponding to said sensor measurements.

10. The method of claim 8, wherein said determining further comprises:

determining said slope and said offset for said function using a linear Kalman filter technique in which a parameter vector includes a calfactor variable and an offset variable.

11. The method of claim 1, further comprising:

taking said sensor measurements that are to be correlated using one or more subcutaneous current sensors; and

infusing insulin into the patient based on said function for estimating a blood-glucose concentration in the patient.

12. A apparatus comprising:

a filter unit to receive one or more signals based on blood-glucose sensor measurements, said filter unit comprising one or more processors to;

correlate blood glucose reference samples with sensor measurements to provide at least one output signal responsive to a delay associated with said sensor measurements; and

determine a function for estimating a blood-glucose concentration in a patient from sensor measurements based, at least in part, on said at least one output signal.

13. The apparatus of claim 12, wherein said filter unit is capable of correlating said blood glucose reference samples with said sensor measurements by:

applying said blood glucose reference samples and said sensor measurements to a matched filter at a plurality of time shift delays to ascertain said delay.

14. The apparatus of claim 12, wherein said filter unit is capable of correlating said blood glucose reference samples with said sensor measurements by:

correlating said sensor measurements with said blood glucose reference samples at a plurality of different time delays to ascertain said delay.

15. The apparatus of claim 12, wherein said filter unit is capable of determining said function for estimating said blood-glucose concentration in the patient by:

applying said blood glucose reference samples and said sensor measurements to a Wiener filter in conjunction with said delay to determine multiple filter coefficients.

16. The apparatus of claim 12, wherein said filter unit is capable of determining said function for estimating said blood-glucose concentration in the patient by:

determining a function for estimating a blood-glucose concentration in the patient from sensor measurements based, at least in part, on a noise signal that is associated with said sensor measurements.

17. The apparatus of claim 12, wherein said sensor measurements comprise current sensor measurements taken from interstitial fluid of the patient.

18. The apparatus of claim 12, wherein said filter unit is capable of determining said function for estimating said blood-glucose concentration in the patient by:

determining a function for estimating a blood-glucose concentration in the patient, said function to account for said delay;

said delay representing, at least partially, an approximated delay associated with blood glucose diffusion between one or more blood vessels and interstitial fluid of the patient.

19. The apparatus of claim 12, wherein said filter unit is capable of determining said function for estimating said blood-glucose concentration in the patient by:

determining a slope and an offset for said function for estimating a blood-glucose concentration in a patient.

20. The apparatus of claim 19, wherein said filter unit is capable of determining said function for estimating said blood-glucose concentration in the patient by:

determining said slope and said offset for said function using a Bayesian technique in which a parameter vector includes a calfactor variable and an offset variable and in which an independent variable includes a current signal corresponding to said sensor measurements.

21. The apparatus of claim 19, wherein said filter unit is capable of determining said function for estimating said blood-glucose concentration in the patient by:

determining said slope and said offset for said function using a linear Kalman filter technique in which a parameter vector includes a calfactor variable and an offset variable.

22. The apparatus of claim 12, further comprising:

one or more blood-glucose subcutaneous current sensors adapted to be coupled to the patient to obtain blood-glucose sensor measurements and adapted to provide said one or more signals based on said blood-glucose sensor measurements;

wherein said filter unit is capable of obtaining said blood-glucose sensor measurements via said one or more blood-glucose subcutaneous current sensors using said one or more signals; and

at least one insulin delivery system adapted to infuse insulin into the patient based on said function for estimating a blood-glucose concentration in the patient.

23. A system comprising:

means for correlating blood glucose reference samples with sensor measurements to provide at least one output signal responsive to a delay associated with said sensor measurements; and

means for determining a function for estimating a blood-glucose concentration in a patient from sensor measurements based, at least in part, on said at least one output signal.

24. An article comprising:

at least one storage medium having stored thereon instructions executable by one or more processors to;

correlate blood glucose reference samples with sensor measurements to provide at least one output signal responsive to a delay associated with said sensor measurements; and

determine a function for estimating a blood-glucose concentration in a patient from sensor measurements based, at least in part, on said at least one output signal.

25. A method comprising:

obtaining a plurality of blood glucose reference sample-sensor measurement pairs;

estimating one or more parameters of a probability model based, at least in part, on said blood glucose reference sample-sensor measurement pairs; and

determining a function for estimating a blood-glucose concentration in a patient from sensor measurements based, at least in part, on said estimated one or more parameters.

26. The method of claim 25, wherein said obtaining comprises:

taking sensor measurements for said plurality of blood glucose reference sample-sensor measurement pairs using at least one subcutaneous current sensor.

27. The method of claim 25, wherein said function is defined at least partly by a slope and an offset.

28. The method of claim 25, wherein said one or more parameters comprise a calfactor variable and an offset variable.

29. The method of claim 28, wherein a relationship between current sensor measurements corresponding to said blood glucose reference sample-sensor measurement pairs and sensor glucose concentration for the patient may be represented by a linear model, said linear model associated with a slope and an offset;

and wherein said slope and said offset of said linear model are determinable from said calfactor variable and/or said offset variable.

30. The method of claim 25, wherein said estimating further comprises:

estimating said one or more parameters using a linear Kalman filter estimator in which process noise and measurement noise are modeled as being constant.

31. The method of claim 25, wherein said estimating further comprises:

estimating said one or more parameters using a linear Kalman filter estimator in which process noise is adapted as a function of model performance.

32. The method of claim 25, wherein said estimating further comprises:

estimating said one or more parameters using a linear Kalman filter estimator in which measurement noise is adapted as a function of model performance.

33. The method of claim 25, wherein said estimating further comprises:

estimating said one or more parameters using a Bayesian estimator in which sensor measurements corresponding to said blood glucose reference sample-sensor measurement pairs comprise an independent variable and blood glucose reference samples corresponding to said blood glucose reference sample-sensor measurement pairs comprise a measured variable.

34. The method of claim 25, further comprising:

estimating a composite sensor glucose concentration value for the patient using a plurality of functions and at least one weighting factor that is derived from one or more quality indicators for a plurality of probability models.

35. The method of claim 25, wherein said determining further comprises determining a plurality of functions for estimating a blood-glucose concentration in the patient from sensor measurements;

and wherein the method further comprises;

estimating a plurality of sensor glucose concentration values for the patient using said plurality of functions;

said plurality of functions associated with a plurality of probability models, which include said probability model; and

determining a composite sensor glucose concentration value for the patient based, at least partly, on said plurality of sensor glucose concentration values.

36. The method of claim 35, wherein said determining a composite sensor glucose concentration value for the patient comprises:

weighting said plurality of sensor glucose concentration values based, at least in part, on a plurality of quality indicators indicative of an accuracy of said plurality of probability models.

37. The method of claim 36, wherein said plurality of quality indicators comprise a plurality of likelihood values;

and wherein said method further comprises;

calculating said plurality of likelihood values based, at least partly, on error differences between sensor glucose concentration values estimated from said plurality of probability models and reference blood glucose values from blood glucose reference samples.

38. An apparatus comprising:

a calibration unit to receive one or more signals based on blood-glucose sensor measurements, said calibration unit comprising one or more processors to;

obtain a plurality of blood glucose reference sample-sensor measurement pairs;

estimate one or more parameters of a probability model based, at least in part, on said blood glucose reference sample-sensor measurement pairs; and

determine a function for estimating a blood-glucose concentration in a patient from sensor measurements based, at least in part, on said estimated one or more parameters.

39. The apparatus of claim 38, wherein said calibration unit is capable of obtaining said plurality of blood glucose reference sample-sensor measurement pairs by:

taking sensor measurements for said plurality of blood glucose reference sample-sensor measurement pairs using at least one subcutaneous current sensor.

40. The apparatus of claim 38, wherein said function is defined at least partly by a slope and an offset.

41. The apparatus of claim 38, wherein said one or more parameters comprise a calfactor variable and an offset variable.

42. The apparatus of claim 41, wherein a relationship between current sensor measurements corresponding to said blood glucose reference sample-sensor measurement pairs and sensor glucose concentration for the patient may be represented by a linear model, said linear model associated with a slope and an offset;

and wherein said slope and said offset of said linear model are determinable from said calfactor variable and/or said offset variable.

43. The apparatus of claim 38, wherein said calibration unit is capable of estimating said one or more parameters of a probability model by:

estimating said one or more parameters using a linear Kalman filter estimator in which process noise and measurement noise are modeled as being constant.

44. The apparatus of claim 38, wherein said calibration unit is capable of estimating said one or more parameters of a probability model by:

estimating said one or more parameters using a linear Kalman filter estimator in which process noise is adapted as a function of model performance.

45. The apparatus of claim 38, wherein said calibration unit is capable of estimating said one or more parameters of a probability model by:

estimating said one or more parameters using a linear Kalman filter estimator in which measurement noise is adapted as a function of model performance.

46. The apparatus of claim 38, wherein said calibration unit is capable of estimating said one or more parameters of a probability model by:

estimating said one or more parameters using a Bayesian estimator in which sensor measurements corresponding to said blood glucose reference sample-sensor measurement pairs comprise an independent variable and blood glucose reference samples corresponding to said blood glucose reference sample-sensor measurement pairs comprise a measured variable.

47. The apparatus of claim 38, wherein said one or more processors of said calibration unit are further to:

estimate a composite sensor glucose concentration value for the patient using a plurality of functions and at least one weighting factor that is derived from one or more quality indicators for a plurality of probability models.

48. The apparatus of claim 38, wherein said calibration unit is capable of determining said function for estimating a blood-glucose concentration in the patient by determining a plurality of functions for estimating a blood-glucose concentration in the patient from sensor measurements;

and wherein said one or more processors of said calibration unit are further to;

estimate a plurality of sensor glucose concentration values for the patient using said plurality of functions;

said plurality of functions associated with a plurality of probability models, which include said probability model; and

determine a composite sensor glucose concentration value for the patient based, at least partly, on said plurality of sensor glucose concentration values.

49. The apparatus of claim 48, wherein said calibration unit is capable of determining said composite sensor glucose concentration value for the patient by:

weighting said plurality of sensor glucose concentration values based, at least in part, on a plurality of quality indicators indicative of an accuracy of said plurality of probability models.

50. The apparatus of claim 49, wherein said plurality of quality indicators comprise a plurality of likelihood values;

and wherein said one or more processors of said calibration unit are further to;

calculate said plurality of likelihood values based, at least partly, on error differences between sensor glucose concentration values estimated from said plurality of probability models and reference blood glucose values from blood glucose reference samples.

51. The apparatus of claim 38, further comprising:

one or more blood-glucose sensors adapted to be coupled to the patient to obtain blood-glucose sensor measurements and adapted to provide said one or more signals based on said blood-glucose sensor measurements,

wherein said calibration unit is capable of obtaining said plurality of blood glucose reference sample-sensor measurement pairs via said one or more blood-glucose sensors using said one or more signals.

52. A system comprising:

means for obtaining a plurality of blood glucose reference sample-sensor measurement pairs;

means for estimating one or more parameters of a probability model based, at least in part, on said blood glucose reference sample-sensor measurement pairs; and

means for determining a function for estimating a blood-glucose concentration in a patient from sensor measurements based, at least in part, on said estimated one or more parameters.

53. An article comprising:

at least one storage medium having stored thereon instructions executable by one or more processors to;

obtain a plurality of blood glucose reference sample-sensor measurement pairs;

estimate one or more parameters of a probability model based, at least in part, on said blood glucose reference sample-sensor measurement pairs; and

determine a function for estimating a blood-glucose concentration in a patient from sensor measurements based, at least in part, on said estimated one or more parameters.