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1. WO2019055237 - METHODS, SYSTEMS, AND DEVICES FOR CALIBRATION AND OPTIMIZATION OF GLUCOSE SENSORS AND SENSOR OUTPUT

Note: Text based on automatic Optical Character Recognition processes. Please use the PDF version for legal matters

[ EN ]

WHAT IS CLAIMED IS:

1. A method for external calibration of a glucose sensor used for measuring the level of glucose in the body of a user, said sensor including physical sensor electronics, a microcontroller, and a working electrode, the method comprising:

periodically measuring, by the physical sensor electronics, electrode current (Isig) signals for the working electrode;

performing, by the microcontroller, an Electrochemical Impedance Spectroscopy (EIS) procedure to generate EIS-related data for the working electrode;

based on the Isig signals and EIS-related data and a plurality of calibration-free sensor glucose (SG) -predictive models, calculating, by the microcontroller, a respective SG value for each of the SG-predictive models;

calculating, by said microcontroller, a modification factor based on respective values of a physiological calibration factor (PCF), an environmental calibration factor (ECF), or both, and determining, by said microcontroller, whether said calculated modification factor is valid;

when the modification factor is valid, calculating, by the microcontroller, a calibrated respective SG value for each of the SG-predictive models based on said modification factor and said respective SG values;

fusing, by the microcontroller, the calibrated respective SG values to calculate a single, calibrated, fused SG value;

performing, by the microcontroller, error detection diagnostics on said calibrated, fused SG value to determine whether a correctable error exists in the calibrated, fused, SG value;

correcting, by the microcontroller, said correctable error; and

displaying a corrected, calibrated, fused SG value to the user.

2. The method of claim 1, wherein, when it is determined that an error in the calibrated, fused SG value is not correctable, the calibrated, fused SG value is blanked to the user.

3. The method of claim 1, wherein said plurality of calibration- free SG-predictive models include at least two of a genetic programming model, an analytical model, a bag of trees model, and a decision tree model.

4. The method of claim 1, wherein said plurality of calibration- free SG-predictive models include a genetic programming model, an analytical model, a bag of trees model, and a decision tree model.

5. The method of claim 1, wherein said modification factor is calculated by fusing said respective values of the physiological calibration factor and the environmental calibration factor.

6. The method of claim 1, wherein said physiological calibration factor is calculated based on one or more external physiological measurements.

7. The method of claim 6, wherein said one or more external physiological measurements are selected from the group consisting of activity level or exercise status, heart rate status, blood pressure status, and body temperature status.

8. The method of claim 1, wherein said environmental calibration factor is calculated based on one or more environmental measurements.

9. The method of claim 8, wherein said one or more environmental measurements are selected from the group consisting of ambient temperature status, ambient pressure status, relative altitude status, and ambient humidity status.

10. The method of claim 1, wherein said modification factor is calculated in real time.

11. A method for external calibration of a glucose sensor used for measuring the level of glucose in the body of a user, said sensor including physical sensor electronics, a microcontroller, and a working electrode, the method comprising:

periodically measuring, by the physical sensor electronics, electrode current (Isig) signals for the working electrode;

performing, by the microcontroller, an Electrochemical Impedance Spectroscopy (EIS) procedure to generate EIS-related data for the working electrode;

based on the Isig signals and EIS-related data and a plurality of calibration-free sensor glucose (SG) -predictive models, calculating, by the microcontroller, a respective SG value for each of the SG-predictive models;

fusing, by the microcontroller, the respective SG values to calculate a single, fused SG value;

calculating, by said microcontroller, a modification factor based on respective values of a physiological calibration factor (PCF), an environmental calibration factor (ECF), or both, and determining, by said microcontroller, whether said calculated modification factor is valid;

when the modification factor is valid, calculating, by the microcontroller, a single, calibrated, fused SG value based on said modification factor and said single, fused SG value;

performing, by the microcontroller, error detection diagnostics on said calibrated, fused SG value to determine whether a correctable error exists in the calibrated, fused, SG value;

correcting, by the microcontroller, said correctable error; and

displaying a corrected, calibrated, fused SG value to the user.

12. The method of claim 11, wherein, when it is determined that an error in the calibrated, fused SG value is not correctable, the calibrated, fused SG value is blanked to the user.

13. The method of claim 11, wherein said plurality of calibration- free SG-predictive models include at least two of a genetic programming model, an analytical model, a bag of trees model, and a decision tree model.

14. The method of claim 11, wherein said plurality of calibration- free SG-predictive models include a genetic programming model, an analytical model, a bag of trees model, and a decision tree model.

15. The method of claim 11, wherein said modification factor is calculated by fusing said respective values of the physiological calibration factor and the environmental calibration factor.

16. The method of claim 11, wherein said physiological calibration factor is calculated based on one or more external physiological measurements.

17. The method of claim 16, wherein said one or more external physiological measurements are selected from the group consisting of activity level or exercise status, heart rate status, blood pressure status, and body temperature status.

18. The method of claim 11, wherein said environmental calibration factor is calculated based on one or more environmental measurements.

19. The method of claim 18, wherein said one or more environmental measurements are selected from the group consisting of ambient temperature status, ambient pressure status, relative altitude status, and ambient humidity status.

20. The method of claim 11, further comprising applying, by the microcontroller, a filter to the single, fused SG value prior to calculating said single, calibrated, fused SG value.

21. A method for using factory calibration to correct for manufacturing batch variations in one or more sensor parameters of a glucose sensor used for measuring the level of glucose in the body of a user, said sensor including physical sensor electronics, a microcontroller, and a working electrode, the method comprising:

periodically measuring, by the physical sensor electronics, electrode current (Isig) signal values for the working electrode;

performing, by the microcontroller, an Electrochemical Impedance Spectroscopy (EIS) procedure to generate values of one or more EIS-related parameters for the working electrode;

calculating, by the microcontroller, values of counter voltage (Vcntr) for the sensor; applying, by said microcontroller, a factory calibration factor to said Isig, EIS parameter, and Vcntr values to generate respective modified Isig, EIS parameter, and Vcntr values;

based on the modified Isig, EIS parameter, and Vcntr values and a plurality of calibration-free sensor glucose (SG)-predictive models, calculating, by the microcontroller, a respective SG value for each of the SG-predictive models;

fusing, by the microcontroller, the respective SG values to calculate a single, fused SG value;

performing, by the microcontroller, error detection diagnostics on said calibrated, fused SG value to determine whether a correctable error exists in the calibrated, fused, SG value;

correcting, by the microcontroller, said correctable error; and

displaying a corrected, calibrated, fused SG value to the user.

22. The method of claim 21, wherein, when it is determined that an error in the calibrated, fused SG value is not correctable, the calibrated, fused SG value is blanked to the user.

23. The method of claim 21, wherein said plurality of calibration- free SG-predictive models include at least two of a genetic programming model, an analytical model, a bag of trees model, and a decision tree model.

24. The method of claim 21, wherein said plurality of calibration- free SG-predictive models include a genetic programming model, an analytical model, a bag of trees model, and a decision tree model.

25. The method of claim 21, wherein said factory calibration factor is a correction parameter that mitigates deviations in sensor performance characteristics between sensors of a prior batch and sensors of a subsequent batch.

26. The method of claim 25, wherein said correction parameter is a weighting factor that is multiplied by said Isig, EIS parameter, and Vcntr values.

27. The method of claim 26, wherein said weighting factor has a first value that is applied to the Isig values, a second value that is applied to the EIS parameter values, and a third value that is applied to the Vcntr values.

28. The method of claim 27, wherein each of said first, second, and third values of the weighting factor is determined by using one or more calibration scales.

29. The method of claim 28, wherein said Isig value is weighted by said first value of the weighting factor, said EIS parameter value is weighted by said second value of the weighting factor, and said Vcntr value is weighted by said third value of the weighting factor.

30. The method of claim 29, wherein the each of the weighted Isig value, the weighted EIS parameter value, and the weighted Vcntr value is clamped to respective pre-defined acceptable range.

31. The method of claim 21, wherein said factory calibration factor is determined for each of said Isig, EIS parameter, and Vcntr values based on respective reference values for a previously-manufactured sensor batch.

32. The method of claim 21, wherein said one or more EIS-related parameters includes 1 kHz real impedance.

33. The method of claim 21, wherein said one or more EIS-related parameters includes 1 kHz imaginary impedance.

34. The method of claim 21, further comprising applying, by the microcontroller, a filter to the single, fused SG value.

35. The method of claim 21, wherein the glucose sensor is used in a hybrid closed-loop (HCL) glucose monitoring system.

36. A method of optimizing glucose sensor estimation for a glucose sensor used for measuring the level of glucose in the body of a user, said sensor including physical sensor electronics, a microcontroller, and a working electrode, the method comprising:

periodically measuring, by the physical sensor electronics, electrode current (Isig) signals for the working electrode;

performing, by the microcontroller, an Electrochemical Impedance Spectroscopy (EIS) procedure to generate EIS-related data for the working electrode;

calculating, by the microcontroller, an adjusted calibration factor for the sensor based on the EIS -related data;

calculating, by the microcontroller, an adjusted offset value for the sensor based on at least one of a stabilization time adjustment and a non-linear sensor response adjustment; and calculating, by the microcontroller, an optimized measured glucose value (SG) based on the adjusted calibration factor and the adjusted offset value,

wherein SG = (adjusted calibration factor) x (Isig + adjusted offset value).

37. The method of claim 36, further including displaying the optimized measured glucose value to the user.

38. The method of claim 36, wherein said adjusted calibration factor is calculated based on 128 Hz real impedance values.

39. The method of claim 36, wherein said adjusted calibration factor is calculated based on at least one of a foreign body response, an oxygen response, a dip adjustment response, and a stabilization response for the sensor.

40. The method of claim 39, wherein said foreign body response is calculated as foreign body response wherein imag1000 is the imaginary 1000

Hz frequency input, and c1, c2, and c3 are experimentally determined calibration coefficients.

41. The method of claim 40, wherein c1 is -1.4, c2 is 0.008, and c3 is 1.3.

42. The method of claim 39, wherein said oxygen response is calculated as oxygen response = c1 × Vcntr2 + c2 × Vcntr + c3, wherein Vcntr is the counter voltage input and c1, c2, and c3 are experimentally determined calibration coefficients.

43. The method of claim 42, wherein c1 is 2.0/V, c2 is 2.0, and c3 is 2.0V.

44. The method of claim 39, wherein said dip adjustment response is calculated as dip adjustment wherein IsigTrend is the long-term sensor current trend

input and c1, c2 and c3 are experimentally determined calibration coefficients.

45. The method of claim 44, wherein c1 is 4.68, c2 is -0.21, and c3 is 0.97.

46. The method of claim 44, wherein the long-term sensor current trend is implemented at 6, 12, 18, 24, and 48-hour average sensor current values.

47. The method of claim 39, wherein said stabilization response is calculated as stabilization response = c1 × Vcntr + c2, where Vcntr is the counter voltage input and c1 and c2 are experimentally determined calibration coefficients.

48. The method of claim 47, wherein c1 is 0.48/V and c2 is 1.24.

49. The method of claim 36, wherein said adjusted calibration factor is calculated based on a foreign body response, an oxygen response, a dip adjustment response, and a stabilization response for the sensor.

50. The method of claim 36, wherein said stabilization time adjustment for calculation of the adjusted offset value is calculated as stabilization time adjustment
wherein age is the sensor age, and c1, c2 and c3 are experimentally determined calibration coefficients.

51. The method of claim 50, wherein c1 is -5.4, c2 is -0.50, and c3 is -1.5nA.

52. The method of claim 50, wherein the sensor age is measured from one of sensor warm-up completion, sensor insertion, and sensor calibration completion.

53. The method of claim 36, said non-linear sensor response adjustment for calculation of the adjusted offset value is calculated as non-linear sensor response adjustment = c1 + (age × c2 + c3) × Isig, wherein age is the sensor age, and c1, c2 and c3 are experimentally determined calibration coefficients.

54. The method of claim 53, wherein c1 is 13.8nA, c2 is -0.1/day, and c3 is -0.7.

55. The method of claim 53, wherein the sensor age is measured from one of sensor warm-up completion, sensor insertion, and sensor calibration completion.