IP Library Granted Patent US 11,311,217
Granted Patent B2
US 11,311,217 · App. 16/117,617 · Granted Apr 26, 2022

Methods, systems, and devices for calibration and optimization of glucose sensors and sensor output

Inventors: Peter Ajemba (Canyon Country, CA); Keith Nogueira (Mission Hills, CA); Brian T. Kannard (Northridge, CA)
Assignee: MEDTRONIC MINIMED, INC.
A61B5/1495A61B5/1468A61B5/14532A61B5/14865A61B5/686A61B5/6849G01N27/026G06N5/022G16H20/17G16H40/40G16H50/30G16H50/70A61B5/0075A61B5/021A61B5/024A61B5/02055A61B5/1118A61B5/1455A61B5/14546A61B5/7203A61B5/7221A61B5/7267A61B5/742A61B2505/07A61B2560/0223A61B2560/0252A61B2560/0257A61B2562/028A61B2562/029A61B2562/164
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Quick Facts
Patent No.
US 11,311,217
App. No.
16/117,617
Granted
Apr 26, 2022
Kind
B2
Abstract

A continuous glucose monitoring system may utilize externally sourced information regarding the physiological state and ambient environment of its user for externally calibrating sensor glucose measurements. Externally sourced factory calibration information may be utilized, where the information is generated by comparing metrics obtained from the data used to generate the sensor's glucose sensing algorithm to similar data obtained from each batch of sensors to be used with the algorithm in the future. The output sensor glucose value of a glucose sensor may also be estimated by analytically optimizing input sensor signals to accurately correct for changes in sensitivity, run-in time, glucose current dips, and other variable sensor wear effects. Correction actors, fusion algorithms, EIS, and advanced ASICs may be used to implement the foregoing, thereby achieving the goal of improved accuracy and reliability without the need for blood-glucose calibration, and providing a calibration-free, or near calibration-free, sensor.

Claims (24)

1. 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;

measuring, by the physical sensor electronics, 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.

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 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.

6. The method of claim 5 , wherein said correction parameter is a weighting factor that is multiplied by said Isig, EIS parameter, and Vcntr values.

7. The method of claim 6 , 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.

8. The method of claim 7 , wherein each of said first, second, and third values of the weighting factor is determined by using one or more calibration scales.

9. The method of claim 8 , 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.

10. The method of claim 9 , 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.

11. The method of claim 1 , 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.

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

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

14. The method of claim 1 , further comprising applying, by the microcontroller, a filter to the single, fused SG value.

15. The method of claim 1 , wherein the glucose sensor is used in a hybrid closed-loop (HCL) glucose monitoring system.

Assignments (4)
SECURITY INTEREST Recorded Jan 16, 2026
From: MEDTRONIC MINIMED, INC.; COMPANION MEDICAL, INC.
To: CITIBANK, N.A.
Reel/Frame 074394/0237 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Mar 21, 2025
From: AJEMBA, PETER; NOGUEIRA, KEITH; KANNARD, BRIAN T.; NISHIDA, JEFFREY; ENGEL, TALY G.; TSAI, ANDY Y.
To: MEDTRONIC MINIMED, INC.
Reel/Frame 070582/0675 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Feb 28, 2022
From: KANNARD, BRIAN T.
To: MEDTRONIC MINIMED, INC.
Reel/Frame 059111/0512 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Aug 28, 2019
From: AJEMBA, PETER; NOGUEIRA, KEITH
To: MEDTRONIC MINIMED, INC.
Reel/Frame 050198/0322 →
Cited By (5)
US 12,343,144 US 12,507,921 US 12,507,951 US 12,558,009 US 12,667,315