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

Patent metadata

Field	Value
Publication number	US-11445951-B2
Application number	US-201816117466-A
Country	US
Kind code	B2
Filing date	Aug 30, 2018
Priority date	Sep 13, 2017
Publication date	Sep 20, 2022
Grant date	Sep 20, 2022

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.

First claim

What is claimed is:

1. A method for external calibration of a glucose sensor used for measuring a level of glucose in a body of a user, the glucose sensor including physical sensor electronics, a microcontroller, and a working electrode, the method comprising:
accessing electrode current (Isig) signals for the working electrode, the Isig signals being measured by the physical sensor electronics;
accessing Electrochemical Impedance Spectroscopy (EIS) related data for the working electrode, the EIS-related data generated by an EIS procedure;
based on the Isig signals, the 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 the microcontroller, a modification factor based on a value of a physiological calibration factor (PCF), a value of an environmental calibration factor (ECF), or both;
determining, by the microcontroller, whether the calculated modification factor is valid; and
in a case where the modification factor is determined to be valid;
calculating, by the microcontroller, a calibrated respective SG value for each of the SG-predictive models based on the modification factor and the respective SG value for the respective SG-predictive model;
fusing, by the microcontroller, the calibrated SG values for the SG-predictive models to calculate a single, calibrated, fused SG value;
performing, by the microcontroller, error detection diagnostics on the calibrated, fused SG value to determine whether a correctable error exists in the calibrated, fused SG value; and
displaying the calibrated, fused SG value to the user,

wherein the PCF is based on status information of an activity level, a heart rate, a blood pressure, and a body temperature of the user, and
wherein, in a case where it is determined that the correctable error exists, the correctable error in the calibrated, fused SG value is corrected by the microcontroller.

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 the 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 the 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 the modification factor is calculated by fusing the respective values of the physiological calibration factor and the environmental calibration factor.

6. The method of claim 1 , wherein the status information is based on one or more external physiological measurements.

7. The method of claim 1 , wherein the ECF is calculated based on one or more environmental measurements.

8. The method of claim 7 , wherein the 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.

9. The method of claim 1 , wherein the modification factor is calculated in real time.

10. A system comprising:
a glucose sensor configured to measure a level of glucose in a body of a user and comprising:
a working electrode;
physical sensor electronics configured to measure electrode current (Isig) signals for the working electrode;
a microcontroller configured to:
access the Isig signals;
access Electrochemical Impedance Spectroscopy (EIS)-related data for the working electrode, the EIS-related data generated by an EIS procedure;
based on the Isig signals, the EIS-related data, and a plurality of calibration-free sensor glucose (SG)-predictive models, calculate a respective SG value for each of the SG-predictive models;
calculate a modification factor based on a value of a physiological calibration factor (PCF), a value of an environmental calibration factor (ECF), or both;
determine whether the calculated modification factor is valid; and
in a case where the modification factor is determined to be valid:
calculate a calibrated respective SG value for each of the SG-predictive models based on the modification factor and the respective SG values value for the respective SG-predictive model;
fuse the calibrated SG values for the SG-predictive models to calculate a single, calibrated, fused SG value;
perform error detection diagnostics on the calibrated, fused SG value to determine whether a correctable error exists in the calibrated, fused SG value; and
display the calibrated, fused SG value to the user,

wherein the PCF is based on status information of an activity level, a heart rate, a blood pressure, and a body temperature of the user, and
wherein, in a case where it is determined that the correctable error exists, the microcontroller is further configured to correct the correctable error in the calibrated, fused SG value.

11. The system of claim 10 , wherein, in a case where it is determined that an error in the calibrated, fused SG value is not correctable, the microcontroller is further configured to blank out the calibrated, fused SG value.

12. The system of claim 10 , wherein the 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.

13. The system of claim 10 , wherein the 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.

14. The system of claim 10 , wherein the modification factor is calculated by fusing the value of the PCF and the value of the ECF.

15. The system of claim 10 , wherein the status information is based on one or more external physiological measurements.

16. The system of claim 10 , wherein the ECF is calculated based on one or more environmental measurements.

17. The system of claim 16 , wherein the 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.

18. The system of claim 10 , wherein the modification factor is calculated in real time.

Assignees

• Medtronic Minimed Inc

Inventors

• Ajemba Peter
• Nogueira Keith
• Nishida Jeffrey
• Tsai Andy Y

Classifications

• G16H50/70

  for mining of medical data, e.g. analysing previous cases of other patients · CPC title

• G16H50/30

  for calculating health indices; for individual health risk assessment · CPC title

• G16H40/40

  for the management of medical equipment or devices, e.g. scheduling maintenance or upgrades · CPC title

• G16H20/17

  delivered via infusion or injection · CPC title

• G06N20/10

  using kernel methods, e.g. support vector machines [SVM] · CPC title