Methods, systems, and devices for calibration and optimization of glucose sensors and sensor output
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.
1. A method for calibrating one or more sensor parameters 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:
periodically measuring, by the physical sensor electronics, electrode current signal (Isig) values for the working electrode;
generating, by the microcontroller, Electrochemical Impedance Spectroscopy (EIS) parameter values for the working electrode based on an EIS procedure;
measuring, by the physical sensor electronics, counter voltage (Vcntr) values for the glucose sensor;
weighting the Isig values, the EIS parameter values, and the Vcntr values based on respective weight metrics to generate respective resulting Isig values, resulting EIS parameter values, and resulting Vcntr values, wherein the respective weight metrics are converted from a factory calibration factor;
based on the resulting Isig values, the resulting EIS parameter values, the resulting Vcntr values, and a plurality of sensor glucose (SG) predictive models, calculating, by the microcontroller, a respective SG value for each of the SG predictive models;
fusing the respective SG values to calculate a single, fused SG value;
performing, by the microcontroller, error detection diagnostics on the single, fused SG value to determine whether a correctable error exists in the single, fused SG value;
in a case where the error detection diagnostics determine that a correctable error exists, correcting, by the microcontroller, the correctable error in the single, fused SG value to generate a corrected, single, fused SG value; and
providing the corrected, single, fused SG value.
2. The method of claim 1 , further comprising: in a case where the error detection diagnostics determine that an error in the single, fused SG value is not correctable, blanking the single, fused SG value.
3. The method of claim 1 , wherein the plurality of SG predictive models includes at least two of a genetic programming model, an analytical model, a bag of trees model, or a decision tree model.
4. The method of claim 1 , wherein the plurality of SG predictive models includes a genetic programming model, an analytical model, a bag of trees model, and a decision tree model.
5. The method of claim 1 , further comprising:
accessing the factory calibration factor,
determining whether the factory calibration factor is valid; and
in response to a determination that the factory calibration factor is valid, converting the factory calibration factor to the respective weight metrics.
6. The method of claim 5 , wherein the determining whether the factory calibration factor is valid comprises determining whether the factory calibration factor falls within a predetermined range of values.
7. The method of claim 5 , wherein the determining whether the factory calibration factor is valid comprises:
determining whether the factory calibration factor is divisible by a predetermine prime number; and
in response to a determination that the factory calibration factor is divisible by the predetermined prime number, determining whether a result of dividing the factory calibration factor by the predetermined prime number is within a predetermine range of values.
8. The method of claim 1 , wherein the weighting the Isig values, the EIS parameter values, and the Vcntr values based on the respective weight metrics comprises:
weighting the Isig values, the EIS parameter values, and the Vcntr values based on the respective weight metrics to generate respective weighted Isig values, weighted EIS parameter values, and weighted Vcntr values; and
clamping the respective weighted Isig values, weighted EIS parameter values, and weighted Vcntr values to a predetermine range of values to generate the respective resulting Isig values, resulting EIS parameter values, and resulting Vcntr values.
9. The method of claim 1 , further comprising applying, by the microcontroller, a filter to the single, fused SG value.
10. The method of claim 1 , wherein the glucose sensor is used in a hybrid closed-loop glucose monitoring system.
11. A glucose sensor for calibrating one or more sensor parameters used for measuring a level of glucose in a body of a user, the glucose sensor comprising:
a working electrode;
physical sensor electronics configured to periodically measure electrode current signal (Isig) values for the working electrode and counter voltage (Vcntr) values for the glucose sensor; and
a microcontroller configured at least to perform:
generating Electrochemical Impedance Spectroscopy (EIS) parameter values for the working electrode based on an EIS procedure;
weighting the Isig values, the EIS parameter values, and the Vcntr values based on respective weight metrics to generate respective resulting Isig values, resulting EIS parameter values, and resulting Vcntr values, wherein the respective weight metrics are converted from a factory calibration factor;
based on the resulting Isig values, the resulting EIS parameter values, the resulting Vcntr values, and a plurality of sensor glucose (SG) predictive models, calculating a respective SG value for each of the SG predictive models;
fusing the respective SG values to calculate a single, fused SG value;
performing error detection diagnostics on the single, fused SG value to determine whether a correctable error exists in the single, fused SG value;
in a case where the error detection diagnostics determine that a correctable error exists, correcting the correctable error to generate a corrected, single, fused SG value; and
providing the corrected, single, fused SG value.
12. The glucose sensor of claim 11 , wherein in a case where the error detection diagnostics determine that an error in the single, fused SG value is not correctable, the microcontroller blanks the single, fused SG value.
13. The glucose sensor of claim 11 , wherein the plurality of SG predictive models includes at least two of a genetic programming model, an analytical model, a bag of trees model, or a decision tree model.
14. The glucose sensor of claim 11 , wherein the plurality of SG predictive models includes a genetic programming model, an analytical model, a bag of trees model, and a decision tree model.
15. The glucose sensor of claim 11 , wherein the microcontroller is further configured at least to perform:
accessing the factory calibration factor;
determining whether the factory calibration factor is valid; and
in response to a determination that the factory calibration factor is valid, converting the factory calibration factor to the respective weight metrics.
16. The glucose sensor of claim 15 , wherein the determining whether the factory calibration factor is valid comprises determining whether the factory calibration factor falls within a predetermined range of values.
17. The glucose sensor of claim 15 , wherein the determining whether the factory calibration factor is valid comprises:
determining whether the factory calibration factor is divisible by a predetermine prime number; and
in response to a determination that the factory calibration factor is divisible by the predetermined prime number, determining whether a result of dividing the factory calibration factor by the predetermined prime number is within a predetermine range of values.
18. The glucose sensor of claim 11 , wherein the weighting the Isig values, the EIS parameter values, and the Vcntr values based on the respective weight metrics comprises:
weighting the Isig values, the EIS parameter values, and the Vcntr values based on the respective weight metrics to generate respective weighted Isig values, weighted EIS parameter values, and weighted Vcntr values; and
clamping the respective weighted Isig values, weighted EIS parameter values, and weighted Vcntr values to a predetermine range of values to generate the respective resulting Isig values, resulting EIS parameter values, and resulting Vcntr values.
19. The glucose sensor of claim 11 , wherein the microcontroller is further configured to apply a filter to the single, fused SG value.
20. A non-transitory, computer-readable medium having instructions that, when executed by one or more processors, cause the one or more processors to perform a method for calibrating one or more sensor parameters of a glucose sensor used for measuring a level of glucose in a body of a user, the method comprising:
periodically measuring electrode current signal (Isig) values for a working electrode;
generating Electrochemical Impedance Spectroscopy (EIS) parameter values for the working electrode based on an EIS procedure;
measuring counter voltage (Vcntr) values for the glucose sensor;
weighting the Isig values, the EIS parameter values, and the Vcntr values based on respective weight metrics to generate respective resulting Isig values, resulting EIS parameter values, and resulting Vcntr values, wherein the respective weight metrics are converted from a factory calibration factor;
based on the resulting Isig values, the resulting EIS parameter values, the resulting Vcntr values, and a plurality of sensor glucose (SG) predictive models, calculating a respective SG value for each of the SG predictive models;
fusing the respective SG values to calculate a single, fused SG value;
performing error detection diagnostics on the single, fused SG value to determine whether a correctable error exists in the single, fused SG value;
in a case where the error detection diagnostics determine that a correctable error exists, correcting the correctable error to generate a corrected, single, fused SG value; and
providing the corrected, single, fused SG value.