Microprocessors, devices, and methods for use in analyte monitoring systems
First Claim
1. One or more microprocessors, comprising programming to control obtaining a measured charge signal over time, comprising a measured charge signal response curve specifically related to the amount or concentration of the glucose extracted from the subject, wherein said measured charge signal response curve comprises a kinetic region;
- using (i) a mathematical model as presented in Eq. (3A) wherein “
Q”
represents the charge, “
t”
represents the elapsed time, “
So”
is a fitted parameter, “
c1” and
“
c2”
are pre-exponential terms that correspond to the electric current contribution at t=0 for first and second reactions, respectively, “
k1” and
“
k2”
are rate constants for the first and second reactions, respectively, and (ii) an error minimization method, to iteratively estimate values of parameters So, c1, c2, k1, and k2 using said model and error minimization method to fit a predicted response curve to said kinetic region of said measured charge signal response curve, wherein (a) the error minimization method provides a calculated error based on differences between kinetic regions of said predicted and measured charge signal response curves and (b) said estimating is iteratively performed until the calculated error between the predicted and measured charge signal response curves is minimized or until no further statistically significant change is seen in the calculated error, at which time iterative estimation of the parameters is stopped, said iterative estimation and error minimization results in estimated values of said parameters; and
correlating 1/k2 with a glucose amount or concentration to provide a measurement of the amount or concentration of the glucose in the subject.
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Abstract
The present invention comprises one or more microprocessors programmed to execute methods for improving the performance of an analyte monitoring device including prediction of glucose levels in a subject by utilizing a predicted slower-time constant (1/k2). In another aspect of the invention, pre-exponential terms (1/c2) can be used to provide a correction for signal decay (e.g., a Gain Factor). In other aspects, the present invention relates to one or more microprocessors comprising programming to control execution of (i) methods for conditional screening of data points to reduce skipped measurements, (ii) methods for qualifying interpolated/extrapolated analyte measurement values, (iii) various integration methods to obtain maximum integrals of analyte-related signals, as well as analyte monitoring devices comprising such microprocessors. Further, the present invention relates to algorithms for improved optimization of parameters for use in prediction models that require optimization of adjustable parameters.
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Citations
65 Claims
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1. One or more microprocessors, comprising programming to control
obtaining a measured charge signal over time, comprising a measured charge signal response curve specifically related to the amount or concentration of the glucose extracted from the subject, wherein said measured charge signal response curve comprises a kinetic region; -
using (i) a mathematical model as presented in Eq. (3A) wherein “
Q”
represents the charge, “
t”
represents the elapsed time, “
So”
is a fitted parameter, “
c1” and
“
c2”
are pre-exponential terms that correspond to the electric current contribution at t=0 for first and second reactions, respectively, “
k1” and
“
k2”
are rate constants for the first and second reactions, respectively, and (ii) an error minimization method, to iteratively estimate values of parameters So, c1, c2, k1, and k2 using said model and error minimization method to fit a predicted response curve to said kinetic region of said measured charge signal response curve, wherein (a) the error minimization method provides a calculated error based on differences between kinetic regions of said predicted and measured charge signal response curves and (b) said estimating is iteratively performed until the calculated error between the predicted and measured charge signal response curves is minimized or until no further statistically significant change is seen in the calculated error, at which time iterative estimation of the parameters is stopped, said iterative estimation and error minimization results in estimated values of said parameters; and
correlating 1/k2 with a glucose amount or concentration to provide a measurement of the amount or concentration of the glucose in the subject. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21)
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22. A method of providing a glucose amount or concentration in a subject, comprising
obtaining a measured charge signal over time, comprising a measured charge signal response curve specifically related to the amount or concentration of the glucose extracted from the subject, wherein said measured charge signal response curve comprises a kinetic region; -
using (i) a mathematical model as presented in Eq. (3A) wherein “
Q”
represents the charge, “
t”
represents the elapsed time, “
So”
is a fitted parameter, “
c1” and
“
c2”
are pre-exponential terms that correspond to the electric current contribution at t=0 for first and second reactions, respectively, “
k1” and
“
k2”
are rate constants for the first and second reactions, respectively, and (ii) an error minimization method, to iteratively estimate values of parameters So, c1, c2, k1, and k2 using said model and error minimization method to fit a predicted response curve to said kinetic region of said measured charge signal response curve, wherein (a) the error minimization method provides a calculated error based on differences between kinetic regions of said predicted and measured charge signal response curves, and (b) said estimating is iteratively performed until the calculated error between the predicted and measured charge signal response curves is minimized or until no further statistically significant change is seen in the calculated error, at which time iterative estimation of the parameters is stopped, said iterative estimation and error minimization results in estimated values of said parameters; and
correlating 1/k2 with a glucose amount or concentration to provide a measurement of the amount or concentration of the glucose in the subject.
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23. One or more microprocessors, comprising programming to control
obtaining a measured charge signal over time using an electrochemical sensor, said measured charge signal comprising a measured charge signal response curve specifically related to an amount or concentration of glucose extracted from a subject, wherein said measured charge signal response curve comprises a kinetic region; -
using (i) a mathematical model as presented in Eq. (3A) wherein “
Q”
represents the charge, “
t”
represents the elapsed time, “
So”
is a fitted parameter, “
c1” and
“
c2”
are pre-exponential terms that correspond to the electric current contribution at t=0 for first and second reactions, respectively, “
k1” and
“
k2”
are rate constants for the first and second reactions, respectively, and (ii) an error minimization method, to iteratively estimate values of parameters So, c1, c2, k1, and k2 using said model and error minimization method to fit a predicted response curve to said kinetic region of said measured charge signal response curve, wherein (a) the error minimization method provides a calculated error based on differences between kinetic regions of said predicted and measured charge signal response curves, and (b) said estimating is iteratively performed until the calculated error between the predicted and measured charge signal response curves is minimized or until no further statistically significant change is seen in the calculated error, at which time iterative estimation of the parameters is stopped, said iterative estimation and error minimization results in estimated values of said parameters; and
correcting for signal decay of the electrochemical sensor by multiplying the measured charge signal by a gain factor estimated from 1/c2. - View Dependent Claims (24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44)
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45. A method of correcting for signal decay of an electrochemical sensor used for the detection of an amount or concentration of glucose in a subject, said method comprising
obtaining a measured charge signal over time using said electrochemical sensor, said measured charge signal comprising a measured charge signal response curve specifically related to the amount or concentration of glucose extracted from the subject, wherein said measured charge signal response curve comprises a kinetic region; -
using (i) a mathematical model as presented in Eq. (3A) wherein “
Q”
represents the charge, “
t”
represents the elapsed time, “
So”
is a fitted parameter, “
c1” and
“
c2”
are pre-exponential terms that correspond to the electric current contribution at t=0 for first and second reactions, respectively, “
k1” and
“
k2”
are rate constants for the first and second reactions, respectively, and (ii) an error minimization method, to iteratively estimate values of parameters So, c1, c2, k1, and k2 using said model and error minimization method to fit a predicted response curve to said kinetic region of said measured charge signal response curve, wherein (a) the error minimization method provides a calculated error based on differences between kinetic regions of said predicted and measured charge signal response curves, and (b) said estimating is iteratively performed until the calculated error between the predicted and measured charge signal response curves is minimized or until no, further statistically significant change is seen in the calculated error, at which time iterative estimation of the parameters is stopped, said iterative estimation and error minimization results in estimated values of said parameters; and
correcting for signal decay of the electrochemical sensor by multiplying the measured charge signal by a gain factor estimated from 1/c2.
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46. One or more microprocessors, comprising programming to control
providing a measurement value related to glucose amount or concentration in a subject, a skin conductance reading associated in time with said glucose measurement value, and one or more further data integrity screens associated with said glucose measurement value; - and
accepting said measurement value when either (i) said skin conductance reading and said one or more further data integrity screens fall within predetermined acceptable ranges or within predetermined threshold values, or (ii) said skin conductance reading falls outside of predetermined acceptable range or beyond predetermined threshold value and said one or more further data integrity screens fall within predetermined acceptable ranges or with predetermined threshold values, or skipping said measurement value when said skin conductance reading falls outside of predetermined acceptable range or beyond predetermined threshold value and one or more of said one or more further data integrity screens fall outside of predetermined acceptable ranges or beyond predetermined threshold values. - View Dependent Claims (47, 48)
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49. One or more microprocessors, comprising programming to control
providing a measurement signal, comprising data points, related to glucose amount or concentration in a subject, wherein said data points typically have a monotonic trend; - and
evaluating said data points for one or more non-monotonic event, wherein (i) if the data points have an acceptable monotonic trend the measurement signal is accepted for further processing, or (ii) if the data points comprise one or more non-monotonic events then a percent contribution of said one or more non-monotonic events relative to total measurement signal is further evaluated, wherein if the percent contribution is less than a predetermined threshold value or falls within a predetermined range relative to total measurement signal, then the measurement signal is accepted for further processing;
however, if the percent contribution is greater than a predetermined threshold value or falls outside a predetermined range relative to total measurement signal, then the measurement signal is not accepted for further processing and the measurement signal is skipped. - View Dependent Claims (50, 51)
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52. One or more microprocessors, comprising programming to control:
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qualifying whether an unusable analyte-related electrochemical current signal from a given measurement cycle should be replaced by interpolation or extrapolation by applying one or more of the following criteria;
(i) if a sensor consistency check value for the measurement cycle falls within a predetermined acceptable range or within a predetermined threshold then the corresponding analyte-related signal may be replaced;
(ii) if a change in background current for the measurement cycle falls within a predetermined acceptable range or within a predetermined threshold then the corresponding analyte-related signal may be replaced;
(iii) if a change in temperatures falls within a predetermined acceptable range or within a predetermined threshold then the corresponding analyte-related signal may be replaced; and
replacing, in a series of analyte-related signals, an unusable analyte-related signal with an estimated signal by either;
(A) if one or more analyte-related signals previous to the unusable analyte-related signal and one or more analyte-related signals subsequent to the unusable analyte related signal are available, then interpolation is used to estimate the unusable, intervening analyte-related signal, or (B) if two or more analyte-related signals previous to the unusable analyte-related signal are available, then extrapolation is used to estimate the unusable, subsequent analyte-related signal;
wherein said series of analyte-related signals is obtained from an analyte monitoring device over time, and each analyte-related signal is related to an amount or concentration of analyte in a subject being monitored with the analyte monitoring device. - View Dependent Claims (53, 54, 55, 56)
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57. One or more microprocessors, comprising programming to control
selecting a current integration method for an analyte-related current signal, wherein said analyte-related current signal comprises data points, a two sensor system is used for detecting said analyte-related current signal, each of said two sensors are electrochemical sensors, each sensor alternately acts as cathode and anode, a current signal, comprising data points, is detected in a half-measurement cycle from the anode and the cathode, and the analyte-related current signal is obtained from the cathode; -
determining a background baseline for a given sensor when acting as cathode is determined from the last two data points of the current signal detected for the same sensor in a previous half-cycle when the sensor acted as an anode; and
subtracting the background baseline from the analyte-related current signal and if over-subtraction of the analyte-related current signal occurs, employing one of the following integration methods to determine an analyte-related charge signal based on the analyte-related current signal;
(i) stopping integration when the maximum integral is reached and using the maximum integral as the analyte-related charge signal;
or (ii) recalculating a background baseline based on the last two data points from the analyte-related current signal at the cathode, subtracting the recalculated background baseline from the analyte-related current signal, and integrating the background subtracted analyte-related current signal to obtain the analyte-related charge signal. - View Dependent Claims (58)
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59. One or more algorithms to optimize parameters for use in a model that requires optimization of adjustable parameters, said one or more algorithms comprising
dividing a data set into a training set and a validation set; -
training the model to determine the adjustable parameters using said training set;
stopping said training before the model parameters fully converged; and
validating the parameters using the validation set, wherein said validated parameters are optimized parameters for use in the model. - View Dependent Claims (60)
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61. One or more algorithms to optimize parameters for use in a prediction model used by an analyte monitoring device, wherein said prediction model requires optimization of adjustable parameters, said one or more algorithms comprising
optimizing the parameters based on multiple analyte readings that quantify two or more regions corresponding to various levels of accuracy for the prediction model used by the analyte monitoring device, wherein one or more of the regions have an associated higher risk relative to one or more other regions, such that optimization of the parameters is carried out until error associated with the prediction model is minimized in the regions associated with higher risk.
Specification