Methods for improving performance and reliability of biosensors
First Claim
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1. A method for measuring glucose present in a subject, said method comprising:
- (A) transdermally extracting a sample comprising glucose from the subject using a sampling system that is in operative contact with a skin or mucosal surface of said subject;
(B) obtaining a measured signal over time, comprising a measured signal response curve, from the extracted glucose, wherein said measured signal is specifically related to the amount or concentration of glucose, and said measured signal response curve comprises kinetic and equilibrium regions;
(C) using (i) a mathematical model comprising selected parameters, wherein said model describes the measured signal response curve, and said mathematical model is selected from the group consisting of a first order process, combined first order and zero order process, a parallel multiple first order process, a flux process, and an nth order process, and (ii) an error minimization method, to iteratively estimate values of the parameters using said model and error minimization method to fit a predicted response curve to said measured signal response curve, wherein (a) the error minimization method provides a calculated error based on differences between said predicted and measured signal response curves, and (b) said estimating is iteratively performed until the calculated error between the predicted and measured signal response curves falls within an acceptable range 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 a predicted response curve corresponding to said measured signal response curve, said predicted response curve yields a predicted end-point value and a measurement correlated to the amount or concentration of the glucose.
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Abstract
The present invention relates to a predictive-kinetic method for use with data processing of a sensor-generated signal, as well as, microprocessors and monitoring systems employing such a predictive-kinetic method. Data from a transient region of a signal is used with suitable models and curve-fitting methods to predict the signal that would be measured for the system at the completion of the reaction. The values resulting from data processing of sensor response using the methods of the present invention are less sensitive to measurement variables.
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Citations
85 Claims
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1. A method for measuring glucose present in a subject, said method comprising:
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(A) transdermally extracting a sample comprising glucose from the subject using a sampling system that is in operative contact with a skin or mucosal surface of said subject;
(B) obtaining a measured signal over time, comprising a measured signal response curve, from the extracted glucose, wherein said measured signal is specifically related to the amount or concentration of glucose, and said measured signal response curve comprises kinetic and equilibrium regions;
(C) using (i) a mathematical model comprising selected parameters, wherein said model describes the measured signal response curve, and said mathematical model is selected from the group consisting of a first order process, combined first order and zero order process, a parallel multiple first order process, a flux process, and an nth order process, and (ii) an error minimization method, to iteratively estimate values of the parameters using said model and error minimization method to fit a predicted response curve to said measured signal response curve, wherein (a) the error minimization method provides a calculated error based on differences between said predicted and measured signal response curves, and (b) said estimating is iteratively performed until the calculated error between the predicted and measured signal response curves falls within an acceptable range 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 a predicted response curve corresponding to said measured signal response curve, said predicted response curve yields a predicted end-point value and a measurement correlated to the amount or concentration of the glucose. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49)
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50. A method for measuring glucose present in a subject, said method comprising:
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(a) transdermally extracting a sample comprising the glucose from the subject using a sampling system that is in operative contact with a skin or mucosal surface of said subject;
(b) obtaining a measured signal over time, comprising a measured signal response curve, from the extracted glucose, wherein said measured signal is specifically related to the amount or concentration of glucose, and said measured signal response curve comprises kinetic and equilibrium regions;
(c) selecting a mathematical model comprising selected parameters, wherein said model describes the measured signal response curve, and said mathematical model is selected from the group consisting of a first order process, combined first order and zero order process, a parallel multiple first order process, a flux process, and an nth order process;
(d) iteratively estimating values of the parameters using said model and an error minimization method to fit a predicted response curve to said measured signal response curve, wherein (i) the error minimization method provides a calculated error based on differences between said predicted and measured signal response curves, and (ii) said estimating is iteratively performed until the calculated error between the predicted and measured signal response curves falls within an acceptable range 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 a predicted response curve corresponding to said measured signal response curve, said predicted response curve yields a predicted end-point value and a measurement correlated to the amount or concentration of the glucose. - View Dependent Claims (51, 52, 53, 54)
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55. A method for measuring glucose present in a subject, said method comprising:
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(A) transdermally extracting a sample comprising glucose from the subject using a sampling system that is in operative contact with a skin or mucosal surface of said subject;
(B) obtaining a measured current signal over time, comprising a measured current signal response curve, from the extracted glucose, wherein said measured current signal is specifically related to the amount or concentration of glucose, and said measured current signal response curve comprises kinetic and equilibrium regions;
(C) using (i) a mathematical model comprising selected parameters, wherein said model describes the measured current signal response curve, and said mathematical model is selected from the group consisting of a first order process, combined first order and zero order process, a parallel multiple first order process, a flux process, and an nth order process, and (ii) an error minimization method, to iteratively estimate values of the parameters using said model and error minimization method to fit a predicted response curve to said measured current signal response curve, wherein (a) the error minimization method provides a calculated error based on differences between said predicted and measured signal response curves, and (b) said estimating is iteratively performed until the calculated error between the predicted and measured signal response curves falls within an acceptable range 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 a predicted response curve corresponding to said measured signal response curve, said predicted response curve yields a predicted end-point value and a measurement correlated to the amount or concentration of the glucose;
(D) performing a background subtraction correction of the predicted response curve using the predicted end-point value as a final background value; and
(E) integrating the background corrected predicted response curve to obtain a measurement of the amount or concentration of glucose in the subject at the time of sampling. - View Dependent Claims (57, 58, 59)
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56. A method for compensating for an incomplete reaction involving the detection of an analyte by predicting a background signal, said method comprising
(A) providing a measured signal over time, comprising a measured signal response curve, wherein said measured signal is specifically related to an amount or concentration of analyte, and said measured signal response curve comprises kinetic and equilibrium regions; -
(B) using (i) a mathematical model comprising selected parameters, wherein said model describes the measured signal response curve, and said mathematical model is selected from the group consisting of a first order process, combined first order and zero order process, a parallel multiple first order process, a flux process, and an nth order process, and (ii) an error minimization method, to iteratively estimate values of the parameters using said model and error minimization method to fit a predicted response curve to said measured signal response curve, wherein (a) the error minimization method provides a calculated error based on differences between said predicted and measured signal response curves, and (b) said estimating is iteratively performed until the calculated error between the predicted and measured signal response curves falls within an acceptable range 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 a predicted response curve corresponding to said measured signal response curve, said predicted response curve yields a predicted final background value and a measurement correlated to the amount or concentration of the analyte; and
(C) performing a background subtraction correction of the predicted response curve using the predicted final background value, wherein said background subtraction compensates for an incomplete reaction involved in the detection of the analyte.
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60. One or more microprocessors, comprising programming to control
(i) a measurement cycle comprising (a) operating a sampling device for extracting a sample from the biological system, said sample comprising glucose, and (b) operating a sensing device for obtaining a measured signal over time, comprising a measured signal response curve, from the extracted glucose, wherein said measured signal is specifically related to the amount or concentration of glucose, and said measured signal response curve comprises kinetic and equilibrium regions; - and
(ii) a computation method using (a) a mathematical model comprising selected parameters, wherein said model describes the measured signal response curve, and said mathematical model is selected from the group consisting of a first order process, combined first order and zero order process, a parallel multiple first order process, a flux process, and an nth order process, and (b) an error minimization method, to iteratively estimate values of the parameters using said model and error minimization method to fit a predicted response curve to said measured signal response curve, wherein (a′
) the error minimization method provides a calculated error based on differences between said predicted and measured signal response curves, and (b′
) said estimating is iteratively performed until the calculated error between the predicted and measured signal response curves falls within an acceptable range 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 a predicted response curve corresponding to said measured signal response curve, said predicted response curve yields a predicted end-point value and a measurement correlated to the amount or concentration of the glucose. - View Dependent Claims (61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73)
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74. A monitoring system for frequent measurement of glucose amount or concentration present in a subject, said system comprising, in operative combination:
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(A) a sampling device for frequently extracting a sample comprising glucose from the subject, wherein said sampling device is adapted for extracting the glucose across a skin or mucosal surface of said subject;
(B) a sensing device in operative contact with the glucose extracted by the sampling device, wherein said sensing device obtains a measured signal over time, comprising a measured signal response curve, from the extracted glucose, wherein said measured signal is specifically related to the amount or concentration of glucose, and said measured signal response curve comprises kinetic and equilibrium regions;
(C) one or more microprocessor(s) in operative communication with the sampling device and the sensing device, wherein said microprocessor is capable of (i) controlling the sampling device and the sensing device to obtain a series of measured signals, in the form of measured signal response curves, at selected time intervals, (ii) predicting measurement values for each measured signal in the series by employing (a) a mathematical model comprising selected parameters, wherein said model describes the measured signal response curve of (B), and said mathematical model is selected from the group consisting of a first order process, combined first order and zero order process, a parallel multiple first order process, a flux process, and an nth order process, and (b) an error minimization method, to iteratively estimate values of the parameters using said model and error minimization method to fit a predicted response curve to said measured signal response curve, wherein (a′
) the error minimization method provides a calculated error based on differences between said predicted and measured signal response curves, and (b′
) said estimating is iteratively performed until the calculated error between the predicted and measured signal response curves falls within an acceptable range 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 a predicted response curve corresponding to said measured signal response curve, said predicted response curve yields a predicted end-point value and a measurement correlated to the amount or concentration of the glucose, and (iii) converting each measurement correlated to the amount or concentration of the glucose in the series to a measurement value indicative of the amount or concentration of glucose present in the subject. - View Dependent Claims (75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85)
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Specification