Microprocessors for use in a device for predicting physiological values

• US 6,326,160 B1
• Filed: 09/27/1999
• Issued: 12/04/2001
• Est. Priority Date: 09/30/1998
• Status: Expired due to Term

• Current Assignee: LifeScan IP Holdings, LLC
• Original Assignee: Cygnus, Inc. (Johnson & Johnson)
• Inventors: Kurnik, Ronald T., Potts, Russell O., Tamada, Janet A., Jayalakshmi, Yalia, Waterhouse, Steven Richard, Oliver, Jonathan James, Dunn, Timothy C., Lesho, Matthew J., Wei, Charles W.
• Primary Examiner(s): Chin, Christopher L.
• Application Number:
  US09/405,976
• Time in Patent Office: 799 Days
• Field of Search: 422/62, 422/63, 422/67, 422/82.01, 435/287.1, 435/287.2, 435/287.3, 435/286.1, 435/25, 435/808, 435/14, 436/503, 436/50, 436/63, 436/164, 436/805, 128/632, 128/635, 128/639, 128/640, 604/20, 204/400, 204/403, 204/153.12
• US Class Current: 435/14
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/1486 using enzyme electrodes, e.g. with immobilised oxidase
• CPC Class Codes: A61B 5/441 Skin evaluation, e.g. for skin disorder diagnosis
• CPC Class Codes: Y10S 435/808 Optical sensing apparatus
• CPC Class Codes: Y10S 435/817 Enzyme or microbe electrode
• CPC Class Codes: Y10S 436/805 Optical property
• CPC Class Codes: Y10S 436/806 Electrical property or magnetic property
• CPC Class Codes: Y10S 436/815 Test for named compound or class of compounds
• CPC Class Codes: Y10T 436/115831 Condition or time responsive

Abstract

The invention relates generally to methods, systems, and devices for measuring the concentration of target analytes present in a biological system using a series of measurements obtained from a monitoring system and a Mixtures of Experts (MOE) algorithm. In one embodiment, the present invention describes a method for measuring blood glucose in a subject.

Claims

1. One or more microprocessors for use in an analyte monitoring system, said one or more microprocessors programmed to:

(i) control a measurement cycle comprising

(a) operating a sampling device for extracting a series of samples at selected time intervals from a biological system, wherein each sample comprises the analyte, and

(b) operating a sensing device for sensing the analyte in each extracted sample to obtain a raw signal from each sample that is related to the analyte amount or concentration in the biological system, thus providing a series of raw signals at selected time intervals, said sensing device comprising a sensor,

(ii) correlate the raw signals with measurement values indicative of the amount or concentration of analyte present in the biological system, and

(iii) predict a measurement value using a Mixtures of Experts algorithm, where the individual experts have a linear form $\begin{array}{cc}\mathrm{An}=\underset{i=1}{\overset{n}{\sum <br><br>}}<br><br>\text{\hspace{1em}<br><br>}<br><br>{\mathrm{An}}_i<br><br>w_i& \left(1\right)\end{array}$

 

wherein (An) is an analyte of interest, n is the number of experts, An_i is the analyte predicted by Expert i; and

w_i is a weighting value, and the individual experts An_i are further defined by the expression shown as Equation (2) $\begin{array}{cc}{\mathrm{An}}_i=\underset{j=1}{\overset{m}{\sum <br><br>}}<br><br>\text{\hspace{1em}<br><br>}<br><br>a_{\mathrm{ij}}<br><br>P_j+z_i& \left(2\right)\end{array}$

 

wherein, An_i is the analyte predicted by Expert i;

P_j is one of m parameters, m is typically less than 100;

α

_ij are coefficients; and

z_i is a constant; and

further where the weighting value, w_i, is defined by the formula shown as Equation (3) $\begin{array}{cc}{w}_i=\frac{{\mathrm{<br><br>}}^{d_i}}{[\underset{k=1}{\overset{n}{\sum <br><br>}}<br><br>\text{\hspace{1em}<br><br>}<br><br>{\mathrm{<br><br>}}^{d_k}]}& \left(3\right)\end{array}$

 

where e refers to the exponential function and the d_k are a parameter set analogous to Equation 2 that is used to determine the weights w_i, the d_k are given by Equation 4 $\begin{array}{cc}{d}_k=\underset{j=1}{\overset{m}{\sum <br><br>}}<br><br>\text{\hspace{1em}<br><br>}<br><br>{\mathrm{\alpha <br><br>}}_{\mathrm{jk}}<br><br>P_j+{\mathrm{\omega <br><br>}}_k& \left(4\right)\end{array}$

 

where α

_jk is a coefficient, P_j is one of m parameters, and where ω

_k is a constant.

2. The one or more microprocessors of claim 1, wherein the sampling device comprises one or more collection reservoirs for containing the extracted sample.

3. The one or more microprocessors of claim 2, wherein the sampling device uses an iontophoretic current to extract the sample comprising analyte from the biological system.

4. The one or more microprocessors of claim 3, wherein the one or more collection reservoirs comprise an enzyme that reacts with the extracted analyte to produce an electrochemically detectable signal.

5. The one or more microprocessors of claim 4, wherein the analyte is glucose and the enzyme is glucose oxidase.

6. The one or more microprocessors of claim 1, wherein the analyte comprises glucose.

7. The one or more microprocessor of claim 6, wherein said predicting is carried out using said series of measurement values in an algorithm represented by the Mixtures of Experts algorithm, where the individual experts have a linear form

8. The one or more microprocessor of claim 7, wherein said algorithm includes further parameters for measurement values selected from the group consisting of temperature, iontophoretic voltage, and skin conductivity.

9. The one or more microprocessor of claim 6, wherein said predicting is carried out using said series of measurement values in an algorithm represented by the Mixtures of Experts algorithm, where the individual experts have a linear form

10. The one or more microprocessor of claim 9, wherein said algorithm includes further parameters for measurement values selected from the group consisting of temperature, iontophoretic voltage, and skin conductivity.

11. The one or more microprocessors of claim 1, wherein the sensing device comprises a biosensor having an electrochemical sensing element.

12. The one or more microprocessors of claim 1, wherein the sensing device comprises a near-IR spectrometer.

13. One or more microprocessors for use in a glucose monitoring system, said one or more microprocessors programmed to:

(i) control a sensing device, in operative contact with a sample comprising glucose extracted from a subject, to obtain a series of raw signals at selected time intervals, wherein each raw signal is specifically related to blood glucose in the subject,

(ii) correlate the raw signals with measurement values indicative of an amount or concentration of blood glucose present in the subject, and

(iii) predict a measurement value using a Mixtures of Experts algorithm, where the individual experts have a linear form

14. The one or more microprocessors of claim 13, wherein said algorithm includes further parameters for raw signals selected from the group consisting of temperature, iontophoretic voltage, and skin conductivity.

15. The one or more microprocessors of claim 13, wherein the sensing device comprises a biosensor having an electrochemical sensing element.

16. The one or more microprocessors of claim 13, wherein the sensing device comprises a near-IR spectrometer.

17. One or more microprocessors for use in a glucose monitoring system, said one or more microprocessors programmed to:

(i) control a sensing device, in operative contact with a sample comprising glucose extracted from a subject, to obtain a series of raw signals at selected time intervals, wherein each raw signal is specifically related to blood glucose in the subject,

(ii) correlate the raw signals with measurement values indicative of an amount or concentration of blood glucose present in the subject, and

(iii) predict a measurement value using a Mixtures of Experts algorithm, where the individual experts have a linear form

18. The one or more microprocessors of claim 17, wherein said algorithm includes further parameters for raw signals selected from the group consisting of temperature, iontophoretic voltage, and skin conductivity.

19. The one or more microprocessors of claim 17, wherein the sensing device comprises a biosensor having an electrochemical sensing element.

20. The one or more microprocessors of claim 17, wherein the sensing device comprises a near-IR spectrometer.