METHOD AND DEVICE FOR PREDICTING PHYSIOLOGICAL VALUES

• US 20080270039A1
• Filed: 06/30/2008
• Published: 10/30/2008
• Est. Priority Date: 09/30/1998
• Status: Abandoned Application

• Current Assignee: Animas Technologies LLC (Johnson & Johnson)
• Original Assignee: Animas Technologies LLC (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.
• Application Number:
  US12/164,461
• Publication Number:
  US 20080270039A1
• Time in Patent Office: Days
• US Class Current: 702/19
• 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

1-24. -24. (canceled)

25. A monitoring system for measuring an amount or concentration of analyte present in a biological system, said system comprising, in operative combination:

a sensing device in operative contact with the analyte, wherein said sensing device obtains a raw signal from the analyte and said raw signal is specifically related to the amount or concentration of analyte; and

one or more microprocessors in operative communication with the sensing device, wherein said one or more microprocessors comprises programming to control

(i) operation of the sensing device; and

(ii) providing two or more ranges of measurement values, wherein said measurement values are indicative of amounts or concentrations of analyte present in the biological system;

identifying the range in which a selected measurement value falls; and

employing an algorithm for prediction of further measurement values wherein said algorithm is optimized for performance in the identifying range.

26. The monitoring system of claim 25, wherein a Mixtures of Experts algorithm is used to determine said selected measurement value and said Mixtures of Experts algorithm is trained using a global training set.

27. The monitoring system of claim 25, wherein said algorithm for prediction of further measurement values is a Mixtures of Experts algorithm and said-Mixtures of Experts algorithm is trained using data from the identified range.

28. The monitoring system of claim 25, wherein said sensing device provides a raw signal specifically related to analyte amount or concentration in the biological system and said one or more microprocessors are further programmed to control correlating the raw signal with a measurement value indicative of analyte amount or concentration in the biological system.

29. The monitoring system of claim 25, wherein for the Mixtures of Experts algorithm the individual experts have a linear form

$\begin{array}{c}\mathrm{An}=\sum _{}^{}\end{array}$

i = 1 n 

An i 

w i ( 1 )

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 parameter, and the individual experts An_i are further defined by the expression shown as Equation (2)

$\begin{array}{cc}{\mathrm{An}}_i=\underset{j=1}{\overset{\mathrm{nt}}{\sum <br><br>}}<br><br>a_{\mathrm{ij}}<br><br>P_j+z_i& \left(2\right)\end{array}$

wherein, An_i, is the analyte predicted by Expert i;

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

a_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)

$\begin{array}{cc}{w}_i=\frac{{\mathrm{<br><br>}}^{d_i}}{\left[\underset{k=1}{\overset{n}{\sum <br><br>}}<br><br>{\mathrm{<br><br>}}^{d_k}\right]}.& \left(3\right)\end{array}$

where e refers to the expotential function, d_i is one of the d_k, d_i and d_k are parameter sets analogous to Equation 2 used to determine the weight w_i, the dk are given by Equation 4

$\begin{array}{cc}{d}_k=\underset{j=1}{\overset{m}{\sum <br><br>}}<br><br>{\mathrm{\alpha <br><br>}}_{\mathrm{jk}}<br><br>P_j+{\mathrm{\omega <br><br>}}_k& \left(4\right)\end{array}$

where a_jk is coefficient, P_j is one of m parameters, and where ω

_k is a constant.

30. The monitoring system of claim 25, wherein the analyte is glucose.