METHOD AND DEVICE FOR PREDICTING PHYSIOLOGICAL VALUES

• US 20080262334A1
• Filed: 06/30/2008
• Published: 10/23/2008
• Est. Priority Date: 09/30/1998
• Status: Active Grant

• Current Assignee: LifeScan IP Holdings, LLC
• 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.
• Granted Patent: US 7,935,499 B2
• Time in Patent Office: Days
• US Class Current: 600/365
• 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 method to measure an amount of concentration of analyte present in a biological system, comprising:

determining a calibration ratio (CalRatio) value, wherein

$\mathrm{CalRatio}=\frac{{\mathrm{BG}}_{\mathrm{cp}}}{\left({\mathrm{active}}_{\mathrm{cp}}+\mathrm{offset}\right)}$

where BG_cp is a blood glucose concentration at the calibration point, actively is an active signal that corresponds to an electrochemical sensor signal at the calibration point, and offset is a value that takes into account a non-zero y-intercept value;

providing two or more ranges;

of CalRatio values;

identifying the range in which said determined CalRatio value falls;

employing an algorithm for prediction of further measurement values selected from one of a first algorithm and a second;

algorithm, the first algorithm comprising;

BG=w₁BG₁+w₂BG₂+w₃BG₃

where

BG₁=p₁(time)+q₁(active)+r₁(signal)+s₁(BG|cp)+t₁

BG₂=p₂(time)+q₂(active)+r₂(signal)+s₂(BG|cp)+t₂

BG₃=p₃(time)+q₃(active)+r₃(signal)+s₃(BG|cp)+t₃

$\begin{array}{cc}{w}_1=\frac{{\mathrm{<br><br>}}^{d_1}}{{\mathrm{<br><br>}}^{d_1+}<br><br>{\mathrm{<br><br>}}^{d_2}+{\mathrm{<br><br>}}^{d_3}}& \\ w_2=\frac{{\mathrm{<br><br>}}^{d_2}}{{\mathrm{<br><br>}}^{d_1+}<br><br>{\mathrm{<br><br>}}^{d_2}+{\mathrm{<br><br>}}^{d_3}}& \\ w_3=\frac{{\mathrm{<br><br>}}^{d_3}}{{\mathrm{<br><br>}}^{d_1+}<br><br>{\mathrm{<br><br>}}^{d_2}+{\mathrm{<br><br>}}^{d_3}}& \end{array}$
d₁=τ

₁(time)+β

₁(active)+γ

₁(signal)+δ

₁(BG|cp)+∈

₁

d₂=τ

₂(time)+β

₂(active)+γ

₂(signal)+δ

₂(BG|cp)+∈

₂

d₃=τ

₃(time)+β

₃(active)+γ

₃(signal)+δ

₃(BG|cp)+∈

₃

in which BG₁ is the analyte, predicted, BG|cp is the blood glucose value at a calibration point, time is the elapsed time, active is the active signal, signal being the calibrated signal, p_i, q_i, r_i are coefficients, t_i is a constant, e indicates an exponential function, d_i is a parameter set usable to determine weightings w_i, with τ

_i, β

_i, γ

_i, δ

_i, and ∈

_i are constants, and the second equation comprising;

BG=w₁BG₁+w₂BG₂+w₃BG₃

where

BG₁=p₁(time)+q₁(active)+r₁(signal)+s₁(BG|cp)+t₁

BG₂=p₂(time)+q₂(active)+r₂(signal)+s₂(BG|cp)+t₂

BG₃=p₃(time)+q₃(active)+r₃(signal)+s₃(BG|cp)+t₃

$\begin{array}{cc}{w}_1=\frac{{\mathrm{<br><br>}}^{d_1}}{{\mathrm{<br><br>}}^{d_1+}<br><br>{\mathrm{<br><br>}}^{d_2}+{\mathrm{<br><br>}}^{d_3}}& \\ w_2=\frac{{\mathrm{<br><br>}}^{d_2}}{{\mathrm{<br><br>}}^{d_1+}<br><br>{\mathrm{<br><br>}}^{d_2}+{\mathrm{<br><br>}}^{d_3}}& \\ w_3=\frac{{\mathrm{<br><br>}}^{d_3}}{{\mathrm{<br><br>}}^{d_1+}<br><br>{\mathrm{<br><br>}}^{d_2}+{\mathrm{<br><br>}}^{d_3}}& \end{array}$
d₁=τ

₁(time)+β

₁(active)+γ

₁(signal)+δ

₁(BG|cp)+∈

₁

d₂=τ

₂(time)+β

₂(active)+γ

₂(signal)+δ

₂(BG|cp)+∈

₂

d₃=τ

₃(time)+β

₃(active)+γ

₃(signal)+δ

₃(BG|cp)+∈

₃

in which BG_i is the analyte predicted, time_c is the elapsed time since calibration, active is the active signal, signal is the calibrated signal, BG/cp is the blood glucose value at a calibration point, p_i;

q_i, r_i are coefficients, t_i is a constant, e indicates an exponential function, d_i is a parameter set usable to determine weightings w_i, with τ

_i, β

_i, γ

_i, δ

_i, and ∈

_i are constants, wherein said algorithm is optimized for performance in the identified range; and

generating further measurement values indicative of amount or concentration of analyte present in the biological system, said generating comprising obtaining a raw signal specifically related to analyte amount or concentration in the biological system and using said algorithm to correlate the raw signal with a measurement value.

26. 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) determining a calibration ratio (CalRatio) value, wherein

$\mathrm{CalRatio}=\frac{{\mathrm{BG}}_{\mathrm{cp}}}{\left({\mathrm{active}}_{\mathrm{cp}}+\mathrm{offset}\right)}$

wherein BG_cp is a blood glucose concentration at the calibration point, active_cp is an active signal that corresponds to an electrochemical sensor signal at the calibration point, and offset was a constant value;

providing two or more ranges of CalRatio values;

identifying the range in which said determined CalRatio value falls;

employing an algorithm for prediction of further measurement values selected from one of a first algorithm and a second algorithm, the first algorithm comprising;

BG=w₁BG₁+w₂BG₂+w₃BG₃

where

BG₁=p₁(time)+q₁(active)+r₁(signal)+s₁(BG|cp)+t₁

BG₂=p₂(time)+q₂(active)+r₂(signal)+s₂(BG|cp)+t₂

BG₃=p₃(time)+q₃(active)+r₃(signal)+s₃(BG|cp)+t₃

$\begin{array}{c}{w}_1=\frac{{\mathrm{<br><br>}}^{d_1}}{{\mathrm{<br><br>}}^{d_1+}<br><br>{\mathrm{<br><br>}}^{d_2}+{\mathrm{<br><br>}}^{d_3}}\\ w_2=\frac{{\mathrm{<br><br>}}^{d_2}}{{\mathrm{<br><br>}}^{d_1+}<br><br>{\mathrm{<br><br>}}^{d_2}+{\mathrm{<br><br>}}^{d_3}}\\ w_3=\frac{{\mathrm{<br><br>}}^{d_3}}{{\mathrm{<br><br>}}^{d_1+}<br><br>{\mathrm{<br><br>}}^{d_2}+{\mathrm{<br><br>}}^{d_3}}\end{array}$
d₁=τ

₁(time)+β

₁(active)+γ

₁(signal)+δ

₁(BG|cp)+∈

₁

d₂=τ

₂(time)+β

₂(active)+γ

₂(signal)+δ

₂(BG|cp)+∈

₂

d₃=τ

₃(time)+β

₃(active)+γ

₃(signal)+δ

₃(BG|cp)+∈

₃

in which BG_i is the analyte predicted, BG/cp is the blood glucose value at a calibration point, time is the elapsed time, active is the active signal, signal being the calibrated signal, p_i, q_i, r_i are coefficients, t_i is a constant, e indicates an exponential function, d_i is a parameter set usable to determine weightings w_i, with τ

_i, β

_i, γ

_i, δ

_i, and ∈

_i are constants, and the second equation comprising;

BG=w₁BG₁+w₂BG₂+w₃BG₃

where

BG₁=p₁(time)+q₁(active)+r₁(signal)+s₁(BG|cp)+t₁

BG₂=p₂(time)+q₂(active)+r₂(signal)+s₂(BG|cp)+t₂

BG₃=p₃(time)+q₃(active)+r₃(signal)+s₃(BG|cp)+t₃

$\begin{array}{c}{w}_1=\frac{{\mathrm{<br><br>}}^{d_1}}{{\mathrm{<br><br>}}^{d_1+}<br><br>{\mathrm{<br><br>}}^{d_2}+{\mathrm{<br><br>}}^{d_3}}\\ w_2=\frac{{\mathrm{<br><br>}}^{d_2}}{{\mathrm{<br><br>}}^{d_1+}<br><br>{\mathrm{<br><br>}}^{d_2}+{\mathrm{<br><br>}}^{d_3}}\\ w_3=\frac{{\mathrm{<br><br>}}^{d_3}}{{\mathrm{<br><br>}}^{d_1+}<br><br>{\mathrm{<br><br>}}^{d_2}+{\mathrm{<br><br>}}^{d_3}}\end{array}$
d₁=τ

₁(time)+β

₁(active)+γ

₁(signal)+δ

₁(BG|cp)+∈

₁

d₂=τ

₂(time)+β

₂(active)+γ

₂(signal)+δ

₂(BG|cp)+∈

₂

d₃=τ

₃(time)+β

₃(active)+γ

₃(signal)+δ

₃(BG|cp)+∈

₃

in which BG_i is the analyte predicted, time_c is the elapsed time since calibration, active is the active signal, signal is the calibrated signal, BG/cp is the blood glucose value at a calibration point p_i, q_i, r_i are coefficients, t_i is a constant, e indicates an exponential function, d_i is a parameter set usable to determine weightings w_i, with τ

_i, β

_i, γ

_i, δ

_i and ∈

_i are constants, wherein said algorithm is optimized for performance in the identified range; and

generating further measurement values indicative of amount or concentration of analyte present in the biological system, said generating comprising obtaining a raw signal specifically related to analyte amount or concentration in the biological system and using said algorithm to correlate the raw signal with a measurement value.