METHOD AND DEVICE FOR CONTINUOUS MONITORING OF THE CONCENTRATION OF AN ANALYTE

• US 20080139902A1
• Filed: 10/11/2007
• Published: 06/12/2008
• Est. Priority Date: 09/23/2003
• Status: Active Grant

• Current Assignee: Roche Diabetes Care Inc. (Roche Holding AG)
• Original Assignee: Roche Diagnostics Operations Incorporated (Roche Holding AG)
• Inventors: Kotulla, Reinhard, Staib, Arnulf, Gillen, Ralph
• Granted Patent: US 7,389,133 B1
• Time in Patent Office: Days
• US Class Current: 600/309
• CPC Class Codes: A61B 2560/0223 of calibration, e.g. protocols for calibrating sensors
• CPC Class Codes: A61B 5/14503 invasive, e.g. introduced into the body by a catheter or needle or using implanted sensors A61B5/1459, A61B5/1464, A61B5/1473, A61B5/1482, A61B5/14865 take precedence
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/7207 of noise induced by motion artifacts
• CPC Class Codes: A61B 5/7239 using differentiation including higher order derivatives

Abstract

The disclosure generally relates to continuous monitoring of an analyte by determining its change over time in the living body of a human or animal. A measurement, variable value correlating with the desired concentration of the analyte are measured as the measurement signal (z_t) and the change over time of the concentration is determined from the measurement signal as the useful signal (y_t) using a calibration. A filter algorithm is used to reduce errors of thee useful signal, which result from noise contained in the measurement signal. The filter algorithm includes an operation in which the influence of an actual measurement value on the useful signal is weighted using a weighting factor (V).

Claims

1. A method for continuous monitoring concentration of an analyte by determining the analyte'"'"'s change over time in the living body of a human or animal, the method comprising:

measuring at sequential points in time measurement values of a measurement variable correlating with a desired concentration of the analyte;

measuring the measurement variable as a measurement signal (z_t);

determining the change over time of the concentration of the analyte from the measurement signal as a useful signal (y_t) by means of a calibration;

providing a filter algorithm in the time domain for determination of the useful signal (y_t) from the measurement signal (z_t), wherein the filter algorithm reduces errors of the useful signal resulting from noise contained in the measurement signal, wherein the filter algorithm includes an operation in which the influence of an actual measurement value on the useful signal is weighted by means of a weighting factor (V);

determining a time dependent signal variation parameter (σ

_t) related to an actual point of time on the basis of signal variations detected in close chronological relation to the measurement of the actual measurement value;

wherein the time dependent signal variation parameter being a measure for signal variations for a period of time preceding an actual measurement value and being determined oil the basis of measurement values including values which were measured less than 30 minutes before the measurement of the actual value; and

adapting dynamically the weighting factor as a function of the signal variation parameter determined for the point in time of the actual measurement, the weighting factor being changed in such a direction that the influence of the actual measurement value is reduced with increasing standard deviation of the measurement signal.

3. The method according to claim 1 wherein measurement values, which are measured less than 15 minutes before the measurement of the actual measurement value, are used in the determination of the signal variations.

4. The method according to claim 1, wherein measurement values, which are measured less than 5 minutes before measurement of the actual measurement value, are used in the determination of the signal variations.

5. The method according to claim 1, wherein the filter algorithm is a recursive filter algorithm.

6. The method according to claim 5, wherein the filter algorithm is a Kalman filter algorithm.

7. The method according to claim 6, characterized in that the filter algorithm is a linear Kalman filter algorithm.

8. The method according to claim 1, wherein the variables of a system model upon which the filter algorithm is based comprise a check variable.

9. The method according to claim 8, wherein the check variable is a time derivative wherein the time derivative is the first time derivative of the analyte concentration.

2. (canceled)

10. A device for continuous monitoring of a concentration of an analyte by determining the analyte'"'"' s change over time in the living body of a human or animal, the device comprising:

a measurement unit, by which measurement values of a measurement variable correlating with the desired concentration are measured as the measurement signal (z_t) at sequential points in time;

an analysis unit, by which the change over time of the concentration is determined by means of a calibration as a useful signal (y_t) from the measurement signal, and

a filter algorithm in the time domain for determination of the useful signal (y_t) from the measurement signal (z_t) to reduce errors of the useful signal, which result from noise contained in the measurement signal;

wherein the filter algorithm includes operations, in which the influence of an actual measurement value on the useful signal is weighted using a weighting factor (V), such that a time dependent signal variation parameter (σ

_t) is determined on the basis of signal variations detected in close chronological relationship with the measurement of the actual measurement value, wherein the time dependent signal variation parameter being a measure for signal variations for a period of time preceding an actual measurement value and being determined on the basis of measurement values including values which were measured less than 30 minutes before the measurement of the actual value; and

the weighting factor is dynamically adapted as a function of the signal variation parameter determined for the point in time of the actual measurement, the weighting factor being changed in such a direction that the influence of the actual measurement value is reduced with increasing standard deviation of the measurement signal.