Real time self-adjusting calibration algorithm

• US 9,204,840 B2
• Filed: 07/24/2008
• Issued: 12/08/2015
• Est. Priority Date: 02/23/2000
• Status: Active Grant

• Current Assignee: Medtronic Minimed Incorporated (Medtronic Plc)
• Original Assignee: Medtronic Minimed Incorporated (Medtronic Plc)
• Inventors: Shin, John J., Patel, Nandita N., Kanderian, Sami Jr., Wang, Lu, Yoon, Richard
• Primary Examiner(s): Riggs, II, Larry D
• Application Number:
  US12/179,536
• Publication Number:
  US 20090005666A1
• Time in Patent Office: 2,693 Days
• US Class Current: 1/1
• CPC Class Codes: A61B 2560/0223 of calibration, e.g. protocols for calibrating sensors
• CPC Class Codes: A61B 5/0002 Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network A61B5/07 takes precedence; transmission systems for measured values G08C; transmission H04B; transmission of digital information H04L; wireless communication networks H04W
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/14546 for measuring analytes not otherwise provided for, e.g. ions, cytochromes
• CPC Class Codes: A61B 5/14865 invasive, e.g. introduced into the body by a catheter or needle or using implanted sensors
• CPC Class Codes: A61B 5/1495 Calibrating or testing of in-vivo probes
• CPC Class Codes: A61B 5/6848 Needles
• CPC Class Codes: A61B 5/6849 in combination with a needle set
• CPC Class Codes: A61M 2005/1726 the body parameters being measured at, or proximate to, the infusion site

Abstract

A method of calibrating glucose monitor data includes collecting the glucose monitor data over a period of time at predetermined intervals. It also includes obtaining at least two reference glucose values from a reference source that temporally correspond with the glucose monitor data obtained at the predetermined intervals. Also included is calculating the calibration characteristics using the reference glucose values and corresponding glucose monitor data to regress the obtained glucose monitor data. And, calibrating the obtained glucose monitor data using the calibration characteristics is included. In preferred embodiments, the reference source is a blood glucose meter, and the at least two reference glucose values are obtained from blood tests. In additional embodiments, calculation of the calibration characteristics includes linear regression and, in particular embodiments, least squares linear regression. Alternatively, calculation of the calibration characteristics includes non-linear regression. Data integrity may be verified and the data may be filtered.

Claims

1. A method for filtering signals from a sensor comprising:

receiving a plurality of signal values from the sensor generated in response to a concentration of a substance;

at a processor, computing a quantifier of a variance of a measurement error based, at least in part, on a standard deviation of an absolute value of consecutive data points within the plurality of signal values; and

at said processor, filtering the plurality of signal values with an adaptive filter, wherein the quantifier is an input to the adaptive filter.

2. The method of claim 1, where the sensor comprises a glucose sensor.

3. The method of claim 2, wherein the plurality of data values are responsive to blood glucose concentrations.

4. The method of claim 1, wherein formulating the standard deviation comprises formulating a windowed, unweighted standard deviation.

5. The method of claim 4, wherein the quantifier is equal to

R_k=c*δ

_k+b,

where

6. The method of claim 1, wherein the standard deviation is formulated as a recursive, weighted standard deviation.

7. The method of claim 6, wherein the quantifier is equal to

R_k=c*δ

_k+b,

where

8. The method of claim 1, wherein the adaptive filter comprises a Kalman filter.

9. A method for calibrating a sensor comprising:

receiving a plurality of signal values from the sensor generated in response to a concentration of a substance;

computing, at a processor, a reliability metric for each signal value of the plurality of data values based, at least in part, on a standard deviation of an absolute value of consecutive data points within the plurality of signal values;

selectively discarding each of one or more of the signal values of the plurality of signal values in response to application of a threshold value to said reliability metric for the signal value;

filtering, at said processor, the signal values of the plurality of signal values that have not been discarded using an adaptive filter; and

adjusting an output of the sensor using the filtered signal values.

20. The method of claim 9, wherein the adaptive filter comprises a Kalman filter.

10. A method for calibrating a sensor comprising:

receiving a plurality of signal values from the sensor responsive to a concentration of a substance;

computing, at a processor, a reliability of each signal value of the plurality of signal values;

selectively discarding each of one Or more of the signal values of the plurality of signal values in response to application of a threshold value to said computed reliability metric for the signal;

filtering, at said processor, the signal values of the plurality of signal values that have not been discarded; and

adjusting an output of the sensor using the filtered signal values;

wherein filtering the signal values comprises filtering the signal values with an adaptive filter;

wherein filtering the signal values with an adaptive filter comprises using the adaptive filter with a parameter based, at least in part, on the signal values of the plurality of signal values that have not been discarded; and

wherein the parameter is representative of a standard deviation of the signal values of the plurality of signal values that have not been discarded or is representative of a standard deviation of an absolute value of signal values within the signal values of the plurality of signal values that have not been discarded.

11. The method of claim 10, wherein the standard deviation comprises a windowed, unweighted standard deviation.

12. The method of claim 10, wherein the standard deviation comprises a recursive, weighted standard deviation.

13. An apparatus for filtering signal values from a sensor comprising:

means, comprising a processor, for receiving a plurality of signal values from the sensor;

means, comprising said processor, for Obtaining a quantifier of a variance of a measurement error associated with the plurality of signal values based, at least in part, on a standard deviation of an absolute value of consecutive data points within the plurality of signal values; and

means, comprising said processor, for filtering the plurality of signal values with an adaptive filter, wherein the quantifier is an input value to the adaptive filter.

14. The apparatus of claim 13, where the sensor comprises a glucose sensor.

15. The apparatus of claim 14, wherein the plurality of signal ues are generated by said sensor in response to blood glucose concentrations.

16. The apparatus of claim 13, wherein said means for obtaining a quantifier comprises means for formulating a standard deviation of an absolute value of consecutive signal values within the plurality of signal values.

17. The apparatus of claim 16, wherein said means for formulating the standard deviation comprises means for formulating a windowed, unweighted standard deviation.

18. The apparatus of claim 16, wherein said means for formulating the standard deviation comprises means for formulating a recursive, weighted standard deviation.

19. The apparatus of claim 13, wherein the adaptive filter comprises a Kalman filter.