Method and apparatus for improving lag correction during in vivo measurement of analyte concentration with analyte concentration variability and range data

• US 9,907,492 B2
• Filed: 09/18/2013
• Issued: 03/06/2018
• Est. Priority Date: 09/26/2012
• Status: Active Grant

• Current Assignee: Abbott Diabetes Care Incorporated (Abbott Laboratories Incorporated)
• Original Assignee: Abbott Diabetes Care Incorporated (Abbott Laboratories Incorporated)
• Inventors: Budiman, Erwin Satrya
• Primary Examiner(s): WINAKUR, ERIC FRANK
• Application Number:
  US14/431,168
• Publication Number:
  US 20150216456A1
• Time in Patent Office: 1,630 Days
• CPC Class Codes: A61B 2560/0475 Special features of memory means, e.g. removable memory cards
• 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 5/14546 for measuring analytes not otherwise provided for, e.g. ions, cytochromes
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/7235 Details of waveform analysis A61B5/349 takes precedence
• CPC Class Codes: A61B 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: G06F 2218/10 by analysing the shape of a waveform, e.g. extracting parameters relating to peaks
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

Methods, devices, and systems are provided for correcting lag in measurements of analyte concentration level in interstitial fluid. The invention includes receiving a signal representative of sensor data from an analyte monitoring system related to an analyte level measured over time, computing rates of change of the sensor data for a time period of the sensor data, computing a rate distribution of the rates of change, transforming the rate distribution into a linear arrangement, determining a best-fit line for the transformed rate distribution, computing a slope of the best-fit line; and using the slope of the best-fit line as a representation of a variability of the analyte level to adjust an amount of lag correction applied to the sensor data. Numerous additional features are disclosed.

Claims

1. A computer-implemented method, comprising:

receiving a signal representative of sensor data from an analyte sensor related to an analyte level measured over time;

computing rates of change of the sensor data for a time period;

computing a rate distribution of the rates of change;

transforming the rate distribution into a linear arrangement;

determining a best-fit line for the transformed rate distribution;

computing a slope of the best-fit line; and

using the slope of the best-fit line as a representation of a variability of the analyte level to adjust an amount of lag correction applied to the sensor data.

2. The method of claim 1, wherein the signal representative of sensor data includes real-time data generated by the analyte sensor while the method is performed.

3. The method of claim 1, wherein the signal representative of sensor data includes stored data previously generated by the analyte sensor.

4. The method of claim 1, wherein computing the rates of change of the sensor data includes computing rates of change of the sensor data from a past time up to a present time.

5. The method of claim 1, wherein transforming the rate distribution includes at least one of applying a logarithmic transformation, a square-root transformation, and a power transformation to the rate distribution.

6. The method of claim 1, wherein determining the best-fit line includes using at least one of a least-squares error fit method, an orthogonal fit method, and a method of averages.

7. The method of claim 1, wherein a relatively steep slope of the best-fit line indicates a relatively low variability of the analyte level compared to a relatively gentle slope of the best-fit line which indicates a relatively high variability of the analyte level.

8. The method of claim 7, wherein the amount of lag correction applied to the sensor data is reduced as the slope of the best-fit line becomes steeper.

9. The method of claim 7, wherein the amount of lag correction applied to the sensor data is increased as the slope of the best-fit line becomes gentler.

10. A system for determining analyte concentration, the system comprising:

a processor; and

a memory coupled to the processor, the memory storing processor executable instructions to;

receive a signal representative of sensor data from an analyte sensor related to an analyte level measured over time;

compute rates of change of the sensor data for a time period;

compute a rate distribution of the rates of change;

transform the rate distribution into a linear arrangement;

determine a best-fit line for the transformed rate distribution;

compute a slope of the best-fit line; and

use the slope of the best-fit line as a representation of a variability of the analyte level to adjust an amount of lag correction applied to the sensor data.

11. The system of claim 10, wherein the signal representative of sensor data includes real-time data generated by the analyte sensor as the instructions are executed.

12. The system of claim 10, wherein the signal representative of sensor data includes stored data previously generated by the analyte sensor.

13. The system of claim 10, wherein the instruction to compute the rates of change of the sensor data includes an instruction to compute rates of change of the sensor data from a past time up to a present time.

14. The system of claim 10, wherein the instruction to transform the rate distribution includes at least one of an instruction to apply a logarithmic transformation, a square-root transformation, and a power transformation to the rate distribution.

15. The system of claim 10, wherein the instruction to determine the best-fit line includes an instruction to use at least one of a least-squares error fit method, an orthogonal fit method, and a method of averages.

16. The system of claim 10, wherein a relatively steep slope of the best-fit line indicates a relatively low variability of the analyte level compared to a relatively gentle slope of the best-fit line which indicates a relatively high variability of the analyte level.

17. The system of claim 16, wherein the instruction to use the slope of the best-fit line to adjust the amount of lag correction applied to the sensor data includes an instruction to reduce the amount of lag correction applied to the sensor data as the slope of the best-fit line becomes steeper.

18. The system of claim 16, wherein the instruction to use the slope of the best-fit line to adjust the amount of lag correction applied to the sensor data includes an instruction to increase the amount of lag correction applied to the sensor data as the slope of the best-fit line becomes gentler.