Sensor fault detection using analyte sensor data pattern comparison

Patent metadata

Field	Value
Publication number	US-10709392-B2
Application number	US-201816110663-A
Country	US
Kind code	B2
Filing date	Aug 23, 2018
Priority date	Mar 15, 2013
Publication date	Jul 14, 2020
Grant date	Jul 14, 2020

Abstract

Analyte sensor faults are detected. Datasets of glucose values sensor electronics are coupled to a glucose sensor in fluid contact with interstitial fluid under a skin surface. Baseline median glucose value and glucose variability values are computed, based on the first dataset. A baseline data point is stored. Evaluation median glucose value and variability are computed, based on the second dataset of glucose values. An evaluation data point is stored. A magnitude of a vector that extends between the baseline data point and the evaluation data point is computed. A component of the magnitude of the vector that is parallel to a hypoglycemia risk contour line is computed and compared to a predefined threshold value. An indication that a sensor fault has been detected if the component is greater than a threshold is displayed.

First claim

What is claimed is:

1. A computer-implemented method, comprising:
computing a magnitude of a vector extending from a baseline analyte data point to an evaluation analyte data point;
computing a component of the magnitude of the vector between the baseline analyte data point and the evaluation analyte data point that is parallel to a hypoglycemic risk contour line;
determining whether the component of the magnitude of the vector is greater than a first threshold value; and
displaying, based on the determination, an analyte sensor fault indication on a display.

2. The computer-implemented method of claim 1 , further comprising:
determining a baseline median glucose value and a baseline glucose variability value; and
determining the baseline analyte data point based on the baseline median glucose value and the baseline glucose variability value.

3. The computer-implemented method of claim 2 , wherein determining the baseline median glucose value and the baseline glucose variability value comprises determining the baseline median glucose value and the baseline glucose variability value based on a first dataset of glucose values.

4. The computer-implemented method of claim 2 , further comprising:
determining an evaluation median glucose value and an evaluation glucose variability value; and
determining the evaluation analyte data point based on the evaluation median glucose value and the evaluation glucose variability value.

5. The computer-implemented method of claim 4 , wherein determining the baseline median glucose value and the baseline glucose variability value comprises determining the baseline median glucose value and the baseline glucose variability value based on a first dataset of glucose values, wherein determining the evaluation median glucose value and the evaluation glucose variability value comprises determining the evaluation median glucose value and the evaluation glucose variability value based on a second dataset of glucose values, and wherein the second dataset of glucose values includes glucose values that are not in the first dataset of glucose values.

6. The computer-implemented method of claim 1 , wherein the analyte sensor fault indication is indicative that an analyte sensor fault has been detected.

7. The computer-implemented method of claim 6 , wherein determining whether the component of the magnitude of the vector is greater than the first threshold value comprises determining that the component of the magnitude of the vector is greater than the first threshold value.

8. The computer-implemented method of claim 6 , wherein the analyte sensor fault indication includes information regarding whether to maintain use of a glucose sensor.

9. The computer-implemented method of claim 6 , wherein the analyte sensor fault indication includes a request for a reference glucose measurement.

10. The computer-implemented method of claim 1 , wherein the analyte sensor fault indication is indicative that an early signal attenuation (ESA) fault has been detected.

11. The computer-implemented method of claim 10 , wherein determining whether the component of the magnitude of the vector is greater than the first threshold value comprises determining that the component of the magnitude of the vector is greater than the first threshold value.

12. The computer-implemented method of claim 10 , wherein the analyte sensor fault indication includes information regarding whether to maintain use of a glucose sensor.

13. The computer-implemented method of claim 10 , wherein the analyte sensor fault indication includes a request for a reference glucose measurement.

14. The computer-implemented method of claim 1 , wherein the analyte sensor fault indication is indicative that an analyte sensor fault has not been detected.

15. The computer-implemented method of claim 14 , wherein determining whether the component of the magnitude of the vector is greater than the first threshold value comprises determining that the component of the magnitude of the vector is less than the first threshold value.

16. The computer-implemented method of claim 1 , wherein the analyte sensor fault indication is indicative that an early signal attenuation (ESA) fault has not been detected.

17. The computer-implemented method of claim 16 , wherein determining whether the component of the magnitude of the vector is greater than the first threshold value comprises determining that the component of the magnitude of the vector is less than the first threshold value.

18. A non-transitory computer-readable medium having software stored thereon that, when executed by a processor, causes the processor to:
compute a magnitude of a vector extending from a baseline analyte data point to an evaluation analyte data point;
compute a component of the magnitude of the vector between the baseline analyte data point and the evaluation analyte data point that is parallel to a hypoglycemic risk contour line;
determine whether the component of the magnitude of the vector is greater than a first threshold value; and
cause, based on the determination, an analyte sensor fault indication to be displayed on a display.

19. A processor configured to:
compute a magnitude of a vector extending from a baseline analyte data point to an evaluation analyte data point;
compute a component of the magnitude of the vector between the baseline analyte data point and the evaluation analyte data point that is parallel to a hypoglycemic risk contour line;
determine whether the component of the magnitude of the vector is greater than a first threshold value; and
cause, based on the determination, an analyte sensor fault indication to be displayed on a display.

Assignees

• Abbott Diabetes Care Inc

Inventors

• Ou Junli
• Budiman Erwin Satrya

Classifications

• A61B5/1459

  invasive, e.g. introduced into the body by a catheter · CPC title

• A61B5/7271 Primary

  Specific aspects of physiological measurement analysis (specific diagnostics methods using bioelectric or biomagnetic signals A61B5/316) · CPC title

• A61B2560/0228

  using calibration standards · CPC title

• A61B5/14532

  for measuring glucose, e.g. by tissue impedance measurement · CPC title

• A61B5/742

  using visual displays (displays for heart-related electrical signals, e.g. ECG, A61B5/339) · CPC title