OUTLIER DETECTION FOR ANALYTE SENSORS

• US 20140188402A1
• Filed: 01/03/2013
• Published: 07/03/2014
• Est. Priority Date: 01/03/2013
• Status: Abandoned Application

• Current Assignee: DexCom Incorporated
• Original Assignee: DexCom Incorporated
• Inventors: Garcia, Arturo, Bhavaraju, Naresh C., Hampapuram, Hari, Kamath, Apurv Ullas, Mahalingam, Aarthi, Sokolovskyy, Dmytro, Vanslyke, Stephen J.
• Application Number:
  US13/733,810
• Publication Number:
  US 20140188402A1
• Time in Patent Office: Days
• US Class Current: 702/23
• CPC Class Codes: A61B 2560/0223 of calibration, e.g. protocols for calibrating sensors
• CPC Class Codes: A61B 2560/0266 for monitoring or limiting apparatus function
• CPC Class Codes: A61B 2560/0276 Determining malfunction
• CPC Class Codes: A61B 2560/028 Arrangements to prevent overuse, e.g. by counting the number of uses
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/1495 Calibrating or testing of in-vivo probes
• CPC Class Codes: A61B 5/7221 Determining signal validity, reliability or quality A61B5/7207 and A61B5/7217 take precedence
• CPC Class Codes: G01M 99/008 by doing functionality tests
• CPC Class Codes: G01N 33/48792 Data management, e.g. communication with processing unit for in vivo diagnostics A61B5/0002; transmission systems for measured values G08C
• CPC Class Codes: G01N 33/49 Blood chemical methods for determining blood cell populations G01N33/5094; chemical analysis of blood groups or blood types G01N33/80
• CPC Class Codes: G01N 33/66 involving blood sugars, e.g. galactose
• CPC Class Codes: G16C 20/80 Data visualisation
• CPC Class Codes: G16C 99/00 Subject matter not provided for in other groups of this subclass

Abstract

Systems and methods for processing sensor data and end of life detection are provided. In some embodiments, a method for determining the end of life of a continuous analyte sensor includes evaluating a plurality of risk factors using an end of life function to determine an end of life status of the sensor and providing an output related to the end of life status of the sensor. The plurality of risk factors may be selected from the list including the number of days the sensor has been in use, whether there has been a decrease in signal sensitivity, whether there is a predetermined noise pattern, whether there is a predetermined oxygen concentration pattern, and error between reference BG values and EGV sensor values.

Claims

1. A method for detecting outliers in analyte sensor data, comprising:

iteratively evaluating a plurality of subsets of a calibration data set to determine a best subset;

identifying a boundary or confidence interval associated with the best subset;

identifying values outside the boundary or confidence interval as possible outliers;

evaluating the relevancy of the possible outliers to determine outlier information; and

processing responsive to the outlier information.

2. The method of claim 1, wherein the evaluation of subsets to determine a best subset includes generating a regression line or a convex hull.

3. The method of claim 2, wherein the regression line is generated using at least ½

of data points in the calibration set.

4. The method of claim 1, wherein the evaluating the relevancy of the possible outliers to determine outlier information comprises at least one of evaluating the clinical relevancy of the possible outliers, discrimination of the root cause of the error in the possible outliers, and trends of the outlier information.

5. The method of claim 1, wherein the evaluating the relevancy comprises examining one or more factors from the following list:

the amplitude of error of a data point relative to the best line, the direction of error a data point relative to the best line, a clinical risk of the data at a time stamp of the data point, a rate of change of the analyte concentration or derivative of the sensor data associated with a data point, a rate of acceleration or deceleration of the analyte concentration or second derivative of the sensor data associated with the data point.

6. The method of claim 1, wherein the processing responsive to the outlier information comprises removing the outlier temporarily or permanently from the calibration set, prospectively or retrospectively.

7. The method of claim 1, wherein the processing responsive to the outlier information comprises flagging an outlier and keeping the outlier in the calibration data set until the next data point is collected.

8. A system for detecting outliers in analyte sensor data, the system comprising sensor electronics configured to be operably connected to a continuous analyte sensor, the sensor electronics configured to:

iteratively evaluate a plurality of subsets of a calibration data set to determine a best subset;

identify a boundary or confidence interval associated with a best subset;

identify values outside the boundary or confidence interval as possible outliers;

evaluate the relevancy of the possible outliers to determine outlier information; and

process responsive to the outlier information.

9. The system of claim 8, wherein the evaluation of subsets to determine a best subset includes generating a regression line or a convex hull.

10. The system of claim 9, wherein the regression line is generated using at least ½

of data points in the calibration set.

11. The system of claim 8, wherein the evaluating the relevancy of the possible outliers to determine outlier information comprises at least one of evaluating the clinical relevancy of the possible outliers, discrimination of the root cause of the error in the possible outlier, and trends of outlier information.

12. The system of claim 8, wherein the evaluating the relevancy comprises examining one or more factors from the following list:

the amplitude of error of a data point relative to the best line, the direction of error a data point relative to the best line, a clinical risk of the data at a time stamp of the data point, a rate of change of the analyte concentration or derivative of the sensor data associated with a data point, a rate of acceleration or deceleration of the analyte concentration or second derivative of the sensor data associated with the data point.

13. The system of claim 8, wherein the processing comprises removing the outlier temporarily or permanently from the calibration set, prospectively or retrospectively.

14. The system of claim 8, wherein the processing comprises flagging an outlier and keeping the outlier in the calibration data set until the next data point is collected.

15. The system of claim 8, wherein the sensor electronics comprise a processor module, the processor module comprising instructions stored in computer memory, wherein the instructions, when executed by the processor module, cause the sensor electronics to perform the evaluating and the processing.

16. A method for detecting outliers in analyte sensor data, comprising:

iteratively evaluating a plurality of subsets of a calibration data set;

identifying a possible outlier based on one or more first outlier criteria;

evaluating the relevancy of the possible outlier based on one or more relevancy criteria to discriminate a root case of the possible outlier; and

processing outlier information responsive thereto.

17. The method of claim 16, wherein the evaluating the relevancy comprises evaluating at least one of:

time since sensor implant, trends in outlier evaluation, the amplitude of error of a data point relative to the best line, the direction of error a data point relative to the best line, a clinical risk of the data at a time stamp of the data point, a rate of change of the analyte concentration or derivative of the sensor data associated with a data point, a rate of acceleration or deceleration of the analyte concentration or second derivative of the sensor data associated with the data point.

18. A system for detecting outliers in analyte sensor data, the system comprising sensor electronics configured to be operably connected to a continuous analyte sensor, the sensor electronics configured to:

iteratively evaluate a plurality of subsets of a calibration data set;

identify a possible outlier based on one or more first outlier criteria;

evaluate the relevancy of the possible outlier based on one or more relevancy criteria to discriminate a root case of the possible outlier; and

process outlier information responsive thereto.

19. The system of claim 18, wherein the evaluating the relevancy comprises evaluating at least one of:

time since sensor implant, trends in outlier evaluation, the amplitude of error of a data point relative to the best line, the direction of error a data point relative to the best line, a clinical risk of the data at a time stamp of the data point, a rate of change of the analyte concentration or derivative of the sensor data associated with a data point, a rate of acceleration or deceleration of the analyte concentration or second derivative of the sensor data associated with the data point.

20. The system of claim 18, wherein the sensor electronics comprise a processor module, the processor module comprising instructions stored in computer memory, wherein the instructions, when executed by the processor module, cause the sensor electronics to perform the evaluating and the processing.