Dropout detection in continuous analyte monitoring data during data excursions

• US 10,132,793 B2
• Filed: 08/20/2013
• Issued: 11/20/2018
• Est. Priority Date: 08/30/2012
• Status: Active Grant

• Current Assignee: Abbott Diabetes Care Incorporated (Abbott Laboratories Incorporated)
• Original Assignee: Abbott Diabetes Care Incorporated (Abbott Laboratories Incorporated)
• Inventors: Ou, Junli, Budiman, Erwin Satrya
• Primary Examiner(s): Byrne, Harry W
• Application Number:
  US14/424,026
• Publication Number:
  US 20150241407A1
• Time in Patent Office: 1,918 Days
• Field of Search: 702104
• CPC Class Codes: A61B 2560/0276 Determining malfunction
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• 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: G06F 16/9024 Graphs; Linked lists G06F16/9027 takes precedence
• CPC Class Codes: G06F 2218/16 by matching signal segments
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

Methods, devices, and systems are provided for identifying dropouts in analyte monitoring system sensor data including segmenting sensor data into a plurality of time series wherein each time series is associated with a different instance of a repeating event, selecting a first time series to analyze for dropouts from the plurality of time series; comparing the selected first time series to a second time series among the plurality of time series, determining whether the selected first time series includes a portion that is more than a predefined threshold lower than a corresponding portion of the second time series, and displaying, on a computer system display, an indication that the selected first time series includes a dropout if the selected first time series includes a portion that is more than the predefined threshold lower than the corresponding portion of the second time series.

Claims

1. A method of identifying a signal dropout in analyte sensor data, comprising:

segmenting sensor data into a plurality of time series;

generating a plurality of plots for each time series of the plurality of time series;

generating a visual depiction of an analyte concentration over a period of time by at least overlaying each plot of the plurality of plots on each other to form a graph associating the analyte concentration with one or more user behaviors over the period of time;

determining an appropriate smoothing window from a plurality of smoothing windows, by at least determining that smoothing the graph using the appropriate smoothing window would not change an overall shape of the graph more than an acceptability threshold;

smoothing the graph using the appropriate smoothing window to obtain a smoothed graph;

displaying the visual depiction, including the smoothed graph, on a computer system display;

selecting a first time series to analyze for dropouts from the plurality of time series;

comparing the selected first time series to a second time series among the plurality of time series, based on the smoothed graph;

determining that the selected first time series includes a portion that is more than a predefined threshold lower than a corresponding portion of the second time series, based on the smoothed graph;

identifying a location on the smoothed graph corresponding to the portion that is more than the predefined threshold lower than the corresponding portion of the second time series; and

displaying, on the computer system display, an indication that the identified location on the smoothed graph includes a dropout.

2. The method of claim 1, wherein segmenting the sensor data further comprises identifying a meal start time during each one of the plurality of time series.

3. The method of claim 1, wherein comparing the selected first time series further comprises time dilating the second time series.

4. The method of claim 1, wherein comparing the selected first time series further comprises time dilating and dynamic range adjusting the second time series.

5. The method of claim 4, wherein the graph includes a plot for the time dilated and dynamic range adjusted second time series.

6. The method of claim 1, wherein displaying the indication that the identified location on the smoothed graph includes the dropout further comprises displaying an indication of a period of time on the graph associated with the dropout.

7. The method of claim 1, wherein comparing the selected first time series further comprises normalizing the first time series to the second time series using at least one of dynamic range matching and curve smoothing.

8. The method of claim 1, wherein each time series is associated with a different instance of a repeating event.

17. The method of claim 1, wherein determining the appropriate smoothing window comprises determining that the appropriate smoothing window would facilitate comparison of an appearance of the plot for the selected first time series to an appearance of the plot for the second time series, wherein smoothing the graph comprises smoothing the plot for the selected first time series and smoothing the plot for the second time series, and wherein comparing the selected first time series to the second time series comprises comparing the smoothed plot for the selected first time series to the smoothed plot for the second time series.

19. The method of claim 1, wherein determining that smoothing the graph using the appropriate smoothing window would not change the overall shape of the graph more than the acceptability threshold comprises determining that the appropriate smoothing window would remove one or more points determined to be jagged.

9. An apparatus for identifying a signal dropout in sensor data, comprising:

a display;

a processor operatively coupled to the display; and

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

segment sensor data into a plurality of time series;

generate a plurality of plots for each time series of the plurality of time series;

generate a visual depiction of an analyte concentration over a period of time by at least overlaying each plot of the plurality of plots on each other to form a graph associating the analyte concentration with one or more user behaviors over the period of time;

determine an appropriate smoothing window from a plurality of smoothing windows, by at least determining that smoothing the graph using the appropriate smoothing window would not change an overall shape of the graph more than an acceptability threshold;

smooth the graph using the appropriate smoothing window to obtain a smoothed graph;

provide the visual depiction, including the smoothed graph, on the display;

select a first time series to analyze for dropouts from the plurality of time series;

compare the selected first time series to a second time series among the plurality of time series, based on the smoothed graph;

determine that the selected first time series includes a portion that is more than a predefined threshold lower than a corresponding portion of the second time series, based on the smoothed graph;

identify a location on the smoothed graph corresponding to the portion that is more than the predefined threshold lower than the corresponding portion of the second time series; and

provide, on the display, an indication that the identified location on the smoothed graph includes a dropout.

10. The apparatus of claim 9, wherein the instructions to segment the sensor data further comprise instructions to identify a meal start time during each one of the plurality of time series.

11. The apparatus of claim 9, wherein the instructions to compare the selected first time series further comprise instructions to time dilate the second time series.

12. The apparatus of claim 9, wherein the instructions to compare the selected first time series further comprise instructions to time dilate and dynamic range adjust the second time series.

13. The apparatus of claim 12, wherein the graph includes a plot for the time dilated and dynamic range adjusted second time series.

14. The apparatus of claim 9, wherein the instructions to provide the indication that the identified location on the smoothed graph includes the dropout further comprise instructions to display an indication of a period of time on the graph associated with the dropout.

15. The apparatus of claim 9, wherein comparing the selected first time series further comprises normalizing the first time series to the second time series using at least one of dynamic range matching and curve smoothing.

16. The apparatus of claim 9, wherein each time series is associated with a different instance of a repeating event.

18. The apparatus of claim 9, further storing instructions to determine the appropriate smoothing window by at least determining that the appropriate smoothing window would facilitate comparison of an appearance of the plot for the selected first time series to an appearance of the plot for the second time series, to smooth the graph by at least smoothing the plot for the selected first time series and smoothing the plot for the second time series, and to compare the selected first time series to the second time series by at least comparing the smoothed plot for the selected first time series to the smoothed plot for the second time series.

20. The apparatus of claim 9, further storing instructions to determine that smoothing the graph using the appropriate smoothing window would not change the overall shape of the graph more than the acceptability threshold comprises determining that the appropriate smoothing window would remove one or more points determined to be jagged.