Dropout Detection in Continuous Analyte Monitoring Data During Data Excursions

• US 20150241407A1
• Filed: 08/20/2013
• Published: 08/27/2015
• 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: Budiman, Erwin Satrya, Ou, Junli
• Granted Patent: US 10,132,793 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• 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

1-22. -22. (canceled)

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

segmenting sensor data into a plurality of time series;

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 the portion that is more than the predefined threshold lower than the corresponding portion of the second time series.

24. The method of claim 23, wherein segmenting the sensor data further comprises smoothing the sensor data.

25. The method of claim 23, wherein segmenting the sensor data further comprises identifying a meal start time during each one of the plurality of time series.

26. The method of claim 23, wherein comparing the selected first time series further comprises time dilating the second time series.

27. The method of claim 23, wherein comparing the selected first time series further comprises time dilating and dynamic range adjusting the second time series.

28. The method of claim 27, further comprising displaying a graph including overlaid plots of the first time series and the time dilated and dynamic range adjusted second time series.

29. The method of claim 23, wherein displaying the indication that the selected first time series includes the dropout further comprises displaying a graph of the first time series and an indication of a period of time on the graph associated with the dropout.

30. The method of claim 23, wherein comparing the selected first time series further comprises normalizing the first time series to the second time series using at least one of time dilation, dynamic range matching, and curve smoothing.

31. The method of claim 23, wherein each time series is associated with a different instance of a repeating event.

32. The method of claim 23, wherein the sensor data is related to an analyte level of a patient monitored over time.

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

a processor; and

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

segment sensor data into a plurality of time series;

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;

determine 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

display an indication that the selected first time series includes a dropout if the selected first time series includes the portion that is more than the predefined threshold lower than the corresponding portion of the second time series.

34. The apparatus of claim 33, wherein the instructions to segment the sensor data further comprise instructions to smooth the sensor data.

35. The apparatus of claim 33, 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.

36. The apparatus of claim 33, wherein the instructions to compare the selected first time series further comprise instructions to time dilate the second time series.

37. The apparatus of claim 33, wherein the instructions to compare the selected first time series further comprise instructions to time dilate and dynamic range adjust the second time series.

38. The apparatus of claim 37, wherein the instructions further comprise instructions to display a graph including overlaid plots of the first time series and the time dilated and dynamic range adjusted second time series.

39. The apparatus of claim 33, wherein the instructions to display the indication that the selected first time series includes the dropout further comprise instructions to display a graph of the first time series and an indication of a period of time on the graph associated with the dropout.

40. The apparatus of claim 33, wherein comparing the selected first time series further comprises normalizing the first time series to the second time series using at least one of time dilation, dynamic range matching, and curve smoothing.

41. The apparatus of claim 33, wherein each time series is associated with a different instance of a repeating event.

42. The apparatus of claim 33, wherein the sensor data is related to an analyte level of a patient monitored over time.