Dropout detection in continuous analyte monitoring data during data excursions

• US 10,345,291 B2
• Filed: 11/16/2018
• Issued: 07/09/2019
• 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:
  US16/193,881
• Publication Number:
  US 20190086385A1
• Time in Patent Office: 235 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, comprising:

receiving sensor data from an analyte monitoring device;

segmenting the sensor data by time to obtain segmented data;

identifying a periodic event from the segmented data, the periodic event independently occurring at a first window of time and a second window of time;

defining a set of time dilation parameters;

applying the time dilation parameters to the second window of time to obtain a baseline-normalized ratio for a portion of the first window of time;

determining that the baseline-normalized ratio for the portion of the first window of time is below a predetermined threshold; and

displaying an indication that the portion of the first window of time includes a dropout.

2. The method of claim 1, wherein the analyte monitoring device is configured to monitor at least one of lactate or glucose.

3. The method of claim 1, wherein the periodic event comprises a meal.

4. The method of claim 3, wherein identifying the periodic event comprises determining a meal marker based on at least one of a user input or an estimation of a meal start time.

5. The method of claim 4, wherein the meal marker corresponds to a start of the first window of time, and wherein identifying the periodic event further comprises matching a generic meal with a nearest time-of-day, based on data obtained prior to the start of the first window of time.

6. The method of claim 1, further comprising displaying a graph of overlaid plots of the segmented data, the graph including the indication that the portion of the first window of time includes the dropout.

7. The method of claim 1, wherein the time dilation parameters have a range of a factor of two.

8. The method of claim 1, wherein the first window of time is a most recent time series.

9. The method of claim 1, wherein applying the time dilation parameters comprises stretching the first window of time or the second window of time at least vertically or horizontally.

10. The method of claim 1, further comprising generating the sensor data by measuring and storing data representative of analyte concentration levels automatically over time by use of a sensor disposed with at least a portion below a skin surface.

11. The method of claim 10, further comprising transmitting, by a sensor electronics operatively coupled to the sensor, the sensor data to a receiving device at a regular time interval.

12. The method of claim 10, further comprising transmitting, by a sensor electronics operatively coupled to the sensor, the sensor data to a receiving device in response to a request from the receiving device.

13. The method of claim 1, wherein the predetermined threshold comprises a negative glucose level difference.

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

a processor; and

a memory coupled to the processor, the memory storing instructions which, when executed by the processor, cause the processor to;

receive sensor data from an analyte monitoring device;

segment the sensor data by time to obtain segmented data;

identify a periodic event from the segmented data, the periodic event independently occurring at a first window of time and a second window of time;

define a set of time dilation parameters;

apply the time dilation parameters to the second window of time to obtain a baseline-normalized ratio for a portion of the first window of time;

determine that the baseline-normalized ratio for the portion of the first window of time is below a predetermined threshold; and

display an indication that the portion of the first window of time includes a dropout.

15. The apparatus of claim 14, wherein the predetermined threshold comprises a negative glucose level difference.

16. The apparatus of claim 14, the memory storing instructions to display a graph of overlaid plots of the segmented data, the graph including the indication that the portion of the first window of time includes the dropout.

17. The apparatus of claim 14, the memory storing instructions to apply the time dilation parameters by at least stretching the first window of time or the second window of time at least vertically or horizontally.

18. A system, comprising:

an analyte sensor positioned in fluid contact with bodily fluid under a skin layer and configured to generate signals corresponding to a monitored analyte level;

sensor electronics operatively coupled to the analyte sensor and configured to generate analyte data based on the signals generated by the analyte sensor; and

a receiving device comprising a display, one or more processors, and a memory storing instructions which, when executed by the one or more processors, cause the receiving device to;

receive the analyte data from the sensor electronics;

segment the analyte data by time to obtain segmented data;

identify a periodic event from the segmented data, the periodic event independently occurring at a first window of time and a second window of time;

define a set of time dilation parameters;

apply the time dilation parameters to the second window of time to obtain a baseline-normalized ratio for a portion of the first window of time;

determine that the baseline-normalized ratio for the portion of the first window of time is below a predetermined threshold; and

provide an indication on the display that the portion of the first window of time includes a dropout.

19. The system of claim 18, wherein the predetermined threshold comprises a negative glucose level difference.

20. The system of claim 18, the memory storing instructions to display a graph of overlaid plots of the segmented data, the graph including the indication that the portion of the first window of time includes the dropout.