Dropout detection in continuous analyte monitoring data during data excursions

US10656139B2 · US · B2

Patent metadata
FieldValue
Publication numberUS-10656139-B2
Application numberUS-201916504986-A
CountryUS
Kind codeB2
Filing dateJul 8, 2019
Priority dateAug 30, 2012
Publication dateMay 19, 2020
Grant dateMay 19, 2020

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  5. First independent claim

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Abstract

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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.

First claim

Opening claim text (preview).

What is claimed is: 1. A method, comprising: receiving sensor data from an analyte sensor configured for positioning in fluid contact with a fluid under a skin layer, the sensor data corresponding to a monitored analyte level of the fluid, the sensor data including multiple instances of a periodic event; segmenting the sensor data into a plurality of time segments, the plurality of time segments including a first segment comprising a first instance of the periodic event, and a second segment comprising a second instance of the periodic event; applying a time dilation operation to the first segment to obtain a time dilated first segment; correlating the sensor data of the time dilated first segment to the sensor data of the second segment, so that sensor data of the time dilated first segment over a first period of time correlates to sensor data of the second segment over a second period of time; determining that the sensor data of the time dilated first segment over the first period of time differs by more than a dynamically varying threshold from the sensor data of the second segment over the second period of time; and displaying an indication that the first segment includes a dropout. 2. The method of claim 1 , wherein the periodic event is a meal, and further comprising receiving a user input for a time marker of at least the first instance of the periodic event or the second instance of the periodic event. 3. The method of claim 1 , wherein applying the time dilation operation to the first segment comprises determining a set of time dilation parameters, and applying the set of time dilation parameters to the first segment to obtain the time dilated first segment; and wherein determining that the sensor data of the time dilated first segment over the first period of time differs by more than the dynamically varying threshold from the sensor data of the second segment comprises determining a ratio for the sensor data of the time dilated first segment and the sensor data of the second segment. 4. The method of claim 1 , wherein the first segment is the most recent segment of the plurality of time segments. 5. The method of claim 1 , wherein displaying the indication that the first segment includes a dropout comprises displaying an indication on a display for a user. 6. The method of claim 1 , wherein the dynamically varying threshold varies according to a noise quality of the analyte sensor. 7. The method of claim 1 , wherein the first period of time and the second period of time are of different lengths. 8. The method of claim 1 , wherein correlating the sensor data of the time dilated first segment to the sensor data of the second segment comprises generating a visual representation of the first segment and a visual representation of the second segment, dilating the visual representation of the first segment to obtain a time dilated visual representation of the first segment, and overlapping the time dilated visual representation of the first segment with the visual representation of the second segment. 9. The method of claim 1 , wherein the sensor data from the analyte sensor is received continuously. 10. The method of claim 1 , further comprising marking the sensor data of the time dilated first segment which differs by more than the dynamically varying threshold from the sensor data of the second segment over the second period of time as invalid. 11. A system, comprising: an analyte sensor configured for positioning in fluid contact with a fluid under a skin layer to generate sensor data corresponding to a monitored analyte level of the fluid, the sensor data including multiple instances of a periodic event; 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 one or more processors to: receive sensor data from the analyte sensor; segment the sensor data into a plurality of time segments, the plurality of time segments including a first segment comprising a first instance of the periodic event, and a second segment comprising a second instance of the periodic event; apply a time dilation operation to the first segment to obtain a time dilated first segment; correlate the sensor data of the time dilated first segment to the sensor data of the second segment, so that sensor data of the time dilated first segment over a first period of time correlates to sensor data of the second segment over a second period of time; determine that the sensor data of the time dilated first segment over the first period of time differs by more than a dynamically varying threshold from the sensor data of the second segment over the second period of time; and display an indication that the first segment includes a dropout. 12. The system of claim 11 , wherein the periodic event is a meal, and wherein the memory further stores instructions which, when executed by the one or more processors, cause the one or more processors to receive a user input for a time marker of at least the first instance of the periodic event or the second instance of the periodic event. 13. The system of claim 11 , wherein the one or more processors apply the time dilation operation to the first segment by at least determining a set of time dilation parameters, and applying the set of time dilation parameters to the first segment to obtain the time dilated first segment; and wherein the one or more processors determine that the sensor data of the time dilated first segment over the first period of time differs by more than the dynamically varying threshold from the sensor data of the second segment by at least determining a ratio for the sensor data of the time dilated first segment and the sensor data of the second segment. 14. The system of claim 11 , wherein the first segment is the most recent segment of the plurality of time segments. 15. The system of claim 11 , wherein the dynamically varying threshold varies according to a noise quality of the analyte sensor. 16. The system of claim 11 , wherein the first period of time and the second period of time are of different lengths. 17. The system of claim 11 , wherein the one or more processors correlate the sensor data of the time dilated first segment to the sensor data of the second segment by at least generating a visual representation of the first segment and a visual representation of the second segment, dilating the visual representation of the first segment to obtain a time dilated visual representation of the first segment, and overlapping the time dilated visual representation of the first segment with the visual representation of the second segment. 18. The system of claim 11 , wherein the one or more processors receive the sensor data from the analyte sensor continuously. 19. The system of claim 11 , wherein the memory further stores instructions which, when executed by the one or more processors, cause the one or more processors to mark the sensor data of the time dilated first segment which differs by more than the dynamically varying threshold from the sensor data of the second segment over the second period of time as invalid. 20. The system of claim 11 , wherein the memory further stores instructions which, when executed by the one or more processors, cause the one or more processors to replace the sensor data of the time dilated first segment which differs by more than the dynamically varying threshold from the sensor data of the second segment over the second period of time with

Assignees

Inventors

Classifications

  • Graphs; Linked lists (G06F16/9027 takes precedence) · CPC title

  • for patient-specific data, e.g. for electronic patient records · CPC title

  • G01N33/49Primary

    Blood {(chemical methods for determining blood cell populations G01N33/5094; chemical analysis of blood groups or blood types G01N33/80)} · CPC title

  • Determining malfunction · CPC title

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

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What does patent US10656139B2 cover?
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 s…
Who is the assignee on this patent?
Abbott Diabetes Care Inc
What technology area does this patent fall under?
Primary CPC classification G01N33/49. Mapped technology areas include Physics.
When was this patent published?
Publication date Tue May 19 2020 00:00:00 GMT+0000 (Coordinated Universal Time) (B2). Legal status and post-grant events are not shown on this page.
What related patents are in patentsdb?
We list 12 related publications on this page (citations in our corpus or others sharing the same primary CPC).