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
First Claim
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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.

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