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DROPOUT DETECTION IN CONTINUOUS ANALYTE MONITORING DATA DURING DATA EXCURSIONS

发明专利审中
1权利要求 · 1 独立
§ Ⅰ

卷宗概要

发明人

Junli Ou; Erwin Satrya Budiman

IPC 分类

G1N 33/49A61B 5/145G6F 16/901G16H 10/60G16Z 99/

CPC 分类

G1N33/49A61B5/14532G6F16/9024G16H10/60G16Z99/A61B2560/276G6F2218/16

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.

原文(中文)

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.