OUTLIER DETECTION FOR ANALYTE SENSORS
First Claim
1. A method for detecting outliers in analyte sensor data, comprising:
- iteratively evaluating a plurality of subsets of a calibration data set to determine a best subset;
identifying a boundary or confidence interval associated with the best subset;
identifying values outside the boundary or confidence interval as possible outliers;
evaluating the relevancy of the possible outliers to determine outlier information; and
processing responsive to the outlier information.
1 Assignment
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Accused Products
Abstract
Systems and methods for processing sensor data and end of life detection are provided. In some embodiments, a method for determining the end of life of a continuous analyte sensor includes evaluating a plurality of risk factors using an end of life function to determine an end of life status of the sensor and providing an output related to the end of life status of the sensor. The plurality of risk factors may be selected from the list including the number of days the sensor has been in use, whether there has been a decrease in signal sensitivity, whether there is a predetermined noise pattern, whether there is a predetermined oxygen concentration pattern, and error between reference BG values and EGV sensor values.
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Citations
20 Claims
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1. A method for detecting outliers in analyte sensor data, comprising:
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iteratively evaluating a plurality of subsets of a calibration data set to determine a best subset; identifying a boundary or confidence interval associated with the best subset; identifying values outside the boundary or confidence interval as possible outliers; evaluating the relevancy of the possible outliers to determine outlier information; and processing responsive to the outlier information. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A system for detecting outliers in analyte sensor data, the system comprising sensor electronics configured to be operably connected to a continuous analyte sensor, the sensor electronics configured to:
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iteratively evaluate a plurality of subsets of a calibration data set to determine a best subset; identify a boundary or confidence interval associated with a best subset; identify values outside the boundary or confidence interval as possible outliers; evaluate the relevancy of the possible outliers to determine outlier information; and process responsive to the outlier information. - View Dependent Claims (9, 10, 11, 12, 13, 14, 15)
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16. A method for detecting outliers in analyte sensor data, comprising:
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iteratively evaluating a plurality of subsets of a calibration data set; identifying a possible outlier based on one or more first outlier criteria; evaluating the relevancy of the possible outlier based on one or more relevancy criteria to discriminate a root case of the possible outlier; and processing outlier information responsive thereto. - View Dependent Claims (17)
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18. A system for detecting outliers in analyte sensor data, the system comprising sensor electronics configured to be operably connected to a continuous analyte sensor, the sensor electronics configured to:
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iteratively evaluate a plurality of subsets of a calibration data set; identify a possible outlier based on one or more first outlier criteria; evaluate the relevancy of the possible outlier based on one or more relevancy criteria to discriminate a root case of the possible outlier; and process outlier information responsive thereto. - View Dependent Claims (19, 20)
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Specification