SIGNAL PROCESSING FOR CONTINUOUS ANALYTE SENSOR
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Accused Products

Abstract
Systems and methods for dynamically and intelligently estimating analyte data from a continuous analyte sensor, including receiving a data stream, selecting one of a plurality of algorithms, and employing the selected algorithm to estimate analyte values. Additional data processing includes evaluating the selected estimative algorithms, analyzing a variation of the estimated analyte values based on statistical, clinical, or physiological parameters, comparing the estimated analyte values with corresponding measure analyte values, and providing output to a user. Estimation can be used to compensate for time lag, match sensor data with corresponding reference data, warn of upcoming clinical risk, replace erroneous sensor data signals, and provide more timely analyte information encourage proactive behavior and preempt clinical risk.
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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DexCom Incorporated
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DexCom Incorporated
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DexCom Incorporated
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DexCom Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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Abbott Diabetes Care Incorporated
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DexCom Incorporated
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Abbott Diabetes Care Incorporated
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53 Claims
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1-16. -16. (canceled)
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17. A method for processing sensor data from a continuous analyte sensor, the method comprising:
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receiving sensor data from a continuous analyte sensor for a first time period, wherein the sensor data comprises at least one sensor data point; processing the sensor data point; and providing an output based at least in part on the sensor data point, wherein the output comprises a representation of a range of possible analyte values associated with the sensor data point. - View Dependent Claims (18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32)
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33. A system for estimating an analyte value from a continuous analyte sensor, the system comprising:
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an input module operatively connected to a continuous analyte sensor, wherein the input module is configured to receive sensor data from the continuous analyte sensor for a first time period, wherein the sensor data comprises at least one sensor data point; a processor module that processes the sensor data point; and an output module that provides output based on the sensor data, wherein the output comprises a representation of a range of possible analyte values associated with the sensor data point. - View Dependent Claims (34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53)
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1 Specification
This application is a divisional of U.S. application Ser. No. 11/007,920, filed Dec. 8, 2004, which claims the benefit of U.S. Provisional Application No. 60/528,382, filed Dec. 9, 2003, U.S. Provisional Application 60/587,787, filed Jul. 13, 2004, and U.S. Provisional Application 60/614,683, filed Sep. 30, 2004. Each above-referenced prior application is incorporated by reference herein in its entirety and is hereby made a portion of this specification.
The present invention relates generally to systems and methods for measuring and analyzing data obtained from a continuous analyte sensor. More particularly, the present invention relates to dynamic and intelligent estimation of analyte values from a continuous analyte sensor.
Diabetes mellitus is a disorder in which the pancreas cannot create sufficient insulin (Type I or insulin dependent) and/or in which insulin is not effective (Type 2 or non-insulin dependent). In the diabetic state, the victim suffers from high blood sugar, which may cause an array of physiological derangements (for example, kidney failure, skin ulcers, or bleeding into the vitreous of the eye) associated with the deterioration of small blood vessels. A hypoglycemic reaction (low blood sugar) may be induced by an inadvertent overdose of insulin, or after a normal dose of insulin or glucose-lowering agent accompanied by extraordinary exercise or insufficient food intake.
Conventionally, a person with diabetes carries a self-monitoring blood glucose (SMBG) monitor, which typically comprises uncomfortable finger pricking methods. Due to the lack of comfort and convenience, a person with diabetes will normally only measure his or her glucose levels two to four times per day. Unfortunately, these time intervals are so far apart that the person with diabetes will likely find out too late, sometimes incurring dangerous side effects, of a hyper- or hypo-glycemic condition. In fact, it is not only unlikely that a person with diabetes will take a timely SMBG value, but the person with diabetes will not know if their blood glucose value is going up (higher) or down (lower) based on conventional methods, inhibiting their ability to make educated insulin therapy decisions.
Some attempts have been made to continuously measure the glucose concentration in a person with diabetes. Typically, these continuous glucose sensors have required a reference glucose monitor (for example, SMBG) to provide reference glucose values in order to calibrate and/or interpret data from the continuous glucose monitor. While the use of these reference glucose values can be helpful, they can also cause numerous inconsistencies and instabilities in the data output of the continuous glucose sensor. As one example, a time lag can be caused by an interstitial fluid sample measured by an implantable glucose sensor as compared with a blood sample measured by an external reference glucose monitor, which can cause inaccurate calibration, outlier detection, and data output. Additionally, the static use of algorithms may not adequately represent physiological trends in a human, for example.
There exists a need for improvements in data processing of continuous glucose sensors in order to better handle the inconsistencies and instabilities that occur in glucose measurements and associated data analysis.
Accordingly, in a first embodiment, a method for estimating an analyte value from a continuous analyte sensor is provided, the method comprising receiving a data stream from the continuous analyte sensor for a first time period, thereby obtaining a measured analyte value; estimating at least one analyte value for a second time period based on the data stream; and comparing the estimated analyte value with the measured analyte value.
In an aspect of the first embodiment, the step of receiving a data stream comprises receiving a data stream that has been algorithmically smoothed.
In an aspect of the first embodiment, the step of receiving the data stream comprises receiving a raw data stream.
In an aspect of the first embodiment, the step of estimating at least one analyte value further comprises selecting an algorithm from a plurality of algorithms based on an analysis of the data stream prior to estimating at least one analyte value.
In an aspect of the first embodiment, the step of selecting an algorithm is conditional upon at least one value selected from the group consisting of analyte concentration, rate of change, acceleration, and an individual historical pattern of the data stream.
In an aspect of the first embodiment, the step of selecting an algorithm comprises employing the plurality of algorithms on the data stream and determining which of the plurality of algorithms best correlates with the data stream.
In an aspect of the first embodiment, the algorithm is selected from the group consisting of polynomial regression, autoregressive algorithms, Fourier transform, Wavelet transform, neural network-based mapping, fuzzy logic based pattern matching, and Genetic-Algorithms based pattern matching.
In an aspect of the first embodiment, the step of selecting an algorithm further comprises applying a physiological boundary to the selected algorithm.
In an aspect of the first embodiment, the method further comprises evaluating the selected algorithm by applying an evaluation function prior to employing the selected algorithm to estimate the analyte value, wherein the evaluation function is selected from the group consisting of a data association function, a curvature formula, and a physiological boundary.
In an aspect of the first embodiment, the step of analyzing a variation comprises analyzing a variation of the estimated analyte value based on a parameter selected from a statistical parameter, a clinical parameter, or a physiological parameter.
In an aspect of the first embodiment, the step of analyzing a variation comprises determining a physiological variation from the estimated analyte value.
In an aspect of the first embodiment, the step of analyzing a variation comprises determining a statistical variation from the estimated analyte value based on a statistical parameter.
In an aspect of the first embodiment, the step of comparing comprises determining a deviation between the estimated analyte value and the measured analyte value.
In an aspect of the first embodiment, the step of analyzing a variation comprises analyzing a variation of the estimated analyte value based on the deviation determined by the step of comparing.
In an aspect of the first embodiment, the method further comprises a step of recognizing a pattern by monitoring a physiological parameter over time.
In an aspect of the first embodiment, the step of analyzing a variation comprises analyzing a variation in the physiological parameter to determine a variation of the estimated analyte value.
In an aspect of the first embodiment, the step of analyzing a variation comprises determining a variation of the estimated analyte value based on a clinical risk of the estimated analyte value to the user.
In an aspect of the first embodiment, the method further comprises providing output based on the estimated analyte data.
In an aspect of the first embodiment, the output displays an estimated analyte value to the user.
In an aspect of the first embodiment, the output displays an estimated path of analyte values for a future time period.
In an aspect of the first embodiment, the output displays an estimated analyte value at a future point in time.
In an aspect of the first embodiment, the output displays an estimated future time period of clinical risk.
In an aspect of the first embodiment, the time period of clinical risk is determined when an estimated analyte value falls outside of a normal analyte threshold.
In an aspect of the first embodiment, the analyte is blood glucose and the normal analyte threshold is from about 100 mg/dL to about 160 mg/dL.
In an aspect of the first embodiment, the normal analyte threshold is from about 80 mg/dL to about 200 mg/dL.
In an aspect of the first embodiment, the normal analyte threshold is from about 55 mg/dL to about 220 mg/dL.
In an aspect of the first embodiment, the output displays at least one clinically acceptable target analyte value.
In an aspect of the first embodiment, the target analyte value is customizable by the user.
In an aspect of the first embodiment, the target analyte value is based on an individual physiological pattern.
In an aspect of the first embodiment, a parameter utilized in determining a therapy recommendation is customizable by the user.
In an aspect of the first embodiment, the output comprises an icon that has a shape representative of the analyzed variation of the estimated analyte value.
In an aspect of the first embodiment, the output comprises a dynamic visual representation of the analyzed variation of estimated analyte value.
In an aspect of the first embodiment, the output prompts the user to obtain a reference analyte value.
In an aspect of the first embodiment, the output provides at least one alarm selected from the group consisting of a visual alarm, an audible alarm, and a tactile alarm, wherein the alarm is provided based on a clinical risk associated with the estimated analyte value.
In an aspect of the first embodiment, the alarm is based on at least one parameter selected from the group consisting of an analyte value, a rate of change, acceleration of a rate of change, and an individual physiological pattern.
In an aspect of the first embodiment, the output continuously provides estimated analyte values.
In an aspect of the first embodiment, the output selectively provides the estimated analyte value based on an event trigger.
In an aspect of the first embodiment, the output provides a clinical risk zone that is displayed on a screen.
In an aspect of the first embodiment, the clinical risk zone comprises at least one of a shaded region, a colored region, and a patterned region.
In an aspect of the first embodiment, the clinical risk zone is bounded by a threshold.
In an aspect of the first embodiment, the output provides the estimated analyte value and the variation of the estimated analyte value on a trend graph.
In an aspect of the first embodiment, the output provides the estimated analyte value and the variation of the estimated analyte value on a gradient bar.
In an aspect of the first embodiment, the output is sent to a personal computer.
In an aspect of the first embodiment, the output is sent to a modem.
In an aspect of the first embodiment, the output is sent to an insulin pen.
In an aspect of the first embodiment, the output is sent to an insulin pump.
In a second embodiment, a method for estimating an analyte value from a continuous analyte sensor is provided, the method comprising receiving a data stream from the continuous analyte sensor for a time period; estimating at least one analyte value for a future time based on the data stream; analyzing a variation of the estimated analyte value based on a parameter selected from the group consisting of a statistical parameter, a clinical parameter, or a physiological parameter; and providing an output based on the estimated analyte value and the variation of the estimated analyte value.
In an aspect of the second embodiment, the method further comprises evaluating the selected algorithm by applying an evaluation function prior to employing the selected algorithm to estimate the analyte value, wherein the evaluation function is selected from the group consisting of a data association function, a curvature formula, and a physiological boundary.
In a third embodiment, a method for estimating an analyte value from a continuous analyte sensor is provided, the method comprising receiving a data stream from the continuous analyte sensor for a time period; selecting at least one algorithm from a plurality of algorithms based on an analysis of the data stream; evaluating the algorithm based on at least one parameter selected from the group consisting of a statistical parameter, a physiological parameter, and a clinical parameter; and employing the selected algorithm based on the step of evaluating to estimate at least one analyte value.
In an aspect of the third embodiment, the method further comprises evaluating the selected algorithm by applying an evaluation function prior to employing the selected algorithm to estimate the analyte value, wherein the evaluation function is selected from the group consisting of a data association function, a curvature formula, and a physiological boundary.
In an aspect of the third embodiment, the method further comprises analyzing a variation of the estimated analyte value based on a parameter selected from a statistical parameter, a clinical parameter, and a physiological parameter.
In an aspect of the third embodiment, the output further comprises a therapy recommendation to help the user obtain a target analyte value.
In a fourth embodiment, a method for matching a data pair using data from a continuous analyte sensor with data from a reference analyte source is provided, the method comprising receiving a data stream from the continuous analyte sensor, the data comprising at least one sensor data point; receiving reference data from a reference analyte monitor, the data comprising at least one reference data point; estimating at least one analyte value for a time period during which no data exists based on the data stream; and creating at least one matched data pair by matching the reference data to the analyte value.
In an aspect of the fourth embodiment, the step of receiving the data stream comprises receiving a data stream that has been algorithmically smoothed.
In an aspect of the fourth embodiment, the step of receiving reference data comprises downloading reference data via a cabled connection.
In an aspect of the fourth embodiment, the step of receiving reference data comprises downloading reference data via a wireless connection.
In an aspect of the fourth embodiment, the step of receiving reference data from a reference analyte monitor comprises receiving within a receiver an internal communication from a reference analyte monitor integral with the receiver.
In an aspect of the fourth embodiment, the algorithm is selected from the group consisting of polynomial regression, autoregressive algorithms, Fourier transform, Wavelet transform, neural network-based mapping, fuzzy logic based pattern matching, and Genetic-Algorithm matching.
In an aspect of the fourth embodiment, the method further comprises evaluating the selected algorithm by applying an evaluation function prior to employing the selected algorithm to estimate the analyte value, wherein the evaluation function is selected from the group consisting of a data association function, a curvature formula, and a physiological boundary.
In an aspect of the fourth embodiment, the method further comprises comparing the estimated analyte value with the corresponding measured analyte value to determine a time lag between the estimated analyte value and the corresponding measured analyte value.
In a fifth embodiment, a method for compensating for a time lag of continuous analyte sensor data by estimating an analyte value for a present time from which the continuous analyte sensor data is delayed is provided, the method comprising receiving a data stream from the continuous analyte sensor, wherein the data stream comprises a physiological time lag from the present time or a computational time lag from the present time; continuously estimating or periodically estimating analyte values for a present time period based on the data stream to compensate for the physiological time lag or the computational time lag in the analyte sensor data; and continuously providing or periodically providing an output to a user based on the estimated analyte values, such that the output of the estimated analyte values provides present time analyte values to the user.
In an aspect of the fifth embodiment, the analyte value estimation step further comprises selecting an algorithm from a plurality of algorithms based on analysis of the data stream prior to estimating the analyte values.
In an aspect of the fifth embodiment, the algorithm selection is conditional upon at least one value selected from the group consisting of analyte concentration, rate of change, acceleration, and an individual historical pattern of the data stream.
In an aspect of the fifth embodiment, the method further comprises evaluating the selected algorithm by applying a data association function, a curvature formula, or physiological boundaries prior to employing the selected algorithm to estimate analyte values.
In an aspect of the fifth embodiment, the method further comprises analyzing a variation of the estimated analyte values based on parameters selected from the group consisting of statistical parameters, clinical parameters, and physiological parameters.
In an aspect of the fifth embodiment, the step of analyzing a variation comprises determining a physiological variation from estimated analyte values.
In an aspect of the fifth embodiment, the step of analyzing a variation comprises determining a statistical variation from the estimated analyte values based on a statistical parameter.
In an aspect of the fifth embodiment, the method further comprises comparing the estimated analyte values with the measured analyte values to determine a deviation between the estimated analyte values and the measured analyte values.
In an aspect of the fifth embodiment, the step of analyzing a variation comprises analyzing a variation of the estimated analyte values based on the deviation determined by the step of comparing.
In an aspect of the fifth embodiment, the output displays estimated analyte values to the user.
In an aspect of the fifth embodiment, the output further comprises a therapy recommendation to help the user obtain a target analyte value.
In an aspect of the fifth embodiment, the output comprises an icon that has a shape representative of the analyzed variation of the estimated analyte value.
In a sixth embodiment, a method for estimating analyte values from a continuous analyte sensor is provided, the method comprising receiving a data stream from the continuous analyte sensor for a time period; and estimating one or more analyte values for a time period based on the data stream, wherein analyte estimation comprises performing an algorithm to estimate analyte values and applying physiological boundaries to the estimated analyte values.
In an aspect of the sixth embodiment, the analyte value estimation step further comprises selecting an algorithm from a plurality of algorithms based on analysis of the data stream prior to estimating the analyte values.
In an aspect of the sixth embodiment, the algorithm selection is conditional upon at least one of analyte concentration, rate of change, acceleration, and individual historical patterns of the data stream.
In an aspect of the sixth embodiment, the algorithm is selected from the group consisting of polynomial regression, autoregressive algorithms, Fourier transform, Wavelet transform, neural network-based mapping, fuzzy logic based pattern matching, and Genetic-Algorithm matching.
In an aspect of the sixth embodiment, the method further comprises evaluating the selected algorithm by applying a data association function, a curvature formula, or physiological boundaries prior to employing the selected algorithm to estimate analyte values.
In an aspect of the sixth embodiment, the method further comprises analyzing a variation of the estimated analyte values based on statistical, clinical, or physiological parameters.
In an aspect of the sixth embodiment, the time period of clinical risk is determined when an estimated analyte value falls outside of a normal analyte threshold.
In an aspect of the sixth embodiment, the output selectively provides estimated analytes value based on an event trigger.
In a seventh embodiment, a method for displaying analyte data from a continuous analyte sensor is provided, the method comprising receiving a data stream from the continuous analyte sensor for a time period; calibrating the data stream using a conversion function to determine at least one calibrated analyte value; analyzing a variation of at least one calibrated analyte value based on a parameter selected from the group consisting of a statistical parameter, a clinical parameter, and a physiological parameter; and providing an output based on the calibrated analyte value and the variation of the calibrated analyte value.
In an aspect of the seventh embodiment, the output comprises a numerical representation of a calibrated analyte value and a variation of the calibrated analyte value.
In an aspect of the seventh embodiment, the output comprises a numerical representation of a range of possible analyte values.
In an aspect of the seventh embodiment, the output further comprises an arrow representing a rate of change of the calibrated analyte values.
In an eighth embodiment, a system for estimating analyte data from a continuous analyte sensor is provided, the system comprising an input module operatively connected to the continuous analyte sensor that receives a data stream comprising a plurality of time spaced sensor data points from the analyte sensor; and a processor module comprising programming that estimates at least one analyte value for a time period based on the data stream and compares the estimated analyte value with a corresponding measured analyte value.
In an aspect of the eighth embodiment, the input module is adapted to receive a data stream that has been algorithmically smoothed.
In an aspect of the eighth embodiment, the input module is adapted to receive a raw data stream.
In an aspect of the eighth embodiment, the programming to estimate an analyte value further comprises programming to select an algorithm from a plurality of algorithms based on analysis of the data stream prior to estimating the analyte value.
In an aspect of the eighth embodiment, the processor module further comprises programming to select the algorithm conditional upon at least one parameter selected from the group consisting of an analyte concentration, a rate of change, acceleration of a rate of change, and an individual historical pattern of the data stream.
In an aspect of the eighth embodiment, the processor module runs the plurality of algorithms on the data stream and determines which of the plurality of algorithms best correlates with the data stream.
In an aspect of the eighth embodiment, the processor module comprises programming to apply a physiological boundary to the selected algorithm.
In an aspect of the eighth embodiment, the processor module further comprises programming to evaluate the selected algorithm by applying a data association function, a curvature formula, or physiological boundaries prior to employing the selected algorithm to estimate analyte values.
In an aspect of the eighth embodiment, the processor module further comprises programming to analyze a variation of the estimated analyte value based on a parameter selected from the group consisting of statistical parameters, clinical parameters, and physiological parameters.
In an aspect of the eighth embodiment, the programming to analyze a variation comprises determining a physiological variation from the estimated analyte value.
In an aspect of the eighth embodiment, the programming to analyze a variation comprises determining statistical variation from the estimated analyte value based on statistical parameters.
In an aspect of the eighth embodiment, the programming to compare the estimated analyte value with the measured analyte value further comprises determining a deviation between the estimated analyte value and the measured analyte value.
In an aspect of the eighth embodiment, the programming to analyze a variation comprising analyzing a variation of the estimated analyte values based on the deviation determined by the comparison step.
In an aspect of the eighth embodiment, the processor module further comprises programming to recognize a pattern by monitoring a physiological pattern over time.
In an aspect of the eighth embodiment, the programming to analyze a variation comprises analyzing the physiological pattern to determine a variation of the estimated analyte value.
In an aspect of the eighth embodiment, the programming to analyze a variation comprises determining the variation of the estimated analyte value based on a clinical risk of the estimated analyte value to the user.
In an aspect of the eighth embodiment, the system further comprises an output module comprising programming to output data based on the estimated analyte data.
In a ninth embodiment, a system for estimating analyte values from a continuous analyte sensor, the system comprising an input module operatively connected to the continuous analyte sensor that receives a data stream comprising a plurality of time spaced sensor data points from the analyte sensor; a processor module comprising programming that estimates at least one analyte value for a future time based on the data stream, and analyzes a variation of the estimated analyte value based on a parameter selected from the group consisting of a statistical parameter, a clinical parameter, and a physiological parameter; and an output module associated with the processor module and comprising programming to output analyte data based on at least one estimated analyte value and the variation of the estimated analyte value.
In an aspect of the ninth embodiment, the input module is adapted to receive a data stream that has been algorithmically smoothed.
In an aspect of the ninth embodiment, the input module is adapted to receive a raw data stream.
In an aspect of the ninth embodiment, the programming to estimate an analyte value further comprises programming to select an algorithm from a plurality of algorithms based on analysis of the data stream prior to estimating the analyte value.
In an aspect of the ninth embodiment, the programming to select an algorithm is conditional upon at least one parameter selected from the group consisting of an analyte concentration, a rate of change, an acceleration of a rate of change, and an individual historical pattern of the data stream.
In an aspect of the ninth embodiment, the programming to select an algorithm further comprises employing the plurality of algorithms on the data stream and determining which of the plurality of algorithms best correlates with the data stream.
In an aspect of the ninth embodiment, the algorithms are selected from the group consisting of polynomial regression, autoregressive algorithms, Fourier transform, Wavelet transform, neural network-based mapping, fuzzy logic based pattern matching, and Genetic-Algorithm matching.
In an aspect of the ninth embodiment, the processor module further comprises programming to apply a physiological boundary to the selected algorithm.
In an aspect of the ninth embodiment, the processor module further comprises programming to evaluate the selected algorithm by applying a data association function, a curvature formula, or physiological boundaries prior to employing the selected algorithm to estimate the analyte value.
In an aspect of the ninth embodiment, the programming to analyze a variation comprises determining a physiological variation from the estimated analyte value.
In an aspect of the ninth embodiment, the programming to analyze a variation comprises determining statistical variation from the estimated analyte value based on a statistical parameter.
In an aspect of the ninth embodiment, the processor module compares the estimated analyte value with the measured analyte value to determine a deviation between the estimated analyte value and the measured analyte value.
In an aspect of the ninth embodiment, the programming to analyze a variation comprises analyzing a variation of the estimated analyte value based on the deviation.
In an aspect of the ninth embodiment, the processor module further comprises programming to recognize a pattern by monitoring a physiological pattern over time.
In an aspect of the ninth embodiment, the programming to analyze a variation comprises analyzing the physiological pattern to determine a variation of the estimated analyte value.
In an aspect of the ninth embodiment, the programming to analyze a variation comprises determining the variation of the estimated analyte value based the clinical risk of the estimated analyte value to the user.
In an aspect of the ninth embodiment, the output displays estimated analyte value to the user.
In a tenth embodiment, a system for estimating analyte values from a continuous analyte sensor is provided, the system comprising an input module operatively connected to the continuous analyte sensor that receives a data stream comprising a plurality of time spaced sensor data points from the analyte sensor; and a processor module comprising programming that selects an algorithm from a plurality of algorithms based on an analysis of the data stream, that evaluates the algorithm based on a parameter selected from the group consisting of statistical parameters, physiological parameters, and clinical parameters, and that employs a selected algorithm based on the algorithm evaluation to estimate at least one analyte value.
In an eleventh embodiment, a system for matching data pairs from a continuous analyte sensor with data from a reference analyte source, the system comprising a sensor input module operatively connected to the continuous analyte sensor that receives a data stream comprising a plurality of time spaced sensor data points from the analyte sensor; a reference input module receiving reference data from a reference analyte monitor, including at least one reference data point; and a processor module comprising programming that estimates at least one analyte value for a time period during which no data exists based on the data stream and creates at least one matched data pair by matching reference analyte data to the estimated analyte value.
In a twelfth embodiment, a system for compensating for a time lag of continuous analyte sensor data by estimating an analyte value for a present time from which the continuous analyte sensor data is delayed is provided, the system comprising an input module operatively connected to the continuous analyte sensor that receives a data stream comprising a plurality of time spaced sensor data points from the analyte sensor; a processor module comprising programming that continuously estimates or periodically estimates analyte values for the present time period based on the data stream to compensate for the physiological or computational time lag in the analyte sensor data; and an output module associated with the processor module and comprising programming to continuously provide or periodically provide an output to the user based on the estimated analyte values, such that output of the estimated analyte values provides present time analyte values to the user.
In a thirteenth embodiment, a system for estimating analyte values from a continuous analyte sensor is provided, the system comprising an input module operatively connected to the continuous analyte sensor that receives a data stream comprising a plurality of time spaced sensor data points from the analyte sensor; and a processor module comprising programming that estimates at least one analyte value for a time period based on the data stream, wherein the analyte estimation comprises performing an algorithm to estimate an analyte value and applying a physiological boundary to the estimated analyte value.
In a fourteenth embodiment, a system for displaying analyte data from a continuous analyte sensor is provided, the system comprising an input module operatively connected to the continuous analyte sensor that receives a data stream comprising a plurality of time spaced sensor data points from the analyte sensor; a processor module comprising programming that calibrates the data stream using a conversion function to determine a calibrated analyte value and analyze a variation of the calibrated analyte value based on statistical, clinical, or physiological parameters; and an output module associated with the processor module and comprising programming to output data based on the calibrated analyte value and the variation of calibrated analyte value.
The following description and examples illustrate some exemplary embodiments of the disclosed invention in detail. Those of skill in the art will recognize that there are numerous variations and modifications of this invention that are encompassed by its scope. Accordingly, the description of a certain exemplary embodiment should not be deemed to limit the scope of the present invention.
In order to facilitate an understanding of the disclosed invention, a number of terms are defined below.
The term “analyte,” as used herein, is a broad term and is used in its ordinary sense, including, without limitation, to refer to a substance or chemical constituent in a biological fluid (for example, blood, interstitial fluid, cerebral spinal fluid, lymph fluid or urine) that can be analyzed. Analytes can include naturally occurring substances, artificial substances, metabolites, and/or reaction products. In some embodiments, the analyte for measurement by the sensor heads, devices, and methods is analyte. However, other analytes are contemplated as well, including but not limited to acarboxyprothrombin; acylcarnitine; adenine phosphoribosyl transferase; adenosine deaminase; albumin; alpha-fetoprotein; amino acid profiles (arginine (Krebs cycle), histidine/urocanic acid, homocysteine, phenylalanine/tyrosine, tryptophan); andrenostenedione; antipyrine; arabinitol enantiomers; arginase; benzoylecgonine (cocaine); biotinidase; biopterin; c-reactive protein; carnitine; carnosinase; CD4; ceruloplasmin; chenodeoxycholic acid; chloroquine; cholesterol; cholinesterase; conjugated 1-β hydroxy-cholic acid; cortisol; creatine kinase; creatine kinase MM isoenzyme; cyclosporin A; d-penicillamine; de-ethylchloroquine; dehydroepiandrosterone sulfate; DNA (acetylator polymorphism, alcohol dehydrogenase, alpha 1-antitrypsin, cystic fibrosis, Duchenne/Becker muscular dystrophy, analyte-6-phosphate dehydrogenase, hemoglobin A, hemoglobin S, hemoglobin C, hemoglobin D, hemoglobin E, hemoglobin F, D-Punjab, beta-thalassemia, hepatitis B virus, HCMV, HIV-1, HTLV-1, Leber hereditary optic neuropathy, MCAD, RNA, PKU, Plasmodium vivax, sexual differentiation, 21-deoxycortisol); desbutylhalofantrine; dihydropteridine reductase; diptheria/tetanus antitoxin; erythrocyte arginase; erythrocyte protoporphyrin; esterase D; fatty acids/acylglycines; free β-human chorionic gonadotropin; free erythrocyte porphyrin; free thyroxine (FT4); free tri-iodothyronine (FT3); fumarylacetoacetase; galactose/gal-1-phosphate; galactose-1-phosphate uridyltransferase; gentamicin; analyte-6-phosphate dehydrogenase; glutathione; glutathione perioxidase; glycocholic acid; glycosylated hemoglobin; halofantrine; hemoglobin variants; hexosaminidase A; human erythrocyte carbonic anhydrase I; 17-alpha-hydroxyprogesterone; hypoxanthine phosphoribosyl transferase; immunoreactive trypsin; lactate; lead; lipoproteins ((a), B/A-1, β); lysozyme; mefloquine; netilmicin; phenobarbitone; phenytoin; phytanic/pristanic acid; progesterone; prolactin; prolidase; purine nucleoside phosphorylase; quinine; reverse tri-iodothyronine (rT3); selenium; serum pancreatic lipase; sissomicin; somatomedin C; specific antibodies (adenovirus, anti-nuclear antibody, anti-zeta antibody, arbovirus, Aujeszky'"'"'s disease virus, dengue virus, Dracunculus medinensis, Echinococcus granulosus, Entamoeba histolytica, enterovirus, Giardia duodenalisa, Helicobacter pylori, hepatitis B virus, herpes virus, HIV-1, IgE (atopic disease), influenza virus, Leishmania donovani, leptospira, measles/mumps/rubella, Mycobacterium leprae, Mycoplasma pneumoniae, Myoglobin, Onchocerca volvulus, parainfluenza virus, Plasmodium falciparum, poliovirus, Pseudomonas aeruginosa, respiratory sync