SIGNAL PROCESSING FOR CONTINUOUS ANALYTE SENSOR

• US 20090036758A1
• Filed: 10/16/2008
• Published: 02/05/2009
• Est. Priority Date: 12/09/2003
• Status: Active Grant

• Current Assignee: DexCom Incorporated
• Original Assignee: DexCom Incorporated
• Inventors: Brauker, James H., Goode, Paul V., Kamath, Apurv U., Carr-Brendel, Victoria E., Thrower, James P., Xavier, Ben
• Granted Patent: US 8,657,745 B2
• Time in Patent Office: Days
• US Class Current: 600/309
• CPC Class Codes: A61B 5/145 Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissuemeasuring of blood pressure or blood flow A61B5/02; non-radiation detecting or locating of foreign bodies in blood A61B5/06
• CPC Class Codes: A61B 5/14503 invasive, e.g. introduced into the body by a catheter or needle or using implanted sensors A61B5/1459, A61B5/1464, A61B5/1473, A61B5/1482, A61B5/14865 take precedence
• CPC Class Codes: A61B 5/1451 for interstitial fluid
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/1468 using chemical or electrochemical methods, e.g. by polarographic means A61B5/1486 takes precedence
• CPC Class Codes: A61B 5/1473 invasive, e.g. introduced into the body by a catheter A61B5/1482 takes precedence
• CPC Class Codes: A61B 5/1486 using enzyme electrodes, e.g. with immobilised oxidase
• CPC Class Codes: A61B 5/14865 invasive, e.g. introduced into the body by a catheter or needle or using implanted sensors
• CPC Class Codes: A61B 5/1495 Calibrating or testing of in-vivo probes
• CPC Class Codes: A61B 5/7257 using Fourier transforms
• CPC Class Codes: A61B 5/726 using Wavelet transforms
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A61B 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: A61B 5/742 using visual displays A61B5/339 takes precedence
• CPC Class Codes: A61B 5/7455 characterised by tactile indication, e.g. vibration or electrical stimulation
• CPC Class Codes: A61B 5/746 Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms alarms per se G08B
• CPC Class Codes: A61B 5/7475 User input or interface means, e.g. keyboard, pointing device, joystick
• CPC Class Codes: A61M 5/1723 using feedback of body parameters, e.g. blood-sugar, pressure measurement of body parameters A61B5/00
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass
• CPC Class Codes: Y02A 90/10 Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

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.

Claims

1. A method for compensating for a time lag of continuous analyte sensor data, the method comprising:

receiving sensor data from a continuous analyte sensor, wherein the sensor data comprises a) a physiological time lag with respect to blood analyte data or reference analyte data, b) a membrane-induced time lag, and/or c) a computationally-induced time lag;

continuously and/or intermittently estimating one or more analyte values based on the sensor data, wherein the step of estimating comprises compensating for a time lag associated with the sensor data; and

continuously and/or intermittently providing an output based on the time-lag compensated estimated analyte values.

2. The method of claim 1, further comparing evaluating time-lag compensated estimated analyte values with time-corresponding received analyte values to determine an updated time lag associated with the sensor data.

3. The method of claim 2, wherein the step of estimating one or more analyte values based on the sensor data comprises dynamically adjusting the time lag based on the updated time lag.

4. The method of claim 1, wherein the step of estimating one or more analyte values based on the sensor data comprises predicting one or more analyte values for a time period associated with the time lag, wherein the time period is from about 1 second to about 60 minutes.

5. The method of claim 4, wherein the time period is from about 15 minutes to about 20 minutes.

6. The method of claim 1, further comprising a step of forming at least one matched data pair by matching the time-lag compensated estimated analyte values with reference data.

12. The system of claim 1, wherein the processor module further comprises programming configured to form at least one matched data pair by matching the time-lag compensated estimated analyte values with reference data.

7. A system for compensating for a time lag of continuous analyte sensor data, the system comprising:

an input module operatively connected to the continuous analyte sensor that receives sensor data, wherein the sensor data comprises a) a physiological time lag with respect to blood analyte data or reference analyte data, b) a membrane-induced time lag, and/or c) a computationally-induced time lag; and

a processor module comprising programming configured to continuously and/or intermittently estimate one or more analyte values based on the sensor data including a time lag compensation, wherein the processor module further comprises programming configured to continuously and/or intermittently provide an output based on the time-lag compensated estimated analyte values.

8. The system of claim 7, further comprising comparing time-lag compensated estimated analyte values with time-corresponding received analyte values to determine the time lag associated with the sensor data.

9. The system of claim 8, wherein the time lag compensation is dynamically adjusted based on the determined time lag.

10. The system of claim 7, wherein the step of estimating one or more analyte values based on the sensor data comprises predicting one or more analyte values for a time period associated with the time lag, wherein the time period is from about 1 second to about 60 minutes.

11. The system of claim 10, wherein the time period is from about 15 minutes to about 20 minutes.