SIGNAL PROCESSING FOR CONTINUOUS ANALYTE SENSOR

• US 20160345879A1
• Filed: 08/15/2016
• Published: 12/01/2016
• Est. Priority Date: 12/09/2003
• Status: Active Grant

• Current Assignee: DexCom Incorporated
• Original Assignee: DexCom Incorporated
• Inventors: Brauker, James H., Carr-Brendel, Victoria E., Goode, Paul V. Jr., Kamath, Apurv Ullas, Thrower, James Patrick, Xavier, Ben
• Granted Patent: US 9,750,441 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• 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 processing sensor data, the method comprising:

receiving sensor data from a continuous analyte sensor;

receiving reference data from a reference analyte monitor measured at a reference time period;

estimating at least one analyte value based on the sensor data for the reference time period; and

forming at least one matched data pair by matching the reference data to the at least one estimated analyte value.

2. The method of claim 1, further comprising a step of performing outlier detection based at least in part on the at least one matched data pair.

3. The method of claim 1, further comprising a step of calibrating the sensor data based at least in part on the at least one matched data pair.

4. The method of claim 1, further comprising receiving sensor data for the reference time period and comparing the estimated analyte value for the reference time period with sensor data for the reference time period to determine a time lag between the estimated analyte value and the time-corresponding analyte value.

5. The method of claim 1, wherein the step of estimating at least one analyte value based on the sensor data for the reference time period includes compensating for a time lag between the reference data and the sensor data.

6. The method of claim 1, wherein the step of estimating at least one analyte value based on the sensor data for the reference time period comprises estimating one or more analyte values for a time period during which no sensor data exists.

7. The method of claim 6, wherein the step of estimating one or more analyte values for a time period during which no sensor data exists comprises estimating one or more analyte values for a future time period.

8. The method of claim 6, wherein the step of estimating one or more analyte values for a future time period is configured to compensate for a time lag.

9. The method of claim 1, wherein the step of estimating at least one analyte value based on the sensor data for the reference time period comprises estimating one or more analyte values for a time period when data is determined to be unreliable and/or inaccurate.

10. The method of claim 1, wherein the step of forming at least one matched data pair by matching the reference data to the estimated analyte value comprises forming a plurality of matched data pairs by matching a plurality a reference data points with a plurality of sensor data points over a predetermined time period.

11. The method of claim 10, further comprising the step of determining a best matched pair by evaluating the plurality of matched data pairs.

12. The method of claim 11, wherein the step of evaluating the plurality of matched data pairs is based at least in part on a statistical deviation and/or clinical risk analysis.

13. A system for processing sensor data, the system comprising:

a sensor input module operatively connected to a continuous analyte sensor that receives sensor data from the continuous analyte sensor;

a reference input module that receives reference data from a reference analyte monitor; and

a processor module comprising programming configured to estimate at least one analyte value based on the sensor data for the reference time period, wherein the processor module further comprises programming configured to form at least one matched data pair by matching the reference data to the at least one estimated analyte value.

14. The system of claim 13, wherein the processor module further comprises programming configured to perform outlier detection based at least in part on the at least one matched data pair.

15. The system of claim 13, wherein the processor module further comprises programming configured to calibrate the sensor data based at least in part on the at least one matched data pair.

16. The system of claim 13, wherein the sensor input module further receives sensor data for the reference time period, and wherein the processor module further comprises programming configured to compare the estimated analyte value for the reference time period with sensor data for the reference time period to determine a time lag between the estimated analyte value and the time-corresponding analyte value.

17. The system of claim 13, wherein the programming configured to estimate at least one analyte value based on the sensor data for the reference time period compensates for a time lag between the reference data and the sensor data.

18. The system of claim 13, wherein the programming configured to estimate at least one analyte value based on the sensor data for the reference time period estimates one or more analyte values for a time period during which no sensor data exists.

19. The system of claim 18, wherein the programming configured to estimate one or more analyte values for a time period during which no sensor data exists estimates one or more analyte values for a future time period.

20. The system of claim 18, wherein the programming configured to estimate one or more analyte values for a future time period is configured to compensate for a time lag.