Signal processing for continuous analyte sensor

• US 8,801,610 B2
• Filed: 07/24/2009
• Issued: 08/12/2014
• Est. Priority Date: 12/09/2003
• Status: Active Grant

• Current Assignee: DexCom Incorporated
• Original Assignee: DexCom Incorporated
• Inventors: Brauker, James H., Carr-Brendel, Victoria, Goode, Paul V., Kamath, Apurv U., Thrower, James P., Xavier, Ben
• Primary Examiner(s): Natnithithadha, Navin
• Application Number:
  US12/509,396
• Publication Number:
  US 20090299162A1
• Time in Patent Office: 1,845 Days
• Field of Search: 600/309, 600345-366
• US Class Current: 600/365
• 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 from a continuous analyte sensor, the method comprising:

receiving sensor data from a continuous analyte sensor measuring an analyte concentration of a host for a first time period, wherein the sensor data comprises at least one sensor data point;

determining, using one or more processors, a range of possible analyte values for the host at a point in time based at least in part on the sensor data point; and

providing, using the one or more processors, an output based at least in part on the sensor data point, wherein the output comprises a representation of the range of possible analyte values for the host at the point in time.

2. The method of claim 1, wherein the representation of the range of possible analyte values is a gradient bar.

3. The method of claim 1, wherein the range of possible analyte values is a range of possible analyte values at a point in time later than the first time period.

4. The method of claim 1, wherein the representation comprises a range of target analyte values for a future time period.

5. The method of claim 1, wherein determining comprises weighting the range based on a clinical risk.

6. The method of claim 1, wherein providing an output comprises transmitting the output to at least one of a server, a personal computer, a personal digital assistant, a modem, an insulin pen, or an insulin pump.

7. The method of claim 1, further comprising determining a therapy recommendation based at least in part on the range of possible analyte values.

8. The method of claim 7, wherein providing an output further comprises displaying the therapy recommendation on a user interface.

9. The method of claim 7, wherein providing an output further comprises transmitting the therapy recommendation to an external device.

10. The method of claim 9, wherein the external device is an insulin delivery device.

11. The method of claim 1, wherein the representation comprises a numerical representation of analyte values.

12. The method of claim 1, wherein the range of possible analyte values is based on a variation analysis.

13. The method of claim 1, wherein the range of possible analyte values is based on physiological patterns.

14. The method of claim 1, wherein the range of possible analyte values is based on a confidence level.

15. The method of claim 1, wherein the range of possible analyte values is adjustable.

16. The method of claim 1, wherein the analyte is glucose.

17. A system for estimating an analyte value from a continuous analyte sensor, the system comprising:

an input module configured to operatively connect to a continuous analyte sensor measuring an analyte concentration of a host, 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 determines a range of possible analyte values for the host at a point in time based at least in part on the sensor data point; and

an output module that provides output based on the sensor data, wherein the output comprises a representation of the range of possible analyte values for the host at the point in time.

18. The system of claim 17, wherein the representation comprises a numerical representation of analyte values.

19. The system of claim 17, the range of possible analyte values is a range of possible analyte values at a point in time later than the first time period.

20. The system of claim 17, further comprising a screen configured to display the representation of the range of possible analyte values, wherein the representation is a gradient bar.

21. The system of claim 17, wherein the representation comprises a range of target analyte values for a future time period.

22. The system of claim 21, wherein the range of target analyte values is determined based additionally on an individual physiological pattern.

23. The system of claim 17, wherein determining the range comprises weighting based on a clinical risk.

24. The system of claim 17, wherein the output module is configured to provide at least one alarm selected from the group consisting of a visual alarm, an audible alarm, and a tactile alarm.

25. The system of claim 24, wherein 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.

26. The system of claim 17, wherein the output module is configured to continuously provide the representation of the range of possible analyte values.

27. The system of claim 17, wherein the output module is configured to transmit the range of possible analyte values to at least one of a server, a personal computer, a personal digital assistant, a modem, an insulin pen, or an insulin pump.

28. The system of claim 17, wherein the processor module is configured to determine a therapy recommendation.

29. The system of claim 28, wherein the therapy recommendation is based at least in part on a parameter, and wherein the parameter is customizable by a user.

30. The system of claim 28, wherein the output module is configured to display the therapy recommendation on a user interface.

31. The system of claim 28, wherein the output module is configured to transmit the therapy recommendation to an external device.

32. The system of claim 31, wherein the external device is an insulin delivery device.

33. The system of claim 17, wherein the range of possible analyte values is determined based on a variation analysis.

34. The system of claim 17, wherein the range of possible analyte values is determined based on physiological patterns.

35. The system of claim 17, wherein the range of possible analyte values is determined based on a confidence level.

36. The system of claim 17, wherein the range of possible analyte values is adjustable.

37. The system of claim 17, wherein the analyte is glucose.