Signal processing for continuous analyte sensor

• US 8,747,315 B2
• Filed: 09/23/2009
• Issued: 06/10/2014
• 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:
  US12/565,156
• Publication Number:
  US 20100030484A1
• Time in Patent Office: 1,721 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 estimating an analyte value from a continuous analyte sensor, the method comprising:

receiving a data stream from the continuous analyte sensor for a first time period, the data stream indicative of a measured analyte concentration of the host over the first time period;

estimating, using a processor module, at least one analyte value for a second time period based on the data stream, wherein the step of estimating at least one analyte value comprises selecting an algorithm from a plurality of algorithms based at least in part on an analysis of a rate of change of the measured analyte concentration; and

providing output based on the estimated at least one analyte value.

2. The method of claim 1, the step of selecting an algorithm is additionally based on one or more of an analyte concentration, an acceleration, and an individual historical pattern of the data stream.

3. The method of claim 1, wherein the step of selecting an algorithm comprises employing a plurality of algorithms on the data stream and determining which of the plurality of algorithms best correlates with the data stream.

4. The method of claim 1, wherein the plurality of algorithms comprise an algorithm 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.

5. The method of claim 1, wherein the step of selecting an algorithm further comprises applying a physiological boundary to the selected algorithm.

6. The method of claim 1, further comprising a step of evaluating a 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.

7. The method of claim 1, wherein the second time period is a future time period.

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

an input module operatively connected to the continuous analyte sensor that receives a data stream for a first time period, the data stream indicative of a measured analyte concentration of the host over the first time period; and

a processor module comprising programming configured to select an algorithm from a plurality of algorithms based on an analysis of an individual historical pattern of the data stream, estimate at least one analyte value using the selected algorithm, and initiate output based on the estimated at least one analyte value.

9. The system of claim 8, wherein the programming configured to select an algorithm from a plurality of algorithms selects the algorithm additionally based on one or more of an analyte concentration, a rate of change, and an acceleration of the data stream.

10. The system of claim 8, wherein the programming configured to select the an algorithm from a plurality of algorithms employs a plurality of algorithms on the data stream and determines which of the plurality of algorithms best correlates with the data stream.

11. The system of claim 8, wherein the plurality of algorithms comprise an algorithm 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.

12. The system of claim 8, wherein the programming configured to select an algorithm from a plurality of algorithms applies a physiological boundary to the selected algorithm.

13. The system of claim 8, wherein the processor module further comprises programming configured to evaluate a 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.

14. The system of claim 8, wherein the second time period is a future time period.

15. A method for estimating an analyte value from a continuous analyte sensor, the method comprising:

receiving a data stream from the continuous analyte sensor for a first time period, the data stream indicative of a measured analyte concentration of the host over the first time period;

selecting, using a processor module, at least one algorithm from a plurality of algorithms based on an analysis of an acceleration of the measured analyte concentration; and

evaluating, using the processor module, 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, using the processor module, the selected algorithm based on the step of evaluating to estimate at least one analyte value.

16. The method of claim 15, wherein the step of employing the selected algorithms comprises estimating at least one analyte value for a second time period based on the data stream.

17. The method of claim 16, wherein the second time period is a future time period.

18. 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 for a first time period, the data stream indicative of a measured analyte concentration of the host over the first time period; and

a processor module comprising programming configured to select an algorithm from a plurality of algorithms based on an analysis of a rate of change of the measured analyte concentration the data stream, evaluate the algorithm based on a parameter selected from the group consisting of statistical parameters, physiological parameters, and clinical parameters, and employ the selected algorithm based on the algorithm evaluation to estimate at least one analyte value.

19. The system of claim 18, wherein the programming configured to employ the selected algorithms estimates at least one analyte value for a second time period based on the data stream.

20. The system of claim 19, wherein the second time period is a future time period.

21. The system of claim 19, wherein selecting an algorithm additionally based on one or more of an analyte concentration, an acceleration, and an individual historical pattern of the data stream.

22. The system of claim 19, wherein the processor is configured to select an algorithm by employing the plurality of algorithms on the data stream and determining which of the plurality of algorithms best correlates with the data stream.

23. The system of claim 19, wherein the processor is configured to select an algorithm by employing at least one of polynomial regression, autoregressive algorithms, Fourier transform, Wavelet transform, neural network-based mapping, fuzzy logic based pattern matching, and Genetic-Algorithms based pattern matching.

24. The system of claim 19, wherein the processor is configured to select an algorithm by applying a physiological boundary to the selected algorithm.

25. The system of claim 19, wherein the processor is configured to evaluate 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.