SIGNAL PROCESSING FOR CONTINUOUS ANALYTE SENSOR

• US 20110263958A1
• Filed: 07/01/2011
• Published: 10/27/2011
• 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
• Granted Patent: US 9,420,965 B2
• Time in Patent Office: Days
• 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. An analyte monitoring system, the system comprising:

an analyte sensor configured to generate sensor data;

a processor module configured to;

receive the sensor data;

receive reference data generated by a reference analyte monitor;

determine a time lag between the sensor data and the reference data;

match one or more sensor data points of the sensor data with one or more data points of the reference data based on the determined time lag; and

calibrate at least some of the sensor data using the matched data;

and

an output module configured to output information representative of the calibrated sensor data.

2. The system of claim 1, wherein the processor module is configured to calculate a first time lag between sensor data and reference data associated with a first time period and calculate a second time lag between sensor data and reference data associated with a second time period, wherein the first time lag is different than the second time lag.

3. The system of claim 2, wherein the processor is configured to match by forming a first matched data by matching one or more sensor data points of the sensor data with one or more data points of the reference data based on the first calculated time lag, and forming a second matched data by matching one or more sensor data points of the sensor data with one or more data points of the reference data based on the second calculated time lag.

4. The system of claim 3, wherein the processor module is configured to form a conversion function or to modify a conversion function using the first and second matched data.

5. The system of claim 4, wherein the processor module is configured to calibrate by converting the at least some of the sensor data into glucose concentration values using the conversion function.

6. The system of claim 1, wherein the sensor is a subcutaneous glucose sensor and wherein the reference analyte monitor is a blood glucose meter.

7. The system of claim 1, wherein the sensor is a transcutaneous glucose sensor and wherein the reference analyte monitor is a blood-glucose meter.

8. The system of claim 2, wherein the processor module is configured to calculate by taking into account one or more of a) a physiological time lag with respect to reference data and sensor data, b) a membrane-induced time lag, or c) a computationally-induced time lag.

9. The system of claim 1, wherein the analyte sensor is configured to measure an interstitial glucose concentration of a host and the reference monitor is configured to measure a blood glucose concentration of the host.

10. The system of claim 1, wherein the processor module is configured to match by selecting an algorithm from a plurality of algorithms based on the time lag information.

11. The system of claim 1, wherein the system is further configured to measure a time lag associated with at least some of the sensor data and at least some of the reference data, wherein the processor module is configured to calculate the time lag using the measured time lag.

12. The system of claim 1, wherein the output module comprises a user interface configured to provide information representative of the calibrated sensor data to a user.

13. The system of claim 1, wherein the output module is configured to transmit information representative of the calibrated sensor data to one or more of a computer or an insulin pump.

14. A method for monitoring an analyte concentration in a host, comprising:

generating sensor data using a continuous analyte sensor;

receiving reference data generated by a reference analyte monitor;

periodically determining one or more time lag factors associated with a time lag between the sensor data and the reference data;

dynamically adjusting a time lag value using the determined time lag factors;

forming matched data pairs by matching one or more sensor data points of the sensor data with one or more reference data points of the reference data based on the dynamically adjusted time lag value;

calibrating at least some of the sensor data using the matched data pairs; and

outputting information indicative of the calibrated sensor data.

15. The method of claim 14, wherein the time lag factors include one of a) a physiological time lag with respect to reference data and sensor data, b) a membrane-induced time lag, or c) a computationally-induced time lag.

16. The method of claim 14, wherein forming matched data pairs comprises averaging a plurality of sensor data points and matching the averaged plurality of sensor data points with a reference data point to form a matched data pair.

17. The method of claim 14, wherein forming matched data pairs comprises forming a first matched data pair based on the dynamically adjusted time lag value and forming a second data pair based on the dynamically adjusted time lag value, wherein the value of the dynamically adjusted time lag value is different when forming the first matched data pair that when forming the second matched data pair.

18. The method of claim 14, wherein the sensor is a subcutaneous glucose sensor, and wherein the reference analyte monitor is a blood glucose meter.

19. The method of claim 14, wherein the sensor is a transcutaneous glucose sensor, and wherein the reference analyte monitor is a blood glucose meter.

20. The method of claim 14, further comprising forming a conversion function or modifying a conversion function using the matched data pairs.

21. The method of claim 20, wherein the calibrating includes converting the at least some of the sensor data into glucose concentration values using the conversion function.

22. The method of claim 14, wherein outputting comprises displaying information representative of the calibrated sensor data on a user interface.

23. The method of claim 14, wherein outputting comprises transmitting information representative of the calibrated sensor data to one or more of a computer and an insulin pump.