Signal processing for continuous analyte sensor

• US 9,420,965 B2
• Filed: 07/01/2011
• Issued: 08/23/2016
• Est. Priority Date: 12/09/2003
• Status: Active Grant

• Current Assignee: DexCom Incorporated
• Original Assignee: DexCom Incorporated
• Inventors: Brauker, James H., Carr-Brendel, Victoria, Goode, Paul V., Kamath, Apurv U., Thrower, James P., Xavier, Ben
• Primary Examiner(s): Natnithithadha, Navin
• Assistant Examiner(s): MESSERSMITH, ERIC J
• Application Number:
  US13/175,392
• Publication Number:
  US 20110263958A1
• Time in Patent Office: 1,880 Days
• Field of Search: 600/345, 600/347, 600/365
• 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. An analyte monitoring system, the system comprising:

an analyte sensor configured to generate a stream of sensor data over a time period;

a processor module configured to;

receive the sensor data;

receive reference data generated by a reference analyte monitor;

match one or more sensor data points of the sensor data with one or more data points of the reference data;

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

determine a first time lag of the calibrated sensor data associated with a first portion of the time period and determine a second time lag of the calibrated sensor data associated with a second, discrete portion of the time period, wherein the first time lag is different than the second time lag; and

estimate analyte values based on the calibrated sensor data and the first and second time lags; and

an output module configured to output information representative of the estimated analyte values.

2. The system of claim 1, wherein the processor module is configured to form a conversion function or to modify a conversion function using the matched data.

3. The system of claim 2, 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.

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

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

6. The system of claim 1, wherein the processor module is configured to determine 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.

7. 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.

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

9. 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 determine one or more of the first and second time lags using the measured time lag.

10. The system of claim 1, wherein the output module comprises a user interface configured to provide information representative of the estimated analyte values to a user.

11. The system of claim 1, wherein the output module is configured to transmit information representative of the estimated analyte values to one or more of a computer or an insulin pump.

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

generating a sensor data stream over a time period using a continuous analyte sensor;

receiving reference data generated by a reference analyte monitor;

periodically determining, using electronics circuitry, one or more time lag factors associated with a time lag of sensor data of the sensor data stream over the time period;

dynamically adjusting, using the electronics circuitry, a time lag value using the determined time lag factors;

forming, using the electronics circuitry, 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;

calibrating, using the electronics circuitry, at least some of the sensor data using the matched data pairs;

estimating, using the electronics circuitry, analyte values based on the calibrated sensor data and the dynamically adjusted time lag value; and

outputting, using the electronics circuitry, information indicative of the estimated analyte values.

13. The method of claim 12, 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.

14. The method of claim 12, 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.

15. The method of claim 12, wherein the sensor is a subcutaneous glucose sensor, and wherein the reference analyte monitor is a blood glucose meter.

16. The method of claim 12, wherein the sensor is a transcutaneous glucose sensor, and wherein the reference analyte monitor is a blood glucose meter.

17. The method of claim 12, further comprising forming a conversion function or modifying a conversion function using the matched data pairs.

18. The method of claim 17, wherein the calibrating includes converting the at least some of the sensor data into glucose concentration values using the conversion function.

19. The method of claim 12, wherein outputting comprises displaying information representative of the estimated analyte values on a user interface.

20. The method of claim 12, wherein outputting comprises transmitting information representative of the estimated analyte values to one or more of a computer and an insulin pump.