Signal processing for continuous analyte sensor

• US 9,351,668 B2
• Filed: 10/12/2009
• Issued: 05/31/2016
• 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., Kamath, Apurv Ullas, Thrower, James Patrick, Xavier, Ben
• Primary Examiner(s): Natnithithadha, Navin
• Assistant Examiner(s): MESSERSMITH, ERIC J
• Application Number:
  US12/577,690
• Publication Number:
  US 20100045465A1
• Time in Patent Office: 2,423 Days
• Field of Search: 600/300, 600/309, 600345-366, 702/19, 702/22, 702/23
• 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 glucose sensor system for monitoring glucose concentration in a biological sample of a host, the system comprising:

a continuous glucose sensor configured to produce sensor data indicative of a host'"'"'s glucose concentration, wherein the continuous glucose sensor comprises a sensing region and a sensing membrane located over the sensing region, and wherein at least a portion of the glucose sensor is configured for transcutaneous implantation; and

an electronics module configured to receive sensor data from the continuous glucose sensor, the electronics module comprising a processor configured to process sensor data received from the continuous glucose sensor for a first time period so as to generate real-time glucose readings from the sensor data, to detect an approaching clinical risk based at least in part on an acceleration of the host'"'"'s glucose concentration, and to determine a possibility of avoiding the approaching clinical risk, wherein the electronics module delays activation of an alarm when the possibility of avoiding the approaching clinical risk is determined.

2. The system of claim 1, wherein detecting an approaching clinical risk further comprises determining that a predicted potential glucose concentration value is greater than a high threshold value or less than a low threshold value.

3. The system of claim 1, wherein the alarm comprises an audible signal.

4. The system of claim 1, wherein the alarm comprises a tactile signal.

5. The system of claim 2, wherein the high threshold value and the low threshold value are user configurable.

6. The system of claim 1, wherein the processor module is further configured to calibrate the sensor data using a glucose concentration measured by a reference glucose monitor operatively linked to the electronics module, wherein the reference glucose monitor is integral with the electronics module.

7. The system of claim 1, wherein detecting an approaching clinical risk further comprises predicting a potential glucose concentration for a second time period based at least in part on both an extrapolation of real time glucose readings and on a variation analysis, and wherein the variation analysis comprises a statistical analysis.

8. The system of claim 1, wherein detecting an approaching clinical risk further comprises predicting a potential glucose concentration for a second time period based at least in part on both an extrapolation of real time glucose readings and on a variation analysis, and wherein the variation analysis comprises a clinical analysis.

9. The system of claim 1, wherein detecting an approaching clinical risk further comprises predicting a potential glucose concentration for a second time period based at least in part on both an extrapolation of real time glucose readings and on a variation analysis, and wherein the variation analysis comprises a physiological analysis.

10. The system of claim 1, wherein delay is for a predetermined amount of time.

11. A device for processing continuous glucose sensor data, the device comprising:

a housing;

a receiver configured for receiving sensor data from a continuous glucose sensor;

a processor; and

a non-transitory computer readable memory comprising;

instructions configured to cause the processor to process sensor data received from the continuous glucose sensor for a first time period so as to generate real-time glucose readings from the sensor data for the first time period;

instructions configured to cause the processor to detect an approaching clinical risk based at least in part on an acceleration determined from the sensor data and to determine a first possibility of avoiding the approaching clinical risk;

instructions configured to cause the processor to wait a predetermined amount of time to determine a second possibility of avoiding the detected clinical risk when the first possibility meets a criterion; and

instructions configured to cause the processor to activate an alarm when the first or second possibility meets a second, different criterion.

12. The device of claim 11, wherein detecting an approaching clinical risk further comprises determining that a predicted potential glucose concentration is greater than a high threshold value or less than a low threshold value.

13. The device of claim 11, wherein the alarm comprises an audible signal.

14. The device of claim 11, wherein the alarm comprises a tactile signal.

15. The device of claim 12, wherein the high threshold value and the low threshold value are user configurable.

16. The device of claim 11, wherein generating the real-time glucose readings further comprise the instruction causing the processor to calibrate the processed sensor data using a glucose concentration measured by a reference glucose monitor operatively linked to the device, wherein the reference glucose monitor is integrally formed with the housing and is configured to measure a glucose concentration in a biological sample.

17. The device of claim 11, wherein detecting an approaching clinical risk further comprises predicting a potential glucose concentration for a second time period based at least in part on both an extrapolation of real time glucose readings and on a variation analysis, and wherein the variation analysis comprises a statistical analysis.

18. The device of claim 11, wherein detecting an approaching clinical risk further comprises predicting a potential glucose concentration for a second time period based at least in part on both an extrapolation of real time glucose readings and on a variation analysis, and wherein the variation analysis comprises a clinical analysis.

19. The device of claim 11, wherein detecting an approaching clinical risk further comprises predicting a potential glucose concentration for a second time period based at least in part on both an extrapolation of real time glucose readings and on a variation analysis, and wherein the variation analysis comprises a physiological analysis.