Signal processing for continuous analyte sensor

• US 9,107,623 B2
• Filed: 04/15/2009
• Issued: 08/18/2015
• 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): D'ANGELO, MICHAEL J
• Application Number:
  US12/424,391
• Publication Number:
  US 20090203981A1
• Time in Patent Office: 2,316 Days
• Field of Search: 600345-347, 600/365, 435/4, 435/14, 422/50, 422420-429, 20440301-40315, 702/23, 604 64- 66
• 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 over a current time period, the electronics module comprising a processor configured to process continuous glucose sensor data based solely from the host over a past time period using a pattern recognition algorithm to recognize a time of day associated with a physiological pattern of the host'"'"'s glucose concentration over the past time period, and to detect an approaching clinical risk associated with the recognized pattern based on a comparison of the sensor data over the current time period with the recognized pattern,

wherein the system is configured to present, on a display, a recommendation to aid the host in proactively avoiding the clinical risk associated with the recognized pattern.

2. The system of claim 1, further comprising an input module configured to allow a user to input physiological information comprising one or more of mealtime, exercise, insulin administration and customized therapy recommendation information, and wherein the physiological information is also used by the pattern recognition algorithm to recognize the physiological pattern.

3. The system of claim 1, wherein the time period is about one year.

4. The system of claim 1, wherein the recognized pattern is further associated with an eating behavior of the host.

5. The system of claim 1, wherein the pattern is a pattern of reoccurring hypoglycemia or reoccurring hyperglycemia during the time of day over the time period.

6. The system of claim 1, wherein recognizing the time of day is based on a determination that the sensor data associated with the time of day meets predetermined criteria.

7. 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, the sensor data representative of a host'"'"'s glucose concentration;

a processor;

a display; and

a computer readable memory comprising;

instructions configured to cause the processor to recognize a time of day associated with a physiological pattern of the host'"'"'s glucose concentration during a time of day by processing glucose sensor data based solely from the host for a past time period using a pattern recognition algorithm;

instructions configured to cause the processor to detect an approaching clinical risk for a future time period based at least in part on a comparison of sensor data of a current time period and the recognized physiological pattern; and

instructions configured to cause the processor to display, on the display, a therapy recommendation to aid the host in proactively avoiding the detected approaching clinical risk.

8. The device of claim 7, further comprising an input module configured to allow a user to input physiological information comprising one or more of mealtime, exercise, insulin administration and customized therapy recommendation information, and wherein the physiological information is also used by the pattern recognition algorithm to recognize the physiological pattern.

9. The device of claim 7, wherein the past time period is about one year.

10. The device of claim 7, wherein the recognized pattern is further associated with an eating behavior of the host.

11. The device of claim 7, wherein the pattern is a pattern of reoccurring hypoglycemia or reoccurring hyperglycemia at a specific time of day over a time period.

12. The device of claim 7, wherein recognizing the time of day is based on a determination that the sensor data associated with the time of day meets predetermined criteria.

13. A method for processing sensor data, the method comprising:

receiving, with an electronic device, sensor data from one or more continuous glucose sensor, the sensor data indicative of a host'"'"'s glucose concentration;

calibrating sensor data by using at least one glucose concentration measurement by a reference glucose monitor operatively linked to the electronic device;

detecting, using the electronic device, an approaching clinical risk based on the sensor data solely from the host, the detecting comprising applying a pattern recognition algorithm to the calibrated sensor data to recognize a time of day associated with a physiological pattern of the host'"'"'s glucose concentration over a past time period and comparing sensor data of a current time period to the physiological pattern;

determining, using the electronic device, a therapy recommendation based on the detected approaching clinical risk; and

displaying, on a display of the electronic device, the therapy recommendation to aid the host in proactively avoiding the detected approaching clinical risk.

14. The method of claim 13, further comprising receiving physiological information from a user using a manual input module of the electronic device, the physiological information comprising one or more of mealtime, exercise, insulin administration and customized therapy recommendation information, and wherein the physiological information is also used by the pattern recognition algorithm to recognize the physiological pattern.

15. The method of claim 13, wherein the time period is about one year.

16. The method of claim 13, wherein the recognized pattern is further associated with an eating behavior of the host.

17. The method of claim 13, wherein the pattern is a pattern of reoccurring hypoglycemia or reoccurring hyperglycemia at a specific time of day over a time period.

18. The method of claim 13, wherein recognizing the time of day is based on a determination that the sensor data associated with the time of day meets predetermined criteria.