Systems and methods for processing sensor data

• US 9,839,395 B2
• Filed: 10/24/2008
• Issued: 12/12/2017
• Est. Priority Date: 12/17/2007
• Status: Active Grant

• Current Assignee: DexCom Incorporated
• Original Assignee: DexCom Incorporated
• Inventors: Shariati, Mohammad Ali, Kamath, Apurv Ullas, Mensinger, Michael Robert
• Primary Examiner(s): JANG, CHRISTIAN YONGKYUN
• Application Number:
  US12/258,325
• Publication Number:
  US 20090192722A1
• Time in Patent Office: 3,336 Days
• Field of Search: 600345-347, 600365
• CPC Class Codes: A61B 2560/0223 of calibration, e.g. protocols for calibrating sensors
• CPC Class Codes: A61B 2560/04 Constructional details of apparatus
• CPC Class Codes: A61B 2562/085 combined with means for recording calibration data
• CPC Class Codes: A61B 5/0008 Temperature signals
• CPC Class Codes: A61B 5/0031 Implanted circuitry
• CPC Class Codes: A61B 5/01 Measuring temperature of body parts ; Diagnostic temperature sensing, e.g. for malignant or inflamed tissue clinical contact thermometers G01K13/20
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/14542 for measuring blood gases A61B5/14551 takes precedence
• CPC Class Codes: A61B 5/14546 for measuring analytes not otherwise provided for, e.g. ions, cytochromes
• 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 5/4839 combined with drug delivery
• CPC Class Codes: A61B 5/6848 Needles
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/7221 Determining signal validity, reliability or quality A61B5/7207 and A61B5/7217 take precedence
• 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 5/742 using visual displays A61B5/339 takes precedence
• CPC Class Codes: A61B 5/743 Displaying an image simultaneously with additional graphical information, e.g. symbols, charts, function plots
• 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: G16H 15/00 : ICT specially adapted for medical reports, e.g. generation or transmission thereof
• CPC Class Codes: G16H 20/00 : ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
• CPC Class Codes: G16H 20/10 : relating to drugs or medications, e.g. for ensuring correct administration to patients
• CPC Class Codes: G16H 20/17 : delivered via infusion or injection
• CPC Class Codes: G16H 40/40 : for the management of medical equipment or devices, e.g. scheduling maintenance or upgrades
• CPC Class Codes: G16H 40/63 : for local operation
• CPC Class Codes: G16H 50/50 : for simulation or modelling of medical disorders
• 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 processing sensor data are provided. In some embodiments, systems and methods are provided for calibration of a continuous analyte sensor. In some embodiments, systems and methods are provided for classification of a level of noise on a sensor signal. In some embodiments, systems and methods are provided for determining a rate of change for analyte concentration based on a continuous sensor signal. In some embodiments, systems and methods for alerting or alarming a patient based on prediction of glucose concentration are provided.

Claims

1. A method for processing continuous glucose sensor data, the method comprising:

receiving sensor data from a continuous glucose sensor, wherein the sensor data comprises one or more sensor data points;

obtaining an estimated sensor glucose value from the one or more sensor data points;

determining, using a processor module, a predicted amount of time to hypoglycemia or a predicted amount of time to hyperglycemia, wherein the determining is based at least in part on the estimated sensor glucose value and a glucose value threshold; and

programmatically producing, using the processor module, insulin delivery and/or insulin therapy instructions based at least in part on one or both of;

a) the predicted amount of time to hyperglycemia meeting a criterion, and b) the predicted amount of time to hypoglycemia meeting a criterion, wherein the produced insulin delivery and/or insulin therapy instructions are usable to automate control of an insulin delivery device to automatically administer insulin.

2. The method of claim 1, wherein the glucose threshold is user selectable.

3. The method of claim 1, further comprising displaying the predicted amount of time on a user interface.

4. The method of claim 3, wherein displaying the predicted amount of time is determined to be performed only when the predicted time is less than a predetermined amount of time.

5. The method of claim 1, further comprising calculating at least two rate of change values;

and filtering the at least two rate of change values to obtain a filtered rate of change value.

6. The method of claim 5, wherein the at least two rate of change values are point-to-point rate of change values.

7. The method of claim 5, wherein the predicted amount of time is further based on the filtered rate of change value.

8. The method of claim 5, further comprising determining an insulin therapy based at least in part on the filtered rate of change value.

9. The method of claim 5, wherein the filtering to obtain a filtered rate of change value is performed continuously.

10. The method of claim 5, wherein the filtering to obtain a filtered rate of change value is not performed when a level of noise is above a noise threshold.

11. The method of claim 5, further comprising displaying a trend arrow representative of the filtered rate of change values.

12. The method of claim 1, further comprising triggering a predicted hypoglycemia alarm based on the predicted amount of time to hyperglycemia meeting a criterion.

13. The method of claim 1, further comprising triggering a predicted hyperglycemia alarm based on the predicted amount of time to hypoglycemia meeting a criterion.

14. The method of claim 1, further comprising using the insulin delivery device to automatically administer insulin based on the produced insulin delivery and/or insulin therapy instructions.

15. A system for processing continuous glucose sensor data, the system comprising:

a continuous glucose sensor configured to generate sensor data associated with glucose concentration in a host; and

a computer system comprising programming configured to obtain an estimated sensor glucose value and determine a predicted amount of time to hypoglycemia or a predicted amount of time to hyperglycemia, wherein the predicted amount of time is based at least in part on the estimated sensor glucose value and a glucose threshold, and wherein the programming is further configured to produce insulin delivery and/or insulin therapy instructions based at least in part on one or both of;

a) a determination that the predicted amount of time to hypoglycemia meets a criterion, and b) a determination that the predicted amount of time to hyperglycemia meets a criterion, wherein the produced insulin delivery and/or insulin therapy instructions are usable to automate control of an insulin delivery device to automatically administer insulin.

16. The system of claim 15, wherein the glucose threshold is user selectable.

17. The system of claim 15, wherein the computer system is configured to display the predicted amount of time on a user interface.

18. The system of claim 17, wherein the computer system is configured to display the predicted amount of time only when the predicted amount of time is less than a predetermined amount of time.

19. The system of claim 15, wherein the computer system is incorporated in a hand-held device.

20. The system of claim 15, wherein the computer system is incorporated in sensor electronics configured to be physically coupled to the continuous glucose sensor.

21. The system of claim 15, wherein the programming is further configured to calculate at least two rate of change values and filter the at least two rate of change values to obtain a filtered rate of change value.

22. The system of claim 21, wherein the at least two rate of change values are point-to-point rate of change values.

23. The system of claim 21, wherein the computer system determines an insulin therapy based at least in part on the filtered rate of change value.

24. The system of claim 21, wherein computer system continuously filters the at least two rate of change values to obtain a filtered rate of change value.

25. The system of claim 21, wherein the computer system displays a trend arrow representative of the filtered rate of change values.

26. The system of claim 21, wherein the computer system filters the at least two rate of change values to obtain a filtered rate of change value only when a level of noise is below a threshold.

27. The method of claim 21, wherein the predicted amount of time is further based on the filtered rate of change value.

28. The system of claim 15, wherein the programming is further configured to trigger a predicted hypoglycemia alarm based on the predicted amount of time to hyperglycemia meeting a criterion.

29. The system of claim 15, wherein the programming is further configured to trigger a predicted hyperglycemia alarm based on the predicted amount of time to hypoglycemia meeting a criterion.

30. The system of claim 15, further comprising the insulin delivery device configured to automatically administer insulin based on the produced insulin delivery and/or therapy instructions.