Systems and methods for processing sensor data

• US 9,339,238 B2
• Filed: 05/16/2012
• Issued: 05/17/2016
• Est. Priority Date: 12/17/2007
• Status: Active Grant

• Current Assignee: DexCom Incorporated
• Original Assignee: DexCom Incorporated
• Inventors: Shariati, Mohammad Ali, Kamath, Apurv Ullas, Dobbles, J. Michael, Mahalingam, Aarthi
• Primary Examiner(s): Natnithithadha, Navin
• Assistant Examiner(s): MESSERSMITH, ERIC J
• Application Number:
  US13/473,201
• Publication Number:
  US 20120245448A1
• Time in Patent Office: 1,462 Days
• Field of Search: 600/309, 600/316, 600345-347, 600/365
• US Class Current: 1/1
• 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 computer-implemented method for classifying a level of noise in a signal obtained from a continuous glucose sensor based on a signal strength, the method comprising executing on a computing system the steps of:

receiving a signal from a continuous glucose sensor during a sensor session;

determining a level of noise in the signal;

classifying the determined level of noise in the signal into one of a plurality of different predetermined noise classifications during the sensor session based a plurality of noise ranges; and

controlling an output of information during the sensor session responsive to the noise classification, wherein the information is selected from the group consisting of rate of change information, alarm information, and medicament delivery information,

wherein the controlling comprises executing instructions to request user interaction, using a user interface of the computing system, with an insulin delivery system based on the noise classification.

2. The method of claim 1, further comprising adaptively determining the one or more noise thresholds for classification of the level of noise in the signal during the sensor session.

3. The method of claim 2, further comprising determining a signal strength of the signal from the continuous glucose sensor during the sensor session.

4. The method of claim 3, wherein the one or more noise thresholds are based on the signal strength of the signal.

5. The method of claim 1, wherein the output provides an indication of the noise classification to an external device based on the noise classification.

6. The method of claim 1, wherein the output provides an indication of the noise classification on a user interface based on the noise classification.

7. The method of claim 1, wherein the controlling further comprises selectively controlling a display of rate of change information on a user interface based on the noise classification.

8. The method of claim 1, wherein the controlling further comprises selectively triggering an alarm indicative of at least one of hypoglycemia, hyperglycemia, predicted hypoglycemia, or predicted hyperglycemia responsive based on the noise classification.

9. The method of claim 1, wherein the controlling further comprises selectively sending medicament therapy instructions based on the noise classification.

10. The system of claim 1, wherein controlling further comprises controlling medicament delivery based on the noise classification.

11. The method of claim 10, wherein controlling medicament delivery comprises controlling insulin delivery based on the noise classification.

12. The method of claim 1, wherein controlling further comprises instructing a closed loop delivery system to open a loop thereof.

13. The method of claim 1, further comprising determining the level of noise in the signal prior to the classifying.

14. A system for classifying a level of noise in a signal obtained from a continuous glucose sensor, the system comprising:

a continuous glucose sensor configured to provide a signal indicative of a glucose concentration in a host; and

a computer system configured to receive a signal from the continuous glucose sensor during a sensor session, to classify a level of noise in the signal into one of a plurality of different predetermined noise classifications during the sensor session based a plurality of noise thresholds, to control an output of information selected from the group consisting of rate of change information, alarm information and medicament delivery information during the sensor session responsive to the noise classification; and

to request, via a user interface of the computer system, user interaction with an insulin delivery system based on the noise classification.

15. The system of claim 14, wherein the computer system is configured to adaptively determine the one or more noise thresholds for classification of the level of noise in the signal during the sensor session.

16. The system of claim 15, wherein the computer system is configured to determine a signal strength of the signal from the continuous glucose sensor during the sensor session.

17. The system of claim 16, wherein the one or more noise thresholds are based the signal strength of the signal.

18. The system of claim 14, wherein the output provides an indication of the noise classification to an external device based on the noise classification.

19. The system of claim 14, wherein the output provides an indication of the noise classification on a user interface based on the noise classification.

20. The system of claim 14, wherein the computer system is configured to control the output of information by selectively controlling a display of rate of change information on a user interface based on the noise classification.

21. The system of claim 14, wherein the computer system is configured to control the output of information by selectively triggering an alarm indicative of at least one of hypoglycemia, hyperglycemia, predicted hypoglycemia, or predicted hyperglycemia responsive based on the noise classification.

22. The system of claim 14, wherein the computer system is configured to control the output of information by selectively sending medicament therapy instructions based on the noise classification.

23. The system of claim 14, wherein the computer system is configured to control medicament delivery based on the noise classification.

24. The system of claim 23, wherein the computer system is configured to control insulin delivery based on the noise classification.

25. The system of claim 14, wherein the computer system is configured to instruct a closed loop delivery system to open a loop thereof.

26. The system of claim 14, wherein the computer system is further configured to determine the level of noise in the signal prior to classifying the level of noise.

27. A computer-implemented method for classifying a level of noise in a signal obtained from a continuous glucose sensor based on a signal strength, the method comprising executing on a computing system the steps of:

determining a level of noise in the signal;

classifying the determined level of noise in the signal into one of a plurality of different predetermined noise classifications during the sensor session based a plurality of noise ranges;

selecting a therapy instruction based on the noise classification, wherein the therapy instruction is more conservative responsive to one of the noise classifications and less conservative responsive to another of the noise classifications; and

controlling an output of information during the sensor session based on the selected therapy instruction.

28. The method of claim 27, further comprising determining the level of noise in the signal prior to the classifying.

29. The method of claim 27, further comprising administering, using a medicament delivery device, medicament to a host based on the information.

30. A system for classifying a level of noise in a signal obtained from a continuous glucose sensor, the system comprising:

a continuous glucose sensor configured to provide a signal indicative of a glucose concentration in a host; and

a computer system configured to receive a signal from the continuous glucose sensor during a sensor session, to classify a level of noise in the signal into one of a plurality of different predetermined noise classifications during the sensor session based a plurality of noise thresholds, to determine a therapy instruction based on the noise classification, and to output information representative of the selected therapy instruction,

wherein the therapy instruction is determined to be more conservative responsive to one noise classification and less conservative responsive to another noise classification.

31. The system of claim 30, wherein the computer system is further configured to determine the level of noise in the signal prior to classifying the level of noise.