Systems and methods for processing sensor data

• US 9,149,233 B2
• Filed: 06/13/2012
• Issued: 10/06/2015
• Est. Priority Date: 12/17/2007
• Status: Active Grant

• Current Assignee: DexCom Incorporated
• Original Assignee: DexCom Incorporated
• Inventors: Kamath, Apurv Ullas, Mensinger, Michael Robert, Li, Ying, Mahalingam, Aarthi, Dobbles, John Michael
• Primary Examiner(s): D'ANGELO, MICHAEL J
• Application Number:
  US13/495,956
• Publication Number:
  US 20120302854A1
• Time in Patent Office: 1,210 Days
• Field of Search: 600345-347, 600/365, 600/309, 600/316
• 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 method for calibration of a continuous glucose sensor, the method comprising:

receiving, using electronic circuitry, sensor data from a continuous analyte sensor, the sensor data comprising one or more sensor data points;

receiving and processing, using the electronic circuitry, calibration information to generate a calibration function;

evaluating, using the electronic circuitry, a predictive accuracy of the calibration information to determine a predictive accuracy metric;

calibrating, using the electronic circuitry, sensor data using the calibration function;

comparing, using the electronic circuitry, the predictive accuracy metric to one or more thresholds; and

processing, using the electronic circuitry, the calibrated sensor data to generate output, wherein the processing is performed differently depending upon the comparison of the predictive accuracy metric to the one or more thresholds, wherein the processing comprises controlling insulin delivery and/or insulin therapy instructions differently based on the comparison of the predictive accuracy metric to the one or more thresholds.

2. The method of claim 1, wherein the processing comprises determining a time period before requesting additional reference data.

3. The method of claim 2, wherein the time period is between about 0 minutes and 7 days.

4. The method of claim 2, further comprising displaying the time period before reference data will be requested.

5. The method of claim 1, wherein the receiving and processing calibration information comprises receiving a matched data pair and forming a calibration set including the matched data pair, and wherein the evaluating a predictive accuracy comprises evaluating a discordance of the matched data pair and/or the matched data pairs in the calibration set.

6. The method of claim 1, wherein the receiving and processing calibration information comprises receiving reference data, and wherein the evaluating a predictive accuracy comprises evaluating a leverage of the reference data based at least in part on a glucose concentration associated with the reference data.

7. The method of claim 1, wherein the processing comprises controlling alarms indicative of at least one of hypoglycemia, hyperglycemia, predicted hypoglycemia, or predicted hyperglycemia differently based on the comparison of the predictive accuracy metric to the one or more thresholds.

8. The method of claim 1, wherein the processing comprises diagnosing a sensor condition differently based at least in part on the comparison of the predictive accuracy metric to the one or more thresholds.

9. The method of claim 1, wherein the one or more thresholds delineate at least three predetermined ranges of accuracy.

10. The method of claim 1, wherein determining the predictive accuracy metric comprises calculating the predictive accuracy metric in terms of a percentage or a deviation value.

11. A system for processing continuous analyte sensor data, comprising:

a continuous analyte sensor configured to continuously measure a concentration of an analyte in a host; and

a computer system that receives sensor data from the continuous analyte sensor, wherein the computer system is configured to

receive and process calibration information to generate a calibration function,

evaluate a predictive accuracy of calibration information to determine a predictive accuracy metric,

calibrate the sensor data using the calibration function,

compare the predictive accuracy metric to one or more thresholds,

process the calibrated sensor data to generate output, wherein the processing is performed differently depending upon the comparison of the predictive accuracy metric to the one or more thresholds, and

control insulin delivery and/or insulin therapy instructions differently based on the comparison of the predictive accuracy metric to the one or more thresholds.

12. The system of claim 11, wherein the computer system is configured to determine a time period to request additional reference data based on the predictive accuracy metric.

13. The system of claim 12, wherein the time period is between about 0 minutes and 7 days.

14. The system of claim 12, wherein the computer system is configured to display the time period before reference data will be requested.

15. The system of claim 11, wherein the calibration information comprises one or more matched data pairs, a calibration set including a plurality of the one or more matched data pairs, and wherein the computer system is configured to evaluate a predictive accuracy by evaluating a discordance of the matched data pair and/or the matched data pairs in the calibration set.

16. The system of claim 11, wherein the calibration information comprises reference data, and wherein the computer system is configured to evaluate a predictive accuracy by evaluating a leverage of the reference data based at least in part on a glucose concentration associated with the reference data.

17. The system of claim 11, wherein the computer system is configured to control an alarm indicative of at least one of hypoglycemia, hyperglycemia, predicted hypoglycemia, and predicted hyperglycemia differently based on the comparison of the predictive accuracy metric to the one or more thresholds.

18. The system of claim 11, wherein the computer system is configured to diagnose a sensor condition differently based at least in part on the comparison of the predictive accuracy metric to the one or more thresholds.

19. The system of claim 11, wherein the one or more thresholds delineate at least three predetermined ranges of accuracy.

20. The system of claim 11, wherein determining the predictive accuracy metric comprises calculating the predictive accuracy metric in terms of a percentage or a deviation value.