System and methods for processing analyte sensor data

• US 8,394,021 B2
• Filed: 10/01/2007
• Issued: 03/12/2013
• Est. Priority Date: 08/01/2003
• Status: Active Grant

• Current Assignee: DexCom Incorporated
• Original Assignee: DexCom Incorporated
• Inventors: Goode, Paul V., Brauker, James H., Kamath, Apurv U.
• Primary Examiner(s): Mallari, Patricia
• Assistant Examiner(s): D'ANGELO, MICHAEL J
• Application Number:
  US11/865,660
• Publication Number:
  US 20080021666A1
• Time in Patent Office: 1,989 Days
• Field of Search: 600/347, 600/365
• US Class Current: 600/309
• 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 3/10 Objective types, i.e. instruments for examining the eyes independent of the patients' perceptions or reactions
• CPC Class Codes: A61B 5/0031 Implanted circuitry
• CPC Class Codes: A61B 5/1433 provided with locking means details of syringes A61M5/315
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/14546 for measuring analytes not otherwise provided for, e.g. ions, cytochromes
• 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 5/14865 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 5/150022 for capillary blood or interstitial fluid
• CPC Class Codes: A61B 5/4839 combined with drug delivery
• CPC Class Codes: A61B 5/743 Displaying an image simultaneously with additional graphical information, e.g. symbols, charts, function plots
• CPC Class Codes: G01N 33/48707 by electrical means G01N33/49, G01N33/493 take precedence
• 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 analyte data, including initiating calibration, updating calibration, evaluating clinical acceptability of reference and sensor analyte data, and evaluating the quality of sensor calibration. During initial calibration, the analyte sensor data is evaluated over a period of time to determine stability of the sensor. The sensor may be calibrated using a calibration set of one or more matched sensor and reference analyte data pairs. The calibration may be updated after evaluating the calibration set for best calibration based on inclusion criteria with newly received reference analyte data. Fail-safe mechanisms are provided based on clinical acceptability of reference and analyte data and quality of sensor calibration. Algorithms provide for optimized prospective and retrospective analysis of estimated blood analyte data from an analyte sensor.

Claims

1. A method for calibrating an analyte sensor, the method comprising:

using a sensor data receiving module to receive sensor data from an analyte sensor, the sensor data comprising one or more sensor data points;

using a reference data receiving module to receive reference data from a reference analyte monitor, the reference data comprising one or more reference data points;

using a processor module to form a matched data pair by matching at least one of the one or more reference data points with at least one of the one or more sensor data points;

using the processor module to form a calibration set comprising n matched data pairs, wherein the calibration set comprises the matched data pair, wherein n represents the number of matched data pairs in the calibration set, and wherein n is a function of signal strength over time;

using the processor module to create a conversion function based on the calibration set; and

using the processor module to transform at least some of the sensor data using the conversion function.

2. The method of claim 1, wherein n is a function of signal strength with respect to analyte concentration over time.

3. The method of claim 1, wherein n is further a function of a frequency of the received reference data points over time.

4. The method of claim 1, further comprising using the processor module to determine a value for n.

5. The method of claim 1, further comprising using the processor module to determine a level of confidence associated with the conversion function.

6. The method of claim 1, further comprising using the processor module to control an output of the transformed sensor data based on the level of confidence.

7. The method of claim 1, wherein the analyte sensor is a glucose sensor.

8. A system for calibrating an analyte sensor, the system comprising:

means for receiving sensor data from an analyte sensor, the sensor data comprising one or more sensor data points;

means for receiving reference data from a reference analyte monitor, the reference data comprising one or more reference data points;

means for forming a matched data pair by matching at least one of the one or more reference data points with at least one of the one or more sensor data points;

means for forming a calibration set comprising n matched data pairs, wherein the calibration set comprises the matched data pair, wherein n represents the number of matched data pairs in the calibration set, and wherein n is a function of signal strength over time;

means for creating a conversion function based on the calibration set; and

means for transforming at least some of the sensor data using the conversion function.

9. The system of claim 8, wherein n is a function of signal strength with respect to analyte concentration over time.

10. The system of claim 8, wherein n is further a function of a frequency of the received reference data points over time.

11. The system of claim 8, further comprising means for determining a value for n.

12. The system of claim 8, further comprising means for determining a level of confidence associated with the conversion function.

13. The system of claim 12, further comprising means for controlling an output of the transformed sensor data based on the level of confidence.

14. The system of claim 8, wherein the analyte sensor is a glucose sensor.

15. The system of claim 8, wherein n is a function of signal strength with respect to analyte concentration over time.

16. The system of claim 8, wherein n is further a function of a frequency of the received reference data points over time.

17. The system of claim 8, wherein the processor module is further configured to determine a value for n.

18. The system of claim 8, wherein the processor module is further configured to determine a level of confidence associated with the conversion function.

19. The system of claim 18, further comprising an output module configured to control an output of the transformed sensor data based on the level of confidence.

20. The system of claim 8, wherein the analyte sensor is a glucose sensor.

21. A system for calibrating an analyte sensor, the system comprising:

a communication unit configured to receive sensor data from an analyte sensor, the sensor data comprising one or more sensor data points, and to receive reference data from a reference analyte monitor, the reference data comprising one or more reference data points; and

a processor module configured to form a matched data pair by matching at least one of the one or more reference data points with at least one of the one or more sensor data points, to form a calibration set comprising n matched data pairs, wherein the calibration set comprises the at least one matched data pair, wherein n represents the number of matched data pairs in the calibration set, and wherein n is a function of signal strength over time, to create a conversion function based on the calibration set to transform at least some of the sensor data using the conversion function.