IP Library Granted Patent US 10,327,688
Granted Patent B2
US 10,327,688 · App. 15/994,905 · Granted Jun 25, 2019

Advanced analyte sensor calibration and error detection

Inventors: Sebastian Böhm (San Diego, CA); Daiting Rong (San Diego, CA); Peter C. Simpson (Cardiff, CA)
Assignee: DexCom, Inc.
A61B5/1495A61B5/1451A61B5/1473A61B5/1486A61B5/14503A61B5/14517A61B5/14532A61B5/7257A61B5/7267G01D18/00G01N27/026G01N27/3274G01N33/49H05K999/00A61B5/14546A61B2560/0276
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Quick Facts
Patent No.
US 10,327,688
App. No.
15/994,905
Granted
Jun 25, 2019
Kind
B2
Abstract

Systems and methods for processing sensor data and self-calibration are provided. In some embodiments, systems and methods are provided which are capable of calibrating a continuous analyte sensor based on an initial sensitivity, and then continuously performing self-calibration without using, or with reduced use of, reference measurements. In certain embodiments, a sensitivity of the analyte sensor is determined by applying an estimative algorithm that is a function of certain parameters. Also described herein are systems and methods for determining a property of an analyte sensor using a stimulus signal. The sensor property can be used to compensate sensor data for sensitivity drift, or determine another property associated with the sensor, such as temperature, sensor membrane damage, moisture ingress in sensor electronics, and scaling factors.

Claims (29)

1. A method for calibrating at least one sensor data point from an analyte sensor, the method comprising:

(a) receiving a priori calibration distribution information;

(b) receiving one or more real-time inputs that influence calibration of the analyte sensor;

(c) forming posterior calibration distribution information based on the one or more real-time inputs; and

(d) converting, in real-time, at least one sensor data point calibrated sensor data based on the posterior calibration distribution information.

2. The method of claim 1 , wherein the a priori calibration distribution information comprises probability distributions for sensitivity (m), sensitivity-related information, baseline (b), or baseline-related information.

3. The method of claim 1 , wherein the a priori calibration distribution information comprises a priori guidance or validation ranges.

4. The method of claim 1 , wherein the one or more real-time inputs comprise data received or determined since a previous calibration process.

5. The method of claim 4 , wherein the one or more real-time inputs comprises at least one of: internally-derived real-time data, externally-derived real-time data, and combinations of internally- and externally-derived real-time data.

6. The method of claim 5 , wherein internally-derived real-time data includes at least one type of information selected from the group consisting of: stimulus signal output of sensor; sensor data measured by the sensor indicative of an analyte concentration; sensor data indicative of analyte rate-of-change; temperature measurements; sensor data from multi-electrode sensors; sensor data generated by redundant sensors; sensor data generated by one or more auxiliary sensors; data representative of a pressure on sensor; data generated by an accelerometer; sensor diagnostic information; impedance; and certainty level.

7. The method of claim 5 , wherein externally-derived real-time data includes at least one type of information selected from the group consisting of: glucose concentration information obtained from a reference monitor; information related to meal; insulin dosing time and amounts; insulin estimates; exercise; sleep; illness; stress; hydration; and hormonal conditions.

8. The method of claim 5 , wherein combinations of internally- and externally-derived real-time data includes at least one type of information selected from the group consisting of: information gathered from population based data; glucose concentration of the host; error at calibration or error in matched data pair; site of sensor implantation specific relationships; time since sensor manufacture; exposure of sensor to temperature, humidity, external factors, on shelf; a measure of noise in an analyte concentration signal; and a level of certainty.

9. The method of claim 1 , further comprising determining a level of certainty associated with the calibration information and/or calibrated sensor data.

10. The method of claim 1 , wherein forming posterior calibration distribution information comprises at least one of: 1) an adjustment of the a priori calibration distribution information or 2) a creation of a new range or distribution information based on the one or more real-time inputs.

11. The method of claim 10 , wherein an adjustment of the a priori calibration distribution information comprises shifting, tightening, or loosening the a priori calibration distribution.

12. The method of claim 1 , wherein the calibration distribution information is selected from the group consisting of: sensitivity; change in sensitivity; rate of change of sensitivity; baseline; change in baseline, rate of change of baseline, baseline profile associated with the sensor; sensitivity profile associated with the sensor; linearity; response time; relationships between properties of the sensor; relationships between particular stimulus signal output; and patient specific relationships between sensor and sensitivity, baseline, drift, impedance, impedance/temperature relationship, site of sensor implantation.

13. The method of claim 1 , further comprising providing output of calibrated sensor data.

14. The method of claim 1 , wherein the method is implemented on a computer having a processor and a memory coupled to said processor, wherein at least one of steps (a) through (e) are performed using said processor.

15. A system for calibrating at least one sensor data point from a continuous analyte sensor, the system comprising sensor electronics configured to be operably connected to a continuous analyte sensor, the sensor electronics configured to:

(a) receive a priori calibration distribution information;

(b) receive one or more real-time inputs that influence calibration of the analyte sensor;

(c) form posterior calibration distribution information using the one or more real-time inputs; and

(d) convert, in real-time, at least one sensor data point calibrated sensor data based on the posterior calibration distribution information.

16. The system of claim 15 , wherein the sensor electronics comprise a processor module, the processor module comprising instructions stored in computer memory, wherein the instructions, when executed by the processor module, cause the sensor electronics to perform the forming and the determining.

17. A system for calibrating at least one sensor data point from an analyte sensor, the system comprising:

means for receiving a priori calibration distribution information;

means for receiving one or more real-time inputs that influence calibration of the analyte sensor;

means for forming posterior calibration distribution information based on the one or more real-time inputs; and

means for converting, in real-time, at least one sensor data point calibrated sensor data based on the posterior calibration distribution information.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Feb 6, 2019
From: BOHM, SEBASTIAN; RONG, DAITING; SIMPSON, PETER C.
To: DEXCOM, INC.
Reel/Frame 048252/0919 →
Continuity (5)
Continuation 14860392 · Sep 21, 2015
Continuation 13446977 · Apr 13, 2012
Continuation 13446848 · Apr 13, 2012
Provisional Application 61476145 · Apr 15, 2011
Related Publication 20180279928A1 · Oct 4, 2018
Cited By (4)
US 12,318,200 US 12,343,143 US 12,507,921 US 12,666,543