Signal processing for continuous analyte sensor

• US 8,005,524 B2
• Filed: 03/24/2010
• Issued: 08/23/2011
• Est. Priority Date: 12/09/2003
• Status: Active Grant

• Current Assignee: DexCom Incorporated
• Original Assignee: DexCom Incorporated
• Inventors: Xavier, Ben, Brauker, James H., Carr-Brendel, Victoria, Goode, Paul V., Kamath, Apurv U., Thrower, James P.
• Primary Examiner(s): Le; Miranda
• Assistant Examiner(s): Natnithithadha; Navin
• Application Number:
  US12/731,046
• Publication Number:
  US 20100179400A1
• Time in Patent Office: 517 Days
• Field of Search: 600/309, 600345-366
• US Class Current: 600/345
• CPC Class Codes: A61B 5/145 Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissuemeasuring of blood pressure or blood flow A61B5/02; non-radiation detecting or locating of foreign bodies in blood A61B5/06
• CPC Class Codes: A61B 5/14503 invasive, e.g. introduced into the body by a catheter or needle or using implanted sensors A61B5/1459, A61B5/1464, A61B5/1473, A61B5/1482, A61B5/14865 take precedence
• CPC Class Codes: A61B 5/1451 for interstitial fluid
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• 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 5/1486 using enzyme electrodes, e.g. with immobilised oxidase
• 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 5/7257 using Fourier transforms
• CPC Class Codes: A61B 5/726 using Wavelet transforms
• 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 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: A61B 5/742 using visual displays A61B5/339 takes precedence
• CPC Class Codes: A61B 5/7455 characterised by tactile indication, e.g. vibration or electrical stimulation
• CPC Class Codes: A61B 5/746 Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms alarms per se G08B
• 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: G16Z 99/00 Subject matter not provided for in other main groups of this subclass
• 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 dynamically and intelligently estimating analyte data from a continuous analyte sensor, including receiving a data stream, selecting one of a plurality of algorithms, and employing the selected algorithm to estimate analyte values. Additional data processing includes evaluating the selected estimative algorithms, analyzing a variation of the estimated analyte values based on statistical, clinical, or physiological parameters, comparing the estimated analyte values with corresponding measure analyte values, and providing output to a user. Estimation can be used to compensate for time lag, match sensor data with corresponding reference data, warn of upcoming clinical risk, replace erroneous sensor data signals, and provide more timely analyte information encourage proactive behavior and preempt clinical risk.

Claims

1. An analyte monitoring system, the system comprising:

an analyte sensor;

a processor module coupled to the sensor and configured to determine the concentration of an analyte based on a signal generated by the analyte sensor; and

a user interface to provide analyte information to a user;

wherein the processor module is configured to dynamically and intelligently match sensor data from the analyte sensor with reference data from a reference analyte monitor based on an evaluation of information associated with a time lag of the sensor data relative to the reference data during calibration of the analyte sensor, and

wherein the processor module is configured to dynamically and intelligently match by estimating a time-correspondence between the sensor data and the reference data based on the information associated with the time lag.

2. A method of calibration an analyte monitoring system, the method comprising:

receiving sensor data from an analyte sensor;

dynamically and intelligently matching, using an electronic system, sensor data from the analyte sensor with reference data from a reference analyte monitor based on an evaluation of information associated with a time lag of the sensor data relative to the reference data during calibration of the analyte sensor, wherein dynamic and intelligent matching comprises estimating a time-correspondence between the sensor data and the reference data based on the information associated with the time lag; and

providing analyte information after calibration of the analyte sensor.