Analyte monitoring system and methods of use

• US 9,339,217 B2
• Filed: 11/21/2012
• Issued: 05/17/2016
• Est. Priority Date: 11/25/2011
• Status: Expired due to Fees

• Current Assignee: Abbott Diabetes Care Incorporated (Abbott Laboratories)
• Original Assignee: Abbott Diabetes Care Incorporated (Abbott Laboratories)
• Inventors: Harper, Wesley Scott, Budiman, Erwin Satrya, Doniger, Kenneth J., Hayter, Gary Alan
• Primary Examiner(s): BERHANU, ETSUB D
• Application Number:
  US13/683,996
• Publication Number:
  US 20130137953A1
• Time in Patent Office: 1,273 Days
• Field of Search: None
• US Class Current: 1/1
• CPC Class Codes: A61B 5/0004 characterised by the type of physiological signal transmitted
• CPC Class Codes: A61B 5/002 Monitoring the patient using a local or closed circuit, e.g. in a room or building A61B5/0017 takes precedence
• CPC Class Codes: A61B 5/0026 characterised by the transmission medium
• 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 5/4839 combined with drug delivery
• CPC Class Codes: A61B 5/6833 Adhesive patches
• CPC Class Codes: A61B 5/7225 Details of analog processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation A61B5/30, A61B5/316 take precedence; transformers for specific applications H01F38/00; amplifiers H03F
• 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: A61M 5/1723 using feedback of body parameters, e.g. blood-sugar, pressure measurement of body parameters A61B5/00
• CPC Class Codes: G01N 33/48792 Data management, e.g. communication with processing unit for in vivo diagnostics A61B5/0002; transmission systems for measured values G08C
• CPC Class Codes: G01N 33/49 Blood chemical methods for determining blood cell populations G01N33/5094; chemical analysis of blood groups or blood types G01N33/80
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

The present disclosure provides methods of processing data provided by a transcutaneous or subcutaneous analyte sensor utilizing different algorithms to strike a balance between signal responsiveness accompanied by signal noise and the introduction of error associated with that noise. The methods utilize the strengths of a lag correction algorithm and a smoothing algorithm to optimize the quality and value of the resulting data (glucose concentrations and the rates of change in glucose concentrations) to a continuous glucose monitoring system. Also provided are systems and kits.

Claims

1. A method for monitoring an analyte using an analyte monitoring system, comprising:

monitoring a data stream including a set of contiguous source data points related to analyte concentration;

providing one or more sets of maximum lag corrected signals from the set of contiguous source data points, wherein each set of maximum lag corrected signals is generated utilizing correction terms based on time derivative estimates and historical source data points, and wherein parameters for the maximum lag correction minimize the correlation between an expected glucose error and time derivative estimates and minimize the correlation between the expected glucose error and a pre-determined array of historical source data points;

providing a first one or more sets of maximum smoothed signals from the set of contiguous source data points, wherein each set of maximum smoothed signals is generated utilizing a first smoothing algorithm;

providing a second one or more sets of maximum smoothed signals from the set of contiguous source data points, wherein each of the second set of maximum smoothed signals is generated utilizing a second smoothing algorithm;

determining analyte concentration utilizing a weighted combination of the one or more sets of maximum lag corrected signals and the first and second one or more sets of maximum smoothed signals, wherein more weight is placed on the one or more sets of maximum lag corrected signals to determine the analyte concentration;

determining a rate of change in the analyte concentration utilizing a weighted combination of the one or more sets of maximum lag corrected signals and the first and second one or more sets of maximum smoothed signals, where more weight is placed on the first and second one or more sets of maximum smoothed signals to determine the rate of change; and

controlling administration of therapy based on the determined analyte concentration.

2. The method of claim 1, wherein the first smoothing algorithm and the second smoothing algorithm are different.

3. The method of claim 1, further comprising averaging the first one or more sets of maximum smoothed signals and the second one or more sets of maximum smoothed signals.