SYSTEMS AND METHODS FOR REPLACING SIGNAL ARTIFACTS IN A GLUCOSE SENSOR DATA STREAM

• US 20120215496A1
• Filed: 04/04/2012
• Published: 08/23/2012
• Est. Priority Date: 08/22/2003
• Status: Active Grant

• Current Assignee: DexCom Incorporated
• Original Assignee: DexCom Incorporated
• Inventors: Kamath, Apurv, Brauker, James H., Mahalingam, Aarthi, Li, Ying
• Granted Patent: US 8,777,853 B2
• Time in Patent Office: Days
• US Class Current: 702/191
• CPC Class Codes: A61B 2560/0252 using ambient temperature
• 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 5/145 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 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/14539 for measuring pH
• CPC Class Codes: A61B 5/14542 for measuring blood gases A61B5/14551 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 5/6848 Needles
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• 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 5/7239 using differentiation including higher order derivatives
• CPC Class Codes: A61B 5/7257 using Fourier transforms
• CPC Class Codes: A61B 5/726 using Wavelet transforms
• CPC Class Codes: A61B 5/742 using visual displays A61B5/339 takes precedence
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• 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 minimizing or eliminating transient non-glucose related signal noise due to non-glucose rate limiting phenomenon such as interfering species, ischemia, pH changes, temperatures changes, known or unknown sources of mechanical, electrical and/or biochemical noise, and the like. The system monitors a data stream from a glucose sensor and detects signal artifacts that have higher amplitude than electronic or diffusion-related system noise. The system processes some or the entire data stream continually or intermittently based at least in part on whether the signal artifact event has occurred.

Claims

1. A method for processing sensor data from a continuous glucose sensor, the method comprising:

receiving continuous glucose sensor data, the continuous glucose sensor data comprising a plurality of sensor data points;

classifying a level of noise associated with the continuous glucose sensor data during a first time period, whereby a noise classification is determined; and

selectively displaying one or more of a representation of raw continuous glucose sensor data, a representation of filtered continuous glucose sensor data, a representation of a range of data values associated with the continuous glucose sensor data, a representation of a direction of the continuous glucose sensor data, a representation of a level of confidence of the continuous glucose sensor data, a low resolution representation of the continuous glucose sensor data or a representation of predicted continuous glucose sensor data for a future time period, wherein the one or more representations are selectively displayed based on the noise classification.

2. The method of claim 1, wherein classifying a level of noise comprises performing at least one of a first derivative analysis or a second derivative analysis of the continuous glucose sensor data for a time period.

3. The method of claim 1, wherein classifying a level of noise comprises comparing raw data and filtered data to obtain at least one residual and comparing the at least one residual to at least one threshold.

4. The method of claim 1, wherein selectively displaying comprises displaying the representation of filtered continuous glucose sensor data based on the noise classification.

5. The method of claim 4, wherein the filtered continuous glucose sensor data is post-processed, and wherein post-processing comprises filtering the data to recalculate data corresponding to the time period and displaying a graphical representation of the recalculated data corresponding to the time period.

6. A system for processing sensor data from a continuous glucose sensor, the system comprising:

a sensor data receiving module configured to receive continuous glucose sensor data, the continuous glucose sensor data comprising a plurality of sensor data points;

a processor module configured to classifying a level of noise associated with the continuous glucose sensor data during a first time period, whereby a noise classification is determined; and

an output module configured to selectively display one or more of a representation of raw continuous glucose sensor data, a representation of filtered continuous glucose sensor data, a representation of a range of data values associated with the continuous glucose sensor data, a representation of a direction of the continuous glucose sensor data, a representation of a level of confidence of the continuous glucose sensor data, a low resolution representation of the continuous glucose sensor data or a representation of predicted continuous glucose sensor data for a future time period, wherein the one or more representations are selectively displayed based on the noise classification.

7. The system of claim 6, wherein processor module is configured to classify a level of noise based one at least one of a first derivative analysis or a second derivative analysis of the continuous glucose sensor data for a time period.

8. The system of claim 6, wherein processor module is configured to classify a level of noise based on a comparison of a signal residual with a threshold, and wherein the signal residual is based on a comparison of raw data and filtered data.

9. The system of claim 6, wherein the processor module is configured to post-process the data corresponding to the time period, and wherein the output module is configured to selectively display a graphical representation of the post-processed data based on the noise classification.