METHOD AND SYSTEM FOR PROCESSING AND ANALYZING ANALYTE SENSOR SIGNALS

• US 20160029966A1
• Filed: 07/29/2015
• Published: 02/04/2016
• Est. Priority Date: 07/31/2014
• Status: Abandoned Application

• Current Assignee: One Drop Biosensor Technologies, LLC
• Original Assignee: Sano Intelligence, Inc. (Informed Data Systems, Inc.)
• Inventors: Chapman, Matthew, Ghosh, Abhijit, Pushpala, Ashwin, Salas-Boni, Rebeca
• Application Number:
  US14/812,302
• Publication Number:
  US 20160029966A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: A61B 2560/0223 of calibration, e.g. protocols for calibrating sensors
• CPC Class Codes: A61B 5/02055 Simultaneously evaluating both cardiovascular condition and temperature
• CPC Class Codes: A61B 5/1118 Determining activity level
• 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 5/14735 comprising an immobilised reagent
• CPC Class Codes: A61B 5/1486 using enzyme electrodes, e.g. with immobilised oxidase
• CPC Class Codes: A61B 5/1495 Calibrating or testing of in-vivo probes
• CPC Class Codes: A61B 5/4812 Detecting sleep stages or cycles
• CPC Class Codes: A61B 5/4866 Evaluating metabolism A61B5/083 takes precedence
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/7246 using correlation, e.g. template matching or determination of similarity
• CPC Class Codes: A61B 5/725 using specific filters therefor, e.g. Kalman or adaptive filters A61B5/316 take precedence
• 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 5/7282 Event detection, e.g. detecting unique waveforms indicative of a medical condition A61B5/4094, A61B5/4818, A61B5/349, A61B5/0823 take precedence

Abstract

A method and system for near-real time and continuous analyte monitoring, the method including: receiving a signal stream associated with an analyte parameter of the user across a set of time points; generating a dataset indicative of values of the analyte parameter across the set of time points, upon processing of the signal stream in near-real time; performing a calibration operation on values of the analyte parameter, based upon a calibration event, thereby generating a set of calibrated values of the analyte parameter; at the computing system, identifying a set of activity events of the user, from a supplemental dataset, during a time window corresponding to the set of time points; generating an analysis indicative of an association between at least one of the set of activity events and the set of calibrated values of the analyte parameter; and rendering information derived from the analysis to the user.

Claims

1. A method for processing and correction of artifacts in glucose sensor signal streams in near-real time, the method comprising:

at a glucose sensor in communication with interstitial fluid of a user, receiving a signal stream associated with a glucose parameter of the user across a set of time points;

at a computing system in communication with the glucose sensor, detecting an artifact within the signal stream in near-real time;

at the computing system, performing a characterization of the artifact as one of a short duration artifact and a long duration artifact, the short duration artifact associated with physical disturbance of the glucose sensor, and the long duration artifact associated with at least one of

     1) sensor equilibration upon application of the analyte sensor to the user and

     2) sensor drift;

at the computing system, performing a correction operation on the artifact according to the characterization in near-real time, thereby producing a dataset indicative of values of the glucose parameter across the set of time points; and

performing a calibration operation on values of the glucose parameter, based upon a calibration event unassociated with a blood-sampled measurement of the user, thereby generating a set of calibrated values of the glucose parameter.

2. The method of claim 1, wherein detecting the artifact comprises:

filtering the signal stream with a median filter and an infinite impulse response filter, computing a difference between successive samples of the signal stream, and comparing the difference to an adaptive threshold.

3. The method of claim 2, wherein performing the characterization comprises comparing a time scale of the artifact to a time threshold, and categorizing the artifact based upon a signal signature associated with the artifact.

4. The method of claim 1, wherein correction of the long-duration artifact comprises performing a parametric fit to a set of samples following the artifact, and subtracting the parametric fit from the signal stream in near-real time.

5. The method of claim 1, wherein performing the calibration operation comprises limiting at least one of the set of calibrated values of the glucose parameter based upon determination of a trajectory in the signal stream, and comparing the trajectory to a physiologically feasible trajectory in generating the set of calibrated values of the glucose parameter.

6. The method of claim 1, wherein performing the correction operation and performing the calibration operation comprise implementing physiological limitations of the user, analyte sensor equilibration behavior, signal behavior, diurnal variation in analyte levels, and signal latency associated with interstitial fluid as conditions in models of the correction operation and the calibration operation.

7. The method of claim 1, wherein performing the calibration operation comprises generating a distribution of frequencies of different values of the analyte parameter, transforming the distribution of frequencies into a state-space model for the user, producing the dataset indicative of values of the glucose parameter across the set of time points, from the state-space model.

8. A method for processing and correction of artifacts in analyte sensor signal streams in near-real time, the method comprising:

at an analyte sensor in communication with body fluid of a user, receiving a signal stream associated with an analyte parameter of the user across a set of time points;

at a computing system in communication with the analyte sensor, detecting an artifact within the signal stream in near-real time;

at the computing system, performing a characterization of the artifact as one of a short duration artifact and a long duration artifact, the long duration artifact associated with at least one of

     1) sensor equilibration upon application of the analyte sensor to the user and

     2) sensor drift; and

at the computing system, performing a correction operation on the artifact according to the characterization in near-real time, thereby producing a dataset indicative of values of the analyte parameter across the set of time points.

9. The method of claim 8, wherein receiving the signal stream includes receiving the signal stream from a glucose sensor configured to interface with interstitial fluid of the user.

10. The method of claim 8, wherein detecting the artifact comprises:

filtering the signal stream with a median filter and an infinite impulse response filter, computing a difference between successive samples of the signal stream, and comparing the difference to an adaptive threshold.

11. The method of claim 10, wherein performing the characterization comprises comparing a time scale of the artifact to a time threshold, and categorizing the artifact based upon a signal signature associated with the artifact.

12. The method of claim 8, wherein performing the characterization of the artifact as a short duration artifact comprises characterizing the short duration artifact as resulting from at least one of:

a physical disturbance of the analyte sensor, moisture at an interface between the analyte sensor and the user, and environmental temperature.

13. The method of claim 8, wherein performing the correction operation comprises performing a parametric fit to a set of samples following the artifact, and subtracting the parametric fit from the signal stream in near-real time.

14. The method of claim 8, wherein performing the correction operation comprises performing a filtering operation, performing a winsorizing operation, and processing the signal stream with a wavelet approximation algorithm.

15. The method of claim 14, wherein performing the filtering operation comprises filtering the signal stream with at least one of a median filter and an infinite impulse response filter configured as a lowpass filter.

16. The method of claim 14, further comprising transforming discrete points of the signal stream into a continuous function based upon an interpolation operation, wherein the interpolation operation comprising at least one of a spline and a path integral operation.

17. The method of claim 8, further comprising performing a calibration operation on a dataset indicative of values of the analyte parameter across the set of time points, based upon a calibration event unassociated with a blood-sampled measurement of the user, thereby generating a set of calibrated values of the analyte parameter.

18. The method of claim 17, wherein performing the calibration operation comprises detecting a sleep-associated state of the user as the calibration event, and using a fasted state related to the sleep-associated state of the user to generate the set of calibrated values of the analyte parameter.

19. The method of claim 17, wherein performing the calibration operation comprises prompting the user to ingest of substance configured to produce a known physiological response in the user as the calibration event, and generating the set of calibrated values of the analyte parameter based upon the known physiological response.

20. The method of claim 8, wherein performing the calibration operation comprises implementing a molecular model that characterizes at least one of:

enzyme kinetics associated with the analyte sensor, diffusive properties of regions of the analyte sensor, and rates of electrochemical reactions associated with regions of the analyte sensor