Systems and methods for replacing signal artifacts in a glucose sensor data stream

• US 8,010,174 B2
• Filed: 08/22/2003
• Issued: 08/30/2011
• Est. Priority Date: 08/22/2003
• Status: Active Grant

• Current Assignee: DexCom Incorporated
• Original Assignee: DexCom Incorporated
• Inventors: Goode, Paul V. Jr., Brauker, James H., Carr-Brendel, Victoria, Thrower, James Patrick, Kamath, Apurv U.
• Primary Examiner(s): Mallari; Patricia
• Assistant Examiner(s): JANG, CHRISTIAN YONGKYUN
• Application Number:
  US10/648,849
• Publication Number:
  US 20050043598A1
• Time in Patent Office: 2,930 Days
• Field of Search: 600/345, 600/347, 600/365
• US Class Current: 600/347
• CPC Class Codes: A61B 2560/0223 of calibration, e.g. protocols for calibrating sensors
• CPC Class Codes: A61B 2562/0247 Pressure sensors
• CPC Class Codes: A61B 2562/0271 Thermal or temperature sensors
• CPC Class Codes: A61B 2562/08 Sensors provided with means for identification, e.g. barcodes or memory chips
• CPC Class Codes: A61B 5/0004 characterised by the type of physiological signal transmitted
• CPC Class Codes: A61B 5/0031 Implanted circuitry
• 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 5/1473 invasive, e.g. introduced into the body by a catheter A61B5/1482 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 5/1495 Calibrating or testing of in-vivo probes
• 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 5/7257 using Fourier transforms
• CPC Class Codes: A61B 5/726 using Wavelet transforms
• CPC Class Codes: A61B 5/7278 Artificial waveform generation or derivation, e.g. synthesising signals from measured signals A61B5/327 takes precedence
• CPC Class Codes: A61B 5/742 using visual displays A61B5/339 takes precedence
• CPC Class Codes: A61B 5/7475 User input or interface means, e.g. keyboard, pointing device, joystick

Abstract

Systems and methods for minimizing or eliminating transient non-glucose related signal noise due to non-glucose rate limiting phenomenon such as ischemia, pH changes, temperatures changes, 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 replaces some or the entire data stream continually or intermittently including signal estimation methods that particularly address transient signal artifacts. The system is also capable of detecting the severity of the signal artifacts and selectively applying one or more signal estimation algorithm factors responsive to the severity of the signal artifacts, which includes selectively applying distinct sets of parameters to a signal estimation algorithm or selectively applying distinct signal estimation algorithms.

Claims

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

monitoring a data stream indicative of an output current from a glucose sensor;

detecting transient non-glucose related signal artifacts in the data stream and evaluating a severity thereof based at least in part on a frequency content of the transient non-glucose related signal artifacts; and

replacing with an electronic device at least some of the signal artifacts with one or more estimated glucose values.

2. The method of claim 1, wherein replacing transient non-glucose related signal artifacts further comprises selectively applying one of a plurality of signal estimation algorithm factors in response to the severity of the signal artifacts.

3. The method of claim 2, wherein the plurality of signal estimation algorithm factors comprises a single algorithm with a plurality of parameters that are selectively applied to the algorithm.

4. The method of claim 2, wherein the plurality of signal estimation algorithm factors comprises a plurality of distinct algorithms.

5. The method of claim 2, wherein selectively applying one of a plurality of signal estimation algorithm factors comprises selectively applying a predetermined algorithm that comprises a set of parameters whose values depend on the severity of the signal artifacts.

6. The method of claim 1, wherein replacing at least some of the signal artifacts with one or more estimated glucose values comprises outputting data representative of the one or more estimated glucose values, wherein the data comprises at least one of a numeric representation of the one or more estimated glucose values, an indication of directional trend of the one or more estimated glucose values, or a graphical representation of the one or more estimated glucose values.

7. The method of claim 6, further comprising filtering the data stream, wherein the one or more estimated glucose values are based on the filtered data stream.

8. The method of claim 6, wherein the one or more estimated glucose values are based on an unfiltered data stream.

9. The method of claim 1, wherein monitoring a data stream comprises receiving data from at least one of a non-invasive, a minimally invasive, or an invasive glucose sensor.

10. The method of claim 1, wherein monitoring a data stream comprises receiving data from at least one of an enzymatic glucose sensor, a chemical glucose sensor, a physical glucose sensor, an electrochemical glucose sensor, a spectrophotometric glucose sensor, a polarimetric glucose sensor, a calorimetric glucose sensor, an iontophoretic glucose sensor, or a radiometric glucose sensor.

11. The method of claim 1, wherein detecting transient non-glucose related signal artifacts further comprises at least one of:

testing for ischemia within or proximal to the glucose sensor;

monitoring a level of pH proximal to the sensor;

monitoring a temperature proximal to the sensor;

comparing a level of pH proximal to and distal to the sensor;

comparing a temperature proximal to and distal to the sensor;

monitoring a pressure or stress within the glucose sensor;

evaluating historical data for high amplitude noise above a predetermined threshold;

performing a Cone of Possibility Detection Method;

evaluating the data stream for a non-physiological rate-of-change;

performing an orthogonal basis function-based transform;

performing a Fourier Transform;

or performing a wavelet transform.

12. The method of claim 1, wherein replacing at least some of the signal artifacts comprises at least one of:

performing linear or non-linear regression;

performing a trimmed mean;

filtering using a non-recursive filter;

filtering using a finite impulse response filter;

filtering using a recursive filter;

filtering using an infinite impulse response filter;

performing a maximum average algorithm;

or performing a Cone of Possibility Replacement Method.

13. The method of claim 1, wherein replacing at least some of the signal artifacts is substantially continual.

14. The method of claim 1, wherein replacing at least some of the signal artifacts is initiated when the severity of signal artifacts meets a criterion.

15. The method of claim 1 wherein replacing at least some of the signal artifacts is terminated when the severity of signal artifacts meets a criterion.

16. The method of claim 1, further comprising discarding at least some of the signal artifacts.

17. The method of claim 1, further comprising calibrating the data stream.

18. The method of claim 1, wherein replacing at least some of the signal artifacts with one or more estimated glucose values comprises outputting or displaying the one or more estimated glucose values.

19. The method of claim 1, further comprising filtering the monitored data stream.

20. The method of claim 1, wherein the data stream comprises a filtered data stream.

21. The method of claim 1, wherein the data stream comprises a raw data stream.

22. The method of claim 1, wherein the frequency content comprises frequencies contained within the data stream.

23. The method of claim 1, wherein evaluating the severity of signal artifacts based at least in part on a frequency content of the transient non-glucose related signal artifacts comprises detecting high frequency constituents of the data stream.

24. The method of claim 1, further comprising outputting the one or more estimated glucose values.

25. The method of claim 1, further comprising outputting the data stream.

26. The method of claim 1, further comprising evaluating whether the one or more estimated glucose values is outside a predetermined range, wherein the predetermined range is defined by boundaries derived from a projected rate of change and/or acceleration of estimated glucose values.

27. The method of claim 26, further comprising discarding the one or more estimated glucose values if the one or more estimated glucose values is outside the predetermined range.

28. The method of claim 26, further comprising replacing the one or more estimated glucose values with a predetermined limit value if the one or more estimated glucose values is outside the predetermined range.

29. The method of claim 1, further comprising storing the one or more estimated glucose values.

30. The method of claim 1, further comprising displaying the one or more estimated glucose values.

31. The method of claim 1, wherein replacing at least some of the signal artifacts with one or more estimated glucose values comprises a maximum-average calculation.

32. The method of claim 31, wherein the maximum-average calculation comprises selecting a maximum value from the data stream for an interval and averaging the maximum value associated with the interval with at least one maximum value associated with at least one previous interval.

33. The method of claim 32, wherein the interval comprises a time period.

34. The method of claim 1, wherein replacing at least some of the signal artifacts comprises determining whether the ratio of the one or more estimated glucose values to at least one projected value is outside a predetermined ratio range, and replacing the one or more estimated glucose values with the at least one projected value if the ratio is outside a predetermined ratio range.

35. The method of claim 1, further comprising using a time series analysis based on a variance of a signal over a window of data.

36. The method of claim 35, wherein the window of data is about 15 minutes.

37. The method of claim 35, wherein the window of data is about 30 minutes.

38. The method of claim 35, wherein the window of data is about 45 minutes.

39. The method of claim 35, wherein the window of data is about 60 minutes.

40. The method of claim 1, wherein evaluating a severity of signal artifacts based at least in part on the frequency content comprises detecting high frequency cycles.

41. The method of claim 1, further comprising using amperometry to generate a signal response from the glucose sensor.

42. The method of claim 41, further comprising transforming the signal response into the data stream, wherein the data stream represents digitized measurement values directly related to the signal response.

43. The method of claim 1, further comprising generating the data stream, wherein generating the data stream comprises:

applying a voltage potential to an electrode of the glucose sensor; and

measuring the output current of the electrode.

44. The method of claim 43, wherein generating the data stream further comprises digitizing the output current measurement.

45. The method of claim 43, wherein generating the data stream further comprises converting the output current into digital counts, wherein the data stream comprises information representative of the counts, and wherein the counts are directly related to the measured output current.

46. The method of claim 1, wherein detecting further includes converting data represented in the data stream using an orthogonal basis function-based transform.

47. The method of claim 46, wherein the orthogonal basis function-based transform is a transform selected from the group consisting of a Fourier Transform, a Discrete Fourier Transform and a wavelet transform.

48. The method of claim 46, wherein the orthogonal basis function-based transform is a windowed Discrete Fourier Transform, and wherein evaluating a severity comprises identifying frequency cycles in the converted data that exceed a predetermined threshold.

49. The method of claim 1, further comprising identifying a transient non-glucose related signal artifact associated with signal loss based on the evaluation, and wherein replacing comprises replacing the transient non-glucose related signal artifact associated with a signal loss with an estimated glucose value.

50. The method of claim 1, further comprising monitoring the frequency content to identify high frequency cycles, wherein signal artifacts associated with identified high frequency cycles are replaced with estimated glucose values.

51. The method of claim 1, wherein detecting further comprises converting the transient non-glucose related signal artifacts by applying a windowed Discrete Fourier Transform, and wherein evaluating a severity includes comparing the converted transient non-glucose related signal artifacts to a threshold.