Methods and apparatus for spectroscopic calibration model transfer

• US 6,441,388 B1
• Filed: 05/03/2000
• Issued: 08/27/2002
• Est. Priority Date: 10/13/1998
• Status: Expired due to Fees

• Current Assignee: National Technology & Engineering Solutions Of Sandia LLC (Honeywell International Inc.), Rio Grande Medical Technologies Incorporated
• Original Assignee: Sandia Corporation (Martin Marietta Corporation), Rio Grande Medical Technologies Incorporated
• Inventors: Haass, Michael J., Rowe, Robert K., Thomas, Edward V.
• Primary Examiner(s): Kim, Robert H.
• Assistant Examiner(s): Song, Hoon K.
• Application Number:
  US09/563,865
• Time in Patent Office: 846 Days
• Field of Search: 250/573, 250/339.09, 250/339.08, 250/252.1, 250/339.12, 250/559.1, 250/338.05
• US Class Current: 250/573
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/1455 using optical sensors, e.g. spectral photometrical oximeters
• CPC Class Codes: A61B 5/1495 Calibrating or testing of in-vivo probes
• CPC Class Codes: G01J 3/28 Investigating the spectrum using colour filters G01J3/51
• CPC Class Codes: G01N 21/274 Calibration, base line adjustment, drift correction
• CPC Class Codes: G01N 21/35 using infrared light G01N21/39 takes precedence
• CPC Class Codes: G01N 21/359 using near infrared light
• CPC Class Codes: G01N 21/49 within a body or fluid

Abstract

A method and apparatus for measuring a biological attribute, such as the concentration of an analyte, particularly a blood analyte in tissue such as glucose. The method utilizes spectrographic techniques in conjunction with an improved instrument-tailored calibration model. In a calibration phase, calibration model data is modified to reduce or eliminate instrument-specific attributes, resulting in a calibration data set modeling intra-instrument variation. In a prediction phase, the prediction process is tailored for each target instrument separately using a minimal number of spectral measurements from each instrument.

Claims

1. A quantitative analysis instrument for measuring an unknown attribute level based on an indirect measurement of a biological sample, said instrument comprising:

(a) a source of infrared energy generating multiple wavelengths;

(b) an input sensor element for directing said wavelengths of infrared energy into said biological sample and an output sensor element for collecting at least a portion of the diffusely reflected infrared energy from said biological sample, said input and said output sensor elements adapted to optically couple to said biological sample;

(c) at least one detector arranged for measuring the intensities of at least a portion of said wavelengths collected by said output sensor element;

(d) a memory device including master calibration information taken using one or more other instruments which has been developed in a manner that reduces instrument-specific attributes, reference measurement information from said instrument, and indirect measurement information from said instrument; and

(e) electronics for processing said master calibration information, said reference measurement information, and said indirect measurement information to generate a prediction of said unknown attribute level.

2. The instrument of claim 1, wherein multiple instruments are used for development of said master calibration information.

3. The instrument of claim 2, wherein multiple instrument spectra are collected using said multiple instruments, and said multiple instrument spectra are processed to reduce instrument-specific attributes to generate multiple instrument spectra with reduced instrument specific attributes.

4. The instrument of claim 3, wherein the multiple instrument spectra with reduced instrument-specific attributes are created by subtracting some linear combination of each instrument'"'"'s spectra from said each instrument'"'"'s spectra.

5. The instrument of claim 1, wherein said electronics use said reference measurement information to remove the specific instrument attributes from said indirect measurement information.

6. The instrument of claim 1, wherein said electronics combine said reference measurements with said master calibration information to create an instrument-specific model.

7. A quantitative analysis instrument for measuring an unknown attribute level based on an indirect measurement of tissue, said instrument comprising:

(a) a source of infrared energy generating multiple wavelengths;

(b) an input sensor element for directing said wavelengths of infrared energy into said tissue and an output sensor element for collecting at least a portion of the diffusely reflected infrared energy from said tissue, said input and said output sensor elements adapted to optically couple to the surface of said tissue;

(c) at least one detector arranged for measuring the intensities of at least a portion of said wavelengths collected by said output sensor element;

(d) a memory device including master calibration information taken from one or more other instruments which has been developed in a manner that reduces instrument-specific attributes, reference measurement information from said instrument, and indirect measurement information from said instrument; and

(e) electronics for processing said master calibration information, said reference measurement information, and said indirect measurement information to generate a prediction of said unknown attribute level.

8. The instrument of claim 7, wherein multiple instruments are used for development of said master calibration information.

9. The instrument of claim 8, wherein said multiple instruments generate multiple instrument spectra and said multiple instrument spectra are processed to reduce instrument-specific attributes, generating multiple instrument spectra with reduced instrument specific attributes.

10. The instrument of claim 9, wherein the multiple instrument spectra with reduced instrument-specific attributes are created by subtracting some linear combination of each instrument'"'"'s spectra from said each instrument'"'"'s spectra.

11. The instrument of claim 7, wherein said electronics use said reference measurement information to remove specific instrument attributes from said indirect measurement information.

12. The instrument of claim 7, wherein said electronics combine said reference measurements with said master calibration information to create an instrument-specific model.

13. A quantitative analysis instrument for non-invasive measurement of a biological attribute in human tissue, said instrument comprising:

(a) a source of multiple wavelengths of infrared energy;

(b) an input element for directing said wavelengths of infrared energy into said tissue and an output element for collecting at least a portion of the diffusely reflected infrared energy from said tissue, said input and said output elements adapted to optically couple to the surface of said tissue;

(c) at least one detector for measuring the intensities of at least a portion of said wavelengths collected by said output element; and

(d) electronics for processing said measured intensities and indicating a value for said biological attribute, said electronics including a processing method incorporated therein, said method utilizing calibration data taken from one or more other instruments which has been developed in a manner that reduces instrument-specific spectral attributes;

said method utilizing one or more reference measurements from said instrument.

14. The instrument of claim 13, wherein multiple instruments are used for development of said calibration data.

15. The instrument of claim 14, wherein said multiple instruments gather multiple instrument spectra, and said multiple instrument spectra are processed to reduce instrument-specific attributes, generating multiple instrument spectra with reduced instrument-specific attributes.

16. The instrument of claim 15, wherein the multiple instrument spectra with reduced instrument-specific attributes are created by subtracting some linear combination of each instrument'"'"'s spectra from said each instrument'"'"'s spectra.

17. The instrument of claim 13, wherein said processing method uses said reference measurements to remove the specific instrument attributes from said measured intensities.

18. The instrument of claim 13, wherein said processing method combines said reference measurements with said calibration data to create an instrument-specific model.

19. A method for generating a prediction result of a biological attribute on a slave instrument using spectroscopy as a surrogate indirect measurement for a direct measurement of said biological attribute, said method comprising the steps of:

(a) using a calibration model developed by using calibration data from at least one master instrument that was modified to reduce the spectral variation due to instrument-specific attributes; and

(b) using a prediction process to predict an unknown amount of said biological attribute in a target spectroscopic measurement made with said slave instrument, said prediction process utilizing said model in conjunction with one or more reference measurements.

20. The method of claim 19, wherein said reference measurements are spectroscopic measurements.

21. The method of claim 19, wherein said reference measurements include both spectroscopic measurements and direct measurements.

22. The method of claim 21, wherein said direct measurements are a blood analyte measurement.

23. The method of claim 19, wherein said spectroscopic measurements are made on said slave instrument.

24. The method of claim 19, wherein said calibration data is obtained from a series of spectroscopic measurements made from a number of master instruments.

25. The method of claim 24, wherein modifying said calibration data to reduce instrument-specific spectral attributes in said calibration data includes calculating a mean indirect measurement and a mean direct measurement for all instruments from which calibration data is obtained, followed by meancentering the indirect measurement, and meancentering the direct measurement.

26. The method of claim 24, wherein modifying said calibration data to reduce instrument-specific spectral attributes in said calibration data includes subtracting the mean of the first S indirect and direct measurements of a particular instrument from each of said particular instrument'"'"'s indirect measurements, where S is less than a total number of indirect and direct measurements made on said particular instrument in generating said calibration data.

27. The method of claim 24, wherein modifying said calibration data to reduce instrument-specific spectral attributes in said calibration data includes subtracting the mean of the S nearest in time indirect and direct measurements of a particular instrument from each of said particular instrument'"'"'s indirect and direct measurements, where S is less than a total number of indirect measurements used in forming said calibration data.

28. The method of claim 24, wherein modifying said calibration data to reduce instrument-specific spectral attributes in said calibration data includes subtracting each of a particular instrument'"'"'s direct and indirect measurements from every other direct and indirect measurement made on said particular instrument in a round-robin fashion.

29. The method of claim 24, further including providing a stored spectral library wherein modifying said calibration data to reduce instrument-specific spectral attributes in said calibration data includes subtracting a combination of spectral data from said stored spectral library based on matching said series of spectroscopic measurements from each of said number of instruments with a stored measurement in said spectral library.

30. The method of claim 24, wherein modifying said calibration data to reduce instrument-specific spectral attributes in said calibration data includes subtracting simulated data from indirect measurements, said simulated data derived from prior modeling of spectral attributes.

31. The method of claim 21, wherein said prediction process utilizes said model and said references measurements to calculate a prediction of the direct measurement and utilizes the difference between the prediction of the direct measurement and the direct measurement of said biological attribute to estimate a correction factor.

32. The method of claim 19, wherein said reference measurements are replaced by matched measurements.

33. The method of claim 32, wherein said matched measurements are obtained by using a spectral library and corresponding values of said biological attributes.

34. The method of claim 32, wherein said matched measurements are selected from said spectral library by calculating a measure of the difference between said target spectroscopic measurement and said library spectra.

44. The method of claim 41, wherein the tailoring step further comprises:

determining P₀ and G_ref according to the method of claim 22 once with said specific instrument at a relatively high level of said physical characteristic and once with said specific instrument at a relatively low level of said physical characteristic; and

determining a scale factor based on P₀ and G_ref at said high and low levels.

35. A method of generating a calibration model that is essentially free from instrument-specific effects comprising building a model by:

(a) making a series of indirect measurements with a number of instruments, and making a direct measurement for at least some of said corresponding indirect measurements;

(b) calculating a mean indirect measurement and a mean direct measurement for each instrument based on the number of measurements from each instrument;

(c) meancentering the indirect measurements by subtracting said mean indirect measurement of each instrument from each indirect measurement, and meancentering said direct measurement by subtracting said mean direct measurement from each direct measurement of each instrument; and

(d) forming a calibration model from said meancentered direct and indirect measurements.

36. The method of claim 35, wherein said indirect measurements are spectral measurements.

37. The method of claim 35, wherein the measurements are made on a single measurement device, whereby the generic calibration model is to be utilized in said single measurement device.

38. The method of claim 35, wherein said direct measurements are of a desired blood component and said desired blood component is measured by invasively removing blood from a subject and analyzing the blood for said desired blood component.

39. The method of claim 38, wherein the desired component is glucose.

40. The method of claim 35, further comprising the step of tailoring the generic calibration model to a specific instrument.

41. The method of claim 40, wherein the tailoring step comprises:

(a) making a direct measurement, G_ref, and at least one indirect measurement Y_ref, using said specific instrument;

(b) using the generic calibration model with Y_ref to obtain a raw prediction, P₀, of a physical characteristic.

42. The method of claim 41, further comprising:

making a plurality of indirect measurements using said specific instrument, Y_new;

using said generic calibration model with Y_new to obtain an untailored prediction, P_new; and

predicting a physical characteristic G_new for the instrument as a function of P_new, P₀, and G_ref.

43. The method of claim 42, wherein G_new is a function of a known scale factor.

45. The method of claim 41, wherein said measurements are made on a single measurement device, whereby said generic calibration model is to be utilized in said single measurement device.

46. A method for generating a prediction result of a biological attribute on a slave instrument using spectroscopy as a surrogate indirect measurement for a direct measurement of said biological attribute, said method comprising the steps of:

(a) using a modified calibration data set derived from calibration data taken from one or more master instruments processed in a manner that reduces the spectral variation due to instrument-specific attributes;

(b) generating a calibration model through application of a multivariate algorithm that uses a composite calibration data set formed by combining the modified calibration data with one or more reference measurements; and

(c) predicting an unknown amount of said biological attribute in a target spectroscopic measurement utilizing said calibration model.

47. The method of claim 46, wherein said modified calibration data set is generated by modifying calibration data obtained from a series of spectroscopic measurements made with a number of instruments on one or more calibration samples with corresponding direct measurements of said biological attribute on one or more calibration samples.

48. The method of claim 47, wherein modifying said calibration data set to reduce instrument-specific spectral attributes in said calibration data set includes calculating a mean indirect measurement and a mean direct measurement for each instrument based on a number of measurements from said each instrument followed by meancentering said indirect measurement by subtracting said mean indirect measurement of each instrument from each indirect measurement, and meancentering said direct measurement by subtracting said mean direct measurement from each direct measurement of each instrument.

49. The method of claim 47, wherein said modified calibration data set is modified by subtracting the mean of the first S indirect and direct measurements from a particular one of said number of instruments from each of the instrument'"'"'s indirect measurements, where S is less than the total number of indirect and direct measurements made on said particular one of said number of instruments in generating the calibration data.

50. The method of claim 47, wherein said modified calibration data set is modified by subtracting the mean of the S nearest in time indirect and direct measurements of a particular instrument from each of said particular instrument'"'"'s indirect and direct measurements, where S is less than a total number of indirect measurements used in forming said calibration data.

51. The method of claim 47, wherein said modified calibration data set is modified by subtracting each of a particular instrument'"'"'s direct and indirect measurements from every other direct and indirect measurement made on said particular instrument in a round-robin fashion.

52. The method of claim 47, further including providing a stored spectral library wherein said modified calibration data set is modified by subtracting a combination of spectral data from said stored spectral library based on matching said spectroscopic measurements from each of said number of instruments with a stored measurement in said spectral library.

53. The method of claim 47, wherein modifying said calibration data to reduce instrument-specific spectral attributes in said calibration data includes subtracting simulated data from indirect measurements, said simulated data derived from prior modeling of spectral attributes.

54. The method of claim 46, wherein said composite calibration data set is created by combining in a linear manner reference measurements with calibration data, the combining process including both reference spectra and reference analyte measurements.

55. The method of claim 46, wherein said reference measurements are replaced by matched measurements.

56. The method of claim 55, wherein said matched measurements are obtained by using a spectral library and corresponding values of said biological attributes.

57. The method of claim 56, wherein said matched measurements are selected from said spectral library by calculating a measure of the difference between said target spectroscopic measurement and said library spectra.

58. A method for predicting a measure of a biological attribute on a slave instrument by effectively using master calibration data, the method comprising:

(a) using calibration data obtained from at least one master instrument, wherein said calibration data has been modified to reduce variations due to instrument-specific attributes generating calibration data with reduced instrument specific spectral attributes;

(b) obtaining at least one indirect measurement with the slave instrument and a corresponding direct measurement;

(c) developing a prediction process that utilizes said master calibration data and said slave reference measurement; and

(d) using said prediction process to predict a measure of the unknown attribute in an indirect measurement made on said slave instrument.

59. The method of claim 58, wherein the calibration data is modified to reduce variations in said direct measurements of said biological attribute due to instrument-specific attributes for each of said at least one master instrument.

60. The method of claim 58, wherein the calibration data is modified to reduce variations in said indirect measurements of said biological attributes due to instrument-specific attributes for each of said at least one master instrument.

61. The method of claim 58, wherein said slave instrument is not one of the at least one master instruments.

62. The method of claim 58, wherein modifying said calibration data to reduce instrument-specific spectral attributes in said calibration data includes calculating a mean indirect measurement and a mean direct measurement for each calibration instrument based on a number of measurements from said each calibration instrument followed by meancentering said indirect measurement by subtracting said mean direct measurement from each direct measurement for each calibration instrument.

63. The method of claim 58, wherein said calibration data with reduced instrument-specific spectral attributes is modified by subtracting a mean of the first S indirect and direct measurements from a particular instrument from each of said particular instrument'"'"'s indirect measurements, where S is less than a total number of indirect and direct measurements made on said particular instrument in generating said calibration data set.

64. The method of claim 58, wherein said calibration data with reduced instrument-specific spectral attributes is modified by subtracting a mean of the S nearest in time indirect and direct measurements of a particular instrument from each of said particular instrument'"'"'s indirect and direct measurements, where S is less than a total number of indirect measurements used in forming said calibration data set.

65. The method of claim 58, wherein said calibration data with reduced instrument-specific spectral attributes is modified by subtracting each of a particular instrument'"'"'s direct and indirect measurements from every other direct and indirect measurement made on said particular instrument on a round-robin fashion.

66. The method of claim 58, further including providing a stored spectral library wherein said calibration data with reduced instrument-specific spectral attributes is modified by subtracting a combination of spectral data from said stored spectral library based on matching said measurements from each of said calibration instrument with a stored measurement in said spectral library.

67. The method of claim 58, wherein modifying said calibration data to reduce instrument-specific spectral attributes in said calibration data includes subtracting simulated data from indirect measurements, said simulated data derived from prior modeling of spectral attributes.

68. A non-invasive method for measuring a biological attribute in human tissue comprising the steps of:

(a) providing an instrument for measuring infrared absorption, said instrument including an energy source emitting infrared energy at multiple wavelengths operatively connected to an input element, said instrument further including an output element operatively connected to a spectrum analyzer;

(b) coupling said input and output elements to said human tissue;

(c) irradiating said tissue through said input element with multiple wavelengths of infrared energy with resulting attenuation of at least some of said wavelengths;

(d) collecting at least a portion of the non-absorbed infrared energy with said output element followed by determining the intensities of said wavelengths of the collected infrared energy; and

(e) predicting the biological attribute in said measured intensities by utilizing calibration information, obtained on at least one master instrument and modified to reduce between instrument differences, at least one reference measurement from said specific instrument, and a multivariate processing method.

69. A method for predicting a variable, comprising:

(a) obtaining a calibration data set of direct measurements and indirect spectral measurements of the variable from at least one master instrument, wherein the calibration data set has been modified to reduce variations therein due to instrument-specific attributes for each master instrument;

(b) developing an instrument-specific calibration model from said modified calibration data set tailored for a specific instrument with at least one reference measurement of the variable from the specific instrument;

(c) obtaining at least one indirect measurement of the variable for the specific instrument; and

(d) using said instrument-specific calibration model and said at least one indirect measurement of the variable for the specific instrument to predict a measure of said variable in said specific instrument.

70. The method of claim 69, wherein the at least one reference measurement has been obtained from physical samples that have not been measured on the at least one master instrument.

71. The method of claim 69, wherein the variable is a chemical or biological pollutant.