Analyte filter method and apparatus

• US 7,436,511 B2
• Filed: 07/22/2005
• Issued: 10/14/2008
• Est. Priority Date: 01/22/1999
• Status: Expired due to Fees

• Current Assignee: GLT Acquisition Corporation
• Original Assignee: Sensys Medical, Inc. (Sensys Medical Limited)
• Inventors: Ruchti, Timothy L., Hazen, Kevin H., Lorenz, Alexander D.
• Primary Examiner(s): Lauchman; L. G.
• Assistant Examiner(s): Stock, Jr.; Gordon J
• Application Number:
  US11/188,064
• Publication Number:
  US 20060017923A1
• Time in Patent Office: 1,180 Days
• Field of Search: None
• US Class Current: 356/326
• CPC Class Codes: G01J 3/28 Investigating the spectrum using colour filters G01J3/51
• CPC Class Codes: G01J 3/433 Modulation spectrometry; Derivative spectrometry
• CPC Class Codes: G01N 21/274 Calibration, base line adjustment, drift correction
• CPC Class Codes: G01N 21/359 using near infrared light
• CPC Class Codes: G01N 2201/129 Using chemometrical methods
• CPC Class Codes: G01N 2201/1293 resolving multicomponent spectra

Abstract

The invention comprises a method and apparatus for enhancing the analysis of noninvasive spectra, resulting in improved analytical performance. More particularly, the invention comprises a method and apparatus for processing noninvasive spectra with an analyte filter that preferably rejects variation likely to be detrimental to the measurement system, while passing signal that probabilistically is unique to the target analyte. Subsequently, the analyte filtered data are used to estimate an analyte property, such as a glucose concentration, in the presence of noise, interferences, state changes, and/or across analyzers.

Claims

1. A method for determining a property of an analyte using a noninvasive spectrum, comprising the steps of:

processing said noninvasive spectrum using an analyte filter yielding a processed spectrum, wherein said analyte filter correlates with frequency response shape of said analyte;

generating said analyte filter using a set of spectra from a first set of spectrometers; and

estimating said property using a multivariate model and said processed spectrum.

2. The method of claim 1, further comprising the step of:

extracting a feature representative of said response shape of said analyte in frequency response.

3. The method of claim 2, wherein said analyte filter enhances said shape in frequency response below a break frequency.

4. The method of claim 3, wherein said analyte filter enhances said shape in frequency responses above a break frequency.

5. The method of claim 2, wherein said feature is characterized in terms of signal and interference.

6. The method of claim 1, wherein said multivariate model comprises any of:

principal component regression; and

partial least squares.

7. The method of claim 1, wherein said frequency response shape of said analyte comprises any of:

a net analyte signal;

an accessible portion of a pure component analyte spectrum; and

shape of said analyte after orthogonalization with respect to interference.

8. The method of claim 7, further comprising the step of:

calculating said shape of said net analyte signal according to;

nas=└

I−

BB⁺┘

R

wherein said net analyte signal, nas, is given by the projection of the pure component analyte signal, R, onto the null space of the interference contained in B, wherein B is a basis set that spans at least a portion of the space of expected variation and I is the identity matrix.

9. The method of claim 7, further comprising the step of:

estimating said net analyte signal from a regression vector of said multivariate model.

10. The method of claim 1, further comprising the step of:

theoretically generating said analyte filter using frequency response shape of said analyte.

11. The method of claim 10, wherein said step of theoretically generating further comprises the steps of:

applying a-priori knowledge of signal of a system from which said noninvasive spectrum is collected; and

applying a-priori knowledge of interference of said system from which said noninvasive spectrum is collected.

12. The method of claim 11, wherein said step of theoretically generating further comprises the step of:

generating a multi-stage filter.

13. The method of claim 12, wherein said multi-stage filter comprises:

a two-stage filter.

14. The method of claim 13, wherein said two-stage filter comprises:

a lowpass filter; and

a highpass filter.

15. The method of claim 1, further comprising the step of:

preprocessing said noninvasive spectrum prior to said step of processing, wherein preprocessing enhances at least a portion of said shape of said analyte.

16. The method of claim 10, further comprising the step of:

iteratively generating frequency response of said analyte filter.

17. The method of claim 16, wherein said steps of theoretically generating and iteratively generating comprise any of:

sequential performance;

concurrent performance; and

iterative performance.

18. The method of claim 1,further comprising the step of:

iteratively generating frequency response shape of said analyte filter using a set of data from a first set of spectrometers; and

implementing said analyte filter on a second spectrometer, wherein said second spectrometer is not a member of said first set of spectrometers.

19. The method of claim 1, further comprising the step of:

implementing said analyte filter on at least one spectrum from a second set of spectrometers, wherein a union of said first set of spectrometers and said second set of spectrometers results in an empty set.

20. The method of claim 19, wherein said step of estimating further comprises the step of:

performing multivariate analysis.

21. The method of claim 20, where said step of performing multivariate analysis comprises use of any of:

a regression model;

partial least squares; and

a principal component regression.

22. The method of claim 1, wherein said analyte filter comprises an n^th point in the frequency domain not restricted by said analyte filter points at a combination of a first, a second, a third, and/or a fourth point of the analyte filter in the frequency domain.

23. The method of claim 1, wherein said analyte filter comprises:

a cluster filter.

24. The method of claim 23, further comprising any of the steps of:

attenuating spectral interference present in a cluster; and

removing spectral interference present in said cluster.

25. The method of claim 24, wherein said cluster filter facilitates calibration transfer of said noninvasive spectrum.

26. The method of claim 24, wherein said cluster filter compensates for variation in any of:

a sample;

an analyzer; and

an environmental state.

27. The method of claim 24, wherein said multivariate model is associated with said cluster.

28. The method of claim 24, wherein said cluster filter compensates for variation of:

a subject;

a sampling position;

temperature; and

instrument state.

29. The method of claim 25, wherein said cluster filter operates according to:

$y_c^{\mathrm{\prime <br><br>}}=y_c-\frac{1}{L}<br><br>\underset{k=1}{\overset{L}{\sum <br><br>}}<br><br>y_{c,k}$

wherein y_c is a reference concentration, a k^th element of y_c is, y_c,k, and L is a set of spectra used to calculate y′

_c.

30. The method of claim 1, wherein said analyte filter comprises a product of a derivative order filter and a shortpass filter, wherein high frequency noise leaked by said derivative order filter is reduced.

31. The method of claim 1, further comprising the step of:

developing said analyte filter using said analyte property shape.

32. The method of claim 31, further comprising the step of:

using interference of said analyte in said step of developing said analyte filter.

33. The method of claim 1, wherein said analyte filter comprises a multi-stage filter.

34. The method of claim 33, wherein said multi-stage filter comprises a two-stage filter.

35. The method of claim 1, wherein said noninvasive spectrum comprises:

a near-infrared spectrum;

at least a portion of said near-infrared spectrum being in the range 1150 to 1850 nm.

36. The method of claim 35, wherein said analyte property comprises:

a glucose concentration.

37. The method of claim 36, further comprising the step of:

generating said noninvasive spectrum using a photo diode array detector.

38. The method of claim 37, further comprising the step of:

implementing said analyte filter on an analyzer not used to develop said analyte filter.

39. An apparatus for determining an analyte property using a noninvasive spectrum, comprising:

means for obtaining said noninvasive spectrum; and

an analyzer, comprising;

a processor;

an algorithm executed by said processor;

an analyte filter operating as a step in said algorithm; and

a multivariate model operating as a step in said algorithm,

said analyzer operating on said noninvasive spectrum using said analyte filter to yield a processed spectrum;

said analyte filter correlating with frequency response shape of said analyte; and

said multivariate model operating on said processed spectrum to yield said analyte property;

wherein said analyte filter is generated using a set of data from a set of spectrometers, wherein said analyzer is not a member of said set of spectrometers.

40. The apparatus of claim 39, wherein said shape comprises an extracted feature of said analyte in frequency response.

41. The apparatus of claim 40, wherein said algorithm further comprises:

preprocessing means for extracting said shape of said analyte.

42. The apparatus of claim 39, wherein said analyte filter enhances shape in frequency response below a break frequency.

43. The apparatus of claim 42, wherein said analyte filter enhances shape in frequency responses above a break frequency.

44. The apparatus of claim 40, wherein said extracted feature is characterized in terms of signal and interference.

45. The apparatus of claim 39, wherein said frequency response shape of said analyte comprises any of:

a net analyte signal;

an accessible portion of a pure component analyte spectrum; and

shape of said analyte after orthogonalization with respect to interference.

46. The apparatus of claim 45, wherein said shape comprises said net analyte signal according to:

nas=└

I−

BB⁺┘

R

wherein said net analyte signal, nas, is given by the projection of the pure component analyte signal, R, onto the null space of the interference contained in B, wherein B is a basis set that spans at least a portion of the space of expected variation and I is the identity matrix.

47. The apparatus of claim 45, wherein said net analyte signal is estimated from a regression vector of said multivariate model.

48. The apparatus of claim 39, wherein said analyte filter comprises:

a theoretically generated filter using frequency response shape of said analyte.

49. The apparatus of claim 48, wherein said theoretically generated filter uses:

a-priori knowledge of signal of a system from which said noninvasive spectrum is collected; and

a-priori knowledge of interference of said system from which said noninvasive spectrum is collected.

50. The apparatus of claim 49, wherein said theoretically generated filter comprises:

a multi-stage filter.

51. The apparatus of claim 50, wherein said multi-stage filter comprises:

a two-stage filter.

52. The apparatus of claim 51, wherein said two-stage filter comprises:

a lowpass filter; and

a highpass filter.

53. The apparatus of claim 51, wherein said algorithm further comprises:

means for preprocessing said noninvasive spectrum prior to said step of processing.

54. The apparatus of claim 48, wherein said analyte filter is iteratively generated using frequency response shape of said analyte.

55. The apparatus of claim 39, where said multivariate model comprises any of:

a regression model;

partial least squares; and

principal component regression.

56. The apparatus of claim 39, wherein said analyte filter comprises an n^th point in the frequency domain not restricted by said analyte filter points at a combination of a first, a second, a third, and/or a fourth point of the analyte filter in the frequency domain.

57. The apparatus of claim 39, wherein said analyte filter comprises:

a cluster filter.

58. The apparatus of claim 57, wherein said cluster filter either removes spectral interference or attenuates spectral interference present in a cluster.

59. The apparatus of claim 57, wherein said cluster filter facilitates calibration transfer of said noninvasive spectrum.

60. The apparatus of claim 57, wherein said cluster filter is used to compensate for variation in any of:

a sample;

a subject;

a sampling position;

temperature;

instrument state;

an analyzer; and

an environmental state.

61. The apparatus of claim 57, wherein multivariate model is associated with said cluster filter.

62. The apparatus of claim 57, wherein said cluster filter operates according to:

x_c^′

=x_c└

I−

x_b^T(x_b^T)<sup>−

1</sup>┘

wherein a basis set spectra, x_b, a pseudo inverse,(x_b^T)<sup>−

1</sup>, and a measured spectrum, x_c, are used to generate a corrected noninvasive spectrum, x_c′

.

63. The apparatus of claim 62, wherein said cluster filter operates according to:

$y_c^{\mathrm{\prime <br><br>}}=y_c-\frac{1}{L}<br><br>\underset{k=1}{\overset{L}{\sum <br><br>}}<br><br>y_{c,k}$

wherein y_c is a reference concentration, a k^th element of y_c is, y_c,k, and L is a set of spectra used to calculated x_b.

64. The apparatus of claim 39, wherein said analyte filter comprises:

a product of a derivative order filter and a shortpass filter, wherein high frequency noise leaked by said derivative order filter is reduced.

65. The apparatus of claim 39, further comprising:

a photo diode array detector.

66. The apparatus of claim 39, wherein said analyte filter is optimized using said shape.

67. The apparatus of claim 60, wherein said shape comprises:

a roll-on frequency;

a roll-off frequency;

signal; and

noise.

68. The apparatus of claim 67, wherein said noise comprises a homogeneous function in frequency space.

69. The apparatus of claim 66, wherein said analyte filter comprises:

a multi-stage filter.

70. The apparatus of claim 69, wherein said multi-stage filter comprises:

a two-stage filter.

71. The apparatus of claim 66, wherein said analyte filter is generated theoretically.

72. The apparatus of claim 66, wherein said filter comprises:

average attenuation of less than 0.05 magnitude above 0.25 frequency units.

73. The apparatus of claim 72, wherein said filter comprises:

a maximum gain of less than 0.1 magnitude above 0.25 normalized frequency units.

74. The apparatus of claim 73, wherein said average attenuation comprises a value less than 0.025 magnitude and said maximum gain comprises a value less than 0.05 magnitude.

75. The apparatus of claim 66, wherein said analyzer comprises:

a sample module in a first housing; and

a base module in a second housing, wherein said sample module communicates with said base module.

76. The apparatus of claim 66, wherein said analyzer comprises:

a photo-diode array.

77. The apparatus of claim 66, wherein said analyzer further comprises:

a coupling fluid; and

a guide to acquire, wherein said coupling fluid and said guide couple said sample module to a tissue sample during use.

78. The apparatus of claim 66, wherein said analyte filter is generated with a set of spectrometers, wherein said set of spectrometers does not comprise said analyzer.