Targeted interference subtraction applied to near-infrared measurement of analytes

• US 6,697,654 B2
• Filed: 06/25/2002
• Issued: 02/24/2004
• Est. Priority Date: 07/22/1999
• Status: Expired due to Fees

• Current Assignee: GLT Acquisition Corporation
• Original Assignee: Sensys Medical, Inc. (Sensys Medical Limited)
• Inventors: Lorenz, Alexander D., Ruchti, Timothy L., Blank, Thomas B.
• Primary Examiner(s): WINAKUR, ERIC FRANK
• Application Number:
  US10/183,906
• Publication Number:
  US 20030023148A1
• Time in Patent Office: 609 Days
• Field of Search: 600/310, 600/316, 600/322, 600/323, 600/473, 356/300, 356/319, 356/320, 356/326, 356/328, 250/339.01, 250/339.02, 250/339.06, 250/339.07, 250/339.11, 250/339.12, 250/340, 250/341.1, 250/341.2, 250/341.8
• US Class Current: 600/310
• CPC Class Codes: A61B 5/1075 for measuring dimensions by non-invasive methods, e.g. for determining thickness of tissue layer A61B8/0858 takes precedence
• 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: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: G01N 21/4785 Standardising light scatter apparatus; Standards therefor

Abstract

Methods and apparatus for estimating and removing spectral interference improve precision and robustness of non-invasive analyte measurement using Near-infrared (NIR) spectroscopy. The estimation of spectral interference is accomplished, either through multivariate modeling or discrete factor analysis, using a calibration set of samples in which the interference is orthogonal to the analyte signal of interest, or where the shape of the interference is known. Each of the methods results in a multivariate model in which the spectral interference is estimated for a new sample and removed by vector subtraction. Independent models based on classes of sample variability are used to collapse spectral interference and determine more accurately which model is best equipped to estimate the signal of interference in the new sample. Principal components analysis and other commonly known analytical techniques can be used to determine class membership.

Claims

1. An apparatus for modeling and removing targeted interfering signals from spectral measurements comprising:

means for measuring a spectrum of a sample;

at least one interference model, wherein said interference is approximately orthogonal to an analyte signal of interest, said interference model adapted to remove spectral interference from said measured spectrum; and

means for selecting most appropriate interference model for estimating interference to be removed from said measured spectrum.

2. The apparatus of claim 1, wherein said apparatus is adapted to measure a spectrum of a tissue sample noninvasively.

3. The apparatus of claim 1 wherein said means for measuring a spectrum comprises:

a radiation source;

means for coupling radiation to the sample;

means for collecting radiation that is any of diffusely scattered and transmitted from the sample;

means for dispersing said collected radiation;

means for detecting said dispersed radiation at predetermined bands of wavelengths and converting said detected radiation to a proportional voltage; and

means for digitizing said voltage.

4. The apparatus of claim 3, wherein said radiation source comprises a broadband light source.

5. The apparatus of claim 3, further comprising a band pass filter, said band pass filter adapted to limit radiation reaching said sample to a targeted wavelength range.

6. The apparatus of claim 3, said apparatus further comprising a blocking element, said blocking element adapted to intermittently block transmission of radiation toward said sample so that any of a baseline and a detector dark current can be detected.

7. The apparatus of claim 3, wherein said means for coupling radiation to said sample comprises at least one fiber optic and a focusing element.

8. The apparatus of claim 3, wherein said means for collecting light comprises at least one fiber optic.

9. The apparatus of claim 3, further comprising a probe, said probe adapted to establish contact of said means for coupling light and said means for collecting light with surface of said tissue sample.

10. The apparatus of claim 3, wherein said means for dispersing collected radiation comprises a grating.

11. The apparatus of claim 3, further comprising a coupling element adapted to couple said means for collecting radiation to an aperture, said aperture adapted to directed radiation toward said means for dispersing radiation.

12. The apparatus of claim 11, wherein said aperture comprises a slit.

13. The apparatus of claim 3, wherein said means for detecting radiation comprises a detector array with associated amplifiers, said array and said amplifiers adapted to convert said detected radiation to a voltage proportionate to a detected signal.

14. The apparatus of claim 3, wherein said means for digitizing said voltage comprises an analog-to-digital converter.

15. The apparatus of claim 1, wherein said interference model is calculated from one or more calibration sets.

16. The apparatus of claim 1, wherein said interference model estimates interference based on one of:

multivariate modeling, wherein known interference is modeled by regressing spectral data against measured property values of the interference; and

discrete factor analysis, wherein interference is estimated by performing an analysis of spectral scores and loadings and determining which factor best represents the interference; and

wherein signal is removed by reconstructing the spectrum using the spectral scores and loadings less the factor representing the interference.

17. The apparatus of claim 1, said means for selecting most appropriate interference model comprising a processing element, wherein said processing element is programmed to execute a sample variability classification procedure, wherein most appropriate interference model is selected for estimating interference to be removed from said measured spectrum.

18. The apparatus of claim 1, wherein said at least one interference model comprises a plurality of interference models.

19. A method for estimating and removing spectral interference from a measured spectrum comprising the steps of:

providing one or more calibration sets of exemplary spectral measurements and corresponding measured property values;

providing one or more spectral measurements, each of said measurements comprising a sample;

calculating one or more interference models from said one or more calibration sets, wherein said interference is approximately orthogonal to an analyte signal of interest;

selecting most appropriate interference model for estimating interference to be removed;

estimating said interference; and

removing said interference from said sample.

20. The method of claim 19, wherein said spectrum is denoted by a vector mε

^N of absorbance values pertaining to a set of N wavelengths λ

ε

^N.

21. The method of claim 19, wherein said measurements are in the wavelength region 700-2500 nm, said wavelength region corresponding to the near infrared (NIR).

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

detecting outliers, wherein said outliers are invalid measurements caused by spectral variations due to any of instrument malfunction, poor sampling of a subject and subjects outside of the calibration set.

23. The method of claim 22, wherein said outlier detection step employs principal components analysis and analysis of resulting residuals to detect spectral outliers.

24. The method of claim 23, wherein said outlier detection step comprises the steps of:

projecting a spectrum m onto five eigenvectors contained in a matrix o, said matrix o being previously developed through a principal components analysis of said calibration set, where ${\mathrm{xpc}}_o=\underset{k=1}{\overset{5}{\sum <br><br>}}<br><br>\text{\hspace{1em}<br><br>}<br><br>{\mathrm{mo}}_k,$

xpc_o being a 1 by 5 vector of scores and where O_k is the k^th column of the matrix o;

determining the residual q, according to

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

preprocessing, wherein said preprocessing step includes one or more transformations that attenuate noise and instrumental variation without affecting a signal of interest.

26. The method of claim 25, wherein a preprocessed measurement, xε

^N, is determined according to;

27. The method of claim 19, wherein parameters of said interference models are calculated using multivariate regression and wherein a resulting model comprises a multivariate regression vector.

28. The method of claim 27, wherein said multivariate regression vector is computed using one of:

Partial Least Squares (PLSR);

Principal Component Regression (PCR);

Locally Weighted Regression (LWR);

Multiple Linear Regression (MLR); and

Classical Least Squared (CLS).

29. The method of claim 28, wherein said regression vector is computed using Principal Component Regression.

30. The method of claim 29, wherein a Principal Components analysis is performed on one or more spectral measurements, wherein the collected spectra, x contain n number of samples by p number of wavelengths.

31. The method of claim 30, wherein a n by 1 vector of eigenvalues and a p by n eigenvector matrix is computed using Singular Value Decomposition.

32. The method of claim 31, wherein said regression vector, W, is related to said spectral measurements, x, and interference property values, y, by:

33. The method of claim 32, wherein predicted property values of the interference are given by:

34. The method of claim 33, wherein the number of factors, said factors comprising eigenvectors, representing said interference is determined through cross validation on one of the calibration set and an independent set of test samples by iteratively increasing the number of factors used to develop the model and minimizing the standard error of prediction (SEP).

35. The method of claim 34, wherein the SEP is given by:

36. The method of claim 35, wherein acceptability of the model is based on an F-test, wherein said F-test is the ratio of the squared SEP to the variance in the measured property values.

37. The method of claim 31, wherein said eigenvector matrix V and said regression vector W are saved and used to estimate interference of new sample spectra.

38. The method of claim 27, wherein said regression vector, W, is represented by:

39. The method of claim 38, wherein for a new sample , said new sample containing interference, a new spectrum , with interference removed is given by:

${\stackrel{\_}{x}}^{\mathrm{\prime <br><br>}}=x^{\mathrm{\prime <br><br>}}<br><br>\left(I-\frac{W^T<br><br>W}{{<br><br>W<br><br>}^2}\right),$

where I is an appropriately scaled identity matrix and W is the regression vector of the modeled interference.

40. The method of claim 19, wherein parameters of said interference models are calculated using discrete factor analysis.

41. The method of claim 40, wherein a signal of interference is estimated by performing an analysis of spectral scores and loadings and determining which factor best represents the interference.

42. The method of claim 41, wherein said signal of interference is removed from said measured spectrum by reconstructing said spectrum using said spectral loadings and scores minus the factor representing the interference.

43. The method of claim 42, wherein said interference modeling step employs one of principal components analysis and another factor-based analytical method.

44. The method of claim 43, wherein a set of spectra x, having m number of samples and n wavelengths is given by:

45. The method of claim 43, wherein spectra {overscore (x)}, having said interference removed, are given by:

46. The method of claim 43, wherein a set of spectra, x , having n number of samples by p number of wavelengths are used in a principal components analysis to generate a n by 1 vector of eigenvalues, and a p by n eigenvector matrix.

47. The method of claim 46, wherein said eigenvectors and eigenvalues are computed using Singular Value Decomposition.

48. The method of claim 46, wherein spectral scores, T, are given by:

49. The method of claim 48, wherein said eigenvectors are analyzed for shape and variation similarities with shape and variation manifested at each wavelength where the interference is known.

50. The method of claim 48, wherein spectral scores are analyzed for similar changes in magnitude where there are corresponding property values for said interference or if the magnitude of the interference is known.

51. The method of claim 48, wherein a corresponding eigenvector is stored wherein a factor corresponding to said interference is known, and wherein said stored eigenvector is used to correct future samples.

52. The method of claim 19, wherein the step of selecting most appropriate interference model for estimating interference to be removed comprises:

classifying samples, wherein a sample is compared to other samples for which interference has already been modeled so that the most appropriate interference model is selected for estimating interference to be removed.

53. The method of claim 52, wherein said sample classification step comprises a principal components analysis of said sample and an analysis of spectral scores through t-squared and t-square limit computation.

54. The method of claim 53, wherein a spectrum m is projected onto p number of eigenvectors contained in a matrix o previously determined through a principal components analysis on said calibration set, where the calculation is given by:

55. The method of claim 54, wherein eigenvalues corresponding to said eigenvector matrix o are used to normalize new scores to unit variance.

56. The method of claim 55, wherein a t-squared value, t, is determined according to:

57. The method of claim 56, wherein a t-squared limit is computed by performing an f-test with a confidence limit on the scores matrix of said calibration set so that a sample having a t-squared value exceeding said t-squared limit is considered to have spectral characteristics dissimilar to those in the calibration set, and wherein said confidence limit is optimized according to the application.

58. The method of claim 57, wherein said confidence limit is ninety-five percent.

59. The method of claim 58, wherein said t-squared value is compared to t-square limits for multiple interference models until at least one passes the constraints.

60. The method of claim 59, wherein the model having the lowest t-squared limit is applied to a sample that fits several models.

61. The method of claim 60, wherein a sample failing to meet the constraints of any stored models is classified as an outlier.

62. The method of claim 52, wherein said calculating and classifying steps are based on any of:

linear discriminant analysis;

SIMCA;

k nearest neighbor;

fuzzy classification; and

artificial neural networks.

63. The method of claim 19, wherein an appropriate model or regression vector is selected and applied to said sample to remove targeted interference.

64. The method of claim 63, wherein removing said interference comprises subtracting it from said sample.

65. The method of claim 19, wherein said step of estimating said interference comprises:

collecting a plurality of baseline samples

removing interference from said baseline samples.

66. The method of claim 65, further comprising the step of:

subtracting interference calculated from said baseline samples from a sample measurement.

67. The method of claim 66, further comprising the step of measuring an analyte value from said sample measurement.

68. The method of claim 67, wherein measurement of said analyte value is biased from a true blood reference value due to non-linearities and un-modeled synergistic effects in data;

and

wherein said bias is constant for all sample measurements that are spectrally similar to said baseline samples;

further comprising the step of adjusting an interference model by adding mean difference between N number of blood reference values and associated model measurements to an intercept of said model.