Multivariate analysis of green to ultraviolet spectra of cell and tissue samples

• US 20010034477A1
• Filed: 02/20/2001
• Published: 10/25/2001
• Est. Priority Date: 02/18/2000
• Status: Abandoned Application

• Current Assignee: Argose, Inc.
• Original Assignee: Argose, Inc.
• Inventors: Mansfield, James, Freeman, Jenny
• Application Number:
  US09/785,531
• Publication Number:
  US 20010034477A1
• Time in Patent Office: Days
• US Class Current: 600/316
• CPC Class Codes: A61B 5/0059 using light, e.g. diagnosis by transillumination, diascopy, fluorescence A61B5/0093, A61B5/1455, A61B5/02416 and A61B5/0261 take precedence
• CPC Class Codes: A61B 5/0071 by measuring fluorescence emission
• 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 5/441 Skin evaluation, e.g. for skin disorder diagnosis
• CPC Class Codes: A61B 5/445 Evaluating skin irritation or skin trauma, e.g. rash, eczema, wound, bed sore
• 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: G01J 3/28 Investigating the spectrum using colour filters G01J3/51
• CPC Class Codes: G01N 21/6486 Measuring fluorescence of biological material, e.g. DNA, RNA, cells G01N21/6428 takes precedence
• CPC Class Codes: G01N 2201/129 Using chemometrical methods
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

This invention relates to methods for processing in vivo skin auto-fluorescence spectra for determining blood glucose levels. The invention also relates to methods of classifying cells or tissue samples or quantifying a component of a cell or tissue using a multivariate classification or quantification model.

Claims

1. A method for processing in vivo skin auto fluorescence spectra emitted by a skin surface of a patient to determine a blood glucose level of the patient comprising the steps of:

collecting auto fluorescence spectra emitted from the skin surface of the patient; and

correcting the collected spectra using multivariate analysis to account for skin surface variables.

2. The method of

claim 1

wherein the multivariate analysis comprises one or more quantification, classification, or data processing techniques selected from the group consisting of:

partial least squares, principal component regression, linear regression, multiple linear regression, stepwise linear regression, ridge regression, radial basis functions, linear discriminant analysis, cluster analysis, neural network analysis, smoothing filters, laplacian operators, maximum likelihood estimators, maximum entropy, first and second derivatives, peak enhancement, Fourier self-deconvolution, principal components, and varimax rotations.

3. An instrument for determining a correct glucose level of a patient by measuring in vivo autofluorescence of the patient'"'"'s skin comprising:

means for irradiating the skin with a plurality of excitation wavelengths;

means for collecting a plurality of emitted wavelengths; and

means for analyzing the collected wavelengths to determine a preliminary blood glucose level, said means for analyzing comprising means for correcting the preliminary blood glucose level to account for variations in skin, said means for correcting comprising using one or more multivariate analytical methodologies to determine the correct glucose level of the patient.

4. A method of quantifying a component of a cell or tissue sample comprising:

generating a single excitation wavelength or plurality of different excitation wavelengths of green to ultraviolet light;

irradiating the sample with said light and measuring the intensity of the stimulated emission of the sample at a three or more different wavelengths of lower energy than the excitation light; and

quantifying one or more components of the cell or tissue from the measured intensities by using a multivariate quantification model.

5. The method of

claim 4

wherein the green to ultraviolet light is in the green to violet range of wavelengths.

6. The method of

claim 4

wherein the green to ultraviolet light is in the violet to near-ultraviolet range of wavelengths.

7. The method of

claim 4

wherein the component is glucose.

8. The method of

claim 4

wherein the irradiating of the sample is done in vitro.

9. The method of

claim 4

wherein the irradiating of the sample is done in vivo.

10. The method of

claim 4

wherein the step of quantifying the component of the sample includes at least one spectral data pre-processing step.

11. The method of

claim 10

wherein the pre-processing step includes at least one of the steps of selecting wavelengths, correcting for a linear baseline, and normalizing a spectral region surrounding the different wavelengths, used for classification of one spectral band in the spectral region.

12. The method of

claim 10

wherein the pre-processing step includes at least one of the steps of normalizing for total area of the spectrum, filtering or smoothing the data, or pre-sorting by analyte.

13. The method of

claim 4

wherein the multivariate quantification is done by a partial least squares technique.

14. The method of

claim 4

wherein the multivariate quantification is done by a principal component regression technique.

15. The method of

claim 4

wherein the multivariate classification is done by one of multiple linear regression, stepwise linear regression, or ridge regression.

16. The method of

claim 4

wherein the step of quantifying the component of the sample is performed by a multivariate algorithm using the measured intensity information and at least one multivariate quantification model which is a function of determined cell or tissue component quantities from a set of reference samples and a set of spectral intensities as a function of wavelength obtained from irradiating the set of reference samples with green to ultraviolet light and monitoring the stimulated emission.

17. A method of quantifying a component of a cell or tissue sample comprising:

generating a single excitation wavelength or plurality of different excitation wavelengths of mid-ultraviolet light;

irradiating the sample with said light and measuring the intensity of the stimulated emission of the sample at three or more different wavelengths of lower energy than the excitation light;

generating at least one multivariate quantification model, said model quantifying different components of the sample as a function of the intensity characteristics at the measured wavelengths in relation to a reference quantitation result;

calculating the quantity of the component from the measured intensities by using multivariate quantitation of the intensities at the at least three different wavelengths based on the quantitation model; and

quantifying the component from the measured intensities by using said multivariate quantification model.

18. The method of

claim 17

wherein quantifying the component of said sample is done in vitro.

19. The method of

claim 17

wherein quantifying the component of said sample is done in vivo.

20. The method of

claim 17

wherein the component is glucose.

21. The method of

claim 17

wherein the step of quantifying the component of the sample includes at least one spectral data pre-processing step.

22. The method of

claim 21

wherein the pre-processing step includes at least one of the steps of selecting wavelengths, correcting for a linear baseline, and normalizing a spectral region surrounding the different wavelengths, used for classification of one spectral band in the spectral region.

23. The method of

claim 21

wherein the pre-processing step includes at least one of the steps of normalizing for total area of the spectrum, filtering or smoothing the data, or pre-sorting the data by analyte.

24. The method of

claim 17

wherein the multivariate quantification is done by a partial least squares technique.

25. The method of

claim 17

wherein the multivariate quantification is done by a principal component regression technique.

26. The method of

claim 17

wherein the multivariate classification is done by one of multiple linear regression, stepwise linear regression, or ridge regression.

27. The method of

claim 17

wherein the step of quantifying the component of the sample is performed by a multivariate algorithm using the measured intensity information and at least one multivariate quantification model which is a function of determined cell or tissue component quantities from a set of reference samples and a set of spectral intensities as a function of wavelength obtained from irradiating the set of reference samples with green to ultraviolet light and monitoring the stimulated emission.

28. A system for quantifying a component of a cell or tissue sample comprising:

means for generating a single excitation wavelength or a plurality of different excitation wavelengths of green to ultraviolet light;

means for directing at least a portion of the green to ultraviolet light into the sample;

means for collecting at least a portion of the stimulated emission light after the excitation light has interacted with the sample;

means for measuring an intensity of the collected stimulated emission light at least three different wavelengths;

means, coupled to the measuring means, for storing the measured intensities as a function of the wavelength;

means for storing at least one multivariate quantification model which contains data indicative of a correct quantification of components of known cell or tissue samples; and

processor means coupled to the means for storing the measured intensities and the means for storing the model, the processor means serving as means for calculating the quantity of the component of the cell or tissue sample by use of the multivariate quantification model and the measured intensities.

29. The system of

claim 28

wherein the means to direct the light and the means to collect the light comprise an endoscope.

30. The system of

claim 28

wherein the means to direct the light and the means to collect the light comprises a fiber optic bundle.

31. The system of

claim 28

further comprising means to determine outliers.

32. A method of classifying a cell or tissue sample comprising:

generating a single excitation wavelength or plurality of different excitation wavelengths of green to ultraviolet light;

irradiating the sample with said light and measuring the intensity of the stimulated emission of the sample at three or more different wavelengths of lower energy than the excitation light; and

classifying the sample as one of two or more cell or tissue types from the measured intensities by using a multivariate classification model.

33. The method of

claim 32

wherein the green to ultraviolet light is in the green to violet range of wavelengths.

34. The method of

claim 32

wherein the green to ultraviolet light is in the violet to near-ultraviolet range of wavelengths.

35. The method of

claim 32

wherein the sample is classified as normal or abnormal.

36. The method of

claim 32

wherein the irradiating of the sample is done in vitro.

37. The method of

claim 32

wherein the irradiating of the sample is done in vivo.

38. The method of

claim 32

wherein the step of classifying of the samples includes at least one spectral data pre-processing step.

39. The method of

claim 38

wherein the pre-processing step includes at least one of the steps of selecting wavelengths, correcting for a linear baseline, and normalizing a spectral region surrounding the different wavelengths, used for classification of one spectral band in the spectral region.

40. The method of

claim 38

wherein the pre-processing step includes at least one of the steps of normalizing for total area of the spectrum, filtering or smoothing the data, or pre-sorting the data by analyte.

41. The method of

claim 32

wherein the multivariate classification is done by a linear discriminant analysis technique.

42. The method of

claim 41

wherein the linear discriminant analysis is preceded by a principal component analyzing step limiting the number of discriminant variables.

43. The method of

claim 32

wherein the step of classifying the sample is performed by a multivariate algorithm using the measured intensity information and at least one multivariate classification model which is a function of determined cell or tissue sample classes from a set of reference samples and a set of spectral intensities as a function of wavelength obtained from irradiating the set of reference samples with green to ultraviolet light and monitoring the stimulated emission.

44. A method of classifying a cell or tissue sample comprising:

generating a single excitation wavelength or plurality of different excitation wavelengths of mid-ultraviolet light;

irradiating the sample with said light and measuring the intensity of the stimulated emission of the sample at three or more different wavelengths of lower energy than the excitation light;

generating at least one multivariate classification model, said model classifying the sample as a function of the intensity characteristics at the measured wavelengths in relation to a reference classification;

calculating the classification of the sample from the measured intensities by using multivariate classification of the intensities at the at least three different wavelengths based on the classification model; and

classifying the sample as one of two or more cell or tissue types from the measured intensities by using said multivariate classification model.

45. The method of

claim 44

wherein classifying said sample is done in vitro.

46. The method of

claim 44

wherein classifying said sample is done in vivo.

47. The method of

claim 44

wherein the sample is classified as normal or abnormal.

48. The method of

claim 44

wherein the step of classifying of the samples includes at least one spectral data pre-processing step.

49. The method of

claim 48

wherein the pre-processing step includes at least one of the steps of selecting wavelengths, correcting for a linear baseline, and normalizing a spectral region surrounding the different wavelengths, used for classification of one spectral band in the spectral region.

50. The method of

claim 48

wherein the pre-processing step includes at least one of the steps of normalizing for total area of the spectrum, filtering or smoothing the data, or pre-sorting the data by analyte.

51. The method of

claim 44

wherein the multivariate classification is done by a linear discriminant analysis technique.

52. The method of

claim 51

wherein the linear discriminant analysis is preceded by a principal component analyzing step limiting the number of discriminant variables.

53. The method of

claim 44

wherein the step of classifying the sample is performed by a multivariate algorithm using the measured intensity information and at least one multivariate classification model which is a function of determined cell or tissue sample classes from a set of reference samples and a set of spectral intensities as a function of wavelength obtained from irradiating the set of reference samples with green to ultraviolet light and monitoring the stimulated emission.

54. A system for classifying a cell or tissue sample comprising:

means for generating a single excitation wavelength or a plurality of different excitation wavelengths of green to ultraviolet light;

means for directing at least a portion of the green to ultraviolet light into the sample;

means for collecting at least a portion of the stimulated emission light after the excitation light has interacted with the sample;

means for measuring an intensity of the collected stimulated emission light at least three different wavelengths;

means, coupled to the measuring means, for storing the measured intensities as a function of the wavelength;

means for storing at least one multivariate classification model which contains data indicative of a correct classification of known cell or tissue samples; and

processor means coupled to the means for storing the measured intensities and the means for storing the model, the processor means serving as means for calculating the classification of the cell or tissue sample as one of two or more cells or tissues by use of the multivariate classification model and the measured intensities.

55. The system of

claim 54

wherein the means to direct the light and the means to collect the light comprise an endoscope.

56. The system of

claim 54

wherein the means to direct the light and the means to collect the light comprises a fiber optic bundle.

57. The system of

claim 54

further comprising means to determine outliers.