Methods and apparatus for tailoring spectroscopic calibration models

• US 6,528,809 B1
• Filed: 09/28/2000
• Issued: 03/04/2003
• Est. Priority Date: 10/13/1998
• Status: Expired due to Fees

• Current Assignee: Sandia Corporation (Martin Marietta Corporation), Rio Grande Medical Technologies Incorporated
• Original Assignee: Sandia Corporation (Martin Marietta Corporation), Rio Grande Medical Technologies Incorporated
• Inventors: Thomas, Edward V., Rowe, Robert K.
• Primary Examiner(s): LE, QUE TAN
• Application Number:
  US09/672,326
• Time in Patent Office: 887 Days
• Field of Search: 250/573, 250/559.1, 250/339.09, 250/339.08, 250/339.07, 250/339.12, 356/346, 356/303, 356/436, 356/432, 364/498, 364/572, 364/413-1-4, 128/633-665
• 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 21/359 using near infrared light

Abstract

A method and apparatus for non-invasively 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 subject-tailored calibration model. In a calibration phase, calibration model data is modified to reduce or eliminate subject-specific attributes, resulting in a calibration data set modeling within- subject physiological variation, sample location, insertion variations, and instrument variation. In a prediction phase, the prediction process is tailored for each target subject separately using a minimal number of spectral measurements from each subject.

Claims

1. A method for generating a prediction result for use on a specific subject to predict a biological attribute of that subject 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 data set that includes spectroscopic variation from multiple subjects;

(b) generating a model by applying multivariate analysis to said calibration data set; and

(c) using a prediction process to predict an unknown amount of said biological attribute in a target spectroscopic measurement that utilizes said model in conjunction with at least one matched measurement, wherein said matched measurement is obtained by using a spectral library and corresponding values of said biological attributes.

2. The method of claim 1, wherein said spectral library is generated from spectral measurements of multiple persons.

3. The method of claim 1, wherein the at least one matched measurement is obtained by selecting the spectra from said spectral library which best corresponds to said target spectroscopic measurement.

4. The method of claim 3, wherein the best corresponding measurement is selected as being those spectra from the spectral library that appear similar when processed by the calibration model.

5. The method of claim 4, wherein the determination of which spectra appear similar when processed by the calibration model is determined through the calculation of Mahalanobis distance and/or spectral residual metrics.

6. The method of claim 1, wherein said prediction process comprises:

(a) making an indirect measurement, Y_new, of the specific subject;

(b) using said model with Y_new to obtain a raw prediction, P_new, of the biological attribute.

7. The method of claim 6, further comprising:

using said model to predict the biological attribute G_new for the subject as a function of P_new, the prediction of said matched measurements P₀^SIM, and the reference value of said biological attribute associated with said matched measurements G_ref^SIM.

8. The method of claim 1, further comprising creating said matched spectrum by combining spectra from the spectral library.

9. The method of claim 8, further comprising:

creating said matched spectrum through a combination of said spectra, wherein various coefficients applied to the spectra are adjusted such that a better match is obtained.

10. A method for generating a prediction result for use on a specific subject to predict a biological attribute of that subject 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 that has spectroscopic variation from multiple subjects;

(b) generating a calibration model through application of a multivariate algorithm that uses a composite calibration data set that is formed by combining the modified calibration data set with one or more matched measurements, wherein said matched measurements are obtained by using a spectral library and corresponding values of said biological attributes; and

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

11. The method of claim 10, further comprising:

selecting as said matched measurements all spectra of said spectral library which meet a predetermined level of matching to the said target spectroscopic measurement.

12. The method of claim 10, further comprising:

selecting a fixed number of spectra from said spectral library which best match the target spectroscopic measurement.

13. The method of claim 10, wherein said spectral library is generated from spectral measurements of multiple persons.

14. The method of claim 10, further comprising creating said matched measurements by combining spectra from said spectral library.

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

(a) providing an apparatus for measuring infrared absorption, said apparatus including an energy source emitting infrared energy at multiple wavelengths, an input element, an output element and 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 absorption 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 infrared energy; and

(e) predicting the biological attribute of said specific subject utilizing a model, wherein said model uses spectroscopic variation from multiple subjects and one or more reference measurements from said specific subject, each of said reference measurements including spectroscopic and corresponding direct measurement of said biological attribute.