Method and apparatus for enhanced estimation of an analyte property through multiple region transformation

• US 7,620,674 B2
• Filed: 10/29/2004
• Issued: 11/17/2009
• Est. Priority Date: 03/07/2003
• Status: Active Grant

• 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): Mai; Tan V
• Application Number:
  US10/976,530
• Publication Number:
  US 20050149300A1
• Time in Patent Office: 1,845 Days
• Field of Search: 708/400
• US Class Current: 708/400
• CPC Class Codes: A61B 2560/0223 of calibration, e.g. protocols for calibrating sensors
• 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: G01N 21/359 using near infrared light

Abstract

The invention comprises transformation of a section of a data block independently of the transformation of separate or overlapping data blocks to determine a property related to the original matrix, where each of the separate or overlapping data blocks are derived from an original data matrix. The transformation enhances parameters of a first data block over a given region of an axis of the data matrix, such as signal-to-noise, without affecting analysis of a second data block derived from the data matrix. This allows for enhancement of analysis of an analyte property, such as concentration, represented within the original data matrix. A separate decomposition and factor selection for each selected data matrix is performed with subsequent score matrix concatenization. The combined score matrix is used to generate a model that is subsequently used to estimate a property, such as concentration represented in the original data matrix.

Claims

1. A method for developing a calibration score matrix, comprising the steps of:

providing a matrix of calibration spectra;

generating N spectral matrices corresponding with N spectral regions of said calibration spectra, wherein N is an integer greater than or equal to two and wherein at least two of said N spectral regions are non-identical spectral regions;

independently decomposing each of said N spectral matrices to yield N score matrices;

independently selecting a plurality of factors for each of said N score matrices;

concatenating said selected number of factors for each of said N score matrices, to generate a calibration score matrix, and

estimating, with a spectrometer, a target property from a prediction matrix using said calibration score matrix, wherein said spectrometer comprises a base module, a communication bundle, and a sample module, wherein said communication bundle integrates said sampling module and said base module.

2. The method of claim 1, wherein N consists of any of the integers 2, 3, 4, and 5.

3. The method of claim 1, wherein said step of independently decomposing employs multivariate techniques.

4. The method of claim 3, wherein said multivariate techniques comprise any of:

principal component analysis;

partial least squares;

wavelet regression;

Fourier series;

Taylor series; and

factor analysis.

5. The method of claim 1, wherein at least two of said N spectral regions overlap.

6. The method of claim 1, wherein at least two of said N spectral regions are discontinuous regions.

7. The method of claim 1, wherein at least two of said N spectral regions abut.

8. The method of claim 1, further comprising the step of developing a calibration with said calibration score matrix.

9. The method of claim 8, further comprising the step of estimating said target property from an estimation spectrum.

10. The method of claim 1, further comprising the step of independently preprocessing each of said generated N spectral matrices prior to said step of decomposing.

11. The method of claim 10, further comprising the step of developing a calibration with said calibration score matrix.

12. The method of claim 11, wherein said calibration spectra comprise noninvasively obtained near-infrared spectra.

13. The method of claim 12, wherein at least two of said N spectral regions comprise regions within the first and second overtone spectral region.

14. The method of claim 13, further comprising the step of estimating a glucose concentration from a noninvasive spectrum.

18. The method of claim 1, wherein said target property comprises at least one of:

an analyte concentration;

an analyte composition; and

an analyte constituent identification.

15. A method for estimating a property from a noninvasive spectrum, comprising the steps of:

providing a matrix of calibration spectra;

generating N spectral matrices corresponding to N spectral regions of said calibration spectra, wherein N is an integer greater than or equal to two and wherein at least two of said N spectral regions are non-identical spectral regions;

independently decomposing each of said N spectral matrices yielding N score matrices;

independently selecting a plurality of factors to represent each of said N score matrices;

concatenating said selected number of factors for each of said N score matrices to generate a calibration score matrix;

generating a calibration model using said calibration score matrix; and

estimating, with a spectrometer, a target property represented by said noninvasive spectrum, wherein said spectrometer comprises a base module, a communication bundle, and a sample module, wherein said communication bundle integrates said sampling module and said base module.

16. A method for developing a calibration for estimating a target property, comprising the steps of:

providing a first calibration data block;

generating N data blocks from said calibration data block, wherein N is an integer greater than or equal to two;

transforming each of said N selected data blocks;

combining said N transformed data blocks to form a second calibration matrix; and

developing a calibration with said second calibration matrix; and

estimating, with a spectrometer, a target property using said calibration, wherein said spectrometer comprises a base module, a communication bundle, and a sample module, wherein said communication bundle integrates said sampling module and said base module.

17. The method of claim 16, wherein said step of transforming comprises at least one of:

independently preprocessing each of said N data blocks; and

independently decomposing and selecting scores for each of said N data blocks.