USRE41333E1 - Multi-tier method of developing localized calibration models for non-invasive blood analyte prediction - Google Patents
Multi-tier method of developing localized calibration models for non-invasive blood analyte prediction Download PDFInfo
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- A61B5/14532—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue for measuring glucose, e.g. by tissue impedance measurement
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- A61B5/1455—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue using optical sensors, e.g. spectral photometrical oximeters
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Abstract
Description
yGLS=(KT — −1K)−1KT — −1(x−k0) (8)
where _ is defined as the covariance matrix of the interfering substances or spectral effects, Û is defined as the measurement noise, x is the spectral measurement, and k0 is the instrument baseline component present in the spectral measurement.
-
- 1. Co-variation of spectrally interfering species. The near infrared spectral absorption profiles of blood analytes tend to overlap and vary simultaneously over brief time periods. This overlap leads to spectral interference and necessitates the measurement of absorbance at more independently varying wavelengths than the number of interfering species.
- 2. Sample heterogeneity. The tissue measurement site has multiple layers and compartments of varied composition and scattering. The spectral absorbance versus wavelength measurement is related to a complex combination of the optical properties and composition of these tissue components. Therefore, the spectral response with changing blood analyte concentration is likely to deviate from a simple linear model.
- 3. State Variations. Variations in the subject's physiological state effect the optical properties of tissue layers and compartments over a relatively short period of time. Such variations, for example, may be related to hydration levels, changes in the volume fraction of blood in the tissue, hormonal stimulation, skin temperature fluctuations and blood hemoglobin levels. Subtle variations may even be expected in response to contact with an optical probe.
- 4. Structural Variations. The tissue characteristics of individuals differ as a result of factors that include hereditary, environmental influences, the aging process, sex and body composition. These differences are largely anatomical and can be described as slowly varying structural properties producing diverse tissue geometry. Consequently, the tissue of a given subject may have distinct systematic spectral absorbance features or patterns that can be related directly to specific characteristics such as dermal thickness, protein levels and percent body fat. While the absorbance features may be repeatable within a patient, the structural variations in a population of patients may not be amenable to the use of a single mathematical calibration model. Therefore, differences between patients are a significant obstacle to the noninvasive measurement of blood analytes through NIR spectral absorbance.
z=f(λ,x) (1)
where f: N→RM is a mapping from the measurement space to the feature space. Decomposing f(•) will yield specific transformations, fi(•): N→ M i for determining a specific feature. The dimension, Mi, indicates whether the ith feature is a scalar or a vector and the aggregation of all features is the vector z. When a feature is represented as a vector or a pattern, it exhibits a certain structure indicative of an underlying physical phenomenon.
-
- 1. abstract and
- 2. simple.
-
- 1. Thickness of adipose tissue. See J. Conway, K. Norris, C. Bodwell, A new approach for the estimation of body composition: infrared interactance, The American Journal of Clinical Nutrition, vol. 40, pp. 1123-1140 (December 1984) and S. Homma, T. Fukunaga, A. Kagaya, Influence of adipose tissue thickness in near infrared spectroscopic signals in the measurement of human muscle, Journal of Biomedical Optics, vol.1(4), pp. 418-424 (October 1996).
- 2. Tissue hydration. See K. Martin, Direct measurement of moisture in skin by NIR spectroscopy, J. Soc. Cosmet. Chem., vol. 44, pp. 249-261 (September/October 1993).
- 3. Magnitude of protein absorbance. See J. Conway, et al., supra.
- 4. Scattering properties of the tissue. See A. Profio, Light transport in tissue, Applied Optics, vol. 28(12), pp. 2216-2222 (June 1989) and W. Cheong, S. Prahl, A. Welch, A review of the optical properties of biological tissues, IEEE Journal of Quantum Electronics, vol. 26(12), pp. 2166-2185 (December 1990); and R. Anderson, J. Parrish. The optics of human skin, Journal of Investigative Dermatology, vol. 77(1), pp. 13-19 (1981).
- 5. Skin thickness. See Anderson, et al., supra; and Van Gemmert, et al., supra.
- 6. Temperature related effects. See Funkunga, supra.
- 7. Age related effects. See W. Andrew, R. Behnke, T. Sato, Changes with advancing age in the cell population of human dermis, Gerontologia, vol. 10, pp. 1-19 (1964/65); and W. Montagna, K. Carlisle, Structural changes in aging human skin, The Journal of Investigative Dermatology, vol. 73, pp. 47-53 (1979; and 19 J. Brocklehurst, Textbook of Geriatric Medicine and Gerontology, pp.593-623, Churchill Livingstone, Edinburgh and London (1973).
- 8. Spectral characteristics relates to sex. See T. Ruchti, Internal Reports and Presentations, Instrumentation Metrics, Inc.
- 9. Pathlength estimates. See R. Anderson, et al., supra and S. Matcher, M. Cope, D. Delpy, Use of water absorption spectrum to quantify tissue chromophore concentration changes in near-infrared spectroscopy, Phys.
- Med. Biol., vol. 38, pp. 177-196 (1993).
- 10. Volume fraction of blood in tissue. See Wilson, et al., supra.
- 11. Spectral characteristics related to environmental influences.
-
- 1. a mapping step in which a
classification model 53 measures the similarity of the extracted features to predefined classes; and - 2. an assignment step in which a
decision engine 54 assigns class membership. Within this framework, two general methods of classification are proposed. The first uses mutually exclusive classes and therefore assigns each measurement to one class. The second scheme utilizes a fuzzy classification system that allows class membership in more than one class simultaneously. Both methods rely on previously defined classes, as described below.
Class Definition
- 1. a mapping step in which a
c=f(z) (2 1)
where c is an integer on the interval [1,P] and P is the number of classes. The class is used to select or adapt the calibration model as discussed in the Calibration Section.
Fuzzy Classification
ck=fk(z) (2)
where k=1,2, . . . P, fk(•) is the membership function of the kth class, ckε[0,1] for all k and the vector cε P is the set of class memberships. The membership vector provides the degree of membership in each of the predefined classes and is passed to the calibration algorithm.
y=Σw
w=(XTX)−1XTyW. (5)
X=B0+YKT+E (6)
X=B0+YKT+TPT+E (7)
yGLS=(KTΣ−1K)−1KTΣ−1(x−k0); (8)
where Σ is defined as the covariance matrix of the interfering substances or spectral effects, ó is defined as the measurement noise, x is the spectral measurement, and k0 is the instrument baseline component present in the spectral measurement.
Σ=PT(ttT)−1P+diag(ó2) (9)
MSE(yGLS)=trace(KTΣ−1K)−1 (10)
ŷ=g(c,x) (11)
where g(•) is a nonlinear calibration model which maps x and c to an estimate of the blood analyte concentration, ŷ.
ŷ=gk(x), (12)
where gk(•) is the calibration model associated with the kth class.
ŷ=g(c,x) (13)
where g(•) is a nonlinear mapping determined through nonlinear regression, nonlinear partial least squares or artificial neural networks. The mapping is developed from the calibration set described previously and is generally complex.
F=XkM, (14)
where M is the matrix of the first n eigenvectors of P. The weighted covariance matrix P is determined through
P=XkVXk T, (15)
where V is a square matrix with the elements of Ck on the diagonal. The regression matrix, B, is determined through
B=(FTVF)−1FTVY. (16)
ŷ=d(c,[y1y2y3 . . . yp]), (17)
where d(•) is the defuzzification function, c is the class membership vector and yk is the blood analyte prediction of the kth calibration model. Existing methods of defuzzification, such as the centroid or weighted average, are applied for small calibration sets. However, if the number of samples is sufficient, d(•) is generated through a constrained nonlinear model.
INSTRUMENT DESCRIPTION
Claims (68)
z=f(λ,x)
W=(XTX)−1XTy,
z=f(λ,x)
W=(X T X)−1 X T Y,
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US11/065,223 US20060167350A1 (en) | 2005-01-27 | 2005-02-23 | Multi-tier method of developing localized calibration models for non-invasive blood analyte prediction |
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US09/359,191 US6280381B1 (en) | 1999-07-22 | 1999-07-22 | Intelligent system for noninvasive blood analyte prediction |
US09/825,687 US6512937B2 (en) | 1999-07-22 | 2001-04-03 | Multi-tier method of developing localized calibration models for non-invasive blood analyte prediction |
US11/046,673 USRE41333E1 (en) | 1999-07-22 | 2005-01-27 | Multi-tier method of developing localized calibration models for non-invasive blood analyte prediction |
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