Multi-tier method of developing localized calibration models for non-invasive blood analyte prediction
First Claim
1. A method of developing a multi-tiered calibration model for estimating concentration of a target blood analyte from measured tissue spectra, comprising the steps of:
- providing a calibration set, wherein said calibration set comprises a data set of exemplar spectral measurements from a representative sampling of a subject population;
initially, classifying said spectral measurements into previously defined classes based on a priori information pertaining to said subject;
further classifying said measured spectrum into previously defined classes based on at least one instrumental measurement at a tissue measurement site;
extracting at least one feature from said measured spectrum for still further classification;
wherein a decision rule makes class assignments.
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Abstract
A method of multi-tier classification and calibration in noninvasive blood analyte prediction minimizes prediction error by limiting co-varying spectral interferents. Tissue samples are categorized based on subject demographic and instrumental skin measurements, including in vivo near-IR spectral measurements. A multi-tier intelligent pattern classification sequence organizes spectral data into clusters having a high degree of internal consistency in tissue properties. In each tier, categories are successively refined using subject demographics, spectral measurement information and other device measurements suitable for developing tissue classifications.
The multi-tier classification approach to calibration utilizes multivariate statistical arguments and multi-tiered classification using spectral features. Variables used in the multi-tiered classification can be skin surface hydration, skin surface temperature, tissue volume hydration, and an assessment of relative optical thickness of the dermis by the near-IR fat band. All tissue parameters are evaluated using the NIR spectrum signal along key wavelength segments.
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Citations
17 Claims
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1. A method of developing a multi-tiered calibration model for estimating concentration of a target blood analyte from measured tissue spectra, comprising the steps of:
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providing a calibration set, wherein said calibration set comprises a data set of exemplar spectral measurements from a representative sampling of a subject population;
initially, classifying said spectral measurements into previously defined classes based on a priori information pertaining to said subject;
further classifying said measured spectrum into previously defined classes based on at least one instrumental measurement at a tissue measurement site;
extracting at least one feature from said measured spectrum for still further classification;
wherein a decision rule makes class assignments. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17)
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Specification