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 exemplar measurements into previously defined classes based on a priori a priori information pertaining to a corresponding subject;
further classifying said exemplar measurements into previously defined classes based on at least one instrumental measurement at a tissue measurement site;
extracting at least one feature from said exemplar measurements for still further classification, wherein a decision rule makes class assignments; and
calculating at least one localized calibration model based on said classified measurements and an associated set of reference values.
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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
68 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 exemplar measurements into previously defined classes based on a priori a priori information pertaining to a corresponding subject;
further classifying said exemplar measurements into previously defined classes based on at least one instrumental measurement at a tissue measurement site;
extracting at least one feature from said exemplar measurements for still further classification, wherein a decision rule makes class assignments; and
calculating at least one localized calibration model based on said classified measurements and an associated set of reference values. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17)
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18. 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;
in at least one tier, classifying said exemplar measurements into previously defined classes; and
extracting at least one feature from said exemplar measurements for still further classification; and
calculating at least one localized calibration model based on said classified exemplar measurements and a set of associated reference values. - View Dependent Claims (19, 20, 21, 22, 23)
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24. A method for developing a calibration model for estimating a target analyte property from measured tissue spectra, comprising the steps of:
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providing a data set of exemplar spectral measurements from a sampling of a subject population;
classifying a majority of said exemplar measurements into classes using at least one feature of said exemplar measurements;
wherein said feature comprises a spectral feature, wherein said classes comprise groups of measurements wherein similarity between measurements within a group is greater than similarity between groups, and calculating at least one localized calibration model using said classified measurements and an associated set of reference values. - View Dependent Claims (25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48)
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49. A method for developing a calibration model for estimating a target analyte property from measured tissue spectra, comprising the steps of:
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providing a data set of exemplar spectral measurements from a sampling of a subject population;
classifying a majority of said exemplar measurements into classes using at least one feature of said exemplar measurements; and
calculating at least one localized calibration model using said classified measurements and an associated set of reference values, wherein the step of classifying comprises classifying through at least two tiers.
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50. A method for developing a calibration model for estimating a target blood analyte property 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;
extracting at least one feature from at least one of said exemplar measurements;
classifying at least a portion of said exemplar measurements into classes using said feature; and
calculating at least one localized calibration model for at least one of said classes based on said classified measurements and an associated set of reference values, wherein said step of extracting at least one feature comprises;
representing structural properties and physiological state of a tissue measurement site through application of at least one mathematical transformation that enhances a quality or aspect of sample measurement for interpretation, wherein a resulting set of features is used to classify a subject and determine a calibration model. - View Dependent Claims (51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61)
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62. A method for developing a calibration algorithm for calculating concentration of a target blood analyte from measured tissue spectra, comprising the steps of:
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providing a data set of exemplar spectral measurements from a representative sampling of a subject population;
classifying at least one of said exemplar measurements into previously defined classes; and
calculating at least one localized calibration model using said classified measurements and an associated set of reference values, wherein said classes comprise groups of measurements, wherein similarity between measurements within a group is greater than similarity between groups. - View Dependent Claims (63, 64, 65, 66, 67)
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68. A method for developing a multi-tier calibration model for determining 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;
through at least two tiers, classifying said exemplar measurements into classes; and
calculating at least one localized calibration model using said classified measurements and an associated set of reference values.
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Specification