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    • 1. 发明授权
    • Methods and apparatus for tailoring spectroscopic calibration models
    • 用于定制光谱校准模型的方法和装置
    • US06528809B1
    • 2003-03-04
    • US09672326
    • 2000-09-28
    • Edward V. ThomasRobert K. Rowe
    • Edward V. ThomasRobert K. Rowe
    • G01N1506
    • A61B5/1495A61B5/14532A61B5/1455G01J3/28G01N21/359G06F19/00
    • 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.
    • 用于非侵入性地测量生物属性的方法和装置,例如分析物的浓度,特别是诸如葡萄糖的组织中的血液分析物。 该方法利用光谱技术结合改进的主题定制校准模型。 在校准阶段,校准模型数据被修改以减少或消除受试者特异性属性,导致校准数据集建模在主体生理变化,样本位置,插入变化和仪器变化之内。 在预测阶段,使用来自每个受试者的最小数量的光谱测量,分别针对每个目标受试者量身定做预测过程。
    • 6. 发明授权
    • Systematic wavelength selection for improved multivariate spectral
analysis
    • 用于改进多变量光谱分析的系统波长选择
    • US5857462A
    • 1999-01-12
    • US505829
    • 1995-07-24
    • Edward V. ThomasMark R. RobinsonDavid M. Haaland
    • Edward V. ThomasMark R. RobinsonDavid M. Haaland
    • A61B5/00G01N21/31
    • A61B5/1464A61B5/14532A61B5/14546A61B5/1455A61B5/14552A61B5/6826A61B5/6838A61B5/6843G01N21/31
    • Methods and apparatus for determining in a biological material one or more unknown values of at least one known characteristic (e.g. the concentration of an analyte such as glucose in blood or the concentration of one or more blood gas parameters) with a model based on a set of samples with known values of the known characteristics and a multivariate algorithm using several wavelength subsets. The method includes selecting multiple wavelength subsets, from the electromagnetic spectral region appropriate for determining the known characteristic, for use by an algorithm wherein the selection of wavelength subsets improves the model's fitness of the determination for the unknown values of the known characteristic. The selection process utilizes multivariate search methods that select both predictive and synergistic wavelengths within the range of wavelengths utilized. The fitness of the wavelength subsets is determined by the fitness function F=f (cost, performance). The method includes the steps of: (1) using one or more applications of a genetic algorithm to produce one or more count spectra, with multiple count spectra then combined to produce a combined count spectrum; (2) smoothing the count spectrum; (3) selecting a threshold count from a count spectrum to select these wavelength subsets which optimize the fitness function; and (4) eliminating a portion of the selected wavelength subsets. The determination of the unknown values can be made: (1) noninvasively and in vivo; (2) invasively and in vivo; or (3) in vitro.
    • 用于在生物材料中确定至少一种已知特征的一个或多个未知值(例如血液中的分析物的浓度或血液中的葡萄糖浓度或一种或多种血液气体参数的浓度)的方法和装置, 具有已知特征的已知值的样本和使用几个波长子集的多变量算法。 该方法包括从适合于确定已知特征的电磁光谱区域中选择多个波长子集,以便由算法使用,其中波长子集的选择提高了模型对已知特征的未知值的确定的适应度。 选择过程使用多变量搜索方法,其在所使用的波长范围内选择预测和协同波长。 波长子集的适应度由适应度函数F = f(成本,性能)决定。 该方法包括以下步骤:(1)使用遗传算法的一个或多个应用来产生一个或多个计数光谱,然后将多个计数光谱组合以产生组合计数光谱; (2)平滑计数谱; (3)从计数光谱中选择阈值计数,以选择优化适应度函数的这些波长子集; 和(4)消除所选波长子集的一部分。 可以确定未知值:(1)非侵入性和体内; (2)侵入和体内; 或(3)体外。
    • 7. 发明授权
    • Reliable noninvasive measurement of blood gases
    • 可靠的非侵入性测量血气
    • US5355880A
    • 1994-10-18
    • US910004
    • 1992-07-06
    • Edward V. ThomasMark R. RobinsonDavid M. HaalandMary K. Alam
    • Edward V. ThomasMark R. RobinsonDavid M. HaalandMary K. Alam
    • G01N21/27A61B5/00A61B5/0456A61B5/145A61B5/1455G01N21/35
    • A61B5/1491A61B5/02007A61B5/0456A61B5/14539A61B5/14546A61B5/14551A61B5/416A61B5/6826A61B5/6838A61B2503/40A61B5/7264Y10S128/925
    • Methods and apparatus for, preferably, determining noninvasively and in vivo at least two of the five blood gas parameters (i.e., pH, PCO.sub.2, [HCO.sub.3.sup.- ], PO.sub.2, and O.sub.2 sat.) in a human. The non-invasive method includes the steps of: generating light at three or more different wavelengths in the range of 500 nm to 2500 nm; irradiating blood containing tissue; measuring the intensities of the wavelengths emerging from the blood containing tissue to obtain a set of at least three spectral intensities v. wavelengths; and determining the unknown values of at least two of pH, [HCO.sub.3.sup.- ], PCO.sub.2 and a measure of oxygen concentration. The determined values are within the physiological ranges observed in blood containing tissue. The method also includes the steps of providing calibration samples, determining if the spectral intensities v. wavelengths from the tissue represents an outlier, and determining if any of the calibration samples represents an outlier. The determination of the unknown values is performed by at least one multivariate algorithm using two or more variables and at least one calibration model. Preferably, there is a separate calibration for each blood gas parameter being determined. The method can be utilized in a pulse mode and can also be used invasively. The apparatus includes a tissue positioning device, a source, at least one detector, electronics, a microprocessor, memory, and apparatus for indicating the determined values.
    • 优选地,在人体内非侵入性和体内测定五种血气参数(即,pH,PCO2,[HCO3-],PO2和O2中的至少两种)的方法和装置。 非侵入性方法包括以下步骤:在500nm至2500nm的范围内产生三种或更多种不同波长的光; 照射含​​血液的组织; 测量从含血液组织出射的波长的强度,以获得至少三个光谱强度v。波长的集合; 并测定pH值,[HCO3-],PCO2和氧浓度测量中至少两个的未知值。 确定的值在含血液组织中观察到的生理范围内。 该方法还包括以下步骤:提供校准样本,确定来自组织的光谱强度v。波长是否代表异常值,以及确定校准样本中是否存在任何异常值。 通过使用两个或多个变量和至少一个校准模型的至少一个多变量算法来执行未知值的确定。 优选地,针对正在确定的每个血液气体参数进行单独的校准。 该方法可以以脉冲模式使用并且也可以被侵入地使用。 该装置包括组织定位装置,源,至少一个检测器,电子装置,微处理器,存储器和用于指示所确定的值的装置。
    • 8. 发明授权
    • Systematic wavelength selection for improved multivariate spectral
analysis
    • 用于改进多变量光谱分析的系统波长选择
    • US5435309A
    • 1995-07-25
    • US104857
    • 1993-08-10
    • Edward V. ThomasMark R. RobinsonDavid M. Haaland
    • Edward V. ThomasMark R. RobinsonDavid M. Haaland
    • A61B5/00G01N21/31
    • A61B5/1464A61B5/14532A61B5/14546A61B5/1455A61B5/14552A61B5/6826A61B5/6838A61B5/6843G01N21/31
    • Methods and apparatus for determining in a biological material one or more unknown values of at least one known characteristic (e.g. the concentration of an analyte such as glucose in blood or the concentration of one or more blood gas parameters) with a model based on a set of samples with known values of the known characteristics and a multivariate algorithm using several wavelength subsets. The method includes selecting multiple wavelength subsets, from the electromagnetic spectral region appropriate for determining the known characteristic, for use by an algorithm wherein the selection of wavelength subsets improves the model's fitness of the determination for the unknown values of the known characteristic. The selection process utilizes multivariate search methods that select both predictive and synergistic wavelengths within the range of wavelengths utilized. The fitness of the wavelength subsets is determined by the fitness function F=.function.(cost, performance). The method includes the steps of: (1) using one or more applications of a genetic algorithm to produce one or more count spectra, with multiple count spectra then combined to produce a combined count spectrum; (2) smoothing the count spectrum; (3) selecting a threshold count from a count spectrum to select these wavelength subsets which optimize the fitness function; and (4) eliminating a portion of the selected wavelength subsets. The determination of the unknown values can be made: (1) noninvasively and in vivo; (2) invasively and in vivo; or (3) in vitro.
    • 用于在生物材料中确定至少一种已知特征的一个或多个未知值(例如血液中的分析物的浓度或血液中的葡萄糖浓度或一种或多种血液气体参数的浓度)的方法和装置, 具有已知特征的已知值的样本和使用几个波长子集的多变量算法。 该方法包括从适合于确定已知特征的电磁光谱区域中选择多个波长子集,以便由算法使用,其中波长子集的选择提高了模型对已知特征的未知值的确定的适应度。 选择过程使用多变量搜索方法,其在所使用的波长范围内选择预测和协同波长。 波长子集的适应度由适应度函数F = f(成本,性能)决定。 该方法包括以下步骤:(1)使用遗传算法的一个或多个应用来产生一个或多个计数光谱,然后将多个计数光谱组合以产生组合计数光谱; (2)平滑计数谱; (3)从计数光谱中选择阈值计数,以选择优化适应度函数的这些波长子集; 和(4)消除所选波长子集的一部分。 可以确定未知值:(1)非侵入性和体内; (2)侵入和体内; 或(3)体外。