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1. 发明授权

US5596992A Multivariate classification of infrared spectra of cell and tissue samples 失效
标题翻译：细胞和组织样品的红外光谱的多变量分类
公开(公告)号：US5596992A
公开(公告)日：1997-01-28
申请号：US85709
申请日：1993-06-30
申请人： David M. Haaland , Howland D. T. Jones , Edward V. Thomas
发明人： David M. Haaland , Howland D. T. Jones , Edward V. Thomas
IPC分类号： A61B5/00 , G01N21/35 , A61B6/00
CPC分类号： A61B5/0086 , A61B5/7264 , A61B5/0075 , A61B5/0088 , A61B5/415 , A61B5/418 , A61B5/4331 , A61B5/4375 , A61B5/441 , G01N21/359 , Y10S977/949
摘要： Multivariate classification techniques are applied to spectra from cell and tissue samples irradiated with infrared radiation to determine if the samples are normal or abnormal (cancerous). Mid and near infrared radiation can be used for in vivo and in vitro classifications using at least different wavelengths.
摘要翻译：将多变量分类技术应用于用红外辐射照射的细胞和组织样品的光谱，以确定样品是正常还是异常（癌变）。中和近红外辐射可以用于使用至少不同波长的体内和体外分类。

2. 发明授权

US5435309A Systematic wavelength selection for improved multivariate spectral analysis 失效
标题翻译：用于改进多变量光谱分析的系统波长选择
公开(公告)号：US5435309A
公开(公告)日：1995-07-25
申请号：US104857
申请日：1993-08-10
申请人： Edward V. Thomas , Mark R. Robinson , David M. Haaland
发明人： Edward V. Thomas , Mark R. Robinson , David M. Haaland
IPC分类号： A61B5/00 , G01N21/31
CPC分类号： A61B5/1464 , A61B5/14532 , A61B5/14546 , A61B5/1455 , A61B5/14552 , A61B5/6826 , A61B5/6838 , A61B5/6843 , G01N21/31
摘要： 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.
摘要翻译：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。

3. 发明授权

US5857462A Systematic wavelength selection for improved multivariate spectral analysis 失效
标题翻译：用于改进多变量光谱分析的系统波长选择
公开(公告)号：US5857462A
公开(公告)日：1999-01-12
申请号：US505829
申请日：1995-07-24
申请人： Edward V. Thomas , Mark R. Robinson , David M. Haaland
发明人： Edward V. Thomas , Mark R. Robinson , David M. Haaland
IPC分类号： A61B5/00 , G01N21/31
CPC分类号： A61B5/1464 , A61B5/14532 , A61B5/14546 , A61B5/1455 , A61B5/14552 , A61B5/6826 , A61B5/6838 , A61B5/6843 , G01N21/31
摘要： 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.
摘要翻译：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。

4. 发明授权

US5355880A Reliable noninvasive measurement of blood gases 失效
标题翻译：可靠的非侵入性测量血气
公开(公告)号：US5355880A
公开(公告)日：1994-10-18
申请号：US910004
申请日：1992-07-06
申请人： Edward V. Thomas , Mark R. Robinson , David M. Haaland , Mary K. Alam
发明人： Edward V. Thomas , Mark R. Robinson , David M. Haaland , Mary K. Alam
IPC分类号： G01N21/27 , A61B5/00 , A61B5/0456 , A61B5/145 , A61B5/1455 , G01N21/35
CPC分类号： A61B5/1491 , A61B5/02007 , A61B5/0456 , A61B5/14539 , A61B5/14546 , A61B5/14551 , A61B5/416 , A61B5/6826 , A61B5/6838 , A61B2503/40 , A61B5/7264 , Y10S128/925
摘要： 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.
摘要翻译：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。

5. 发明授权

US5630413A Reliable noninvasive measurement of blood gases 失效
标题翻译：可靠的非侵入性测量血气
公开(公告)号：US5630413A
公开(公告)日：1997-05-20
申请号：US257875
申请日：1994-08-12
申请人： Edward V. Thomas , Mark R. Robinson , David M. Haaland , Mary K. Alam
发明人： Edward V. Thomas , Mark R. Robinson , David M. Haaland , Mary K. Alam
IPC分类号： G01N21/27 , A61B5/00 , A61B5/0456 , A61B5/145 , A61B5/1455 , G01N21/35
CPC分类号： A61B5/1491 , A61B5/02007 , A61B5/0456 , A61B5/14539 , A61B5/14546 , A61B5/14551 , A61B5/416 , A61B5/6826 , A61B5/6838 , A61B2503/40 , A61B5/7264 , Y10S128/925
摘要： This invention relates to methods and apparatus for, preferably, determining non-invasively and in vivo at least two of the five blood gas parameters (i.e., pH, [HCO.sub.3.sup.- ], PCO.sub.2, PO.sub.2, and O.sub.2 sat.) in a human.
摘要翻译：本发明涉及用于优选地确定人体内五种血液气参数（即pH，[HCO 3 - ]，PCO 2，PO 2和O 2 S）中的至少两种非侵入性和体内的方法和装置。

6. 发明授权

US06341257B1 Hybrid least squares multivariate spectral analysis methods 有权
标题翻译：混合最小二乘法多变量光谱分析方法
公开(公告)号：US06341257B1
公开(公告)日：2002-01-22
申请号：US09518773
申请日：2000-03-03
申请人： David M. Haaland
发明人： David M. Haaland
IPC分类号： G06F1900
CPC分类号： G01J3/28 , G01J2003/2866
摘要： A set of hybrid least squares multivariate spectral analysis methods in which spectral shapes of components or effects not present in the original calibration step are added in a following estimation or calibration step to improve the accuracy of the estimation of the amount of the original components in the sampled mixture. The “hybrid” method herein means a combination of an initial classical least squares analysis calibration step with subsequent analysis by an inverse multivariate analysis method. A “spectral shape” herein means normally the spectral shape of a non-calibrated chemical component in the sample mixture but can also mean the spectral shapes of other sources of spectral variation, including temperature drift, shifts between spectrometers, spectrometer drift, etc. The “shape” can be continuous, discontinuous, or even discrete points illustrative of the particular effect.
摘要翻译：一组混合最小二乘法多变量光谱分析方法，其中在随后的估计或校准步骤中添加不存在于原始校准步骤中的成分或效应的光谱形状，以提高估计的原始组分的量的准确度采样混合物。这里的“混合”方法是指初始经典最小二乘法分析校准步骤与随后的多元分析方法分析的组合。此处的“光谱形状”通常表示样品混合物中未校准的化学组分的光谱形状，但也可以表示其他光谱变化源的光谱形状，包括温度漂移，光谱仪之间的偏移，光谱仪漂移等。 “形状”可以是说明特定效果的连续，不连续或甚至离散的点。

7. 发明授权

US4975581A Method of and apparatus for determining the similarity of a biological analyte from a model constructed from known biological fluids 失效
标题翻译：用于从已知生物流体构建的模型中确定生物分析物的相似性的方法和装置
公开(公告)号：US4975581A
公开(公告)日：1990-12-04
申请号：US369217
申请日：1989-06-21
申请人： Mark R. Robinson , Kenneth J. Ward , Robert P. Eaton , David M. Haaland
发明人： Mark R. Robinson , Kenneth J. Ward , Robert P. Eaton , David M. Haaland
IPC分类号： G01N33/49 , A61B5/00 , A61B5/145 , A61B5/1455 , A61B5/1459 , G01N21/35 , G01N21/55 , G01N33/487
CPC分类号： G01N21/552 , A61B5/14532 , A61B5/1455 , A61B5/1459 , A61B5/6876 , A61B5/6884 , G01N21/359 , G01N2201/129
摘要： The characteristics of a biological fluid sample having an analyte are determined from a model constructed from plural known biological fluid samples. The model is a function of the concentration of materials in the known fluid samples as a function of absorption of wideband infrared energy. The wideband infrared energy is coupled to the analyte containing sample so there is differential absorption of the infrared energy as a function of the wavelength of the wideband infrared energy incident on the analyte containing sample. The differential absorption causes intensity variations of the infrared energy incident on the analyte containing sample as a function of sample wavelength of the energy, and concentration of the unknown analyte is determined from the thus-derived intensity variations of the infrared energy as a function of wavelength from the model absorption versus wavelength function.

8. 发明授权

US06687620B1 Augmented classical least squares multivariate spectral analysis 有权
标题翻译：增强经典最小二乘法多变量光谱分析
公开(公告)号：US06687620B1
公开(公告)日：2004-02-03
申请号：US10209841
申请日：2002-07-31
申请人： David M. Haaland , David K. Melgaard
发明人： David M. Haaland , David K. Melgaard
IPC分类号： G01N3348
CPC分类号： G01N21/359 , G01N21/274 , G01N21/64 , G01N21/65 , G01N2201/1293
摘要： A method of multivariate spectral analysis, termed augmented classical least squares (ACLS), provides an improved CLS calibration model when unmodeled sources of spectral variation are contained in a calibration sample set. The ACLS methods use information derived from component or spectral residuals during the CLS calibration to provide an improved calibration-augmented CLS model. The ACLS methods are based on CLS so that they retain the qualitative benefits of CLS, yet they have the flexibility of PLS and other hybrid techniques in that they can define a prediction model even with unmodeled sources of spectral variation that are not explicitly included in the calibration model. The unmodeled sources of spectral variation may be unknown constituents, constituents with unknown concentrations, nonlinear responses, non-uniform and correlated errors, or other sources of spectral variation that are present in the calibration sample spectra. Also, since the various ACLS methods are based on CLS, they can incorporate the new prediction-augmented CLS (PACLS) method of updating the prediction model for new sources of spectral variation contained in the prediction sample set without having to return to the calibration process. The ACLS methods can also be applied to alternating least squares models. The ACLS methods can be applied to all types of multivariate data.
摘要翻译：称为增强经典最小二乘法（ACLS）的多变量光谱分析方法在校准样本集中包含未建模的光谱变化源时提供了改进的CLS校准模型。在CLS校准期间，ACLS方法使用从组件或光谱残差得到的信息，以提供改进的校准增强型CLS模型。 ACLS方法基于CLS，因此它们保留了CLS的定性优势，但是它们具有PLS和其他混合技术的灵活性，因为即使没有明确地包括在光谱变化的未建模的光谱变化源中，它们也可以定义预测模型校准模型。未建模的光谱变化源可能是未知的成分，未知浓度的成分，非线性响应，非均匀和相关误差，或存在于校准样品光谱中的其他光谱变化源。此外，由于各种ACLS方法都是基于CLS的，所以它们可以包含用于更新包含在预测样本集中的新的光谱变化源的预测模型的新的预测增加CLS（PACLS）方法，而不必返回到校准过程。 ACLS方法也可以应用于交替的最小二乘模型。 ACLS方法可以应用于所有类型的多变量数据。

9. 发明授权

US06922645B2 Augmented classical least squares multivariate spectral analysis 有权
标题翻译：增强经典最小二乘法多变量光谱分析
公开(公告)号：US06922645B2
公开(公告)日：2005-07-26
申请号：US10963195
申请日：2004-10-12
申请人： David M. Haaland , David K. Melgaard
发明人： David M. Haaland , David K. Melgaard
IPC分类号： G01N21/27 , G01N21/35 , G01N21/64 , G01N21/65 , G01R23/16 , G01N23/48
CPC分类号： G01N21/359 , G01N21/274 , G01N21/64 , G01N21/65 , G01N2201/1293
摘要： A method of multivariate spectral analysis, termed augmented classical least squares (ACLS), provides an improved CLS calibration model when unmodeled sources of spectral variation are contained in a calibration sample set. The ACLS methods use information derived from component or spectral residuals during the CLS calibration to provide an improved calibration-augmented CLS model. The ACLS methods are based on CLS so that they retain the qualitative benefits of CLS, yet they have the flexibility of PLS and other hybrid techniques in that they can define a prediction model even with unmodeled sources of spectral variation that are not explicitly included in the calibration model. The unmodeled sources of spectral variation may be unknown constituents, constituents with unknown concentrations, nonlinear responses, non-uniform and correlated errors, or other sources of spectral variation that are present in the calibration sample spectra. Also, since the various ACLS methods are based on CLS, they can incorporate the new prediction-augmented CLS (PACLS) method of updating the prediction model for new sources of spectral variation contained in the prediction sample set without having to return to the calibration process. The ACLS methods can also be applied to alternating least squares models. The ACLS methods can be applied to all types of multivariate data.
摘要翻译：称为增强经典最小二乘法（ACLS）的多变量光谱分析方法在校准样本集中包含未建模的光谱变化源时提供了改进的CLS校准模型。在CLS校准期间，ACLS方法使用从组件或光谱残差得到的信息，以提供改进的校准增强型CLS模型。 ACLS方法基于CLS，因此它们保留了CLS的定性优势，但是它们具有PLS和其他混合技术的灵活性，因为即使没有明确地包括在光谱变化的未建模的光谱变化源中，它们也可以定义预测模型校准模型。未建模的光谱变化源可能是未知的成分，未知浓度的成分，非线性响应，非均匀和相关误差，或存在于校准样品光谱中的其他光谱变化源。此外，由于各种ACLS方法都是基于CLS的，所以它们可以包含用于更新包含在预测样本集中的新的光谱变化源的预测模型的新的预测增加CLS（PACLS）方法，而不必返回到校准过程。 ACLS方法也可以应用于交替的最小二乘模型。 ACLS方法可以应用于所有类型的多变量数据。

10. 发明授权

US06842702B2 Augmented classical least squares multivariate spectral analysis 有权
公开(公告)号：US06842702B2
公开(公告)日：2005-01-11
申请号：US10661968
申请日：2003-09-11
申请人： David M. Haaland , David K. Melgaard
发明人： David M. Haaland , David K. Melgaard
IPC分类号： G01N21/27 , G01N21/35 , G01N21/64 , G01N21/65 , G06F19/00 , G01N33/48
CPC分类号： G01N21/359 , G01N21/274 , G01N21/64 , G01N21/65 , G01N2201/1293
摘要： A method of multivariate spectral analysis, termed augmented classical least squares (ACLS), provides an improved CLS calibration model when unmodeled sources of spectral variation are contained in a calibration sample set. The ACLS methods use information derived from component or spectral residuals during the CLS calibration to provide an improved calibration-augmented CLS model. The ACLS methods are based on CLS so that they retain the qualitative benefits of CLS, yet they have the flexibility of PLS and other hybrid techniques in that they can define a prediction model even with unmodeled sources of spectral variation that are not explicitly included in the calibration model. The unmodeled sources of spectral variation may be unknown constituents, constituents with unknown concentrations, nonlinear responses, non-uniform and correlated errors, or other sources of spectral variation that are present in the calibration sample spectra. Also, since the various ACLS methods are based on CLS, they can incorporate the new prediction-augmented CLS (PACLS) method of updating the prediction model for new sources of spectral variation contained in the prediction sample set without having to return to the calibration process. The ACLS methods can also be applied to alternating least squares models. The ACLS methods can be applied to all types of multivariate data.

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