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    • 2. 发明授权
    • Method of preparing calibration curve for quantitative analysis of in-vivo component, and quantitative analyzer using the calibration curve
    • 制备体内成分定量分析校准曲线的方法,使用校准曲线进行定量分析
    • US07809416B2
    • 2010-10-05
    • US11210855
    • 2005-08-25
    • Tomohiro OtaKatsuhiro HirataKatsuhiko Maruo
    • Tomohiro OtaKatsuhiro HirataKatsuhiko Maruo
    • A61B5/1455
    • A61B5/1495A61B5/14532A61B2560/0223
    • A method of non-invasively determining a concentration of an in-vivo component such as blood sugar level (glucose) of a subject is provided. An absorption spectrum of the subject is measured by use of near-infrared light. The concentration of the in-vivo component is determined by use of the absorption spectrum of the subject and a calibration curve. The calibration curve is prepared by determining a plurality of difference absorption spectra that are differences between a plurality of near-infrared absorption spectra of a living body and a reference absorption spectrum selected from the near-infrared absorption spectra, determining a plurality of synthetic absorption spectra, which are obtained by synthesizing each of the difference absorption spectra with a previously measured reference absorption spectrum of the subject, and performing a multivariate analysis with use of the obtained synthetic absorption spectra.
    • 提供了非侵入性地确定受试者的体内成分(例如血糖水平(葡萄糖))的浓度的方法。 通过使用近红外光来测量受试者的吸收光谱。 通过使用受试者的吸收光谱和校正曲线来确定体内成分的浓度。 通过确定作为活体的多个近红外吸收光谱与从近红外吸收光谱中选择的参考吸收光谱之间的差异的多个差吸收光谱来确定校准曲线,确定多个合成吸收光谱 ,其通过将每个差分吸收光谱与预先测量的受试者的参考吸收光谱合成并使用所获得的合成吸收光谱进行多变量分析而获得。
    • 3. 发明授权
    • Method and device for calculating a biological component density of a subject
    • 用于计算受试者的生物成分密度的方法和装置
    • US07333841B2
    • 2008-02-19
    • US10478276
    • 2002-11-14
    • Katsuhiko MaruoMitsuhiro Tsurugi
    • Katsuhiko MaruoMitsuhiro Tsurugi
    • A61B5/00
    • A61B5/1495A61B5/1075A61B5/14532A61B5/1455A61B5/7264A61B2562/0242
    • A method for calculating a biological component density of this invention comprises steps of irradiating a light of NIR spectrum to a skin of a subject, receiving the light of NIR reflected from the skin to obtain NIR spectrum data thereof, and substituting the NIR spectrum data into a predetermined calibrating equation to obtain a biological component density of the subject such as glucose density. This invention is characterized by preparing a plurality of the calibrating equations which are different from each other and are specific to each of plural groups which are classified in terms of a skin thickness parameter indicative of a skin thickness with respect to individuals of a species to which the subject belongs, determining the skin thickness parameter of the subject with a non-invasive technique and identifying the group of the subject in accordance with the determined skin thickness parameter, and deriving one of the calibrating equations in match with the identified group in order to calculate the biological component density of the subject.
    • 计算本发明的生物成分密度的方法包括以下步骤:向受试者的皮肤照射NIR光谱,接收从皮肤反射的NIR光,以获得其NIR光谱数据,并将NIR光谱数据代入 一个预定的校准方程,以获得受试者的生物组分密度,例如葡萄糖密度。 本发明的特征在于制备多个彼此不同的校准方程,并且对于多个组中的每一组具有特异性,根据皮肤厚度参数分类,所述皮肤厚度参数指示相对于其中的个体的皮肤厚度 所述受试者属于,使用非侵入性技术确定受试者的皮肤厚度参数,并根据确定的皮肤厚度参数识别受试者的组,并且导出与所识别的组匹配的校准方程之一,以便 计算受试者的生物成分密度。
    • 4. 发明申请
    • BLOOD SUGAR VALUE ESTIMATION APPARATUS
    • 血糖值估算装置
    • US20120166092A1
    • 2012-06-28
    • US13378448
    • 2010-07-28
    • Katsuhiko Maruo
    • Katsuhiko Maruo
    • G06F19/00
    • A61B5/14532A61B5/1455A61B2560/0233G01N21/274G01N21/276G01N21/359
    • The blood sugar value estimation apparatus is configured to non-invasively calculate the blood sugar value with time on the basis of the optical spectrum of the living body measured with time and the calibration model. The apparatus comprises a calibration model creating means configured to create a calibration model from the calibration models or a calibration model from a plurality of the datasets for creating the calibration model. The apparatus is configured to measure a bio-spectrum of a person being tested to set the reference spectrum, and to measure a difference spectrum of a difference between the reference spectrum and a measurement spectrum measured at a time other than a time when the reference spectrum is measured, and to change the calibration model for calculating according to the variation of the difference spectrum. Consequently, the blood sugar value is estimated, especially monitored with time, with high-accuracy.
    • 血糖值估计装置被配置为基于随时间测量的生物体的光谱和校准模型随时间非侵入式地计算血糖值。 该装置包括校准模型创建装置,其被配置为从校准模型创建校准模型或者从多个数据集中创建校准模型以创建校准模型。 该装置被配置为测量被测试者的生物谱以设置参考光谱,并且测量参考光谱与在除参考光谱之外的时间测量的测量光谱之间的差异的差分光谱 并根据差分谱的变化改变校准模型进行计算。 因此,以高精度估计血糖值,特别是随时间监测。
    • 5. 发明授权
    • Device for non-invasive determination of a glucose concentration in the
blood of a subject
    • 非侵入性测定受试者血液中葡萄糖浓度的装置
    • US6016435A
    • 2000-01-18
    • US978266
    • 1997-11-25
    • Katsuhiko MaruoKeisuke ShimizuMasami Oka
    • Katsuhiko MaruoKeisuke ShimizuMasami Oka
    • A61B5/00A61B5/145A61B5/1455G01N21/35G01N21/3577G01N21/359
    • A61B5/1455A61B5/14532A61B5/6843G01N21/359A61B2562/0242
    • A device for non-invasive determination of a glucose concentration in the blood of a subject which includes a light source for producing near-infrared radiation having successive wavelengths within a range of 1300 nm to 2500 nm, a light projecting unit for projecting the near-infrared radiation on a skin of the subject, a light receiving unit for receiving a resulting radiation emitted from the inside of the skin, and a spectrum analyzing unit for making a spectrum analysis of the resulting radiation, and determining the glucose concentration according to the spectrum analysis. The light receiving unit is separated from the light projecting unit by a distance defined within a range of 0.1 mm to 2 mm to selectively sense the resulting radiation emitted from a dermis layer positioned under an epidermis layer of the skin. The glucose concentration in the blood is determined at the spectrum analyzing unit by using the spectrum analysis and a statistically-obtained correlation between glucose concentration in dermis region and glucose concentration in blood of test subjects.
    • 一种用于非侵入式确定受试者血液中葡萄糖浓度的装置,其包括用于产生具有在1300nm至2500nm范围内的连续波长的近红外辐射的光源;投光单元, 在受试者的皮肤上的红外辐射,用于接收从皮肤内部发射的所得辐射的光接收单元和用于对所得到的辐射进行光谱分析的光谱分析单元,以及根据光谱确定葡萄糖浓度 分析。 光接收单元从光投射单元分离出在0.1mm至2mm的范围内的距离,以选择性地感测从位于皮肤表皮层下面的真皮层发射的所得到的辐射。 通过使用频谱分析和统计学上获得的测试对象血液中真皮层葡萄糖浓度与葡萄糖浓度之间的相关性,在频谱分析单元处确定血液中的葡萄糖浓度。
    • 6. 发明授权
    • Method of determining a glucose concentration in a target by using
near-infrared spectroscopy
    • 通过使用近红外光谱测定靶中的葡萄糖浓度的方法
    • US5957841A
    • 1999-09-28
    • US46580
    • 1998-03-24
    • Katsuhiko MaruoMasami Oka
    • Katsuhiko MaruoMasami Oka
    • G01N21/35A61B5/00G01N21/3577G01N21/359
    • A61B5/1495A61B5/14532A61B5/1455G01N21/359
    • A glucose concentration in a living tissue as a target is determined by the following method. Near-infrared radiation is projected on the living tissue, and a resulting radiation emitted from the living tissue is received. A spectrum analysis of the resulting radiation is performed to detect a first absorption signal from a wavelength region, e.g., 1550 nm to 1650 nm, having an absorption peak of OH group derived from glucose molecule, a second absorption signal from a wavelength region, e.g., 1480 nm to 1550 nm, having an absorption peak of NH group in the living tissue, and a third absorption signal from a wavelength region, e.g., 1650 nm to 1880 nm, having an absorption peak of CH group in the living tissue. The glucose concentration is determined by a multivariate analysis of results of the spectrum analysis, in which the first, second and third absorption signals are used as explanatory variables, and the glucose concentration is a criterion variable. This method can predict the glucose concentration of the subject with an improved accuracy.
    • 通过以下方法确定作为目标的生物体组织中的葡萄糖浓度。 近红外辐射被投射在生物体组织上,从生物体组织发射的辐射被接收。 进行所得到的辐射的光谱分析,以检测来自葡萄糖分子的OH基的吸收峰,来自波长区域的第二吸收信号的波长区域例如1550nm至1650nm的第一吸收信号,例如 在生物体组织中具有NH基的吸收峰,1480nm〜1550nm的第三吸收信号,以及来自活体组织中具有CH基吸收峰的波长区域例如1650nm〜1880nm的第三吸收信号。 通过对第一,第二和第三吸收信号用作解释变量的频谱分析结果的多变量分析来确定葡萄糖浓度,葡萄糖浓度是标准变量。 该方法可以精确地预测受试者的葡萄糖浓度。
    • 7. 发明授权
    • Method of determining an optimum workload corresponding to user's target
heart rate and exercise device therefor
    • 确定与用户目标心率对应的最佳工作量的方法及其锻炼装置
    • US5853351A
    • 1998-12-29
    • US151879
    • 1993-11-15
    • Katsuhiko MaruoAkiko OnoMototaka NagaiSatsuki Saeki
    • Katsuhiko MaruoAkiko OnoMototaka NagaiSatsuki Saeki
    • A63B24/00A63B69/00
    • G06N3/02A63B24/00A63B2230/06A63B2230/062
    • An optimum workload corresponding to a target heart rate of an user is determined by the following method, which is preferably utilized for providing a safe training or an accurate examination of physical strength to the user. After the target heart rate is set, a first steady heart rate of the user is measured during an initial exercise cycle in which a first workload is applied to the user. The first workload is derived in accordance with the target heart rate and a statistically obtained workload versus heart rate correlation corresponding to at least one factor selected from the group consisting of the user's age, gender, body weight, and body height, etc. In addition, a second steady heart rate of the user is measured during at least one subsequent exercise cycle in which a second workload is applied to the user. The second workload is derived by entering as input parameters the applied workload and the measured heart rate at the immediately previous exercise cycle into a multiple variate model equation. Consequently, the optimum workload is determined by entering the applied workload and the measure heart rate at the last exercise cycle into the multiple variate model equation. The model equation is prepared by utilizing a neural network analysis or a multiple variate analysis.
    • 通过以下方法确定与用户的目标心率相对应的最佳工作量,其优选地用于提供对用户的身体强度的安全训练或准确检查。 在设定了目标心率之后,在初始运动周期中测量用户的第一稳定心率,其中向用户施加第一工作负荷。 第一工作负载根据目标心率和统计学上得到的工作负荷与对应于从由用户的年龄,性别,体重和体高等组成的组中选择的至少一个因素相对的心率相关性而得出。另外 在至少一个随后的运动周期中测量用户的第二稳定心率,其中向用户施加第二工作负荷。 第二个工作负载是通过将应用的工作量作为输入参数输入,并将紧接在前的锻炼周期的测量心率输入到多变量模型方程式中。 因此,通过将应用的工作量和最后一个运动周期的测量心率输入到多变量模型方程中来确定最佳工作负荷。 通过利用神经网络分析或多变量分析来准备模型方程。