WO1997020495A1 - Glucose monitoring apparatus and method using laser-induced emission spectroscopy - Google Patents

Glucose monitoring apparatus and method using laser-induced emission spectroscopy Download PDF

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Publication number
WO1997020495A1
WO1997020495A1 PCT/US1996/018532 US9618532W WO9720495A1 WO 1997020495 A1 WO1997020495 A1 WO 1997020495A1 US 9618532 W US9618532 W US 9618532W WO 9720495 A1 WO9720495 A1 WO 9720495A1
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WIPO (PCT)
Prior art keywords
glucose
sample
light
concentration
wavelength band
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Application number
PCT/US1996/018532
Other languages
French (fr)
Inventor
Wendy J. Snyder
Warren S. Grundfest
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Cedars-Sinai Medical Center
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Filing date
Publication date
Application filed by Cedars-Sinai Medical Center filed Critical Cedars-Sinai Medical Center
Priority to CA002238518A priority Critical patent/CA2238518C/en
Priority to AU10552/97A priority patent/AU715281B2/en
Priority to JP9521294A priority patent/JP2000501830A/en
Priority to AT96941397T priority patent/ATE249166T1/en
Priority to DE69629937T priority patent/DE69629937T2/en
Priority to DK96941397T priority patent/DK0863718T3/en
Priority to EP96941397A priority patent/EP0863718B1/en
Publication of WO1997020495A1 publication Critical patent/WO1997020495A1/en

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    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/145Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
    • A61B5/1455Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue using optical sensors, e.g. spectral photometrical oximeters
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/145Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
    • A61B5/14532Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue for measuring glucose, e.g. by tissue impedance measurement

Definitions

  • This invention relates to glucose monitoring, and more particularly, to glucose level monitoring using laser-induced emission spectroscopy.
  • infrared absorption techniques are susceptible to accuracy problems, in part because glucose has more than 20 infrared absorption peaks, many of which overlap with the absorption peaks of other constituents in the body.
  • Fluorescence spectroscopy using ultraviolet (UV) excitation light has been introduced for monitoring glucose levels. This technique requires, among other things, the monitoring of a spectral peak within the induced fluorescence spectrum. Accurately locating the peak may be difficult for a low-level fluorescence signal in the presence of noise. Increasing the intensity of the excitation light may not be a desirable option because of concerns of UV exposure to the body. Also, known fluorescence spectroscopic techniques generally fail to take full advantage of information contained in the fluorescence spectrum at wavelengths other than the peak wavelength and fail to account for certain nonlinear relationships between the glucose level and the resulting emission spectra.
  • UV ultraviolet
  • the present invention is embodied in an apparatus, and related method, that determines the concentration of glucose in a sample that includes water, by directly monitoring induced glucose ultraviolet and visible (UV-visible) emission light from the sample.
  • the apparatus compensates for nonlinearities between these parameters and the glucose.
  • the apparatus includes a light source, a sensor, and a processor.
  • the light source emits ultraviolet excitation light of at least one predetermined energy level.
  • the excitation light is directed at a sample to produce return light from the sample.
  • the return light includes induced emissions of light produced as a result of interactions between the excitation light and any glucose with water present in the sample.
  • the sensor monitors the return light and generates at least three electrical signals indicative of the intensity of return light associated with glucose concentration distinguishing characteristics of the emission light.
  • the processor processes the electrical signals, using a predictive model, to determine the concentration of glucose in the sample.
  • the predictive model is defined using six latent variables. The latent variables are used to derive prediction coefficients that are associated with the glucose concentration distinguishing characteristics.
  • the intensity of the excitation light remains relatively constant while the sensor generates the electrical signals.
  • the at least three electrical signals indicate the intensity of return light within a respective number of predetermined wavelength bands within the wavelength band of the emission light.
  • the sensor may includes a first detector adapted to detect the return light within a first wavelength band and generate a first electrical signal, a second detector adapted to detect the return light within a second wavelength band and generate a second electrical signal, and a third detector adapted to detect the return light within a third wavelength band and generate a third electrical signal.
  • the senor monitors the intensity of return light within eight different wavelength bands and generates eight electrical signals, each indicative of the intensity of return light within a respective wavelength band. More particularly, using an excitation light having a wavelength of about 308 nanometers, the eight wavelength bands may be centered at about 342, 344, 347, 352, 360, 370, 385 and 400 nanometers, respectively. Alternatively, the sensor may generate a plurality of electrical signals that indicate the intensity of return light substantially continuously across an extended wavelength spectrum associated with the emission light.
  • the energy of the excitation light is varied over several predetermined energy levels
  • the sensor generates, at each intensity level, a first electrical signal based on the intensity of return light within a wavelength of the emission light associated with ra an scattering, and a second electrical signal based on the intensity of return light within a wavelength band of the emission light associated with a peak of a broad glucose emission band.
  • the apparatus may include one or more waveguides for transmitting the excitation light from the light source to the sample and for transmitting the return light from the sample to the sensor.
  • a regression model is provided that accounts for a nonlinear relationship between the concentration of glucose in a sample and an electrical signal based on certain glucose concentration distinguishing characteristics of a light emission spectrum that includes UV-visible emission light produced by glucose related interactions with the excitation light. Further, a sample is caused to produce a light emission spectrum that includes emission light produced by any glucose related interaction or direct fluorescence, and a plurality of electrical signals are generated that represent the glucose concentration distinguishing characteristics. Finally, the plurality of electrical signals are processed, using the regression model, to determine the glucose concentration and an electrical signal generated based on the glucose concentration determined using the regression model.
  • FIG. 1 is a block diagram of a glucose monitoring system embodying the invention.
  • FIG. 2 is a graph of the intensity of glucose emission versus wavelength for different concentrations of glucose in water illuminated with laser excitation light having a wavelength of 308 nanometers.
  • FIG. 3 is a graph of the intensity of glucose emission at two wavelengths verses glucose concentration in water, illuminated with laser excitation light having a wavelength of 308 nanometers and an excitation energy of 1 millijoule per pulse.
  • FIG. 4 is a graph of the regression coefficient verses the latent variable number, derived from a partial least square (PLS) analysis using the intensities at eight wavelength indicated in the graph of FIG.2.
  • PLS partial least square
  • FIG. 5 is a graph of the prediction residual sum of squares (PRESS) versus number of latent variables, using one spectra at a time to test the PLS model derived from intensities at the eight wavelengths indicated in the graph of FIG.2.
  • FIG. 6 is a graph of the PRESS versus number of latent variables using two spectra at a time to test the PLS model derived from intensities at the eight wavelengths indicated in the graph of FIG.2.
  • FIG. 7 is a graph of the predicted concentration verses the actual concentration of glucose for the PLS model using six latent variables and for a multiple linear regression (MLR) model derived from the graph of FIG. 2.
  • MLR multiple linear regression
  • FIG. 8 is a graph of the predicted concentration verses the actual concentration of glucose for the PLS model using seven latent variables and for a multiple linear regression (MLR) model derived from the graph of
  • FIG. 9 is a graph of the PRESS versus number of latent variables using one spectra at a time to test a PLS model derived from the whole spectra of the graph of FIG.2.
  • FIG. 10 is a graph of the actual concentration verses the predicted concentration for the PLS model using six latent variables derived from the whole spectrum of the graph of FIG. 2.
  • FIG. 11 is a graph of the intensity of glucose emission verses wavelength, at different excitation energy levels, for glucose in water at a concentration of 500 milligrams per deciliter.
  • FIG. 12 is a graph of emission intensity versus wavelength for distilled water excited at an excitation energy of 5 millijoules per pulse.
  • FIG. 13 is a graph of the emission intensity verses wavelength for ultra-anhydrous glucose excited at an excitation energy of 5 millijoules per pulse.
  • FIG. 14 is a graph of the emission intensity verses wavelength for anhydrous glucose excited at 5 millijoules per pulse.
  • FIG. 15 is a graph of the emission intensity versus wavelength for anhydrous glucose excited with excitation light having an energy at different levels between 0.25 and 10 millijoules per pulse.
  • FIG. 16 is a graph of the intensity of glucose emission verses wavelength for different concentrations of glucose in water, illuminated with laser excitation light having a wavelength of 308 nanometers and an excitation energy of 7 millijoules per pulse.
  • the present invention is embodied in a glucose monitoring system 10, and related method, for determining the concentration of glucose in a sample 12 by monitoring the glucose ultraviolet and visible (UV-visible) light emission spectra at several wavelengths or excitation intensities while compensating for the nonlinear relationship between the glucose concentration of these parameters.
  • the system provides good accuracy in spite of the nonlinearities or low signal-to-noise levels.
  • an excitation source 14 directs ultraviolet excitation light to the sample 12 through an optical fiber 16, to induce any glucose within the sample to absorb and reemit or to scatter the excitation light.
  • An optical fiber or fiber bundle 18 collects return light emitted by the sample. The return light includes any glucose emissions induced by the excitation light.
  • a sensor 20 monitors the return light within different wavelength bands of interest and generates a series of electrical signals based on the intensity of return light received in the wavelength band or bands of interest.
  • the sensor includes a spectrograph 22 which resolves the return light by separating the return light by wavelength.
  • An analyzer 24 or processor having a prediction model that associates the intensity of return light of interest with the concentration of glucose in the sample, processes the electrical signals generated by the sensor, compares the results with the model, and generates an electrical signal representing the concentration of glucose in the sample.
  • a useful excitation source 14 is an excimer laser producing light having a wavelength of about 308 nanometers, a full width half maximum (FWHM) pulse width of about 120 nanometers, and a repetition rate of about 5 hertz. It is believed that glucose more efficiently absorbs excitation light having a wavelength between 260 to 280 nanometers and it would be desirable to have an excitation wavelength in that range. However, as presently understood, excitation sources operating at these wavelengths generally are of limited availability.
  • the excitation light can be provided by any type of generally narrow-band ultraviolet light source and generally can have a wavelength from about 250 to 350 nanometers.
  • the excitation light is guided to the sample 12 through a fused silica fiber 16 having a 600 micron core diameter.
  • the excitation light's energy, emitted from the fiber is set to predetermine levels from about 0.5 to 10 millijoules per pulse (0.54 to 36 millijoules per square millimeter per pulse) .
  • the induced emissions from the sample, or return light preferably is collected using a bundle of six fused silica fibers 18, each fiber having a 300 micron core.
  • the collection fibers guide the return light to the sensor 20.
  • the sensor 20 may include individual light-sensitive diodes, with appropriate bandpass filters, or as discussed above, may include a spectrograph 22 that resolves a broad spectrum of the return light.
  • a spectrograph was used to collect the data discussed below.
  • a long pass filter 26 (Schott WG335) is placed within the spectrograph to filter from the return light, any excitation light that may have been collected by the collection fibers 18.
  • An ultraviolet enhanced grating 150 grooves per millimeter
  • a silicon diode array detector 28 having 1024 elements collects the dispersed return light and generates an electrical signal that serially represents the intensity of return light collected in each element.
  • An EG&G optical multichannel analyzer (OMA III) receiving the electrical signal can display a graph representing the intensity of return light within the desired wavelength band or bands of interest.
  • the spectrum shown in FIG. 2 is the emission spectra of different glucose concentrations after excitation with an ultraviolet excimer laser light having a wavelength of 308 nanometers. Each spectrum is shown to have a double peak shape. One spectral peak is associated with a narrow wavelength band centered at about 346 nanometers, apparently produced as a result of vibrational raman scattering, and a broad emission band 28 centered at approximately 440 nanometers, believed to be produced largely by direct glucose and water fluorescence.
  • the wavelength of the peak associated with the narrow raman scattering band is approximately 30 to 50 nanometers longer than the wavelength of the excitation light and shifts generally in proportion to shifts in the wavelength of the excitation light.
  • the shape and emission wavelengths of the broad glucose emission band generally is not a direct function of the excitation wavelength.
  • the emission intensity associated with eight representative wavelengths does not vary linearly with glucose concentration over the clinically relevant range of 80 to 300 milligrams per deciliter.
  • the eight representative wavelength are indicated by the vertical lines in the graph of FIG. 2.
  • the relationship between measured intensity and glucose concentration is highly nonlinear and presents a different profile at different wavelengths. More particularly, as the glucose concentration in water increases, the intensity at a wavelength of 370 nanometers generally increases as the glucose concentration increases until the concentration reaches about 500 milligrams per deciliter. At this point, the intensity then begins to taper off or decrease with increasing concentration. Similarly, the intensity at at a wavelength of 347 nanometers, generally the wavelength of the raman scattering peak generally increases and then decreases with increasing glucose concentration. Note however, that the rate of change for the intensity versus glucose concentration is different for each of the curves.
  • Polynomial curve fitting for providing a predictive model is achieved using statistical techniques based on a least squares regression method.
  • a common regression technique known as partial least squares (PLS) regression has been found to provide a robust model in that the model parameters do not change significantly when new samples are taken.
  • PLS partial least squares
  • the PLS regression technique begins by "autoscaling" each variable such that all the variables are equally influential in the prediction.
  • the PLS regression technique uses principle component analysis, also known as singular value decomposition or eigenvector analysis, to represent the dependent and independent matrices.
  • principle component analysis a NIPALS algorithm is used to define a data matrix of independent variables.
  • PLS regression techniques introduce a weighting factor into the regression model.
  • the PLS algorithm gives a sequence of models, the best model being the one that minimizes the cross-validation.
  • a data matrix of independent variables (the glucose concentration is the dependent variable) , consisting of the emission intensity at the different wavelengths, is provided to a data processing routine that performs the PLS regression.
  • the data processing routine is included in the "PLS_Toolbox Version 1.3" available from Barry M. Wise, 1415 Wright Avenue, Richland, WA 99352 (E-mail: bm_wise@pnl.gov) .
  • the routines in the "Toolbox” are presently intended for use with the MATLABTM software package available from The Mathworks, Inc., 24 Prime Park Way, Natick, MA 01760.
  • the matrix associated with the spectral intensities at each wavelength and the matrix associated with the concentration values have their means removed before processing.
  • the routine calculates a regression vector shown in FIG. 4 and in Table II below.
  • the scalar components of the regression vector are the prediction coefficients for each wavelength.
  • the intensity at each of the eight wavelengths is measured. These eight measured values are scaled and multiplied by the regression vector, i.e., the eight wavelength coefficients in Table II. The result is a scaled concentration prediction.
  • the scaled predicted concentration must be rescaled to provide a concentration value in the original units.
  • the cross- validation calculation is used to determine the optimum number of latent variables to use in the model.
  • the cross-validation is performed by using one spectra chosen at random to test the model.
  • the cross-validation is repeated ten times, randomly choosing a different spectra to test the model.
  • the results of the cross-validation are shown in the press plot of FIG. 5 as a plot of the prediction residual sum of squares (PRESS) versus the number of latent variables used in the model.
  • the PLS analysis yielded a model of six latent variables.
  • FIGS. 5 and 6 shows that the minimum PRESS exists between five to seven latent variables.
  • FIG. 7 shows the results of a prediction test using samples of known glucose concentration in the PLS prediction model using six latent variables, derived from Table I, to define the model.
  • the PLS model provides a fairly accurate prediction of the glucose concentration.
  • the test was repeated for a multiple linear regression (MLR) model based on the same input data.
  • the PLS model generally performs better than the MLR model at lower concentration levels while the MLR model performs better at at higher concentration levels.
  • FIG. 8 shows the results of another prediction test again using samples of known glucose concentration in testing PLS and MLR models.
  • the PLS model uses seven latent variables to define the model.
  • both models provide substantially the same results.
  • using additional latent variables in the model does not necessarily improve the model's prediction accuracy.
  • the predictive model can be improved by using a greater number of wavelengths for generating the model.
  • the emissions spectra from the 1,024 elements of the detector array provides a like number of intensity values. Approximately 200 of these points are in the wavelength range of glucose UV-visible emission light (approximately 335 to 450 nanometers) and the data is truncated to this range. To reduce the effects of noise, the spectra is measured three to five times for each glucose concentration. An average of each of these spectra is used to generate the model.
  • the data for the truncated smoothed spectra was converted into a smaller file by averaging three points at a time to arrive at one point. For example, 180 points become 60 points.
  • 60 wavelengths for each concentration level, preconditioned as discussed above, are analyzed in this example to arrive at a predictive model using the PLS regression technique, instead of the eight different wavelengths from Table 1 used in the previous example.
  • the PRESS plot for the model using the whole spectra indicates a minimum PRESS at six latent variables.
  • a test of the model using samples of known concentration is shown in FIG. 10.
  • the PLS predictive model, using the preconditioned spectra provides a very accurate prediction of the glucose concentration. Given the generally noisy nature of the spectral measurements, and the non-linear relationship between the glucose concentration and the emission intensity at any given wavelength of interest, the results indicated in FIG. 10 are indeed surprising.
  • FIG. 11 shows emission spectra, at a single glucose concentration, resulting from excitation light delivered at different intensity levels. As shown in Table IV below, the emission intensity at a wavelength associated with the raman peak, normalized with respect to the broader florescence peak, is nonlinear with respect to the excitation energy at given concentration level.
  • Excitat ion Energy (mj/pul, se)
  • Table IV can be used to provide a predictive model, using the PLS regression technique, as discussed above, with respect to Table I.
  • the glucose concentration of an unknown sample can be determined using a predictive model provided by PLS analysis.
  • FIG. 12 shows the emission spectrum of distilled water illuminated by excitation light having an energy of 5 millijoules per pulse (18 millijoules per millimeter per square millimeter) .
  • the graph shows that the florescence spectra for distilled water exhibits a broad florescence band with a peak at approximately 370 nanometers and a narrow raman scattering band at approximately 346 nanometers.
  • the raman scattering band results from scattered incident light having its wavelength shifted by the energy (rotational and translational) of the water molecules.
  • the emission spectrum of ultra anhydrous glucose is shown in FIG. 13.
  • the resulting spectrum has a single broad fluorescence band that peaks at approximately 450 nanometers.
  • the emission spectrum of anhydrous glucose which has absorbed a small but spectrally significant amounts of water, exhibits two narrow raman scattering bands that peak at 341 nanometers and 346 nanometers, respectively, and one broad emission band that peaks at about 420 nanometers.
  • the raman scattering peak at 346 nanometers corresponds to the raman peak of water shown in FIG. 12.
  • the raman scattering peak at 341 nanometers apparently results from interaction between the water and glucose molecules.
  • the spectrum of the anhydrous glucose is shifted to shorter wavelengths when compared with the spectrum of the ultra anhydrous glucose shown in FIG. 13.
  • the emissions spectra of anhydrous glucose, as the excitation energy is varied, are shown in FIG. 15.
  • the intensity of spectra generally increase as the excitation energy increases.
  • the intensity ratio between the peaks of the raman bands and the broad emission band does not remain constant as the excitation energy increases.
  • the ratio between the raman scattering band and the broad emission band similarly does not remain constant as the concentration increases. Accordingly, the interaction between the water and glucose molecules, and the energy density of the excitation light all appear to effect the resulting emission spectra. Accordingly, simple linear models are effective as an approximation only along very narrow, discrete segments of possible glucose concentrations of interest.
  • the glucose concentration can be accurately predicted in spite of signal noise and nonlinear relationships between the glucose concentration and certain spectroscopic parameters of interest.
  • the prediction is performed using a model developed from a PLS regression analysis.

Abstract

A glucose monitor, and related method, determines the concentration of glucose in a sample with water, using a predictive regression model. The glucose monitor illuminates the sample with ultraviolet excitation light that induces the water and any glucose present in the sample to emit return light that includes raman scattered light and glucose emission or fluorescence light. The return light is monitored and processed using a predictive regression model to determine the concentration of glucose in the sample. The predictive regression model accounts for nonlinearities between the glucose concentration and intensity of return light within different wavelength bands at a predetermined excitation light energgy or the intensity of return light within a predetermined wavelength band at different excitation energy levels. A fiber-optic waveguide is used to guide the excitation light from a laser excitation source to the sample and the return light from the sample to a sensor.

Description

GLUCOSE MONITORING APPARATUS AND METHOD USING LASER-INDUCED EMISSION SPECTROSCOPY
BACKGROUND OF THE INVENTION
This invention relates to glucose monitoring, and more particularly, to glucose level monitoring using laser-induced emission spectroscopy.
Millions of people, afflicted with diabetes, must periodically monitor their blood glucose level because their bodies are unable to maintain a constant blood glucose level without diet adjustments and periodic insulin injections. Most popular methods for monitoring blood glucose levels require a small blood sample that is periodically drawn from the body for analysis.
Recently, noninvasive optical techniques have been developed to monitor the blood's glucose level using infrared absorption through a portion of the body. However, infrared absorption techniques are susceptible to accuracy problems, in part because glucose has more than 20 infrared absorption peaks, many of which overlap with the absorption peaks of other constituents in the body.
Fluorescence spectroscopy using ultraviolet (UV) excitation light has been introduced for monitoring glucose levels. This technique requires, among other things, the monitoring of a spectral peak within the induced fluorescence spectrum. Accurately locating the peak may be difficult for a low-level fluorescence signal in the presence of noise. Increasing the intensity of the excitation light may not be a desirable option because of concerns of UV exposure to the body. Also, known fluorescence spectroscopic techniques generally fail to take full advantage of information contained in the fluorescence spectrum at wavelengths other than the peak wavelength and fail to account for certain nonlinear relationships between the glucose level and the resulting emission spectra.
From the discussion above, it should be apparent that there is a need for an apparatus, and related method, for monitoring glucose that is simple and rapid to use, and that provides good accuracy in spite of nonlinearities or low signal-to-noise levels. The present invention fulfills these needs.
SUMMARY OF THE INVENTION
The present invention is embodied in an apparatus, and related method, that determines the concentration of glucose in a sample that includes water, by directly monitoring induced glucose ultraviolet and visible (UV-visible) emission light from the sample. The apparatus compensates for nonlinearities between these parameters and the glucose.
The apparatus includes a light source, a sensor, and a processor. The light source emits ultraviolet excitation light of at least one predetermined energy level. The excitation light is directed at a sample to produce return light from the sample. The return light includes induced emissions of light produced as a result of interactions between the excitation light and any glucose with water present in the sample. The sensor monitors the return light and generates at least three electrical signals indicative of the intensity of return light associated with glucose concentration distinguishing characteristics of the emission light. The processor processes the electrical signals, using a predictive model, to determine the concentration of glucose in the sample. In one feature of the invention, the predictive model is defined using six latent variables. The latent variables are used to derive prediction coefficients that are associated with the glucose concentration distinguishing characteristics. In a more detailed feature of the invention, the intensity of the excitation light remains relatively constant while the sensor generates the electrical signals. Further, the at least three electrical signals indicate the intensity of return light within a respective number of predetermined wavelength bands within the wavelength band of the emission light. In another feature, the sensor may includes a first detector adapted to detect the return light within a first wavelength band and generate a first electrical signal, a second detector adapted to detect the return light within a second wavelength band and generate a second electrical signal, and a third detector adapted to detect the return light within a third wavelength band and generate a third electrical signal.
In yet another more detailed feature of the invention, the sensor monitors the intensity of return light within eight different wavelength bands and generates eight electrical signals, each indicative of the intensity of return light within a respective wavelength band. More particularly, using an excitation light having a wavelength of about 308 nanometers, the eight wavelength bands may be centered at about 342, 344, 347, 352, 360, 370, 385 and 400 nanometers, respectively. Alternatively, the sensor may generate a plurality of electrical signals that indicate the intensity of return light substantially continuously across an extended wavelength spectrum associated with the emission light.
In another more detailed feature of the invention, the energy of the excitation light is varied over several predetermined energy levels, and the sensor generates, at each intensity level, a first electrical signal based on the intensity of return light within a wavelength of the emission light associated with ra an scattering, and a second electrical signal based on the intensity of return light within a wavelength band of the emission light associated with a peak of a broad glucose emission band. Further, the apparatus may include one or more waveguides for transmitting the excitation light from the light source to the sample and for transmitting the return light from the sample to the sensor.
In a related method of the invention, a regression model is provided that accounts for a nonlinear relationship between the concentration of glucose in a sample and an electrical signal based on certain glucose concentration distinguishing characteristics of a light emission spectrum that includes UV-visible emission light produced by glucose related interactions with the excitation light. Further, a sample is caused to produce a light emission spectrum that includes emission light produced by any glucose related interaction or direct fluorescence, and a plurality of electrical signals are generated that represent the glucose concentration distinguishing characteristics. Finally, the plurality of electrical signals are processed, using the regression model, to determine the glucose concentration and an electrical signal generated based on the glucose concentration determined using the regression model.
Other features and advantages of the present invention should become apparent from the following description of the preferred embodiment, taken in conjunction with the accompanying drawings, which illustrate, by way of example, the principles of the invention.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 is a block diagram of a glucose monitoring system embodying the invention.
FIG. 2 is a graph of the intensity of glucose emission versus wavelength for different concentrations of glucose in water illuminated with laser excitation light having a wavelength of 308 nanometers. FIG. 3 is a graph of the intensity of glucose emission at two wavelengths verses glucose concentration in water, illuminated with laser excitation light having a wavelength of 308 nanometers and an excitation energy of 1 millijoule per pulse.
FIG. 4 is a graph of the regression coefficient verses the latent variable number, derived from a partial least square (PLS) analysis using the intensities at eight wavelength indicated in the graph of FIG.2.
FIG. 5 is a graph of the prediction residual sum of squares (PRESS) versus number of latent variables, using one spectra at a time to test the PLS model derived from intensities at the eight wavelengths indicated in the graph of FIG.2.
FIG. 6 is a graph of the PRESS versus number of latent variables using two spectra at a time to test the PLS model derived from intensities at the eight wavelengths indicated in the graph of FIG.2.
FIG. 7 is a graph of the predicted concentration verses the actual concentration of glucose for the PLS model using six latent variables and for a multiple linear regression (MLR) model derived from the graph of FIG. 2.
FIG. 8 is a graph of the predicted concentration verses the actual concentration of glucose for the PLS model using seven latent variables and for a multiple linear regression (MLR) model derived from the graph of
FIG. 2.
FIG. 9 is a graph of the PRESS versus number of latent variables using one spectra at a time to test a PLS model derived from the whole spectra of the graph of FIG.2. FIG. 10 is a graph of the actual concentration verses the predicted concentration for the PLS model using six latent variables derived from the whole spectrum of the graph of FIG. 2.
FIG. 11 is a graph of the intensity of glucose emission verses wavelength, at different excitation energy levels, for glucose in water at a concentration of 500 milligrams per deciliter.
FIG. 12 is a graph of emission intensity versus wavelength for distilled water excited at an excitation energy of 5 millijoules per pulse.
FIG. 13 is a graph of the emission intensity verses wavelength for ultra-anhydrous glucose excited at an excitation energy of 5 millijoules per pulse.
FIG. 14 is a graph of the emission intensity verses wavelength for anhydrous glucose excited at 5 millijoules per pulse.
FIG. 15 is a graph of the emission intensity versus wavelength for anhydrous glucose excited with excitation light having an energy at different levels between 0.25 and 10 millijoules per pulse.
FIG. 16 is a graph of the intensity of glucose emission verses wavelength for different concentrations of glucose in water, illuminated with laser excitation light having a wavelength of 308 nanometers and an excitation energy of 7 millijoules per pulse. DESCRIPTION OF THE PREFERRED EMBODIMENTS
As shown in the exemplary drawings, the present invention is embodied in a glucose monitoring system 10, and related method, for determining the concentration of glucose in a sample 12 by monitoring the glucose ultraviolet and visible (UV-visible) light emission spectra at several wavelengths or excitation intensities while compensating for the nonlinear relationship between the glucose concentration of these parameters. The system provides good accuracy in spite of the nonlinearities or low signal-to-noise levels.
In the glucose monitoring system 10 shown in FIG. 1, an excitation source 14 directs ultraviolet excitation light to the sample 12 through an optical fiber 16, to induce any glucose within the sample to absorb and reemit or to scatter the excitation light. An optical fiber or fiber bundle 18 collects return light emitted by the sample. The return light includes any glucose emissions induced by the excitation light. A sensor 20 monitors the return light within different wavelength bands of interest and generates a series of electrical signals based on the intensity of return light received in the wavelength band or bands of interest. In one embodiment, the sensor includes a spectrograph 22 which resolves the return light by separating the return light by wavelength. An analyzer 24 or processor, having a prediction model that associates the intensity of return light of interest with the concentration of glucose in the sample, processes the electrical signals generated by the sensor, compares the results with the model, and generates an electrical signal representing the concentration of glucose in the sample.
A useful excitation source 14 is an excimer laser producing light having a wavelength of about 308 nanometers, a full width half maximum (FWHM) pulse width of about 120 nanometers, and a repetition rate of about 5 hertz. It is believed that glucose more efficiently absorbs excitation light having a wavelength between 260 to 280 nanometers and it would be desirable to have an excitation wavelength in that range. However, as presently understood, excitation sources operating at these wavelengths generally are of limited availability. The excitation light can be provided by any type of generally narrow-band ultraviolet light source and generally can have a wavelength from about 250 to 350 nanometers.
The excitation light is guided to the sample 12 through a fused silica fiber 16 having a 600 micron core diameter. The excitation light's energy, emitted from the fiber, is set to predetermine levels from about 0.5 to 10 millijoules per pulse (0.54 to 36 millijoules per square millimeter per pulse) . The induced emissions from the sample, or return light, preferably is collected using a bundle of six fused silica fibers 18, each fiber having a 300 micron core. The collection fibers guide the return light to the sensor 20.
The sensor 20 may include individual light- sensitive diodes, with appropriate bandpass filters, or as discussed above, may include a spectrograph 22 that resolves a broad spectrum of the return light. A spectrograph was used to collect the data discussed below. A long pass filter 26 (Schott WG335) is placed within the spectrograph to filter from the return light, any excitation light that may have been collected by the collection fibers 18. An ultraviolet enhanced grating (150 grooves per millimeter), located after an entrance slit of the spectrograph disperses the return light into its constituent wavelengths. A silicon diode array detector 28 having 1024 elements collects the dispersed return light and generates an electrical signal that serially represents the intensity of return light collected in each element. An EG&G optical multichannel analyzer (OMA III) receiving the electrical signal can display a graph representing the intensity of return light within the desired wavelength band or bands of interest.
Before the concentration of glucose can be determined in a sample having an unknown glucose concentration, a model must be prepared that accounts for certain nonlinearities between the glucose concentration and certain measured parameters. The process of deriving the model is better understood with reference to FIG. 2. The spectrum shown in FIG. 2 is the emission spectra of different glucose concentrations after excitation with an ultraviolet excimer laser light having a wavelength of 308 nanometers. Each spectrum is shown to have a double peak shape. One spectral peak is associated with a narrow wavelength band centered at about 346 nanometers, apparently produced as a result of vibrational raman scattering, and a broad emission band 28 centered at approximately 440 nanometers, believed to be produced largely by direct glucose and water fluorescence.
The wavelength of the peak associated with the narrow raman scattering band is approximately 30 to 50 nanometers longer than the wavelength of the excitation light and shifts generally in proportion to shifts in the wavelength of the excitation light. The shape and emission wavelengths of the broad glucose emission band generally is not a direct function of the excitation wavelength.
As shown in Table I below, the emission intensity associated with eight representative wavelengths does not vary linearly with glucose concentration over the clinically relevant range of 80 to 300 milligrams per deciliter. The eight representative wavelength are indicated by the vertical lines in the graph of FIG. 2. Table I
Wavelength (nanometers)
Concent 342 344 347 352 360 370 385 400 r(mg/dl) 80 56.3 116 87.4 86.9 95.4 106 80.8 54.6
100 72.5 145 105 103 120 123 98.9 60.3
120 67.8 126 91.9 78.2 92.9 103 74.6 45.9
140 62.1 121 93.9 80.0 95.8 102 76.2 47.6
160 57.9 120 81.4 73.4 87.8 104 75.3 46 200 51.1 102 77.3 80.1 88.3 101 71.3 46.3
220 48.6 104 74.4 74.2 83.8 96.6 71.1 42.4
240 58.6 102 84.6 78.5 84.5 95.9 73.4 46.6
300 55.4 107 71.9 67.9 77.9 86.9 65.1 4.19
Instead, as shown in FIG. 3,the relationship between measured intensity and glucose concentration is highly nonlinear and presents a different profile at different wavelengths. More particularly, as the glucose concentration in water increases, the intensity at a wavelength of 370 nanometers generally increases as the glucose concentration increases until the concentration reaches about 500 milligrams per deciliter. At this point, the intensity then begins to taper off or decrease with increasing concentration. Similarly, the intensity at at a wavelength of 347 nanometers, generally the wavelength of the raman scattering peak generally increases and then decreases with increasing glucose concentration. Note however, that the rate of change for the intensity versus glucose concentration is different for each of the curves.
In designing a model to predict the glucose concentration, several approaches are available to account for the nonlinear effects discussed above. One method is to restrict the calibration curve to small segments which are approximated by a simple linear model. Another method is to perform a transformation on the nonlinear variable. Finally, the calibration curve can be modeled using a polynomial fit.
Polynomial curve fitting for providing a predictive model is achieved using statistical techniques based on a least squares regression method. A common regression technique known as partial least squares (PLS) regression has been found to provide a robust model in that the model parameters do not change significantly when new samples are taken. The algorithms and theoretical basis for PLS predictive modeling cpn be found in
Brereton, R.G. Chemo etrics: Applications of Mathematics and Statistics to Laboratory Systems, New York: Ellis Horwood, 1990. A basic overview of PLS regression can be found in Gerald and Kowalski, "Partial Least-Squares
Regression: A Tutorial" Analytical Chimica Acta 185
(1986) :1-17.
The PLS regression technique begins by "autoscaling" each variable such that all the variables are equally influential in the prediction. The PLS regression technique uses principle component analysis, also known as singular value decomposition or eigenvector analysis, to represent the dependent and independent matrices. In principle component analysis, a NIPALS algorithm is used to define a data matrix of independent variables. PLS regression techniques introduce a weighting factor into the regression model. The PLS algorithm gives a sequence of models, the best model being the one that minimizes the cross-validation.
For example, from Table I, a data matrix of independent variables (the glucose concentration is the dependent variable) , consisting of the emission intensity at the different wavelengths, is provided to a data processing routine that performs the PLS regression. In this example, the data processing routine is included in the "PLS_Toolbox Version 1.3" available from Barry M. Wise, 1415 Wright Avenue, Richland, WA 99352 (E-mail: bm_wise@pnl.gov) . The routines in the "Toolbox" are presently intended for use with the MATLAB™ software package available from The Mathworks, Inc., 24 Prime Park Way, Natick, MA 01760. In using the routine, the matrix associated with the spectral intensities at each wavelength and the matrix associated with the concentration values have their means removed before processing. The routine calculates a regression vector shown in FIG. 4 and in Table II below. The scalar components of the regression vector are the prediction coefficients for each wavelength.
Table II
Number Wavelength Coefficient
1 342 0.8946
2 344 -1.0627
3 347 -1.2613
4 352 -0.2548
5 360 1.1316
6 370 -1.4846
7 385 2.0911
8 400 -0.9403
To make a prediction on a sample of unknown concentration, the intensity at each of the eight wavelengths is measured. These eight measured values are scaled and multiplied by the regression vector, i.e., the eight wavelength coefficients in Table II. The result is a scaled concentration prediction. The scaled predicted concentration must be rescaled to provide a concentration value in the original units.
Because eight different wavelengths were used, the model can yield up to eight latent variables. Table
III below shows the percent of variance that was accounted for with the addition of each latent variable to the model. Table I I I
Percent Variance Captured by PLS Model
X-Block Y-Block
LV # This LV Total This LV Total
1 75.6695 75.6695 77.9674 77.9674
2 8.5652 84.2347 15.3105 93.2779
3 3.4081 87.6428 3.9910 97.7993
4 8.9551 96.5979 0.5305 97.7993
5 1.9529 98.5508 0.4636 98.2629
6 0.5536 99.1045 0.6821 98.9450
7 0.2573 99.3618 0.7112 99.6562
8 0.6382 100.00 0.0031 99.6593
In developing the predictive model, the cross- validation calculation is used to determine the optimum number of latent variables to use in the model. The cross-validation is performed by using one spectra chosen at random to test the model. The cross-validation is repeated ten times, randomly choosing a different spectra to test the model. The results of the cross-validation are shown in the press plot of FIG. 5 as a plot of the prediction residual sum of squares (PRESS) versus the number of latent variables used in the model. The PLS analysis yielded a model of six latent variables.
The cross-validation was repeated using blocks of two spectra at a time to test the model. The press plot associated with the two spectra cross-validation is shown in FIG. 6. FIGS. 5 and 6 shows that the minimum PRESS exists between five to seven latent variables.
The predictive model was tested using samples of known glucose concentration. FIG. 7 shows the results of a prediction test using samples of known glucose concentration in the PLS prediction model using six latent variables, derived from Table I, to define the model. As seen from the graph, the PLS model provides a fairly accurate prediction of the glucose concentration. By way of comparison, the test was repeated for a multiple linear regression (MLR) model based on the same input data. The PLS model generally performs better than the MLR model at lower concentration levels while the MLR model performs better at at higher concentration levels.
FIG. 8 shows the results of another prediction test again using samples of known glucose concentration in testing PLS and MLR models. However, for this test, the PLS model uses seven latent variables to define the model. As can be seen by the graph, both models provide substantially the same results. Thus, using additional latent variables in the model does not necessarily improve the model's prediction accuracy.
However, it can be shown by the following example that the predictive model can be improved by using a greater number of wavelengths for generating the model. The emissions spectra from the 1,024 elements of the detector array provides a like number of intensity values. Approximately 200 of these points are in the wavelength range of glucose UV-visible emission light (approximately 335 to 450 nanometers) and the data is truncated to this range. To reduce the effects of noise, the spectra is measured three to five times for each glucose concentration. An average of each of these spectra is used to generate the model. To further remove noise, a smoothing function is performed on the spectra using a three point moving average (Xx (smoothed) = ( ^ + ,X + X1+1)/3 . The data for the truncated smoothed spectra was converted into a smaller file by averaging three points at a time to arrive at one point. For example, 180 points become 60 points. Thus, 60 wavelengths for each concentration level, preconditioned as discussed above, are analyzed in this example to arrive at a predictive model using the PLS regression technique, instead of the eight different wavelengths from Table 1 used in the previous example.
As shown in FIG. 9, the PRESS plot for the model using the whole spectra indicates a minimum PRESS at six latent variables. A test of the model using samples of known concentration is shown in FIG. 10. As can be seen by the graph, the PLS predictive model, using the preconditioned spectra, provides a very accurate prediction of the glucose concentration. Given the generally noisy nature of the spectral measurements, and the non-linear relationship between the glucose concentration and the emission intensity at any given wavelength of interest, the results indicated in FIG. 10 are indeed surprising.
A second embodiment of the present invention focuses on the nonlinear relationship between the glucose concentration and the intensity of the excitation light. FIG. 11 shows emission spectra, at a single glucose concentration, resulting from excitation light delivered at different intensity levels. As shown in Table IV below, the emission intensity at a wavelength associated with the raman peak, normalized with respect to the broader florescence peak, is nonlinear with respect to the excitation energy at given concentration level. Excitat ion Energy (mj/pul, se)
Concentr (rag/df) .25 .5 1 3 5 7 10
0 1 .92 .95 1 1 1 1
1 .78 .71 .71 .76 .79 .86 .84
10 .8 .7 .73 .73 .8 .84 .82
50 .69 .64 .71 .7 .77 .73 .78
100 .74 .7 .75 .81 .95 .88 .87
500 .72 .73 .67 .8 .86 .72 .85
1000 .84 .83 .84 .84 .97 1 .93
The values in Table IV can be used to provide a predictive model, using the PLS regression technique, as discussed above, with respect to Table I. Thus by varying the intensity or energy of the excitation light, the glucose concentration of an unknown sample can be determined using a predictive model provided by PLS analysis.
The present invention takes into account the nonlinear nature of the physical interaction between the glucose molecules and the water molecules. FIG. 12 shows the emission spectrum of distilled water illuminated by excitation light having an energy of 5 millijoules per pulse (18 millijoules per millimeter per square millimeter) . The graph shows that the florescence spectra for distilled water exhibits a broad florescence band with a peak at approximately 370 nanometers and a narrow raman scattering band at approximately 346 nanometers. The raman scattering band results from scattered incident light having its wavelength shifted by the energy (rotational and translational) of the water molecules.
The emission spectrum of ultra anhydrous glucose is shown in FIG. 13. The resulting spectrum has a single broad fluorescence band that peaks at approximately 450 nanometers. As shown in FIG. 14, the emission spectrum of anhydrous glucose, which has absorbed a small but spectrally significant amounts of water, exhibits two narrow raman scattering bands that peak at 341 nanometers and 346 nanometers, respectively, and one broad emission band that peaks at about 420 nanometers. The raman scattering peak at 346 nanometers corresponds to the raman peak of water shown in FIG. 12. The raman scattering peak at 341 nanometers apparently results from interaction between the water and glucose molecules. Further, the spectrum of the anhydrous glucose is shifted to shorter wavelengths when compared with the spectrum of the ultra anhydrous glucose shown in FIG. 13. The emissions spectra of anhydrous glucose, as the excitation energy is varied, are shown in FIG. 15. The intensity of spectra generally increase as the excitation energy increases. However, the intensity ratio between the peaks of the raman bands and the broad emission band does not remain constant as the excitation energy increases.
Further, as shown in FIG. 16, the ratio between the raman scattering band and the broad emission band similarly does not remain constant as the concentration increases. Accordingly, the interaction between the water and glucose molecules, and the energy density of the excitation light all appear to effect the resulting emission spectra. Accordingly, simple linear models are effective as an approximation only along very narrow, discrete segments of possible glucose concentrations of interest.
From the foregoing, it will be appreciated that the glucose concentration can be accurately predicted in spite of signal noise and nonlinear relationships between the glucose concentration and certain spectroscopic parameters of interest. The prediction is performed using a model developed from a PLS regression analysis. Although the foregoing discloses preferred embodiments of the present invention, it is understood that those skilled in the art may make various changes to the preferred embodiments shown without departing from the scope of the invention. The invention is defined only by the following claims.

Claims

We claim:
1. Apparatus for determining the concentration of glucose in a sample that includes water, comprising: a light source that emits ultraviolet excitation light of at least one predetermined energy level, that is directed at a sample to produce return light from the sample, such return light including induced emissions of light produced as a result of interactions between the excitation light and any glucose with water present in the sample; a sensor that monitors the return light and generates at least three electrical signals indicative of the intensity of return light associated with glucose concentration distinguishing characteristics of the emission light; and a processor that processes the electrical signals, using a predictive model, to determine the concentration of glucose in the sample.
2. Apparatus for determining the concentration of glucose in a sample as defined in claim 1, wherein: the at least three electrical signals indicate the intensity of return light within a respective number of predetermined wavelength bands within the wavelength band of the emission light, and the intensity of the excitation light remains relatively constant while the sensor generates the electrical signals.
3. Apparatus for determining the concentration of glucose in a sample as defined in claim 2, wherein: the sensor monitors the intensity of return light within eight different wavelength bands and generates eight electrical signals, each indicative of the intensity of return light within a respective wavelength band.
4. Apparatus for determining the concentration of glucose in a sample as defined in claim 3, wherein: the wavelength of the excitation light is about 308 nanometers; the first wavelength band is a narrow wavelength band centered at about 342 nanometers; the second wavelength band is a narrow wavelength band centered at about 344 nanometers; the third wavelength band is a narrow wavelength band centered at about 347 nanometers; the fourth wavelength band is a narrow wavelength band centered at about 352 nanometers; the fifth wavelength band is a narrow wavelength band centered at about 360 nanometers; the sixth wavelength band is a narrow wavelength band centered at about 370 nanometers; the seventh wavelength band is a narrow wavelength band centered at about 385 nanometers; and the eighth wavelength band is a narrow wavelength band centered at about 400 nanometers.
5. Apparatus for determining the concentration of glucose in a sample as defined in claim 1, wherein the sensor generates a plurality of electrical signals that indicate the intensity of return light substantially continuously across an extended wavelength spectrum associated with the emission light.
6. Apparatus for determining the concentration of glucose in a sample as defined in claim 1, wherein: the energy of the excitation light is varied over several predetermined energy levels; and the sensor generates, at each intensity level, a first electrical signal based on the intensity of return light within a wavelength of the emission light associated with raman scattering, and a second electrical signal based on the intensity of return light within a wavelength band of the emission light associated with a peak of a broad glucose emission band.
7. Apparatus for determining the concentration of glucose in a sample as defined in claim 1, and further comprising one or more waveguides for transmitting the excitation light from the light source to the sample and for transmitting the return light from the sample to the sensor.
8. Apparatus for determining the concentration of glucose in a sample as defined in claim 1, wherein the sensor includes: a first detector adapted to detect the return light within a first wavelength band and generate a first electrical signal; a second detector adapted to detect the return light within a second wavelength band and generate a second electrical signal; and a third detector adapted to detect the return light within a third wavelength band and generate a third electrical signal.
9. Apparatus for determining the concentration of glucose in a sample as defined in claim 1, wherein the predictive model is defined by six latent variables.
10. Apparatus for determining the concentration of glucose in a sample as defined in claim 1, wherein the predictive model is defined by prediction coefficients that are associated with the glucose concentration distinguishing characteristics.
11. A method of determining the concentration of glucose in a sample with water, comprising: providing a regression model that accounts for a nonlinear relationship between the concentration of glucose in a sample and electrical signal based on certain glucose concentration distinguishing characteristics of a light emission spectrum that includes emission light produced by glucose related interactions with the excitation light; causing a sample to produce a light emission spectrum that includes ultraviolet emission light produced by glucose related interactions and generating a plurality of electrical signals that represent the glucose concentration distinguishing characteristics; and processing, using the regression model, the plurality of electrical signal to determine the glucose concentration and generating an electrical signal based on the glucose concentration determined using the regression model.
PCT/US1996/018532 1995-12-01 1996-11-19 Glucose monitoring apparatus and method using laser-induced emission spectroscopy WO1997020495A1 (en)

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AU10552/97A AU715281B2 (en) 1995-12-01 1996-11-19 Glucose monitoring apparatus and method using laser-induced emission spectroscopy
JP9521294A JP2000501830A (en) 1995-12-01 1996-11-19 Glucose monitoring device and method using laser induced emission spectrometer
AT96941397T ATE249166T1 (en) 1995-12-01 1996-11-19 DEVICE AND METHOD FOR MONITORING GLUCOSE USING SPECTROSCOPY OF EMISSION RADIATION INDUCED BY LASER
DE69629937T DE69629937T2 (en) 1995-12-01 1996-11-19 DEVICE AND METHOD FOR GLUCOSE MONITORING BY SPECTROSCOPY OF EMISSION RADIATION INDUCED BY LASER
DK96941397T DK0863718T3 (en) 1995-12-01 1996-11-19 Device and method for glucose monitoring using laser induced emission spectroscopy
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6044285A (en) * 1997-11-12 2000-03-28 Lightouch Medical, Inc. Method for non-invasive measurement of an analyte
US6289230B1 (en) 1998-07-07 2001-09-11 Lightouch Medical, Inc. Tissue modulation process for quantitative noninvasive in vivo spectroscopic analysis of tissues
WO2004109263A1 (en) * 2003-06-03 2004-12-16 Bayer Healthcare Llc Readhead for optical inspection apparatus

Families Citing this family (310)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5490505A (en) 1991-03-07 1996-02-13 Masimo Corporation Signal processing apparatus
MX9702434A (en) 1991-03-07 1998-05-31 Masimo Corp Signal processing apparatus.
US5638818A (en) * 1991-03-21 1997-06-17 Masimo Corporation Low noise optical probe
EP1905352B1 (en) 1994-10-07 2014-07-16 Masimo Corporation Signal processing method
US8019400B2 (en) 1994-10-07 2011-09-13 Masimo Corporation Signal processing apparatus
US6002952A (en) 1997-04-14 1999-12-14 Masimo Corporation Signal processing apparatus and method
US6229856B1 (en) 1997-04-14 2001-05-08 Masimo Corporation Method and apparatus for demodulating signals in a pulse oximetry system
US6728560B2 (en) 1998-04-06 2004-04-27 The General Hospital Corporation Non-invasive tissue glucose level monitoring
US6721582B2 (en) 1999-04-06 2004-04-13 Argose, Inc. Non-invasive tissue glucose level monitoring
US7899518B2 (en) 1998-04-06 2011-03-01 Masimo Laboratories, Inc. Non-invasive tissue glucose level monitoring
US20020091324A1 (en) * 1998-04-06 2002-07-11 Nikiforos Kollias Non-invasive tissue glucose level monitoring
ATE521277T1 (en) 1998-06-03 2011-09-15 Masimo Corp STEREO PULSE OXIMETER
US7245953B1 (en) 1999-04-12 2007-07-17 Masimo Corporation Reusable pulse oximeter probe and disposable bandage apparatii
US6721585B1 (en) 1998-10-15 2004-04-13 Sensidyne, Inc. Universal modular pulse oximeter probe for use with reusable and disposable patient attachment devices
USRE41912E1 (en) 1998-10-15 2010-11-02 Masimo Corporation Reusable pulse oximeter probe and disposable bandage apparatus
US6463311B1 (en) 1998-12-30 2002-10-08 Masimo Corporation Plethysmograph pulse recognition processor
US6684090B2 (en) 1999-01-07 2004-01-27 Masimo Corporation Pulse oximetry data confidence indicator
US20020140675A1 (en) * 1999-01-25 2002-10-03 Ali Ammar Al System and method for altering a display mode based on a gravity-responsive sensor
US6770028B1 (en) * 1999-01-25 2004-08-03 Masimo Corporation Dual-mode pulse oximeter
US6360114B1 (en) 1999-03-25 2002-03-19 Masimo Corporation Pulse oximeter probe-off detector
US6515273B2 (en) 1999-08-26 2003-02-04 Masimo Corporation System for indicating the expiration of the useful operating life of a pulse oximetry sensor
US6377829B1 (en) 1999-12-09 2002-04-23 Masimo Corporation Resposable pulse oximetry sensor
US6950687B2 (en) 1999-12-09 2005-09-27 Masimo Corporation Isolation and communication element for a resposable pulse oximetry sensor
AU2001237066A1 (en) 2000-02-18 2001-08-27 Argose, Inc. Generation of spatially-averaged excitation-emission map in heterogeneous tissue
EP1257195A2 (en) * 2000-02-18 2002-11-20 Argose, Inc. Multivariate analysis of green to ultraviolet spectra of cell and tissue samples
US6430525B1 (en) 2000-06-05 2002-08-06 Masimo Corporation Variable mode averager
US7193628B1 (en) * 2000-07-13 2007-03-20 C4Cast.Com, Inc. Significance-based display
EP2064989B1 (en) 2000-08-18 2012-03-21 Masimo Corporation Dual-mode pulse oximeter
US6850787B2 (en) * 2001-06-29 2005-02-01 Masimo Laboratories, Inc. Signal component processor
US6697658B2 (en) 2001-07-02 2004-02-24 Masimo Corporation Low power pulse oximeter
US6701170B2 (en) * 2001-11-02 2004-03-02 Nellcor Puritan Bennett Incorporated Blind source separation of pulse oximetry signals
US7355512B1 (en) 2002-01-24 2008-04-08 Masimo Corporation Parallel alarm processor
US6850788B2 (en) 2002-03-25 2005-02-01 Masimo Corporation Physiological measurement communications adapter
US20040010185A1 (en) * 2002-07-11 2004-01-15 Optical Sensors, Inc. Method for measuring a physiologic parameter using a preferred site
US6970792B1 (en) 2002-12-04 2005-11-29 Masimo Laboratories, Inc. Systems and methods for determining blood oxygen saturation values using complex number encoding
US7919713B2 (en) * 2007-04-16 2011-04-05 Masimo Corporation Low noise oximetry cable including conductive cords
US6920345B2 (en) 2003-01-24 2005-07-19 Masimo Corporation Optical sensor including disposable and reusable elements
US7181219B2 (en) * 2003-05-22 2007-02-20 Lucent Technologies Inc. Wireless handover using anchor termination
GB0312151D0 (en) * 2003-05-28 2003-07-02 Suisse Electronique Microtech Optical glucose detector
US7003338B2 (en) 2003-07-08 2006-02-21 Masimo Corporation Method and apparatus for reducing coupling between signals
US7500950B2 (en) 2003-07-25 2009-03-10 Masimo Corporation Multipurpose sensor port
US7254431B2 (en) * 2003-08-28 2007-08-07 Masimo Corporation Physiological parameter tracking system
US7483729B2 (en) 2003-11-05 2009-01-27 Masimo Corporation Pulse oximeter access apparatus and method
US7438683B2 (en) 2004-03-04 2008-10-21 Masimo Corporation Application identification sensor
US7415297B2 (en) * 2004-03-08 2008-08-19 Masimo Corporation Physiological parameter system
CA2464029A1 (en) 2004-04-08 2005-10-08 Valery Telfort Non-invasive ventilation monitor
US9341565B2 (en) 2004-07-07 2016-05-17 Masimo Corporation Multiple-wavelength physiological monitor
US7343186B2 (en) 2004-07-07 2008-03-11 Masimo Laboratories, Inc. Multi-wavelength physiological monitor
US7937128B2 (en) 2004-07-09 2011-05-03 Masimo Corporation Cyanotic infant sensor
US8036727B2 (en) 2004-08-11 2011-10-11 Glt Acquisition Corp. Methods for noninvasively measuring analyte levels in a subject
US7254429B2 (en) 2004-08-11 2007-08-07 Glucolight Corporation Method and apparatus for monitoring glucose levels in a biological tissue
US7060955B1 (en) * 2005-01-31 2006-06-13 Chemimage Corporation Apparatus and method for defining illumination parameters of a sample
US20060189871A1 (en) * 2005-02-18 2006-08-24 Ammar Al-Ali Portable patient monitor
US7596398B2 (en) 2005-03-01 2009-09-29 Masimo Laboratories, Inc. Multiple wavelength sensor attachment
EP1874178A4 (en) 2005-04-13 2009-12-09 Glucolight Corp Method for data reduction and calibration of an oct-based blood glucose monitor
US7330746B2 (en) * 2005-06-07 2008-02-12 Chem Image Corporation Non-invasive biochemical analysis
US7330747B2 (en) * 2005-06-07 2008-02-12 Chemimage Corporation Invasive chemometry
US7962188B2 (en) 2005-10-14 2011-06-14 Masimo Corporation Robust alarm system
US7530942B1 (en) 2005-10-18 2009-05-12 Masimo Corporation Remote sensing infant warmer
US8233955B2 (en) 2005-11-29 2012-07-31 Cercacor Laboratories, Inc. Optical sensor including disposable and reusable elements
US7990382B2 (en) 2006-01-03 2011-08-02 Masimo Corporation Virtual display
US8182443B1 (en) 2006-01-17 2012-05-22 Masimo Corporation Drug administration controller
US20070244377A1 (en) * 2006-03-14 2007-10-18 Cozad Jenny L Pulse oximeter sleeve
US8219172B2 (en) 2006-03-17 2012-07-10 Glt Acquisition Corp. System and method for creating a stable optical interface
US9176141B2 (en) 2006-05-15 2015-11-03 Cercacor Laboratories, Inc. Physiological monitor calibration system
US8998809B2 (en) * 2006-05-15 2015-04-07 Cercacor Laboratories, Inc. Systems and methods for calibrating minimally invasive and non-invasive physiological sensor devices
US7941199B2 (en) 2006-05-15 2011-05-10 Masimo Laboratories, Inc. Sepsis monitor
US8028701B2 (en) 2006-05-31 2011-10-04 Masimo Corporation Respiratory monitoring
US10188348B2 (en) 2006-06-05 2019-01-29 Masimo Corporation Parameter upgrade system
US20080064965A1 (en) * 2006-09-08 2008-03-13 Jay Gregory D Devices and methods for measuring pulsus paradoxus
US8457707B2 (en) * 2006-09-20 2013-06-04 Masimo Corporation Congenital heart disease monitor
USD614305S1 (en) 2008-02-29 2010-04-20 Masimo Corporation Connector assembly
US8315683B2 (en) * 2006-09-20 2012-11-20 Masimo Corporation Duo connector patient cable
USD609193S1 (en) 2007-10-12 2010-02-02 Masimo Corporation Connector assembly
US20080103375A1 (en) * 2006-09-22 2008-05-01 Kiani Massi E Patient monitor user interface
US9161696B2 (en) 2006-09-22 2015-10-20 Masimo Corporation Modular patient monitor
US8840549B2 (en) 2006-09-22 2014-09-23 Masimo Corporation Modular patient monitor
US8265723B1 (en) 2006-10-12 2012-09-11 Cercacor Laboratories, Inc. Oximeter probe off indicator defining probe off space
WO2008045538A2 (en) 2006-10-12 2008-04-17 Masimo Corporation Perfusion index smoother
US9861305B1 (en) 2006-10-12 2018-01-09 Masimo Corporation Method and apparatus for calibration to reduce coupling between signals in a measurement system
US20080094228A1 (en) * 2006-10-12 2008-04-24 Welch James P Patient monitor using radio frequency identification tags
US8255026B1 (en) 2006-10-12 2012-08-28 Masimo Corporation, Inc. Patient monitor capable of monitoring the quality of attached probes and accessories
US9192329B2 (en) 2006-10-12 2015-11-24 Masimo Corporation Variable mode pulse indicator
US7880626B2 (en) 2006-10-12 2011-02-01 Masimo Corporation System and method for monitoring the life of a physiological sensor
US8600467B2 (en) 2006-11-29 2013-12-03 Cercacor Laboratories, Inc. Optical sensor including disposable and reusable elements
US8414499B2 (en) 2006-12-09 2013-04-09 Masimo Corporation Plethysmograph variability processor
US7791155B2 (en) * 2006-12-22 2010-09-07 Masimo Laboratories, Inc. Detector shield
US8852094B2 (en) 2006-12-22 2014-10-07 Masimo Corporation Physiological parameter system
US8652060B2 (en) * 2007-01-20 2014-02-18 Masimo Corporation Perfusion trend indicator
EP2476369B1 (en) 2007-03-27 2014-10-01 Masimo Laboratories, Inc. Multiple wavelength optical sensor
US8374665B2 (en) 2007-04-21 2013-02-12 Cercacor Laboratories, Inc. Tissue profile wellness monitor
US8764671B2 (en) * 2007-06-28 2014-07-01 Masimo Corporation Disposable active pulse sensor
US8048040B2 (en) 2007-09-13 2011-11-01 Masimo Corporation Fluid titration system
US8310336B2 (en) 2008-10-10 2012-11-13 Masimo Corporation Systems and methods for storing, analyzing, retrieving and displaying streaming medical data
US8355766B2 (en) * 2007-10-12 2013-01-15 Masimo Corporation Ceramic emitter substrate
EP2227843B1 (en) 2007-10-12 2019-03-06 Masimo Corporation Connector assembly
US8274360B2 (en) 2007-10-12 2012-09-25 Masimo Corporation Systems and methods for storing, analyzing, and retrieving medical data
US8986253B2 (en) 2008-01-25 2015-03-24 Tandem Diabetes Care, Inc. Two chamber pumps and related methods
WO2009111542A2 (en) 2008-03-04 2009-09-11 Glucolight Corporation Methods and systems for analyte level estimation in optical coherence tomography
JP2009236832A (en) * 2008-03-28 2009-10-15 Jfe Engineering Corp Monitoring method and device for dissolved pollutant
EP2278911A1 (en) 2008-05-02 2011-02-02 Masimo Corporation Monitor configuration system
JP2011519684A (en) 2008-05-05 2011-07-14 マシモ コーポレイション Pulse oximeter system with electrical disconnect circuit
EP2326239B1 (en) 2008-07-03 2017-06-21 Masimo Laboratories, Inc. Protrusion for improving spectroscopic measurement of blood constituents
USD621516S1 (en) 2008-08-25 2010-08-10 Masimo Laboratories, Inc. Patient monitoring sensor
US8203438B2 (en) 2008-07-29 2012-06-19 Masimo Corporation Alarm suspend system
US8203704B2 (en) 2008-08-04 2012-06-19 Cercacor Laboratories, Inc. Multi-stream sensor for noninvasive measurement of blood constituents
SE532941C2 (en) 2008-09-15 2010-05-18 Phasein Ab Gas sampling line for breathing gases
WO2010031070A2 (en) 2008-09-15 2010-03-18 Masimo Corporation Patient monitor including multi-parameter graphical display
AU2009293019A1 (en) 2008-09-19 2010-03-25 Tandem Diabetes Care Inc. Solute concentration measurement device and related methods
US8346330B2 (en) 2008-10-13 2013-01-01 Masimo Corporation Reflection-detector sensor position indicator
US8401602B2 (en) 2008-10-13 2013-03-19 Masimo Corporation Secondary-emitter sensor position indicator
US8771204B2 (en) 2008-12-30 2014-07-08 Masimo Corporation Acoustic sensor assembly
KR101306340B1 (en) * 2009-01-08 2013-09-06 삼성전자주식회사 Method for measuring concentration of component in biochemical sample and for presuming reliability of test result
US8588880B2 (en) 2009-02-16 2013-11-19 Masimo Corporation Ear sensor
EP3605550A1 (en) 2009-03-04 2020-02-05 Masimo Corporation Medical monitoring system
US10032002B2 (en) 2009-03-04 2018-07-24 Masimo Corporation Medical monitoring system
US10007758B2 (en) 2009-03-04 2018-06-26 Masimo Corporation Medical monitoring system
US9323894B2 (en) 2011-08-19 2016-04-26 Masimo Corporation Health care sanitation monitoring system
US8388353B2 (en) 2009-03-11 2013-03-05 Cercacor Laboratories, Inc. Magnetic connector
US8897847B2 (en) 2009-03-23 2014-11-25 Masimo Corporation Digit gauge for noninvasive optical sensor
US8989831B2 (en) 2009-05-19 2015-03-24 Masimo Corporation Disposable components for reusable physiological sensor
US8571619B2 (en) 2009-05-20 2013-10-29 Masimo Corporation Hemoglobin display and patient treatment
US8418524B2 (en) 2009-06-12 2013-04-16 Masimo Corporation Non-invasive sensor calibration device
US8670811B2 (en) * 2009-06-30 2014-03-11 Masimo Corporation Pulse oximetry system for adjusting medical ventilation
US8471713B2 (en) 2009-07-24 2013-06-25 Cercacor Laboratories, Inc. Interference detector for patient monitor
US8473020B2 (en) 2009-07-29 2013-06-25 Cercacor Laboratories, Inc. Non-invasive physiological sensor cover
EP3284494A1 (en) 2009-07-30 2018-02-21 Tandem Diabetes Care, Inc. Portable infusion pump system
US8688183B2 (en) 2009-09-03 2014-04-01 Ceracor Laboratories, Inc. Emitter driver for noninvasive patient monitor
US20110172498A1 (en) * 2009-09-14 2011-07-14 Olsen Gregory A Spot check monitor credit system
US9579039B2 (en) 2011-01-10 2017-02-28 Masimo Corporation Non-invasive intravascular volume index monitor
US20110137297A1 (en) 2009-09-17 2011-06-09 Kiani Massi Joe E Pharmacological management system
DE112010003689T5 (en) * 2009-09-17 2013-01-17 Marcelo Lamego Improved analyte monitoring using one or more accelerometers
US8571618B1 (en) 2009-09-28 2013-10-29 Cercacor Laboratories, Inc. Adaptive calibration system for spectrophotometric measurements
US20110082711A1 (en) 2009-10-06 2011-04-07 Masimo Laboratories, Inc. Personal digital assistant or organizer for monitoring glucose levels
US8845100B2 (en) 2009-10-07 2014-09-30 The University Of Toledo Non-invasive ocular analyte sensing system
US8523781B2 (en) 2009-10-15 2013-09-03 Masimo Corporation Bidirectional physiological information display
US9066680B1 (en) 2009-10-15 2015-06-30 Masimo Corporation System for determining confidence in respiratory rate measurements
WO2011047216A2 (en) 2009-10-15 2011-04-21 Masimo Corporation Physiological acoustic monitoring system
US8715206B2 (en) 2009-10-15 2014-05-06 Masimo Corporation Acoustic patient sensor
WO2011047211A1 (en) 2009-10-15 2011-04-21 Masimo Corporation Pulse oximetry system with low noise cable hub
US8821415B2 (en) 2009-10-15 2014-09-02 Masimo Corporation Physiological acoustic monitoring system
US9848800B1 (en) 2009-10-16 2017-12-26 Masimo Corporation Respiratory pause detector
US9839381B1 (en) 2009-11-24 2017-12-12 Cercacor Laboratories, Inc. Physiological measurement system with automatic wavelength adjustment
DE112010004682T5 (en) 2009-12-04 2013-03-28 Masimo Corporation Calibration for multi-level physiological monitors
US9153112B1 (en) 2009-12-21 2015-10-06 Masimo Corporation Modular patient monitor
GB2490817A (en) 2010-01-19 2012-11-14 Masimo Corp Wellness analysis system
GB2490832B (en) 2010-03-01 2016-09-21 Masimo Corp Adaptive alarm system
EP2544591B1 (en) 2010-03-08 2021-07-21 Masimo Corporation Reprocessing of a physiological sensor
US9307928B1 (en) 2010-03-30 2016-04-12 Masimo Corporation Plethysmographic respiration processor
US8712494B1 (en) 2010-05-03 2014-04-29 Masimo Corporation Reflective non-invasive sensor
US9138180B1 (en) 2010-05-03 2015-09-22 Masimo Corporation Sensor adapter cable
US8666468B1 (en) 2010-05-06 2014-03-04 Masimo Corporation Patient monitor for determining microcirculation state
US9326712B1 (en) 2010-06-02 2016-05-03 Masimo Corporation Opticoustic sensor
US8740792B1 (en) 2010-07-12 2014-06-03 Masimo Corporation Patient monitor capable of accounting for environmental conditions
US9408542B1 (en) 2010-07-22 2016-08-09 Masimo Corporation Non-invasive blood pressure measurement system
WO2012027613A1 (en) 2010-08-26 2012-03-01 Masimo Corporation Blood pressure measurement system
US9775545B2 (en) 2010-09-28 2017-10-03 Masimo Corporation Magnetic electrical connector for patient monitors
JP5710767B2 (en) 2010-09-28 2015-04-30 マシモ コーポレイション Depth of consciousness monitor including oximeter
US9211095B1 (en) 2010-10-13 2015-12-15 Masimo Corporation Physiological measurement logic engine
US8723677B1 (en) 2010-10-20 2014-05-13 Masimo Corporation Patient safety system with automatically adjusting bed
US20120226117A1 (en) 2010-12-01 2012-09-06 Lamego Marcelo M Handheld processing device including medical applications for minimally and non invasive glucose measurements
WO2012109671A1 (en) 2011-02-13 2012-08-16 Masimo Corporation Medical characterization system
US9066666B2 (en) 2011-02-25 2015-06-30 Cercacor Laboratories, Inc. Patient monitor for monitoring microcirculation
US8830449B1 (en) 2011-04-18 2014-09-09 Cercacor Laboratories, Inc. Blood analysis system
US9095316B2 (en) 2011-04-20 2015-08-04 Masimo Corporation System for generating alarms based on alarm patterns
US9622692B2 (en) 2011-05-16 2017-04-18 Masimo Corporation Personal health device
US9986919B2 (en) 2011-06-21 2018-06-05 Masimo Corporation Patient monitoring system
US9532722B2 (en) 2011-06-21 2017-01-03 Masimo Corporation Patient monitoring system
US9245668B1 (en) 2011-06-29 2016-01-26 Cercacor Laboratories, Inc. Low noise cable providing communication between electronic sensor components and patient monitor
US11439329B2 (en) 2011-07-13 2022-09-13 Masimo Corporation Multiple measurement mode in a physiological sensor
US9192351B1 (en) 2011-07-22 2015-11-24 Masimo Corporation Acoustic respiratory monitoring sensor with probe-off detection
US8755872B1 (en) 2011-07-28 2014-06-17 Masimo Corporation Patient monitoring system for indicating an abnormal condition
US9782077B2 (en) 2011-08-17 2017-10-10 Masimo Corporation Modulated physiological sensor
US9808188B1 (en) 2011-10-13 2017-11-07 Masimo Corporation Robust fractional saturation determination
EP2765909B1 (en) 2011-10-13 2019-06-26 Masimo Corporation Physiological acoustic monitoring system
EP3584799B1 (en) 2011-10-13 2022-11-09 Masimo Corporation Medical monitoring hub
US9943269B2 (en) 2011-10-13 2018-04-17 Masimo Corporation System for displaying medical monitoring data
US9778079B1 (en) 2011-10-27 2017-10-03 Masimo Corporation Physiological monitor gauge panel
US9445759B1 (en) 2011-12-22 2016-09-20 Cercacor Laboratories, Inc. Blood glucose calibration system
US11172890B2 (en) 2012-01-04 2021-11-16 Masimo Corporation Automated condition screening and detection
US9392945B2 (en) 2012-01-04 2016-07-19 Masimo Corporation Automated CCHD screening and detection
US10149616B2 (en) 2012-02-09 2018-12-11 Masimo Corporation Wireless patient monitoring device
US10307111B2 (en) 2012-02-09 2019-06-04 Masimo Corporation Patient position detection system
US9480435B2 (en) 2012-02-09 2016-11-01 Masimo Corporation Configurable patient monitoring system
US9195385B2 (en) 2012-03-25 2015-11-24 Masimo Corporation Physiological monitor touchscreen interface
US9131881B2 (en) 2012-04-17 2015-09-15 Masimo Corporation Hypersaturation index
US9555186B2 (en) 2012-06-05 2017-01-31 Tandem Diabetes Care, Inc. Infusion pump system with disposable cartridge having pressure venting and pressure feedback
US10542903B2 (en) 2012-06-07 2020-01-28 Masimo Corporation Depth of consciousness monitor
US9697928B2 (en) 2012-08-01 2017-07-04 Masimo Corporation Automated assembly sensor cable
US10827961B1 (en) 2012-08-29 2020-11-10 Masimo Corporation Physiological measurement calibration
US9877650B2 (en) 2012-09-20 2018-01-30 Masimo Corporation Physiological monitor with mobile computing device connectivity
US9955937B2 (en) 2012-09-20 2018-05-01 Masimo Corporation Acoustic patient sensor coupler
US9749232B2 (en) 2012-09-20 2017-08-29 Masimo Corporation Intelligent medical network edge router
US9717458B2 (en) 2012-10-20 2017-08-01 Masimo Corporation Magnetic-flap optical sensor
US9560996B2 (en) 2012-10-30 2017-02-07 Masimo Corporation Universal medical system
US9787568B2 (en) 2012-11-05 2017-10-10 Cercacor Laboratories, Inc. Physiological test credit method
CA2889504A1 (en) * 2012-11-07 2014-05-15 Medtronic Minimed, Inc. Dry insertion and one-point in vivo calibration of an optical analyte sensor
US9750461B1 (en) 2013-01-02 2017-09-05 Masimo Corporation Acoustic respiratory monitoring sensor with probe-off detection
US9724025B1 (en) 2013-01-16 2017-08-08 Masimo Corporation Active-pulse blood analysis system
US9750442B2 (en) 2013-03-09 2017-09-05 Masimo Corporation Physiological status monitor
US9965946B2 (en) 2013-03-13 2018-05-08 Masimo Corporation Systems and methods for monitoring a patient health network
US10441181B1 (en) 2013-03-13 2019-10-15 Masimo Corporation Acoustic pulse and respiration monitoring system
US9474474B2 (en) 2013-03-14 2016-10-25 Masimo Corporation Patient monitor as a minimally invasive glucometer
US9936917B2 (en) 2013-03-14 2018-04-10 Masimo Laboratories, Inc. Patient monitor placement indicator
US9986952B2 (en) 2013-03-14 2018-06-05 Masimo Corporation Heart sound simulator
US10456038B2 (en) 2013-03-15 2019-10-29 Cercacor Laboratories, Inc. Cloud-based physiological monitoring system
US9891079B2 (en) 2013-07-17 2018-02-13 Masimo Corporation Pulser with double-bearing position encoder for non-invasive physiological monitoring
US10555678B2 (en) 2013-08-05 2020-02-11 Masimo Corporation Blood pressure monitor with valve-chamber assembly
WO2015038683A2 (en) 2013-09-12 2015-03-19 Cercacor Laboratories, Inc. Medical device management system
US11147518B1 (en) 2013-10-07 2021-10-19 Masimo Corporation Regional oximetry signal processor
WO2015054161A2 (en) 2013-10-07 2015-04-16 Masimo Corporation Regional oximetry sensor
US10828007B1 (en) 2013-10-11 2020-11-10 Masimo Corporation Acoustic sensor with attachment portion
US10832818B2 (en) 2013-10-11 2020-11-10 Masimo Corporation Alarm notification system
US10279247B2 (en) 2013-12-13 2019-05-07 Masimo Corporation Avatar-incentive healthcare therapy
US11259745B2 (en) 2014-01-28 2022-03-01 Masimo Corporation Autonomous drug delivery system
US10086138B1 (en) 2014-01-28 2018-10-02 Masimo Corporation Autonomous drug delivery system
US10532174B2 (en) 2014-02-21 2020-01-14 Masimo Corporation Assistive capnography device
US9924897B1 (en) 2014-06-12 2018-03-27 Masimo Corporation Heated reprocessing of physiological sensors
US10123729B2 (en) 2014-06-13 2018-11-13 Nanthealth, Inc. Alarm fatigue management systems and methods
US10231670B2 (en) 2014-06-19 2019-03-19 Masimo Corporation Proximity sensor in pulse oximeter
US10111591B2 (en) 2014-08-26 2018-10-30 Nanthealth, Inc. Real-time monitoring systems and methods in a healthcare environment
WO2016036985A1 (en) 2014-09-04 2016-03-10 Masimo Corportion Total hemoglobin index system
US10383520B2 (en) 2014-09-18 2019-08-20 Masimo Semiconductor, Inc. Enhanced visible near-infrared photodiode and non-invasive physiological sensor
WO2016057553A1 (en) 2014-10-07 2016-04-14 Masimo Corporation Modular physiological sensors
WO2016118922A1 (en) 2015-01-23 2016-07-28 Masimo Sweden Ab Nasal/oral cannula system and manufacturing
US10568553B2 (en) 2015-02-06 2020-02-25 Masimo Corporation Soft boot pulse oximetry sensor
EP3254339A1 (en) 2015-02-06 2017-12-13 Masimo Corporation Connector assembly with pogo pins for use with medical sensors
CA2974830C (en) 2015-02-06 2023-06-27 Masimo Corporation Fold flex circuit for lnop
USD755392S1 (en) 2015-02-06 2016-05-03 Masimo Corporation Pulse oximetry sensor
US10524738B2 (en) 2015-05-04 2020-01-07 Cercacor Laboratories, Inc. Noninvasive sensor system with visual infographic display
WO2016191307A1 (en) 2015-05-22 2016-12-01 Cercacor Laboratories, Inc. Non-invasive optical physiological differential pathlength sensor
US10448871B2 (en) 2015-07-02 2019-10-22 Masimo Corporation Advanced pulse oximetry sensor
JP6855443B2 (en) 2015-08-11 2021-04-07 マシモ・コーポレイション Medical monitoring analysis and regeneration including identification marks that respond to light reduced by body tissue
US10226187B2 (en) 2015-08-31 2019-03-12 Masimo Corporation Patient-worn wireless physiological sensor
US11504066B1 (en) 2015-09-04 2022-11-22 Cercacor Laboratories, Inc. Low-noise sensor system
US11679579B2 (en) 2015-12-17 2023-06-20 Masimo Corporation Varnish-coated release liner
US10537285B2 (en) 2016-03-04 2020-01-21 Masimo Corporation Nose sensor
US10993662B2 (en) 2016-03-04 2021-05-04 Masimo Corporation Nose sensor
US11191484B2 (en) 2016-04-29 2021-12-07 Masimo Corporation Optical sensor tape
WO2018009612A1 (en) 2016-07-06 2018-01-11 Patient Doctor Technologies, Inc. Secure and zero knowledge data sharing for cloud applications
US10617302B2 (en) 2016-07-07 2020-04-14 Masimo Corporation Wearable pulse oximeter and respiration monitor
JP7197473B2 (en) 2016-10-13 2022-12-27 マシモ・コーポレイション System and method for patient fall detection
US11504058B1 (en) 2016-12-02 2022-11-22 Masimo Corporation Multi-site noninvasive measurement of a physiological parameter
WO2018119239A1 (en) 2016-12-22 2018-06-28 Cercacor Laboratories, Inc Methods and devices for detecting intensity of light with translucent detector
US10721785B2 (en) 2017-01-18 2020-07-21 Masimo Corporation Patient-worn wireless physiological sensor with pairing functionality
US10327713B2 (en) 2017-02-24 2019-06-25 Masimo Corporation Modular multi-parameter patient monitoring device
US11086609B2 (en) 2017-02-24 2021-08-10 Masimo Corporation Medical monitoring hub
US10388120B2 (en) 2017-02-24 2019-08-20 Masimo Corporation Localized projection of audible noises in medical settings
WO2018156648A1 (en) 2017-02-24 2018-08-30 Masimo Corporation Managing dynamic licenses for physiological parameters in a patient monitoring environment
WO2018156804A1 (en) 2017-02-24 2018-08-30 Masimo Corporation System for displaying medical monitoring data
WO2018156809A1 (en) 2017-02-24 2018-08-30 Masimo Corporation Augmented reality system for displaying patient data
EP3592231A1 (en) 2017-03-10 2020-01-15 Masimo Corporation Pneumonia screener
US20180271417A1 (en) * 2017-03-21 2018-09-27 Wipro Limited Method and a device for non-invasive monitoring of a blood glucose level of a user
WO2018194992A1 (en) 2017-04-18 2018-10-25 Masimo Corporation Nose sensor
US10918281B2 (en) 2017-04-26 2021-02-16 Masimo Corporation Medical monitoring device having multiple configurations
USD835283S1 (en) 2017-04-28 2018-12-04 Masimo Corporation Medical monitoring device
JP7278220B2 (en) 2017-04-28 2023-05-19 マシモ・コーポレイション Spot check measurement system
USD835282S1 (en) 2017-04-28 2018-12-04 Masimo Corporation Medical monitoring device
USD835284S1 (en) 2017-04-28 2018-12-04 Masimo Corporation Medical monitoring device
USD835285S1 (en) 2017-04-28 2018-12-04 Masimo Corporation Medical monitoring device
EP3622529A1 (en) 2017-05-08 2020-03-18 Masimo Corporation System for pairing a medical system to a network controller by use of a dongle
WO2019014629A1 (en) 2017-07-13 2019-01-17 Cercacor Laboratories, Inc. Medical monitoring device for harmonizing physiological measurements
USD890708S1 (en) 2017-08-15 2020-07-21 Masimo Corporation Connector
USD906970S1 (en) 2017-08-15 2021-01-05 Masimo Corporation Connector
US10637181B2 (en) 2017-08-15 2020-04-28 Masimo Corporation Water resistant connector for noninvasive patient monitor
CA3065746A1 (en) * 2017-08-18 2019-02-21 Abbott Diabetes Care Inc. Systems, devices, and methods related to the individualized calibration and/or manufacturing of medical devices
WO2019079643A1 (en) 2017-10-19 2019-04-25 Masimo Corporation Display arrangement for medical monitoring system
USD925597S1 (en) 2017-10-31 2021-07-20 Masimo Corporation Display screen or portion thereof with graphical user interface
WO2019089655A1 (en) 2017-10-31 2019-05-09 Masimo Corporation System for displaying oxygen state indications
US11766198B2 (en) 2018-02-02 2023-09-26 Cercacor Laboratories, Inc. Limb-worn patient monitoring device
WO2019204368A1 (en) 2018-04-19 2019-10-24 Masimo Corporation Mobile patient alarm display
WO2019209915A1 (en) 2018-04-24 2019-10-31 Cercacor Laboratories, Inc. Easy insert finger sensor for transmission based spectroscopy sensor
CN112512406A (en) 2018-06-06 2021-03-16 梅西莫股份有限公司 Opioid overdose monitoring
US10779098B2 (en) 2018-07-10 2020-09-15 Masimo Corporation Patient monitor alarm speaker analyzer
US11872156B2 (en) 2018-08-22 2024-01-16 Masimo Corporation Core body temperature measurement
CN112997366A (en) 2018-10-11 2021-06-18 迈心诺公司 Patient connector assembly with vertical detent
USD916135S1 (en) 2018-10-11 2021-04-13 Masimo Corporation Display screen or portion thereof with a graphical user interface
USD998630S1 (en) 2018-10-11 2023-09-12 Masimo Corporation Display screen or portion thereof with a graphical user interface
US11406286B2 (en) 2018-10-11 2022-08-09 Masimo Corporation Patient monitoring device with improved user interface
USD917564S1 (en) 2018-10-11 2021-04-27 Masimo Corporation Display screen or portion thereof with graphical user interface
US11389093B2 (en) 2018-10-11 2022-07-19 Masimo Corporation Low noise oximetry cable
USD917550S1 (en) 2018-10-11 2021-04-27 Masimo Corporation Display screen or portion thereof with a graphical user interface
USD999246S1 (en) 2018-10-11 2023-09-19 Masimo Corporation Display screen or portion thereof with a graphical user interface
USD998631S1 (en) 2018-10-11 2023-09-12 Masimo Corporation Display screen or portion thereof with a graphical user interface
USD897098S1 (en) 2018-10-12 2020-09-29 Masimo Corporation Card holder set
AU2019357721A1 (en) 2018-10-12 2021-05-27 Masimo Corporation System for transmission of sensor data using dual communication protocol
US11464410B2 (en) 2018-10-12 2022-10-11 Masimo Corporation Medical systems and methods
US11684296B2 (en) 2018-12-21 2023-06-27 Cercacor Laboratories, Inc. Noninvasive physiological sensor
ES2774983B2 (en) 2019-01-22 2021-06-10 Univ Sevilla PORTABLE DEVICE AND METHOD FOR NON-INVASIVE ESTIMATION OF GLUCOSE LEVEL IN BLOOD
AU2020259445A1 (en) 2019-04-17 2021-12-02 Masimo Corporation Patient monitoring systems, devices, and methods
USD919100S1 (en) 2019-08-16 2021-05-11 Masimo Corporation Holder for a patient monitor
USD985498S1 (en) 2019-08-16 2023-05-09 Masimo Corporation Connector
USD921202S1 (en) 2019-08-16 2021-06-01 Masimo Corporation Holder for a blood pressure device
USD917704S1 (en) 2019-08-16 2021-04-27 Masimo Corporation Patient monitor
USD919094S1 (en) 2019-08-16 2021-05-11 Masimo Corporation Blood pressure device
US11832940B2 (en) 2019-08-27 2023-12-05 Cercacor Laboratories, Inc. Non-invasive medical monitoring device for blood analyte measurements
WO2021077019A1 (en) 2019-10-18 2021-04-22 Masimo Corporation Display layout and interactive objects for patient monitoring
USD927699S1 (en) 2019-10-18 2021-08-10 Masimo Corporation Electrode pad
US11879960B2 (en) 2020-02-13 2024-01-23 Masimo Corporation System and method for monitoring clinical activities
WO2021163447A1 (en) 2020-02-13 2021-08-19 Masimo Corporation System and method for monitoring clinical activities
US20210290177A1 (en) 2020-03-20 2021-09-23 Masimo Corporation Wearable device for monitoring health status
USD933232S1 (en) 2020-05-11 2021-10-12 Masimo Corporation Blood pressure monitor
USD979516S1 (en) 2020-05-11 2023-02-28 Masimo Corporation Connector
USD974193S1 (en) 2020-07-27 2023-01-03 Masimo Corporation Wearable temperature measurement device
USD980091S1 (en) 2020-07-27 2023-03-07 Masimo Corporation Wearable temperature measurement device
USD946596S1 (en) 2020-09-30 2022-03-22 Masimo Corporation Display screen or portion thereof with graphical user interface
USD946597S1 (en) 2020-09-30 2022-03-22 Masimo Corporation Display screen or portion thereof with graphical user interface
USD946598S1 (en) 2020-09-30 2022-03-22 Masimo Corporation Display screen or portion thereof with graphical user interface
USD997365S1 (en) 2021-06-24 2023-08-29 Masimo Corporation Physiological nose sensor
USD1000975S1 (en) 2021-09-22 2023-10-10 Masimo Corporation Wearable temperature measurement device

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO1993001745A1 (en) * 1991-07-17 1993-02-04 Georgia Tech Research Corporation Measuring molecular change in the ocular lens
EP0589191A1 (en) * 1992-09-04 1994-03-30 Edward W. Stark Non-invasive glucose measurement method and apparatus
WO1994016614A1 (en) * 1993-01-28 1994-08-04 Braig James R Noninvasive pulsed infrared spectrophotometer
US5341805A (en) * 1993-04-06 1994-08-30 Cedars-Sinai Medical Center Glucose fluorescence monitor and method
EP0623307A1 (en) * 1993-05-07 1994-11-09 Diasense, Inc. Non-invasive determination of constituent concentration using non-continuous radiation
EP0631137A2 (en) * 1993-06-25 1994-12-28 Edward W. Stark Glucose related measurement method and apparatus
US5435309A (en) * 1993-08-10 1995-07-25 Thomas; Edward V. Systematic wavelength selection for improved multivariate spectral analysis

Family Cites Families (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US3807862A (en) 1972-12-18 1974-04-30 Sybron Corp Raman spectroscopy in the presence of fluorescence
US4031398A (en) 1976-03-23 1977-06-21 Research Corporation Video fluorometer
FR2554586B1 (en) 1983-09-30 1986-03-21 Centre Nat Rech Scient SPECTROMETRY DISCRIMINATION METHOD AND DEVICE FOR IMPLEMENTING THE METHOD
JPH073366B2 (en) * 1988-02-24 1995-01-18 株式会社日立製作所 Spectrofluorometer
US5037200A (en) 1989-07-11 1991-08-06 Tosoh Corporation Laser-operated detector
US5266498A (en) * 1989-10-27 1993-11-30 Abbott Laboratories Ligand binding assay for an analyte using surface-enhanced scattering (SERS) signal
MY107650A (en) * 1990-10-12 1996-05-30 Exxon Res & Engineering Company Method of estimating property and / or composition data of a test sample
US5243983A (en) 1990-12-14 1993-09-14 Georgia Tech Research Corporation Non-invasive blood glucose measurement system and method using stimulated raman spectroscopy
US5212099A (en) 1991-01-18 1993-05-18 Eastman Kodak Company Method and apparatus for optically measuring concentration of an analyte
US5280788A (en) 1991-02-26 1994-01-25 Massachusetts Institute Of Technology Devices and methods for optical diagnosis of tissue
JPH04348258A (en) * 1991-05-27 1992-12-03 Kowa Co Multi-channel optical measuring device
US5348018A (en) 1991-11-25 1994-09-20 Alfano Robert R Method for determining if tissue is malignant as opposed to non-malignant using time-resolved fluorescence spectroscopy
US5491344A (en) * 1993-12-01 1996-02-13 Tufts University Method and system for examining the composition of a fluid or solid sample using fluorescence and/or absorption spectroscopy

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO1993001745A1 (en) * 1991-07-17 1993-02-04 Georgia Tech Research Corporation Measuring molecular change in the ocular lens
EP0589191A1 (en) * 1992-09-04 1994-03-30 Edward W. Stark Non-invasive glucose measurement method and apparatus
WO1994016614A1 (en) * 1993-01-28 1994-08-04 Braig James R Noninvasive pulsed infrared spectrophotometer
US5341805A (en) * 1993-04-06 1994-08-30 Cedars-Sinai Medical Center Glucose fluorescence monitor and method
EP0623307A1 (en) * 1993-05-07 1994-11-09 Diasense, Inc. Non-invasive determination of constituent concentration using non-continuous radiation
EP0631137A2 (en) * 1993-06-25 1994-12-28 Edward W. Stark Glucose related measurement method and apparatus
US5435309A (en) * 1993-08-10 1995-07-25 Thomas; Edward V. Systematic wavelength selection for improved multivariate spectral analysis

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6044285A (en) * 1997-11-12 2000-03-28 Lightouch Medical, Inc. Method for non-invasive measurement of an analyte
US6377828B1 (en) 1997-11-12 2002-04-23 Lightouch Medical, Inc. Method for non-invasive measurement of an analyte
US6289230B1 (en) 1998-07-07 2001-09-11 Lightouch Medical, Inc. Tissue modulation process for quantitative noninvasive in vivo spectroscopic analysis of tissues
WO2004109263A1 (en) * 2003-06-03 2004-12-16 Bayer Healthcare Llc Readhead for optical inspection apparatus
US7499154B2 (en) 2003-06-03 2009-03-03 Siemens Healthcare Diagnostics Inc. Readhead for optical inspection apparatus

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AU715281B2 (en) 2000-01-20
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PT863718E (en) 2004-02-27
EP0863718A1 (en) 1998-09-16
EP0863718B1 (en) 2003-09-10
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ATE249166T1 (en) 2003-09-15
DK0863718T3 (en) 2004-01-26

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