Oximeter for reliable clinical determination of blood oxygen saturation in a fetus

Abstract: With the crude instrumentation now in use to continuously monitor the status of the fetus at delivery, the obstetrician and labor room staff not only over-recognize the possibility of fetal distress with the resultant rise in operative deliveries, but at times do not identify fetal distress which may result in preventable fetal neurological harm. The invention, which addresses these two basic problems, comprises a method and apparatus for non-invasive determination of blood oxygen saturation in the fetus. The apparatus includes a multiple frequency light source which is coupled to an optical fiber. The output of the fiber is used to illuminate blood containing tissue of the fetus. In the preferred embodiment, the reflected light is transmitted back to the apparatus where the light intensities are simultaneously detected at multiple frequencies. The resulting spectrum is then analyzed for determination of oxygen saturation. The analysis method uses multivariate calibration techniques that compensate for nonlinear spectral response, model interfering spectral responses and detect outlier data with high sensitivity.

Description: Background of the Invention This invention relates to both a method and apparatus, as illustrated in Figure 1, for the non-invasive determination of blood oxygen, particularly in a fetus. Oxygen is essential to human life; for the adult, child and fetus. Asphyxia is the condition where the lack of oxygen causes the cessation of life. Hypoxia is a deficiency in the amount of oxygen reaching the tissues. While hypoxia is not fatal it may cause severe neurological damage. Methods currently available to the obstetrician and labor room staff for assessment of fetal status include non-invasive measures such as monitoring the contraction patterns of the expectant mother and monitoring fetal heart rate. In the presence of possible fetal distress suggested by clinical evaluation, or non-invasive monitoring methods, or invasive procedures such as intermittent fetal scalp blood samples (for fetal blood pH determination), or percutaneous umbilical blood sampling (PUBS), emergent caesarean section is often performed. With both the non-invasive or invasive measures for determining fetal status identified above, information concerning the most important physiological parameter of fetal well-being, blood oxygen saturation, is not available to the physician. Changes in fetal heart rate and blood pH are secondary manifestations of a primary condition, fetal hypoxia. The ability to determine blood oxygen saturation in both pediatric (including newborn) and adult populations via oximetry, particularly pulse oximetry, is well known. Oximetry in such applications (but not in fetal monitoring, as explained below) is an accepted method of oxygen determination and has been utilized in clinical medicine for approximately 10 years. It is used to ensure that the patient's oxygen level is adequate to prevent damage to organs such as the brain, heart, lungs, and kidneys. There are two types of oximeters: (1) invasive oximeters; and (2) non-invasive pulse oximeters. The invasive oximeters must have the light beam and detector optics in contact with blood. In clinical medicine the sampling device, typically a fiber optic catheter probe, is placed in a large blood vessel in the body and measurement is made on the blood that passes by the catheter. Non-invasive (i.e., pulse) oximeters do not require direct contact with the blood. The non-invasive pulse oximeters are able to remove the interferences generated by the tissue and bone, determining the difference between data from high and low pulse pressures by performing a ratio. As only arterial blood pulses, non-invasive oximeters only analyze arterial blood. The prior patented technology can be broken down into three categories: 1) Non-invasive blood oxygen saturation determination instruments utilizing a transmission sampling technique, with analysis based on two wavelengths. U.S. patent Nos. 4,770,179, 4,700,708, 4,653,498 and 4,621,643 to New et al. are believed to represent the best examples of this technology. 2) Invasive blood oxygen saturation determination instruments utilizing a fiber optic probe with reflectance sampling in which the probe must be inserted into a blood containing area. U.S. patent No. 4,114,604 to Shaw et al. is believed to represent the best example of this prior art. 3) Non-invasive blood oxygen saturation determination utilizing a reflectance method with analysis of the reflected light by a linear algorithm employing only two wavelengths. This technology is represented by U.S. patent No. 4,859,057 to Taylor et al. The methods disclosed in the above-identified patents on pulse oximetry (e.g. New et al. and Taylor et al.) are based upon several related facts. First, the concentration of blood in a given location of the body varies with each pulse of the heart. With each heart beat a systolic pulse pressure is generated which leads to a maximal expansion of the vascular system. During the resting period of the cardiac cycle (i.e., diastole) there is no pressure generated and the vascular system returns to a minimal size. The light transmitted or reflected during diastole interacts with the skin, fat, bone, muscle and blood. Light transmitted or reflected during systole, interacts with the same skin, fat, bone, muscle, and blood, plus an additional amount of blood which is present due to the expansion of the arterial system. If the diastolic signal is subtracted from the systolic signal the result is a signal which represents the additional amount of blood. The quality and clarity of the subtraction generated signal is related to the amount of additional blood present which, in turn, is proportional to the pulse pressure, (i.e., the difference between systolic pressure and diastolic pressure). See Figure 2 for a graphical representation of the above process. All present pulse instruments assess variations in red blood cell concentration by utilizing a light frequency near or at the isobestic point, where measurement of pulsatile volume is made independent of oxygen saturation. An isobestic wavelength is one which does not change intensity with oxygen saturation but only with blood concentration. A second wavelength in the red portion of the spectrum, which is sensitive to oxygen saturation, is detected by either a transmission or reflection sampling technique. By using the isobestic wavelength as a reference and by comparing its spectral intensity to the intensity of the second wavelength in the red portion of the spectrum, it is possible to determine the oxygen saturation of the blood non-invasively. Oximeters based on invasive procedures also use a frequency at or near the isobestic point. In invasive instruments the intensity at the isobestic frequency is related to the amount of light returning or reflected by the sample which, in turn, is related to the hematocrit (i.e., the percent volume of the blood volume occupied by red blood cells). Basically, invasive methods simply take a ratio of the "red" wavelength divided by the isobestic wavelength. In all prior known applications, the algorithm used for analysis of the two, or sometimes three, wavelengths detected have typically utilized a single analysis frequency with a single background correction frequency to determine a single proportionality constant describing the relationship between absorbance and concentration (i.e., univariate or one variable algorithms). In the patents to New et al. the blood oxygen saturation determination is made by utilizing a ratio between the ambient transmission and the change in transmission occurring during each pulse at both wavelengths. Shaw, et al., U.S. patent No. 3,847,483, describes non-invasive apparatus using two wavelengths of light which originate from two alternately energized light-emitting diodes. The oxygen saturation is then determined by an equation, which may be characterized as a nonlinear, bivariate algorithm, employing 6 calibration constants. U.S. patent No. 4,114,604, also to Shaw, et al., discloses what is described as an improved catheter oximeter which operates on radiation at three or more different wavelengths. Shaw et al. recognize the nonlinear characteristics involved in oxygen saturation determination and recommend a possible method for overcoming this problem. It is important to note that the Shaw methodology utilizes only a few discrete non-overlapping frequencies taken at different non-overlapping time periods. It is also important to note that it is not suited to non-invasive determinations as it does not disclose any method of eliminating background components such as hair, bone and skin. The reflectance oximeter disclosed by Taylor et al., No. 4,859,057, does not disclose a specific mathematical relationship between the two wavelengths utilized. The method of determining oxygen saturation requires calculation of the difference between the minimum and maximum components of an a.c. signal. The resulting difference is then used to determine O 2 sat. through the use of a look-up table. An additional methodology associated with invasive reflectance determination is disclosed by Hoeft et al., "In Vivo Measurement of Blood Oxygen Saturation by Analysis of Whole Blood Reflectance Spectra", SPIE Vol 1067, pp. 62-68, Optical Filters in Medicine IV (1989). The actual instrumentation utilized consists of an optical multichannel instrument that is a grating spectrometer with a CCD array detector. They employ a simple relationship based upon one of the two wavelength regions used being an isobestic range. Oxygen saturation is then assumed to be a linear function of the ratio of the light intensity reflected from the blood at the isobestic and non-isobestic wavelengths as follows: O 2 Saturation = A + B(I 1 /I 2 ) where I 1 is light intensity diffusely scattered back from the blood at the isobestic wavelengths (840-850 nm), I 2 is the light intensity diffusely back scattered at the non-isobestic wavelengths (600-840 nm), and A and B are experimentally determined calibration coefficients. The method of Hoeft et al., differs from the other above identified methodologies in that they allow for simultaneous sampling of multiple frequencies. Before the O 2 saturation of an unknown sample can be determined, the hemoglobin of the blood sample must be known for the calculation of coefficients A & B. Although Hoeft's methodology utilizes information from more than one frequency, it uses a univariate algorithm. Additionally, Hoeft's method is not suited to non-invasive analysis since it requires a determination of hemoglobin concentration via wet chemistry. Over the past few years significant research has been done in the attempt to create a clinically useful pulse oximeter for fetal monitoring, but none have been reliable or accurate enough to reach clinical medicine. The reason for this failure is multi-factorial, including the difficulty of the environment and the parameters under which a fetal pulse oximeter muse operate. This work has focused on modifying existing pulse oximeters for reflectance measurement. The main reasons existing oximeter technology is not suitable for fetal monitoring are: (1) the requirement that the sampling measurement be made by reflectance spectroscopy; (2) fetal circulation has a much lower pulse pressure than that of adults; (3) the critical range for making a decision on operative intervention will be in the 30% to 60% oxygen saturation region; and (4) fetal heart rate is approximately twice that of the average adult. In comparison to transmission measurements (as used by New et al.), the use of reflectance spectroscopy decreases the magnitude of the return signal by approximately a factor of 10. As the signal-to-noise ratio decreases, the precision of the oxygen saturation determination decreases. A requirement of non-invasive arterial blood oxygen saturation determination is that the background components be removed. To remove background components, existing non-invasive oximeters use the difference between diastole and systole signals. The larger the difference between systolic and diastolic, the larger the blood volume analyzed, and the higher the signal-to-noise ratio. While the fetus is in utero, systolic pressures are 75-80 mm Hg and diastolic pressures of 50-55 mm Hg. Thus, the difference between diastole and systole is significantly less, approximately 20 mm Hg, in comparison to 60 mm Hg pulse pressure in the average adult. The environment under which the fetal pulse oximeter is required to operate is further complicated by the low oxygen saturations it is required to determine. The accuracy of oxygen saturation determination with known pulse oximeters becomes quite poor at saturations of less than 75%. The source of this error is the nonlinear relationship between oxygen saturation and reflected or transmitted light intensity. Thus, using data reported by Chapman et al. and Severinghaus et al. with existing technology, the oxygen saturation of the fetus cannot be determined with an error of less than 10% for the expected fetal oxygen saturations below 75%. In summary, the physiological and physical parameters associated with fetal monitoring represent an extreme environment under which existing oximeter technology cannot operate with reasonable, clinically acceptable accuracy. It is essential to realize that all prior art oximeters, both pulse and invasive, have used 3 or less measured intensities and/or two or less variables for analysis. Both New et al. and Shaw et al. use a limited number of wavelengths, but use nonlinear univariate or bivariate algorithms. No algorithm is specified in Taylor. Methods that simultaneously use two or more variables are known as multivariate methods. As used in this application, multivariate will refer to simultaneous analysis of three or more variables. Not only do multivariate statistical methods provide enhanced analysis of component concentrations, but such multivariate methods have also recently made possible the estimation of physical and chemical properties of materials from their spectra. A simple illustration of the increased capability of multivariate methods in component concentration determination is provided by Figures 3A., 3B. and 3C. In Figure 3A. one can see that an impurity component, whose spectrum overlaps that of the analyte, can affect the spectrum of the analytic band and, therefore, the accuracy of the analysis will suffer when the analysis is performed at a single wavelength ν 1 or when ratioing ν 1 to a reference wavelength. The measured absorbance, A m , at the analysis wavelength, ν 1 , for a sample containing the impurity is different than the true absorbance, A t , of the analyte at that wavelength. If the calibration curve in Figure 3.B. is from spectra of samples containing no impurity, then the presence of the impurity in the sample will yield an apparent concentration that may be quite different from the true concentration. This error will remain undetected if the intensity was measured at only one wavelength. If the impurity is included in the calibration samples but varies randomly in concentration in the samples, a calibration plot similar to that in Figure 3.B. will exhibit large scatter among the data, and the result will be both a poor calibration curve and concentration estimates that have poor precision for the unknown samples. However, with analysis at more than one wavelength, not only can the presence of the impurity be detected, Figure 3.C., but if its presence is included in the calibration, quantitative analysis of the analyte is possible with multivariate calibration methods, even if the impurity and its concentration are unknown. An indication that the unknown is different from the set of calibration samples not containing the impurity is obtained by plotting the absorbance of the calibration samples and the unknown sample spectra at two frequencies selected for analysis. As exhibited in Figure 3.C., the spectrum of the sample containing the impurity (indicated by "x") is obviously different than that of the calibration spectra (i.e. it is an outlier). Outliers are those samples or spectra among either the calibration or unknown data which do not exhibit the characteristic relationship between composition and spectra of the other calibration samples. The sensitivity in detecting outliers is increased by increasing the number of frequencies included in the analysis. The number of independently varying impurities that can be accounted for in the analysis is also increased by increasing the number of frequencies utilized. Accurate univariate methods are dependent upon the ability to identify a unique, isolated band for each analyte. Multivariate methods can be used even when there is overlap of spectral information from various components over all measured spectral regions. Unlike univariate methods, multivariate techniques can achieve increased precision from redundant information in the spectra, can account for base-line variations, can more fully model nonlinearities, and can provide outlier detection. The general approach that is used when statistical multivariate methods are applied to quantitative spectroscopy problems requires calibration in which a mathematical model of the spectra is generated. See Figure 4. This calibration model can then be used for prediction of concentrations in unknown samples. The spectra of a series of calibration standards are first obtained, such that the spectra span the range of variation of all factors which can influence the spectra of future unknown samples. Assuming that the calibration uses samples that contain all the components expected in the unknown samples and spans their expected range of variation, the calibration will be able to empirically account for (or at least approximate) non-ideal behavior in Beer's law, independent of the source of the non-ideal behavior. Nonlinearities may arise from spectroscopic instrumentation, dispersion, or intermolecular interactions. As used in this application "nonlinear" refers to any deviation in Beer's law or the inverse Beer's law relationship (i.e., which cannot be modeled with the standard linear expression y = mx + b ; where y represents the dependent variable, x is the independent variable, and m and b are, respectively, the slope and intercept). The spectral response with changing oxygen saturation is not linear. Once the empirical calibration relating spectra and component concentrations has been performed, then the spectrum of the unknown sample can be analyzed by a multivariate prediction step to estimate the component concentration or properties. If the calibration samples are truly representative of the unknown sample, then the result of the analysis will be an estimate which will have a precision similar to that found in the set of calibration samples. In addition, spectral residuals (i.e., the difference between measured and estimated spectra) can be used to determine if the unknown sample is similar to the calibration samples. If the unknown sample is not representative of the calibration samples (i.e., is an outlier) spectroscopic interpretation of the residuals can often be made to determine the source of any differences between unknown and calibration samples. The multivariate methods which are best suited for analysis of oximeter data are those that model the spectra using an inverse Beer's law model, such as principal component regression (PCR) or partial least squares (PLS). In an inverse Beer's law model the concentration of each component in the mixture is represented as a linear function of the sampled absorbance spectrum. An advantage of this multivariate approach is that the nonlinearities in the spectral response to changes in composition can be accommodated without the need for an explicit model. For the chemical components to be predicted, PCR or PLS analysis is used to construct a linearly independent set of factors based upon a set of calibration spectra (i.e., spectra for which the composition to be predicted is known). The number of these component factors which are useful for prediction (the "rank" of the model) is selected by a cross-validation procedure, which is also used to estimate the precision of subsequent predictions. PLS and PCR methods are capable of achieving accurate and precise results in the presence of linear and nonlinear dependencies in the absorbance spectrum at various frequencies. Thus, an entire spectral region can be used in multivariate analysis without the need for the spectroscopist to choose an optimal set of wavelengths for the analysis. Similarly, these methods of computation are not sensitive to linear dependencies introduced by over sampling of information at many frequencies in the construction of the calibration samples. U.S. patent No. 4,975,581 to Robinson et al. discloses a method and apparatus for, particularly, quantitatively determining the amount of glucose in a human. The method relates to determining one or more unknown concentration values of a known characteristic (e.g. glucose) via the steps of: a. Irradiating a biological fluid (i.e., blood) having unknown values of a known characteristic (i.e., glucose) with infrared energy having a least several wavelengths so that there is differential absorption of at least some of the wavelengths by the biological fluid as a function of both the wavelengths and the known characteristic. b. Measuring the absorption variations from the biological fluid. c. Calculating the unknown values of the known characteristic (i.e., glucose) in the biological fluid from the measured absorption variations utilizing a multivariate algorithm and a mathematical calibration model. The model is constructed from the set of samples and is a function of the known values of the characteristic and the intensity variations vs. wavelengths information obtained from irradiating the set of samples. The method can be used in vivo and non-invasively, in vivo and invasively, and in vitro. The applicants recognize that the preferred embodiment of U.S. patent No. 4,975,581 utilizes the partial least squares algorithm. However, the reasons for utilizing the PLS algorithm in the present invention are quite different from the reasons it was utilized to determine glucose concentrations. The limiting factor in the determination of a blood analyte, such as glucose, is the lack of information available. The determination of a blood analyte requires a very high signal-to-noise ratio and a sophisticated algorithm for extraction of a minuscule amount of information (glucose is, normally, 0.1 weight percent of blood). In the case of a pulse oximeter suitable for fetal monitoring, the information is abundant, but the environment of operation is extreme. As has been previously mentioned, the reflected light-oxygen saturation relationship is highly nonlinear, the signal for analysis is extremely noisy and the present invention must remove the interfering background components by correlating with the pulsating blood. Also the frequency regions used for analysis are separate and the basic instrumentation is different. Despite past and continuing failures, an accurate assessment of fetal oxygen saturation can be obtained by measuring the peripheral blood oxygen saturation in the fetus. The technology for the realization of this goal, no more invasive than the electronic heart monitors currently used, is disclosed herein. This improved method and apparatus should lead to a reduction in the rate of Cesarean sections for apparent fetal distress. Such a monitor should improve the survival rate of otherwise compromised fetuses by early and accurate detection of real problems. Thus, the ultimate goal of a healthy mother and baby will be enhanced. It is an object of the present invention to provide a fetal oximeter which can easily and accurately operate in the extreme environment of fetal monitoring, thus overcoming the shortcomings of existing technology. The object of the pulse oximeter of the present invention is to overcome the limitations of prior art oximeters, including their inability to obtain information at a variety of wavelengths simultaneously, and the limitation inherent in the time necessary for the intermittently energized light sources in such prior art oximeters to reach the required brightness and stability. In contrast to Shaw et al., another object of the present invention is to utilize multiple frequencies with simultaneous sampling, employ an algorithm which can model nonlinearities over the entire clinically observed blood oxygen saturation range and which is suitable for non-invasive measurements in the fetus' environment. It is another and important object to determine if a sample's spectrum and subsequently determined oxygen value is representative of the calibration samples. This is crucial for the implementation of an accurate and reliable clinical instrument. Identifying and removing outlier samples from the calibration set will drastically improve the accuracy and precision of the analysis. Identification of outliers among the unknown samples provides information for evaluating the validity of the fetal blood oxygen saturation determination. This ability is especially important in this medical application because the consequences of hypoxia on the fetus can result in death or lifelong disability. According to the present invention, utilization of a pulse oximeter employing outlier detection methods would result in the generation of a "flag" when analyzing a spurious sample, indicating that the analysis was unreliable. It is another object of the invention to provide an oximeter based on a multivariate inverse Beer's law model, such as PLS or PCR, to provide the following benefits: a. Accommodation for nonlinear spectral responses without a degradation in prediction accuracy; b. Compensation for the presence of interferences of undetermined origin (e.g., chemical contaminants or physiological variations); and c. Identification of spurious or outlier samples in both the calibration samples and in the unknown samples. No simple or obvious combination of the prior art will result in an instrument capable of non-invasively and accurately monitoring fetal oxygen saturation over wide ranges of saturation values. For instance, existing oximeters do not measure multiple wavelengths simultaneously. Therefore, the full advantages of using a powerful multivariate algorithm like PLS could not be obtained due to the limited number of frequencies available when using existing instrumentation. Though Taylor, et al., discloses reflectance sampling, all known commercially available pulse oximeters use transmission sampling. Further, conventional oximeters do not use gratings or any mechanism that separates light into its constituent wavelengths. The present invention represents a significant advancement in apparatus and methodology by: a. Simultaneous and rapid sampling at multiple frequencies. Rapid sampling is necessary due to the rapid rate of the fetal heart and large variations in beat-to-beat pulse pressure. To distinguish the true maximum and minimum of the vascular system, a sampling rate ≥ 50 Hz would be desirable, and is feasible using our technology. b. Use of an emitter/detector apparatus, in connection with fiber optics, which is well-suited for attachment to the fetus for reflection sampling. c. Analysis of the spectral information with a multivariate algorithm. A multivariate analysis will be superior to either univariate or bivariate analyses because the information available at multiple frequencies can be combined to yield more information with a higher precision and reliability than the information available at one or several discrete frequencies or ratios. The preferred algorithms are known as partial least squares (PLS) and principle component regression (PCR). Other suitable algorithms are classic least squares (CLS), Q-matrix method, cross correlation, Kalman filtering and multiple linear regression (MLR). MLR is sometimes referred to as inverse least squares (ILS). d. Providing the doctor with a measure of validity or an assurance of accuracy by employing outlier detection methods. The ability to identify false negatives is extremely important because the consequences of hypoxia on the fetus can result in death or life-long neurological deficits. On the other hand, the ability to eliminate false positives will reduce the incidence of unnecessary caesarean sections, a surgical intervention with risks for both fetus and mother. Summary This invention relates to an oximeter as claimed in claim 1 and to a method as claimed in claim 6. Brief Description of the Drawings Figure 1 is a schematic illustration of the preferred embodiment of the invention; Figure 2 is a graphical representation of the basic principle of how a conventional pulse oximeter obtains the "additional" blood signal; Figure 3 is a series of graphs comparing univariate calibration to multivariate calibration; Figure 4 is a chart showing the general approach used in multivariate statistical methods to generate a mathematical calibration model and to use this model to quantitatively determine concentrations and/or properties from the spectra of unknown samples; Figure 5 is a schematic of the test apparatus of the present invention; Figure 5A is an enlarged view of the fiber optic bundle of Figure 5; Figure 6 is a graph illustrating the raw data (reflectance intensity vs. wavelength) obtained at various O 2 saturations with the apparatus of Figure 5; Figure 7 is a graph illustrating the analysis of the raw data with the algorithm of New, et al.; Figure 8 is a graph illustrating the nonlinear relationship between a ratio of reflected light ratios, as specified by New, et al, and oxygen saturation; Figure 9 is a graph illustrating the analysis of the raw data with the algorithm of Hoeft, et al.; Figure 10 is a graph illustrating the analysis of the raw data with the algorithm of Shaw, et al.; Figure 11 is a graph illustrating the correlation between O 2 saturation and frequency and comparing the frequencies utilized by New, et al., Shaw, et al., and the multivariate algorithms of the present invention. Figure 12 is a plot of measured oxygen saturation vs. oxygen saturation predicted by the principle component regression (PCR) algorithm; Figure 13 is a plot of measured oxygen saturation vs. oxygen saturation predicted by the partial least squares (PLS) algorithm; Figure 14 is a graph illustrating the addition of random noise to a single-beam spectrum corresponding to a sample with O 2 saturation of 70% and a hematocrit (HCT) of 35%; Figure 15 is a plot of measured oxygen saturation vs. oxygen saturation predicted by the analysis of single-beam spectra (with noise added) with the algorithm of New, et al.; Figure 16 is a plot of measured oxygen saturation vs. oxygen saturation predicted by the analysis of single-beam spectra (with noise added) with the algorithm of Hoeft, et al.; Figure 17 is a plot of measured oxygen saturation vs. oxygen saturation predicted by the analysis of single-beam spectra (with noise added) with the algorithm of Shaw, et al.; Figure 18 is a plot of measured oxygen saturation vs. oxygen saturation predicted by the analysis of single-beam spectra (with noise added) with the PCR algorithm; Figure 19 is a plot of measured oxygen saturation vs. oxygen saturation predicted by the analysis of single-beam spectra (with noise added) with the PLS algorithm; and Figure 20 is a graphical representation of the electrical activity of the heart and its temporal relationship to the pressure or size of the vascular system. Description of the Preferred Embodiment To demonstrate the nonlinear reflected light response of the blood at various oxygen levels, the effect of physiological hematocrit variation, the inadequacies of the algorithms used in current oximeters, and the superiority of multivariate analysis, a set of human blood samples were examined using reflectance spectroscopy. The samples were examined at hematocrit levels ranging from 25% to 47% and with oxygen saturations ranging from 30% to 100%. Standard blood bank solutions of packed red blood cells were used to create solutions with different hematocrits. The solutions of packed red blood cells were diluted with normal physiological saline to create hematocrit levels commonly encountered in clinical medicine. The four hematocrit levels examined were 47%, 35%, 33% and 25%. Each of the blood solutions at the above identified hematocrit levels was placed in a tonometer which allowed controlled oxygenation of blood while maintaining normal physiological temperature (i.e. 37°C/98.6° F). The blood solutions were gently stirred to prevent settling or separation of the blood components and to provide adequate mixing. The rotational speed of the tonometer stir rod was minimized to prevent cell lysis. The oxygenation of the blood was performed using a gaseous mixture of nitrogen, oxygen and carbon dioxide. The percentages of oxygen and nitrogen were varied to provide adequate changes in the oxygen saturation of the blood solution. The percentage of carbon dioxide was maintained throughout the experiment at a physiological level of between 4 and 8%. Data were obtained by first establishing an appropriate hematocrit level and then varying the oxygen saturation from approximately 30% to 100%, as explained above. For each oxygen saturation examined, a 4 ml blood sample was removed from the tonometer utilizing a standard sealed syringe. A 2 ml amount of sample was placed immediately in a glass cuvette, and the syringe with the remaining 2 ml was capped. The syringe was then placed on ice to prevent changes in oxygen saturation during transport to a laboratory for conventional blood gas analysis. The oxygen saturation determination was performed on a Radiometer OSM3 Hemoximeter. For each oxygen saturation examined, the 2 ml which had been placed in the glass cuvette was examined in reflectance with the test apparatus 11 illustrated in Figure 5. Apparatus 11 includes a spectrometer 13, a cuvette holder 15 and a computer 17. Spectrometer 13 includes a halogen light source 21, a concave focusing mirror 23, a fiber optic housing 25, a second fiber optic housing 27, a grating 29, an array detector 31, and instrument electronics 35. Spectrometer 13 is connected to cuvette holder 15, via fiber optic bundle 37, and to computer 17, via cable 39. Cuvette holder 15 includes a base 41, having a first or cuvette supporting arm 43 and a second arm 45. Arm 43 includes a cavity 47 for receiving and properly positioning a standard laboratory cuvette 49. Arm 45 includes an opening 51, through which passes fiber optic bundle 37, and supports a pair of compression springs 53 and 55. The right-hand end 57 of bundle 37, which is accurately squared off, is securely contained in a rigid sleeve 59 which, in turn, is held in bracket 61 via set screw 63. Springs 53, 55 hold end 57 with reproducible contact against spacer slide 65 which, in turn, is passed into contact with one of the sides of cuvette 49. Computer 17 includes a microprocessor and associated electronics 71, video monitor 73, disk drive 75, and a key board 77. As illustrated in Figures 5 and 5A, bundle 37 includes a central illumination or input fiber 81 and a surrounding bundle of receiving or output fibers 83. Again, with reference to Figure 5, quartz-halogen light source 21, generating light in the 500 nm to 1000 nm frequency region, is coupled into fiber optic bundle 37 to provide illumination of sample 85. The central fiber 81 serves as the illumination fiber while surrounding fibers 83 serve as receivers for transporting the reflected light from the sample back to spectrometer 13. The reflected light is then separated by frequency using a standard grating spectrometer and recorded utilizing a charge coupled device (CCD) array detector 31, specifically a Phillips module type 56470 CCD detector array, at frequencies from 500 to 1000 nm. The detector was scanned 128 times for a total scan time of approximately one minute, with the intensity values from a given frequency subsequently coadded to improve the signal-to-noise ratio. The resulting intensity values at each frequency (i.e. single-beam spectral values) were then stored on a computer disk without further manipulation, to serve as the data set for subsequent analysis. The process of establishing a hematocrit and then varying the oxygen saturation for such hematocrit was performed a number of times at each of the hematocrit levels previously identified (i.e. 47, 35, 33, 25%). Approximately 25 samples were obtained at each hematocrit level with the oxygen saturation values of these samples being distributed from 30% to approximately 100% saturation. The raw data is illustrated in Figure 6. The data set obtained with the apparatus of Figure 5 was then analyzed using a variety of algorithms which represent: (1) algorithms presently utilized on commercially available oximeters and described in prior art patents; (2) algorithms published in the current literature; and (3) multivariate algorithms not previously utilized for oxygen saturation determination. The specific algorithms and how they will be referenced are: (1) Single ratio method as described by New et al. in U.S. patent No. 4,653,498; (2) Sum of intensities ratio method, as described by Hoeft et al.; (3) Multiple ratio method as described by Shaw et al. in U.S. patent No. 4,114,604; (4) Principle component regression (PCR), not previously utilized in oxygen saturation determination; and (5) Partial least squares (PLS), not previously utilized in oxygen saturation determination. Single Ratio Method The single ratio method describes the development of a linear regression using four constants based on a ratio of intensities at 660 nm and 940 nm. Because New et al. specify the light sources as light emitting diodes (LEDs), which emit a narrow range of frequencies about their center frequency, the intensity value used for a given frequency was the average intensity value of several surrounding frequencies. To model the method and apparatus of New et al., the 660 nm intensity value was calculated as the average of the single-beam intensities from 658 nm to 662 nm, (i.e. 5 intensity values). The value for 940 nm was obtained from 938 nm to 942 nm in a similar manner, again using 5 intensity values. In New et al. the equation for determination of the regression constants is: New et al., specify using four different saturation values and their corresponding intensity values for determination of the regression constants (i.e., K B2 , K B2 , K A1 , K A2 ). This represents a condition of four equations and four unknowns. For actual determination of the coefficient values, one of the four coefficients must be arbitrarily set, typically, to 1.0. This method of coefficient determination is feasible, but a better method to determine the constants is to utilize the intensity ratios from all calibration samples and their corresponding saturation values, and create a situation where there are many more equations than unknowns. In a condition with more equations than unknowns, a nonlinear least squares regression analysis can be performed to minimize error. We determined the constants using the modified Gauss-Newton method for the fitting of nonlinear regression functions by least squares. The analysis was performed separately at each individual hematocrit (and at all oxygen saturation levels for each hematocrit), and then upon the entire data set including all hematocrits and oxygen saturation levels together. The results are shown in Figure 7 where Predicted Oxygen Saturation was determined by the modified Gauss-Newton method, and Measured Oxygen Saturation was determined with the Radiometer OSM3 Hemoximeter. The average errors are set forth in Table 1. Hematocrit Average Absolute Error of Percent Oxygen Saturation 25% 4.4 33% 11.1 35% 8.4 47% 4.2 all together 8.9 Intensity sum Ratio Method The method as described by Hoeft et al. consists of a simple linear regression based upon a ratio of the sum of the intensities from 600 nm to 840 nm, R sig , and a second sum of the intensities from 840 to 850 nm, R ref . Specifically, R sig is the sum of 289 intensity values corresponding to frequencies between 600 nm and 840 nm, and R ref is the sum of 13 intensity values from 840 nm to 850 nm. The relation is stated as: O 2 Saturation = A + B (R ref /R sig ) where A and B are hematocrit dependent. Utilizing the data set obtained from the apparatus of Figure 5, a linear regression was performed on each hematocrit group individually and on the four different hematocrit groups combined together. The results are as illustrated in Figure 9. The average errors are set forth in Table 2. Hematocrit Average Absolute Error of Percent Oxygen Saturation 25% 2.9 33% 3.1 35% 3.1 47% 2.6 all together 5.3 Multipie Ratio Method The U.S. patent No. 4,114,604 to Shaw et al. describes the use of multiple ratios utilized in a nonlinear function. The specific ratios described are R 1 = (intensity at 669 nm)/(intensity at 698 nm) and R 2 = (intensity at 798 nm)/(intensity at 698 nm) . Again the specific intensity values used in our analysis were the average of 5 data values surrounding the specific frequency desired. Shaw et al. propose a rational function model of the form Where S is the percent oxygen saturation, the Ai's and Bi's (8 total) are model parameters, all of which have to be estimated. Shaw et al. recommend certain constraints among the parameter estimates, such as A 3 = A 0 + A 1 + A 2 and B 3 = B 0 + B 1 + B 2 . Shaw et al. also suggest that the parameter estimates should be selected such that the partial derivative of the above equation with respect to R 1 should be zero near one extreme of S, while the partial derivative of S with respect to R 2 should be zero near the other extreme of S. If all four of these constraints are used, then there are essentially four parameters remaining in the model. However, Shaw et al. do not provide other details on how to estimate the model parameters. In trying to construct a model according to Shaw's recommendations, the constraints on A 3 and B 3 were easily incorporated into the original model. It was also necessary to set the value B 0 at 1 to obtain a model in which where S i is the model prediction associated with the i th observation, and R 1i and R 2i are the observed values of the spectral ratios (R 1 and R 2 ) associated with the i th observation. The parameter estimates (with associated standard errors in parentheses) associated with the data set (involving data from all hematocrits) over the saturation from 30% to, approximately, 100% are A 0 = 46.6 (38); A 1 = -105 (89); A 2 = 64.5 (53); B 1 = -2.13 (.10); and B 2 = 1.19 (.11). Estimation of the model parameters was made by nonlinear least-squares regression using the Gauss-Newton method. These parameter estimates are very highly correlated. This probably indicates that the model contains more fitted parameters than necessary, with a potential hazard that errors in the calibration data will be excessively incorporated into the model. The analysis for each hematocrit individually and for all the hematocrits grouped together is illustrated in Figure 10. The average errors are set forth in Table 3. Hematocrit Average Absolute Error of Percent Oxygen Saturation 25% 1.4 33% 1.2 35% 1.2 47% 0.9 all together 2.7 Multivariate Analysis There are four full-spectrum multivariate algorithms (PLS, PCR, CLS and MLR/ILS) commonly used in spectroscopy. We have determined that the two methods best suited for accurate determination of oxygen saturation in a fetus are PLS and PCR. To explain the superiority of full-spectrum multivariate algorithms one needs to understand that: (1) information on oxygen saturation is present at multiple frequencies, (2) full-spectrum multivariate methods have a signal averaging effect, and (3) some multivariate methods (particularly PLS and PCR) can accommodate nonlinear spectral responses. Examination of Figure 11, the graph of Correlation (between O 2 saturation and frequency) vs. Frequency, reveals that the correlation is in excess of 0.80 from 600 nm to 710 nm. In contrast, for frequencies above 850 nm, the correlation is less than 0.10. For purposes of comparison, Figure 11 also shows the frequency regions used by New et al., Shaw et al. and the multivariate algorithms. Please note that: (1) the frequency regions used by Hoeft are not shown on the figure; and (2) the height of the various shaded regions is arbitrary. Also note that the widths illustrated for New et al. and Shaw et al. are wider than actually disclosed in these two references. As illustrated in Figure 11, our inclusion of all intensities in the spectral region (in contrast to the discreet limited regions utilized by the prior art) is beneficial to the analysis because most of these intensities contain considerable amounts of information relating to oxygen saturation. The multivariate full-spectrum signal averaging effect arises from the fact that information about each analyte or property is contained at many wavelengths and the statistical analysis serves to simultaneously use all this information. In addition a spurious data point at a given wavelength will be only one of many data points included in the analysis and its influence in the analysis will be diminished. Thus, the relative weight of the intensity of a particular frequency is decreased, and its adverse effect on the quantitative analysis is minimized. An additional advantage of multivariate methods is their ability to model nonlinear relationships between the spectra and concentration. Our experience in determining blood oxygen saturation using reflected light has demonstrated that the relationship between reflected light intensity and oxygen saturation is nonlinear. The sources of this nonlinearity are at least partially due to instrument/detector nonlinearities and the sigmoidal oxygen-hemoglobin binding curve. In summary, the physiological and physical difficulties associated with fetal monitoring such as low pulse pressure and the necessity for reflectance sampling, which result in decreased signal-to-noise ratios and the nonlinear relationship between saturation and reflected light intensity, have led us to the conclusion that the utilization of full spectrum multivariate analysis, as set forth herein, is the correct approach. Analysis of the experimental data with multivariate methods and comparison with prior art algorithms demonstrates the superiority of our methodology and the associated instrumentation. Principal component regression (PCR) and partial least squares (PLS) are similar methods of multivariate analysis. Both are factor analysis methods which are full spectrum in nature; both can model some nonlinearities; and both allow for detection of outliers. PLS and PCR can be employed even when the concentrations or properties of only one component are known in the calibration samples. Principle Component Regression Analysis of the single-beam spectral data by PCR was performed for each hematocrit individually and then upon the entire data set (i.e., all hematocrits together). The results of the analysis are as illustrated in Figure 14. The average errors are set forth in Table 4. Hematocrit Average Absolute Error of Percent Oxygen Saturation 25% 1.8 33% 1.2 35% 1.2 47% 0.4 all together 2.3 Partial Least Squares The analysis of the single beam spectral data by PLS was, like PCR, preformed for each hematocrit individually and then upon the entire data set (i.e., all hematocrits together). The results of the analysis are illustrated in Figure 15. The average errors are set forth in Table 5. Hematocrit Average Absolute Error of Percent Oxygen Saturation 25% 0.6 33% 1.2 35% 1.0 47% 1.6 all together 2.0 Analysis with the Addition of Noise As has been discussed previously, the fetal environment represents a condition in which the "additional" blood spectrum will have poor signal-to-noise ratio characteristics. The experimental spectral data used for the comparison analysis set forth above, was acquired in a manner to minimize noise. Specifically, the blood sample was scanned 128 times and the reflectance intensity values at a given wavelength were subsequently averaged to minimize random noise. To simulate the noise level anticipated when monitoring an actual fetus, random computer generated noise was added to the original data. Thus, the anticipated fetal spectral noise was added at a level of 30% of the average maximum value of all the spectra, and the intensity values at all wavelengths were subjected to the same magnitude and distribution of random noise. Figure 16 sets forth a visual presentation on the amount of noise added. The specific spectrum shown corresponds to an O 2 saturation of 70% and a hematocrit of 35%. The resulting noisy spectral data, from all data points, were then analyzed using the same algorithms as described above. The analysis of the noisy data were done in exactly the same way as the original data. The results of the analysis, which are shown below, clearly demonstrate the superiority of multivariate analysis. The New et al. algorithm was applied to the noisy spectra in the manner as previously described. As can be seen from Figure 17, the results of the analysis did not have any predictive value. The actual results are summarized below: Hematocrit Average Absolute Error of Percent Oxygen Saturation 25% 16.5 33% 13.2 35% 11.3 47% 19.7 all together 16.0 The Hoeft et al. algorithm was applied to the noisy spectra in the same manner as previously described. Again, the results of the analysis did not have any predictive value. See Figure 18. The actual results are summarized below: Hematocrit Average Absolute Error of Percent Oxygen Saturation 25% 16.0 33% 11.0 35% 9.2 47% 18.1 all together 13.6 The Shaw et al. algorithm was also applied to the noisy spectra in the manner as previously described with regard to non-noisy data. As with New et al. and Hoeft et al., and as illustrated in Figure 19, the results of the analysis did not have any predictive value. The actual results are summarized in Table 8. Hematocrit Average Absolute Error of Percent Oxygen Saturation 25% 15.5 33% 12.7 35% 8.0 47% 14.5 all together 13.6 The principle component regression algorithm was applied to the noisy spectra in the manner as previously described. In contrast to New et al., Hoeft et al. and Shaw et al., the results of the analysis, illustrated in Figure 20, showed only a mild decrease in predictive ability. Thus the PCR algorithm still performed well. The actual results are summarized below: Hematocrit Average Absolute Error of Percent Oxygen Saturation 25% 3.7 33% 3.4 35% 3.8 47% 4.2 all together 5.8 Finally, the partial least squares algorithm was applied to the noisy spectra in the manner as previously described. The results of the analysis showed a mild decrease in predictive value but the algorithm still preformed well, especially given the level of noise added to the spectral data. See Figure 21. The average absolute error of prediction changed from 2.0 percent O 2 saturation using the non-noisy spectra to 2.6 percent O 2 saturation on the noisy spectra. The actual results are summarized below: Hematocrit Average Absolute Error of Percent Oxygen Saturation 25% 3.1 33% 2.9 35% 3.4 47% 3.5 all together 2.6 The Preferred Embodiment With reference to Figure 1, oximeter 111 includes a spectrometer 113, an electronics and computer processing module 115, and a visual display module 117. Spectrometer 113 includes a broad band halogen light source 121, a concave focusing mirror 123 a fiber optic housing 125, a second fiber optic housing 127, a grating 129, a CCD array detector 131, and an electric buss 135. Module 115 includes a microprocessor 141, memory 143 in which the multivariate calibration model is stored, and module 145 in which the outlier detection algorithm is stored. Microprocessor 141, memory 143 and module 145 are connected together via suitable electronic connectors, as illustrated schematically at 147. Visual display module 117 includes a blood oxygen saturation display 151, heart rate display 153, an indicator of accuracy of determination 155, oxygen saturation trend 157, and heart rate tracing 159. Finally, apparatus 111 includes a fiber optic bundle 161, including a central control input fiber 163, and a surrounding bundle of output fibers 165. In cross section bundle 161 has the configuration illustrated in Figure 5A. The end of bundle 161 is secured to the scalp of the fetus via a suitable suction or other device. Source 121 emits frequencies from approximately 500 mm to 1000 mm, as illustrated in Figure 11. This light is transmitted to the fetus via input fiber 163 to illuminate a blood containing part of the fetus, such as the scalp illustrated in Figure 1. The back scattered or reflected light is then transmitted back to spectrometer 113 by fiber bundle 165. Alternately the same optical fiber or a secondary optical fiber could be utilized. The returning light is then separated into various frequencies and detected by the charge coupled device (CCD) array detector 131. The reflected light intensities at the various frequencies are then analyzed by computer 141 employing a multivariate algorithm (such as PLS or PCR) utilizing information over the entire spectral range. The spectral data are analyzed to establish which spectra correspond with maximum concentration of blood (or maximum dilation) in the arterial system of the fetus, and which spectra correspond with minimum concentration or dilation of the arterial system. The spectra associated with minimum dilation will contain information on blood, skin, bone, etc. The spectra associated with maximum dilation will contain the same information plus an additional amount of blood information. However, because reflected light does not necessarily follow a Beer's law model, data treatments or spectral transformations for reflection spectra may be different than for absorption spectra. Normally, diffusely reflected light is expected to follow the Kubelka-Munk equation as follows: f(R ∞ ) = (1 - R ∞ ) / 2R ∞ = k/s where R ∞ is the absolute reflectance of an "infinitely thick" layer, s is the scattering coefficient, and k is the molar absorption coefficient. However, the log of the inverse reflectance sometimes yields superior quantitative results. Subtraction of the appropriately transformed spectral data from the maximum and minimum dilation will correspond to the additional amount of blood present due to the pulse pressure generated by the heart. The above process effectively subtracts out the interfering background and provides the multivariate algorithm with a spectrum corresponding to the additional blood. The subtracted spectrum is analyzed by a multivariate algorithm. In the preferred embodiment the algorithm employed would be partial least squares or principle component regression. The algorithm will provide the operator with blood oxygen saturation as indicated by 151. An additional embodiment of the invention includes apparatus for obtaining information regarding the electrical activity of the fetal heart, which activity can provide information to assist in determination of maximal and minimal dilation. With reference to Figure 23, maximum expansion of the arterial system due to ventricular contraction occurs at a set interval following the R peak of the QRS complex. This complex precedes ventricular contraction which results in ejection of blood from the heart. Minimum expansion of the arterial system is present prior to ventricular contraction and corresponds to a time period in the vicinity of the P-wave. Correlation with the electrical activity of the heart may be necessary for effective operation during periods of maximum uterine contraction. If the fetus were in normal vertex position the head could become compressed to the point that the pulse pressure or change in diameter of the vascular system becomes too small to detect rapidly using optical methods. Thus, the electrical activity of the fetal heart would provide the additional information for operation under adverse conditions. It is the authors' experience that pretreatment of the spectral or concentration data can oftentimes improve the analysis precision in the calibration and unknown analyses as well as increase the robustness of the models. Thus, data pretreatments including but not limited to centering, scaling, normalizing, taking first or higher order derivatives, smoothing, Fourier transforming, and/or linearization can all improve the analysis precision and accuracy. These pretreatments can also improve the robustness of the model to instrument drift and can improve the transfer of the calibration model between instruments. It is additionally understood by the inventors that the amount of oxygen in the blood can be recorded as oxygen saturation or partial pressure of oxygen. These two indicators of oxygen level are strongly correlated, although partial pressure of oxygen will be affected by pH and the partial pressure of carbon dioxide. Determination of oxygen saturation is referenced in the specification due to its present use in clinical practice. Whereas the drawings and accompanying description have shown and described the preferred embodiment of the present invention, it should be apparent to those skilled in the art that various changes may be made in the form of the invention without affecting the scope thereof.

Claims: A quantitative analysis instrument for noninvasive measurement of blood oxygen saturation in a fetus, said instrument comprising: (a) a broad band source of light (121), said source generating wavelengths in the range from 500 to 1000 nm; (b) optical means for simultaneously introducing (163) said wavelengths into said fetus and for simultaneously collecting (165) at least a portion of said wavelengths that are reflected back from said fetus; (c) a device (161) for positioning said optical means relative to said fetus, whereby at least a portion of said wavelengths are introduced into said fetus and said portion of said reflected wavelengths are collected; (d) at least one detector (131) positioned relative to said optical means for measuring the spectral intensities of said reflected wavelengths during the diastolic portion of the cardiac cycle of said fetus, to obtain a diastolic set of spectral intensities v. wavelengths, and for measuring the spectral intensities of said reflected wavelengths during the systolic portion of said cardiac cycle, to obtain a systolic set of spectral intensities v. wavelengths; (e) electronics including a microprocessor (141) and memory (143) means for, (i) storing said diastolic set of spectral intensities v. wavelengths and said systolic set of spectral intensities v. wavelengths; (ii) processing said diastolic and systolic sets of spectral intensities to determine a measure of change between said diastolic and systolic sets to obtain a third set of spectral intensities v. wavelengths, and (iii) processing said third set of spectral intensities v. wavelengths to determine a measure of oxygen saturation, said memory means including a multivariate algorithm and a multivariate calibration model, said algorithm using at least three variables; and (f) means (151), for indicating said determined measure of oxygen saturation. The analysis instrument of claim 1, wherein said algorithm is selected from the group including PLS, PCR, CLS, Q-matrix, cross-correlation, Kalman filtering and MLR. The analysis instrument of claim 2, wherein said algorithm is an algorithm capable of utilizing more discrete spectral intensities per sample than the number of calibration samples used to generate said model. The analysis instrument of claim 1, wherein said electronics also includes a means for measuring the electrical activity of the heart of said fetus, to facilitate the determination of said systolic portion and said diastolic portion of said cardiac cycle. The analysis instrument of claim 1, wherein said electronics includes means for detecting outliers. A method for noninvasive measurement of blood oxygen saturation in a fetus, said method comprising the steps of: (a) generating a plurality of different wavelengths of light with a broad band light source, some of said wavelengths being in the range from 500 to 1000 nm; (b) simultaneously irradiating said fetus with said wavelengths and simultaneously collecting a portion of said wavelengths that are reflected back from said fetus; (c) measuring the spectral intensities of said reflected wavelengths during the diastolic portion of the cardiac cycle of said fetus, to obtain a diastolic set of spectral intensities v. wavelengths, and measuring the spectral intensities of said reflected wavelengths during the systolic portion of said cardiac cycle, to obtain a systolic set of spectral intensities v. wavelengths; and (d) determining a third set of spectral intensities v. wavelengths from said diastolic and systolic sets of spectral intensities, and determining a measure of oxygen saturation from said third set of spectral intensities v. wavelengths utilizing a multivariate algorithm and a multivariate calibration model, said algorithm using at least three variables. The method as set forth in claim 6, wherein said step of determining oxygen saturation includes utilizing an algorithm selected from the group including PLS, PCR, CLS, Q-matrix, cross-correlation, Kalman filtering and MLR. The method as set forth in claim 7, wherein said algorithm is capable of utilizing more discrete spectral intensities per sample than the number of calibration samples used to generate said model. The method as set forth in claim 6, further including the step of determining whether said third set of spectral intensities v. wavelengths from said fetus is an outlier by comparing said third set of spectral intensities v. wavelengths from said fetus with a set of spectral intensities v. wavelengths obtained from irradiating calibration samples. The method as set forth in claim 6, further including the step of pretreating said set of spectral intensities v. wavelengths from said fetus. The method as set forth in claim 12, wherein said pretreating includes centering, scaling, normalizing, taking first or higher order derivatives, smoothing, fourier transforming or linearization. The method as set forth in claim 6, including accomplishing said determination of said diastolic portion and said systolic portion of said cardiac cycle by concurrently measuring the electrical activity of the heart of said fetus.

Inventors: HAALAND DAVID M, ROBINSON MARK R, WARD KENNETH J, HAALAND, DAVID M., ROBINSON, MARK R., WARD, KENNETH J.
Applicants: ROBINSON MARK R, SANDIA NATIONAL LAB, UNIV NEW MEXICO, ROBINSON, MARK R., SANDIA NATIONAL LABORATORIES, UNIVERSITY OF NEW MEXICO
Application year: 1992
Main classification: A61B5
IPC codes: A61B5, G01N21