CN113499039A - Method and device for portable and efficient identification of cerebrospinal fluid in intravertebral anesthesia operation - Google Patents
Method and device for portable and efficient identification of cerebrospinal fluid in intravertebral anesthesia operation Download PDFInfo
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Abstract
The invention relates to the technical field of medical health, and provides a method and a device for portable and efficient identification of cerebrospinal fluid in intraspinal anesthesia operation, which comprises the following steps: step one, knowing the spectroscopic characteristics of cerebrospinal fluid, local anesthetic drugs and normal saline; exploring ATR-FTIR spectral characteristic discrimination indexes of cerebrospinal fluid, local anesthetic drugs and physiological saline and establishing a diagnostic standard; step three, further expanding and constructing a characteristic map database; and step four, carrying out Fourier transform infrared spectroscopy analysis on the liquid sample obtained in the intraspinal anesthesia operation by using a handheld portable infrared spectrometer, obtaining a corresponding liquid sample map according to the relative vibration between the atoms in the molecules of the liquid sample and the molecular rotation information, comparing the liquid sample map with the map in the characteristic map database to obtain a comparison result, and judging whether the liquid sample is cerebrospinal fluid, local anesthesia medicine or normal saline according to the comparison result through an ATR-FTIR (attenuated total reflectance-infrared spectroscopy) discrimination standard.
Description
Technical Field
The invention relates to the technical field of medical treatment and health, in particular to a method and a device for conveniently and efficiently identifying cerebrospinal fluid in an intraspinal anesthesia operation.
Background
Intravertebral anesthesia is mainly used for patients who are suitable for abdominal, pelvic and lower limb operations. In the process of operating in the vertebral canal, local anesthetic (lidocaine) is firstly used for carrying out infiltration anesthesia layer by layer, so that pain of a patient caused by subsequent operation is avoided. After the local anesthesia is proper, a thicker epidural puncture needle (17G) is adopted to pass through the skin, the subcutaneous tissue, the supraspinal ligament, the interspinous ligament and the ligamentum flavum in sequence to reach the epidural space, and the needle core is pulled out. Physiological saline bubbles are injected into the epidural space through the non-resistance needle, and when there is no resistance, it is determined that the needle tip has reached the epidural space. If only epidural anesthesia is needed, the epidural local anesthesia medicine is injected at the moment. If the subarachnoid space block needs to be performed, after the epidural space is definitely positioned, a thinner puncture needle is used, the puncture needle is sleeved into the epidural puncture needle, and the needle is inserted forwards to break through the arachnoid space and reach the subarachnoid space. At this time, the fine needle core was pulled out, and the presence or absence of cerebrospinal fluid outflow was observed. If the cerebrospinal fluid flows smoothly, the intraspinal anesthesia effect can be achieved by injecting local anesthesia medicines (such as ropivacaine, bupivacaine and the like) with different proportions into the subarachnoid space. Then the arachnoid puncture needle is pulled out, and the epidural catheter is sent into the epidural needle tube core, so that the continuous epidural block can be carried out. According to the clinical practical requirements, different anesthesia modes are implemented for the patient, such as simple epidural anesthesia, simple subarachnoid anesthesia or combined anesthesia of lumbar vertebra.
Because the normal saline, the local anesthetic and the cerebrospinal fluid are all clear liquids, the clear liquids are difficult to distinguish by naked eyes. If the puncturing process is not smooth, it is easy to determine which kind of the liquid flows out. For example: during the puncture process, due to unclear dissection, when the arachnoid is accidentally punctured and is not detected (for example, the puncture needle point punctures or a retained epidural catheter punctures the arachnoid during the catheterization process), the epidural medicine is applied blindly, and the whole or part of the medicine enters the subarachnoid space due to large epidural administration amount to generate life-threatening total spinal anesthesia. In addition, the large dose of local anesthetic injected in the local anesthetic process or saline injected outside the dura mater can interfere with the judgment of the outflow cerebrospinal fluid, even misjudges that the puncture needle is in the subarachnoid cavity and blindly administers the medicament, so that no anesthesia blocking plane exists, no anesthesia effect exists, and the operation implementation is influenced. The more common condition is that whether the cerebrospinal fluid is damaged by repeated puncture or not cannot be determined, the risk of puncturing the dura mater is increased, the lying-in woman is easy to have common postpartum severe low intracranial pressure headache, and great pain and psychological pressure are brought to the pregnant woman, the lying-in woman and families of the pregnant woman and the lying-in woman.
Currently, there is no good identification method, and identification is assisted only by sensing and judging the temperature of the flowing liquid through the limbs of an operator, which is very inaccurate and violates the aseptic operation principle. How to rapidly identify whether the puncture needle reaches the subarachnoid space and whether the outflow liquid is local anesthetic, normal saline or cerebrospinal fluid is the clinical problem needing to be solved currently by an objective and accurate method.
In addition, some patients after spinal surgery develop cerebrospinal fluid leakage complications, which affect wound healing. How to judge whether the outflowing liquid is cerebrospinal fluid or is simply tissue seepage fluid, and the purposes of early identification and timely treatment are also the problems to be clinically solved.
In summary, the defects of the prior art are as follows:
currently, there is no technology that can effectively, objectively and accurately determine what composition (local anesthetic, cerebrospinal fluid or physiological saline) the outflow liquid is. In most cases, the method only depends on the experience of a clinician, and objective basis is lacked. Repeated puncture injury and puncture failure are caused due to unclear judgment, and even the dura mater is punctured, a large amount of cerebrospinal fluid leakage occurs, so that related complications occur after the operation of a patient.
Disclosure of Invention
The invention aims to provide a method and a device for conveniently and efficiently identifying cerebrospinal fluid in an intraspinal anesthesia operation, so as to solve at least one of the technical problems in the background art.
In order to achieve the purpose, the invention adopts the technical scheme that: a method for identifying cerebrospinal fluid in a portable and efficient manner in an intraspinal anesthesia operation comprises the following steps:
step one, understanding the spectroscopic characteristics of cerebrospinal fluid, local anesthetic drugs and normal saline: diluting the samples of the cerebrospinal fluid, the local anesthetic drugs and the physiological saline, obtaining spectral images of the samples of the cerebrospinal fluid, the local anesthetic drugs and the physiological saline by using a spectrometer, and performing quality control on the obtained spectral images; analyzing the information of the material components and structures contained in the characteristic spectrum bands of the cerebrospinal fluid, the local anesthetic drug and the physiological saline sample;
step two, exploring ATR-FTIR spectral characteristics of cerebrospinal fluid, local anesthetic drugs and physiological saline, proposing a discrimination standard, and establishing a diagnosis basis: selecting a proper multi-mode statistical method to analyze the cerebrospinal fluid, local anesthetic drugs and physiological saline samples, and performing cross validation; ATR-FTIR spectrum discrimination standard for discriminating biological sample classification during intravertebral anesthesia operation is preliminarily constructed;
step three, further expanding and constructing a characteristic map database: based on the existing discrimination model, ATR-FTIR spectrum analysis is continuously expanded to fulfill the aim of perfecting a diagnosis database;
and step four, carrying out Fourier transform infrared spectroscopy analysis on the liquid sample obtained in the intraspinal anesthesia operation by adopting a handheld portable infrared spectrometer, obtaining a corresponding liquid sample map according to the relative vibration between the atoms in the molecules of the liquid sample and the molecular rotation information, comparing the liquid sample map with the map in the characteristic map database to obtain a comparison result, and judging whether the liquid sample is cerebrospinal fluid, local anesthesia medicine or normal saline according to the ATR-FTIR spectrum discrimination standard.
The quality control of the obtained spectral image in the first step specifically comprises:
the spectrometer is applied, an attenuated total reflection mode is adopted, sampling conditions and a sample adding method are set, sampling parameters are accurately regulated and controlled, and the quality of a liquid ATR-FTIR spectrum is ensured; after unqualified spectra are eliminated by spectral quality analysis, 890-4000cm is selected-1In-range spectra are subjected to baseline correction, a second-order reciprocal is obtained through Savitsky-Golay second-order derivation, background interference is eliminated through smoothing processing, and the spectral signal-to-noise ratio and the apparent resolution are increased, so that the difference between different spectral features is highlighted; then, carrying out vector normalization processing on the obtained second order reciprocal by using OPUS software; storing the processed spectrum and performing hierarchical clustering analysis by applying an Euclidean distance calculation method and a Ward's algorithm; and eliminating the sample atlas which does not meet the quality control requirement.
The information of the material components and the structures contained in the characteristic spectrum bands of the cerebrospinal fluid, the local anesthetic drug and the physiological saline sample in the step one specifically comprises the following steps:
carrying out preliminary data processing on the obtained spectrum, carrying out statistical analysis on characteristic peak position, peak intensity, half peak width and related ratio by utilizing OMINIC E.S.P.ver.5.0 software, and carrying out variance homogeneity test and normal distribution test on all parameters; for the data with uniform variance and normal distribution, the difference between the two groups is tested by adopting a t test, and for the data with non-normal distribution, a Mann-Whitney U test is adopted; the qualitative data adopts a percentile method and is checked by using chi-square; and analyzing the ratio of the characteristic peak position, peak intensity, peak width, half-peak height and related peaks of the ATR-FTIR spectrum with the difference, and analyzing the substance content and the structural information corresponding to the difference spectrum band to preliminarily obtain the spectroscopic characteristics represented by the ATR-FTIR spectrum band.
Preferably, the spectral quality analysis in step one includes an absorption intensity threshold test, a signal-to-noise ratio test and a water content test.
Preferably, the content of the statistical analysis performed by using the omiic e.s.p.ver.5.0 software in the first step further includes 9 peak positions and 12 relative peak intensity ratios.
Selecting a suitable multimodal statistical method for analyzing the cerebrospinal fluid, local anesthetic drug and physiological saline samples in the step two specifically comprises the following steps:
using MATLAB software to carry out statistical analysis on all the characteristic spectrum data with the difference; after eliminating extreme values, performing quality inspection, baseline correction and normalization processing on all original spectral data by using expanded multivariate scattering correction; expressing each obtained spectrum as a weighted sum of target spectra, taking an average spectrum of all the spectra as the target spectra, selecting a polynomial function to carry out fourth-order derivation on the light scattering effect, and calculating an error; the corrected spectrum is the result obtained by deducting the estimated polynomial function from the original spectrum; normalizing by dividing the corrected spectrum by the estimated weight of the target spectrum; then, performing principal component analysis to recover the residual spectral information; decomposing the data into weighted sums of uncorrelated principal components by a multivariate method; carrying out statistical analysis on the share of each component, and selecting the main component with the most discriminative power; then using Mann-Whitney U nonparametric statistical test; then, a supervised classification method and a support vector machine are used for carrying out discriminant analysis; the supervised classification method is realized based on a LIBSVM library of sources and an execution support vector machine; carrying out binary classification on data by adopting a supervised learning mode in a linear classification method, solving the maximum edge distance hyperplane of the data, optimizing the structural risk by calculating the empirical risk and adding a regularization term, and carrying out nonlinear analysis on a learning sample; and the recognition efficiency is further improved by adopting a one-out method.
The cross validation in the step two, the ATR-FTIR spectrum discrimination standard for discriminating the biological sample classification during the preliminary construction of the intravertebral anesthesia operation specifically comprises the following steps:
classifying and distinguishing the training set samples, then verifying the test set samples, verifying the established distinguishing model, substituting the verification samples into a distinguishing system, further checking and correcting the distinguishing effect of a diagnosis system, and checking the accuracy of the ATR-FTIR spectrum distinguishing method; and finally, substituting the characteristics of the prediction set sample map into the established discrimination equation, and checking the accuracy and the sensitivity of the discrimination.
Preferably, the portable infrared spectrometer detects a sample volume of the liquid sample at each time, which is equal to the sample volume of the rapid blood glucose analysis.
In one embodiment of the invention, the portable device for efficiently identifying cerebrospinal fluid in the intraspinal anesthesia operation is further provided, and the portable device adopts the method, so that the cerebrospinal fluid can be efficiently identified in the intraspinal anesthesia operation.
The portable device for efficiently identifying cerebrospinal fluid in the intraspinal anesthesia operation comprises a handheld portable infrared spectrometer, wherein a detection port, a display screen and an analysis key are arranged on a shell of the handheld portable infrared spectrometer, and a discriminant analysis chip is also arranged in the shell; the detection port is used for receiving disposable test paper instilled with a body fluid sample; the analysis key is used for starting the discriminant analysis chip to start to analyze and detect the liquid sample on the disposable test paper when being pressed down; the discriminant analysis chip is used for analyzing and detecting the body fluid sample on the disposable test paper according to the method; the display screen is used for displaying the analysis and detection result of the discriminant analysis chip.
The beneficial effects of the invention include:
the portable high-efficiency cerebrospinal fluid identification method in the intraspinal anesthesia operation adopts a Fourier transform infrared spectroscopy technology, obtains a corresponding map according to information such as relative vibration and molecular rotation among atoms in molecules, determines a substance molecular structure by analyzing the intensity, position and shape of an absorption peak, identifies unknown compound components and carries out qualitative and quantitative analysis in a fingerprint area (7.69-25 mu m) with the wavelength larger than 10 mu m by identifying single or combined molecular vibration and rotation modes. FTIR (Fourier transform infrared absorption spectrometer) technology is widely applied to the research of the structure and conformation of biomolecules such as protein, lipid, carbohydrate, nucleic acid and the like, has a series of advantages of high luminous flux, low noise, high measurement speed and the like, and has longer infrared wavelength, small energy and no damage to the structure of living tissues during detection. The FTIR technology has high sensitivity, accurate wave number and good repeatability, can analyze microgram-level or even nanogram-level samples, can be applied to qualitative analysis and quantitative determination, and can analyze unknown substance components, thereby being capable of assisting in judging whether liquid flowing out in the puncture process is cerebrospinal fluid or not or only local anesthetic drugs or physiological saline (local anesthetic or epidural space injection physiological saline or local anesthetic drugs). Cerebrospinal fluid contains various electrolytes, proteins, a small amount of cells and the like, and is different from normal saline or local anesthetic drugs and is an inorganic compound, so the spectroscopy of the cerebrospinal fluid and the normal saline or the local anesthetic drugs has a large difference. By means of infrared spectrum technology, it is possible to distinguish whether the components in the body fluid sample are mixed with cerebrospinal fluid. The current infrared spectrum instrument is a large-scale instrument, and the invention is designed into a handheld portable spectrometer in clinical application according to application scenes, adopts disposable test paper, and measures after absorbing liquid samples, thereby avoiding the inconvenience and inaccuracy of measuring results caused by instrument pollution and repeated cleaning and wiping. By learning the spectral characteristics of different types of samples, a discrimination model and a database are established, and by pre-installing software, the hand-held portable spectrometer can also obtain the discrimination result of the measured sample immediately to assist clinical differential diagnosis.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed for the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a schematic structural diagram of the portable device for efficiently identifying cerebrospinal fluid in the intraspinal anesthesia operation.
Detailed Description
In order to make the technical problems, technical solutions and advantageous effects to be solved by the present invention more clearly apparent, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
It will be understood that when an element is referred to as being "secured to" or "disposed on" another element, it can be directly or indirectly secured to the other element. When an element is referred to as being "connected to" another element, it can be directly or indirectly connected to the other element. The terms "upper", "lower", "left", "right", "front", "rear", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like indicate orientations or positions based on the orientations or positions shown in the drawings, and are for convenience of description only and not to be construed as limiting the technical solution. The terms "first", "second" and "first" are used merely for descriptive purposes and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features. The meaning of "plurality" is two or more unless specifically limited otherwise.
The method for conveniently and efficiently identifying cerebrospinal fluid in the intraspinal anesthesia operation comprises the following steps of:
step one, understanding the spectroscopic characteristics of cerebrospinal fluid, local anesthetic drugs (lidocaine, ropivacaine and bupivacaine) and physiological saline:
(1) samples were run on a 1: 10, diluting; (what proportion is appropriate for the customer to provide details)
(2) And (3) performing quality control on the obtained spectral image:
using FTIR Spectrometers (Thermo Scientific)TM NicoletTM6700) The spectrometer adopts an attenuated total reflection mode, sets sampling conditions and a sample adding method, accurately regulates and controls sampling parameters, and ensures the quality of a liquid ATR-FTIR spectrum. After unqualified spectra are eliminated by spectral quality analysis (including absorption intensity threshold, signal-to-noise ratio and water content test), 890-4000cm is selected-1In-range spectra are then baseline corrected, Savitsky-Golay second order derivatives, and smoothed to remove background interference, increase spectral signal-to-noise ratio and apparent resolution, and thereby highlight differences between different spectral features. Subsequently, the second order reciprocal was vector-normalized using OPUS software (v6.5, Bruker Optics GmbH)And (6) performing normalization treatment. The processed spectra were stored and subjected to hierarchical clustering analysis using Euclidean distance calculations and Ward's algorithm. And eliminating the sample atlas which does not meet the quality control requirement.
(3) Analyzing the information of the material composition and structure contained in the characteristic spectrum band of each of the three samples:
the obtained spectrum is subjected to preliminary data processing, statistical analysis is carried out on characteristic peak positions, peak intensities, half-peak widths and related ratios (including 9 peak positions and 12 relative wave intensity ratios) by utilizing OMINIC E.S.P.ver.5.0(Nicolet Instrument Co., Madison, Wis., USA) software, and normal distribution and uniformity variance are tested on all parameters. For normal distribution data, the t-test was used to test for differences between the two groups, while for non-normal distribution data, the Mann-Whitney U-test was used. Qualitative data were measured by percentile method using chi-square test. P values < 0.05 are statistically significant.
Analyzing and discussing the characteristic peak position, peak intensity, peak width, half-peak height and related peak ratio of ATR-FTIR spectrum with difference; and analyzing the substance content and the structural information corresponding to the different bands to preliminarily obtain the spectroscopic characteristics represented by the ATR-FTIR spectral band.
Step two, exploring ATR-FTIR spectral characteristic discrimination indexes of cerebrospinal fluid, local anesthetic drugs and physiological saline and establishing a diagnostic standard:
(1) obtaining a sample, selecting a suitable multivariate statistical method to analyze the sample:
statistical analysis was included for all characteristic spectral data for the differences using MATLAB software (MATLAB, MathWorks, inc., Natick, mass., USA). After the extreme values are excluded, all raw spectral data are quality checked, baseline corrected and normalized using Extended Multivariate Scatter Correction (EMSC). Each obtained spectrum is expressed as a weighted sum of the target spectra, the average spectrum of all spectra is taken as the target spectrum, a polynomial function is selected to derive the light scattering effect in the fourth order and the error is calculated. The corrected spectrum is the result of subtracting the estimated polynomial function from the original spectrum. Normalization is achieved by dividing the corrected spectrum by the estimated weight of the target spectrum.
Principal Component Analysis (PCA) is then performed to recover the remaining spectral information. This multivariate approach decomposes the data into a weighted sum of uncorrelated principal components. The fractions of each component are statistically analyzed and the most discriminating principal component is selected. The Mann-Whitney nonparametric statistical test was then used, which allowed the principal components to be reorganized in ascending order of P value, the first being the most distinctive parameter between the two groups. Subsequently, discriminant analysis is performed using a supervised classification method, support vector machine. The method is implemented based on a libray for Support vector machines (LIBSVM) and an execution Support Vector Machine (SVM) of a source.
Carrying out binary classification on data by adopting a supervised learning mode in a linear classification method, solving a maximum margin hyperplane (maximum-margin hyperplane) of the data, and carrying out nonlinear analysis on a learning sample well by calculating empirical risk and adding a regularization term to optimize structural risk; has strong recognition effect on ATR-FTIR spectrum. The leave-one-out cross-validation (LOOCV) method is used to further improve the recognition efficiency.
(2) Cross validation, ATR-FTIR spectrum discrimination standard for discriminating biological sample classification during the preliminary construction of intravertebral anesthesia operation:
the accuracy of the ATR-FTIR spectrum discrimination method is tested by classifying and discriminating samples in a training set, then verifying samples in a testing set, verifying established discrimination models, substituting the verified samples into a discrimination system, further testing and correcting the discrimination effectiveness of a diagnosis system. And finally, substituting the atlas characteristics of the prediction set sample (identification set) into the established discriminant equation, and checking the accuracy and the sensitivity of the discriminant equation.
Step three, further expanding and constructing a characteristic map database:
based on the existing discriminant model, ATR-FTIR spectrum analysis is continuously expanded to fulfill the aim of perfecting a diagnostic database.
And step four, carrying out Fourier transform infrared spectroscopy analysis on the liquid sample obtained in the intraspinal anesthesia operation by adopting a handheld portable infrared spectrometer, obtaining a corresponding liquid sample spectrum according to the relative vibration between the atoms in the molecules of the liquid sample and the molecular rotation information, comparing the liquid sample spectrum with the spectrum in the characteristic spectrum database to obtain a comparison result, and judging whether the liquid sample is cerebrospinal fluid, local anesthesia medicine or normal saline according to the comparison result through the ATR-FTIR (attenuated total reflectance-infrared spectroscopy) discrimination standard.
The invention improves the appearance of the current infrared spectrometer, and aims at the current clinical requirement to quickly and accurately distinguish and analyze the liquid sample obtained in the intraspinal anesthesia process and determine whether the liquid sample is cerebrospinal fluid, local anesthesia medicine or normal saline. The spectrometer is structurally modified and parameter set, and the configuration of optical elements in the spectrometer is optimized, so that the spectrometer becomes portable spectrum detection equipment (figure 1) which is small in size and weight and convenient to carry. Only one drop of sample (equal to the sample size in rapid blood sugar analysis) is needed to achieve the purpose of real-time, objective and accurate classification of the liquid sample source.
In one embodiment of the invention, the device for portable high-efficiency identification of cerebrospinal fluid in an intraspinal anesthesia operation is also provided, and the device adopts the method for portable high-efficiency identification of cerebrospinal fluid in an intraspinal anesthesia operation.
As shown in fig. 1, the portable device for efficiently identifying cerebrospinal fluid in the intraspinal anesthesia operation comprises a handheld portable infrared spectrometer, wherein a detection port 1, a display screen 2 and an analysis key 3 are arranged on a shell of the handheld portable infrared spectrometer, and a discrimination analysis chip is also arranged in the shell; the detection port 1 is used for receiving disposable test paper instilled with a body fluid sample; the analysis key 3 is used for starting the discriminant analysis chip to start analyzing and detecting the body fluid sample on the disposable test paper when being pressed; the discriminant analysis chip is used for analyzing and detecting the body fluid sample on the disposable test paper according to the method; the display screen 2 is used for displaying the analysis and detection result of the discriminant analysis chip.
The analysis key 3 may be a physical key or a virtual key disposed on the display screen 2.
During specific operation, a drop of body fluid sample is taken and dripped on disposable test paper used for sucking the sample on the handheld portable infrared spectrometer, and a discriminant analysis chip is carried in the instrument and can be used for testing in real time according to a map obtained by testing and displaying a classification result immediately.
The innovation points and advantages of the invention include:
1. the puncture needle creatively solves the important problem bothering clinic at present, can effectively assist a clinician in identifying and diagnosing the puncture level reached in the process of intraspinal anesthesia puncture, and avoids the risk of spinal anesthesia and the risk of repeated puncture, puncture failure and injury to patients.
2. The characteristics of analyzing and distinguishing the component structures of different substances by the infrared spectrum technology are innovatively applied to distinguishing the type of the biological fluid sample clinically, so that the aim of quickly, accurately and objectively judging the components of the biological fluid is fulfilled.
3. The small and exquisite portable infrared spectrometer is innovatively designed and applied to judging the intraspinal anesthesia puncture process, and classification research on biological body fluid is assisted, so that the infrared spectrometer has a great application prospect.
The key technical points of the invention comprise:
1. and (3) extracting spectral characteristics of cerebrospinal fluid, normal saline and local anesthetic.
2. The establishment of a database of cerebrospinal fluid, normal saline and local anesthetic drugs, and an algorithm for statistical classification.
3. Improvement of miniature portable miniaturisation of infrared spectroscopy instruments.
The above description is only a preferred embodiment of the present invention and should not be taken as limiting, and any modifications, equivalents, improvements, etc. made within the spirit and principles of the present invention are intended to be included within the scope of the present invention, including any improvements in spectrometer equipment and parameters, and improvements in or relating to cerebrospinal fluid, saline, and anesthetic spectral feature databases.
Claims (10)
1. A method for identifying cerebrospinal fluid in a portable and efficient manner in intraspinal anesthesia operation is characterized by comprising the following steps: the method comprises the following steps:
step one, knowing the spectral characteristics of cerebrospinal fluid, local anesthetic drugs and normal saline: diluting the cerebrospinal fluid, local anesthetic drugs and physiological saline samples, obtaining spectral images of the cerebrospinal fluid, local anesthetic drugs and physiological saline samples by using a spectrometer, and performing quality control on the obtained spectral images; analyzing the material components and the structural information contained in the characteristic spectrum bands of the cerebrospinal fluid, the local anesthetic drug and the physiological saline sample respectively;
step two, exploring ATR-FTIR spectral characteristic discrimination indexes of cerebrospinal fluid, local anesthetic drugs and physiological saline, and establishing a diagnostic standard: selecting a proper multivariate statistical method to analyze the cerebrospinal fluid, the local anesthetic drugs and the physiological saline samples; performing cross validation, namely preliminarily constructing an ATR-FTIR spectrum discrimination standard for discriminating biological sample classification during intravertebral anesthesia operation;
step three, further expanding and constructing a characteristic map database: based on the existing discrimination model, the ATR-FTIR spectrum analysis database is continuously expanded to fulfill the aim of perfecting the diagnosis database;
performing Fourier transform infrared spectroscopy analysis on a liquid sample obtained in the intraspinal anesthesia operation by using a handheld portable infrared spectrometer, obtaining a corresponding liquid sample map according to the relative vibration between atoms in molecules of the liquid sample and the molecular rotation information, and comparing the liquid sample map with a map in the characteristic map database to obtain a comparison result; and judging whether the liquid sample is cerebrospinal fluid, local anesthetic drug or normal saline according to the comparison result through the ATR-FTIR spectrum discrimination standard.
2. The method for portable high-efficiency identification of cerebrospinal fluid in an intraspinal anesthesia operation of claim 1, wherein said quality control of the obtained spectral image in the first step comprises:
the spectrometer is applied, an attenuated total reflection mode is adopted, sampling conditions and a sample adding method are set, sampling parameters are accurately regulated and controlled, and the quality of a liquid ATR-FTIR spectrum is ensured; after unqualified spectra are eliminated by spectral quality analysis, 890-4000cm is selected-1In-range spectra are subjected to baseline correction, a second-order reciprocal is obtained through Savitsky-Golay second-order derivation, background interference is eliminated through smoothing processing, and the spectral signal-to-noise ratio and the apparent resolution are increased, so that the difference between different spectral features is highlighted; then, carrying out vector normalization processing on the obtained second order reciprocal by using OPUS software; storing the processed spectrum and performing hierarchical clustering analysis by applying an Euclidean distance calculation method and a Ward's algorithm; and eliminating the sample atlas which does not meet the quality control requirement.
3. The method as claimed in claim 1, wherein the step of analyzing the substance components and structural information contained in the characteristic spectrum bands of the cerebrospinal fluid, the local anesthetic drug and the physiological saline sample in the first step comprises:
(the above does not require a change to the client since the reference to "said" is in full accord with the statement in claim 1.)
Carrying out preliminary data processing on the obtained spectrum, carrying out statistical analysis on characteristic peak position, peak intensity, half peak width and related ratio by utilizing OMINIC E.S.P.ver.5.0 software, and carrying out normal distribution and variance homogeneity detection on all parameters; for normal distribution data, a t test is adopted to test the difference between the two groups, and for non-normal distribution data, a Mann-Whitney U test is adopted; the qualitative data adopts a percentile method and is checked by using chi-square; and analyzing the ratio of the characteristic peak position, peak intensity, peak width, half-peak height and related peaks of the ATR-FTIR spectrum with the difference, and analyzing the substance content and the structural information corresponding to the different spectral bands to preliminarily obtain ATR-FTIR spectrum characteristics of different types of samples.
4. The method for portable high-efficiency identification of cerebrospinal fluid during an intraspinal anesthesia procedure of claim 2, wherein the spectral quality analysis of step one comprises an absorption intensity threshold test, a signal-to-noise ratio test, and a water content test.
5. The method of claim 3, wherein the statistical analysis performed in step one using OMINIC E.S.P.ver.5.0 software further comprises 9 peak positions and 12 relative peak intensity ratios.
6. The method for portable high-efficiency cerebrospinal fluid identification for intraspinal anesthesia procedures of claim 1, wherein said selecting the appropriate multivariate statistical method for analyzing the samples of cerebrospinal fluid, local anesthesia medications and physiological saline in step two specifically comprises:
using MATLAB software to carry out statistical analysis on all the characteristic spectrum data with the difference; after eliminating extreme values, performing quality inspection, baseline correction and normalization processing on all original spectral data by using expanded multivariate scattering correction; expressing each obtained spectrum as the weighted sum of the target spectra, taking the average spectrum of all the spectra as the target spectra, selecting a polynomial function to carry out fourth-order derivation on the light scattering effect, and calculating an error; the corrected spectrum is the result obtained by deducting the estimated polynomial function from the original spectrum; normalizing by dividing the corrected spectrum by the estimated weight of the target spectrum; then, performing principal component analysis to recover the residual spectral information; decomposing the data into weighted sums of uncorrelated principal components by a multivariate method; carrying out statistical analysis on the proportion of each component, and selecting the most discriminative principal component; then using Mann-Whitney U nonparametric statistical test; then, a supervised classification method and a support vector machine are used for carrying out discriminant analysis; the supervised classification method is realized based on an LIBSVM library and an execution support vector machine; carrying out binary classification on data by adopting a supervised learning mode in a linear classification method, solving the maximum edge distance hyperplane of the data, optimizing the structural risk by calculating the empirical risk and adding a regularization term, and carrying out nonlinear analysis on a learning sample; and the recognition efficiency is further improved by adopting a one-out method.
7. The method for portable high-efficiency identification of cerebrospinal fluid during an intravertebral anesthesia operation of claim 1, wherein the cross-validation of step two, the preliminary construction of ATR-FTIR spectral discrimination criteria for biological sample classification during an intravertebral anesthesia operation specifically comprises:
classifying and distinguishing the training set samples, then verifying the test set samples, verifying the established distinguishing model, substituting the verification samples into a distinguishing system, further checking and correcting the distinguishing effect of a diagnosis system, and checking the accuracy of the ATR-FTIR spectrum distinguishing method; and finally, substituting the characteristics of the prediction set sample map into the established discrimination equation, and checking the accuracy and the sensitivity of the discrimination.
8. The method as claimed in claim 1, wherein the portable infrared spectrometer is capable of detecting a sample amount of fluid sample at a time equivalent to a sample amount of rapid blood glucose analysis.
9. A device for portable high-efficiency identification of cerebrospinal fluid during an intravertebral anesthesia procedure, wherein the device is adapted for portable high-efficiency identification of cerebrospinal fluid during an intravertebral anesthesia procedure using the method of any one of claims 1 to 8.
10. The portable device for efficiently identifying cerebrospinal fluid during an intraspinal anesthesia operation as claimed in claim 9, wherein said portable device for efficiently identifying cerebrospinal fluid during an intraspinal anesthesia operation comprises a hand-held portable infrared spectrometer, a detection port, a display screen and an analysis key are arranged on a housing of said hand-held portable infrared spectrometer, and a discriminant analysis chip is further arranged in said housing; the detection port is used for receiving disposable test paper instilled with a body fluid sample; the analysis key is used for starting the discriminant analysis chip to start analyzing and detecting the body fluid sample on the disposable test paper when being pressed down; the discriminant analysis chip is used for analyzing and detecting the body fluid sample on the disposable test paper according to the method for conveniently and efficiently identifying cerebrospinal fluid in the intraspinal anesthesia operation of any one of claims 1 to 8; the display screen is used for displaying the analysis and detection result of the discriminant analysis chip.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114993974A (en) * | 2022-05-26 | 2022-09-02 | 中南大学 | Category identification method based on FTIR and pupa shell |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2010048678A1 (en) * | 2008-10-31 | 2010-05-06 | The University Of Sydney | Classification of biological samples using spectroscopic analysis |
| US20110054353A1 (en) * | 2009-08-19 | 2011-03-03 | Mirador Biomedical | Spinal canal access and probe positioning, devices and methods |
| CN104573742A (en) * | 2014-12-30 | 2015-04-29 | 中国科学院深圳先进技术研究院 | Medical image classification method and system |
| CN105319214A (en) * | 2015-11-30 | 2016-02-10 | 四川大学华西第二医院 | Cerebrospinal fluid detection device used for intraspinal anesthesia |
| CN106691397A (en) * | 2017-03-09 | 2017-05-24 | 四川大学华西第二医院 | Cerebrospinal fluid detection apparatus for intraspinal anesthesia |
| CN106901808A (en) * | 2017-02-28 | 2017-06-30 | 四川大学华西第二医院 | A kind of method of auxiliary judgment intravertebral anesthesia puncture needle tip position |
| CN108765447A (en) * | 2018-04-26 | 2018-11-06 | 深圳博脑医疗科技有限公司 | A kind of image partition method, image segmentation device and electronic equipment |
| CN109863239A (en) * | 2016-08-19 | 2019-06-07 | 莫纳什大学 | The spectroscopy system and method for identification and quantification for pathogen |
| CN111707656A (en) * | 2020-06-29 | 2020-09-25 | 陕西未来健康科技有限公司 | Cerebrospinal fluid cell detection method and system based on Raman scattering spectrum |
| CN212788637U (en) * | 2020-04-20 | 2021-03-26 | 易秋菊 | Intelligent display combined spinal-epidural puncture needle |
-
2021
- 2021-08-25 CN CN202110983711.5A patent/CN113499039B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2010048678A1 (en) * | 2008-10-31 | 2010-05-06 | The University Of Sydney | Classification of biological samples using spectroscopic analysis |
| US20110054353A1 (en) * | 2009-08-19 | 2011-03-03 | Mirador Biomedical | Spinal canal access and probe positioning, devices and methods |
| CN104573742A (en) * | 2014-12-30 | 2015-04-29 | 中国科学院深圳先进技术研究院 | Medical image classification method and system |
| CN105319214A (en) * | 2015-11-30 | 2016-02-10 | 四川大学华西第二医院 | Cerebrospinal fluid detection device used for intraspinal anesthesia |
| CN109863239A (en) * | 2016-08-19 | 2019-06-07 | 莫纳什大学 | The spectroscopy system and method for identification and quantification for pathogen |
| CN106901808A (en) * | 2017-02-28 | 2017-06-30 | 四川大学华西第二医院 | A kind of method of auxiliary judgment intravertebral anesthesia puncture needle tip position |
| CN106691397A (en) * | 2017-03-09 | 2017-05-24 | 四川大学华西第二医院 | Cerebrospinal fluid detection apparatus for intraspinal anesthesia |
| CN108765447A (en) * | 2018-04-26 | 2018-11-06 | 深圳博脑医疗科技有限公司 | A kind of image partition method, image segmentation device and electronic equipment |
| CN212788637U (en) * | 2020-04-20 | 2021-03-26 | 易秋菊 | Intelligent display combined spinal-epidural puncture needle |
| CN111707656A (en) * | 2020-06-29 | 2020-09-25 | 陕西未来健康科技有限公司 | Cerebrospinal fluid cell detection method and system based on Raman scattering spectrum |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114993974A (en) * | 2022-05-26 | 2022-09-02 | 中南大学 | Category identification method based on FTIR and pupa shell |
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