CN119574503A - A method and system for detecting the degree of spinal cord injury based on terahertz spectroscopy - Google Patents
A method and system for detecting the degree of spinal cord injury based on terahertz spectroscopy Download PDFInfo
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Abstract
本发明公开了一种基于太赫兹光谱的脊髓损伤程度检测方法和系统，具体以太赫兹频域光谱为基础开展分析，合理设计数据分析方法强化光谱特征，计算折射率，利用折射率和吸收系数同血脂含量的关系，实现对样本血脂浓度的有效检测，最终达成对脊髓损伤的有效检测和长期监测目标。相比传统的生物化学检测、影像学检查、电生理检查等检测技术，本发明检测用时短、成本低，且不会对生物组织引起有害的电离反应，具有更高的安全性和检测效能。
The present invention discloses a method and system for detecting the degree of spinal cord injury based on terahertz spectroscopy. Specifically, the terahertz frequency domain spectrum is used as the basis for analysis, a data analysis method is rationally designed to strengthen the spectral characteristics, the refractive index is calculated, and the relationship between the refractive index and the absorption coefficient and the blood lipid content is used to achieve effective detection of the blood lipid concentration of the sample, and finally achieve the goal of effective detection and long-term monitoring of spinal cord injury. Compared with traditional biochemical detection, imaging examination, electrophysiological examination and other detection technologies, the present invention has a short detection time, low cost, and will not cause harmful ionization reactions to biological tissues, and has higher safety and detection efficiency.
Description
Technical Field
The invention belongs to the technical field of terahertz spectrum detection, and particularly relates to a method and a system for detecting the degree of spinal cord injury based on terahertz spectrum.
Background
Spinal Cord Injury (SCI) is a neurological disease that is clinically manifested as a permanent defect in the nervous system and a severe impairment of motor function, and has currently raised global health concerns. Existing spinal cord injury detection and assessment methods are mainly divided into three categories.
The first category is neurological examination, which involves assessing a patient's motor ability and sensory function through clinical observation. The method is relatively simple and noninvasive, but has low objectivity and specificity due to subjectivity.
The second category includes imaging examinations such as x-rays, computed Tomography (CT), magnetic Resonance (MRI), and the like. While these methods may provide more specific information about spinal cord injury, they lack analysis of molecular levels and physiological indicators in patients, requiring a combination of other biochemical screening methods.
The third category is electrophysiological examination, which evaluates the integrity of the nervous system by recording the electrophysiological characteristics of the body of a patient with a spinal cord injury. Electrophysiological measurements lack consistent measurement criteria for different spinal cord injury patients, resulting in diagnostic differences.
Generally, the traditional spinal cord injury detection technology has the defects of low detection efficiency, long time consumption, high medical price, potential radiation injury to human body and the like.
Terahertz spectroscopy has now evolved into a mature and reliable technique. Because terahertz waves (electromagnetic waves with the frequency of 0.1THz-10 THz) cover the rotation and vibration energy levels of a plurality of biological molecules, the terahertz waves are particularly sensitive to the tiny changes of molecular types and structures, and therefore the structural information and the constituent elements of substances can be researched by analyzing parameters such as terahertz spectrum information, refractive indexes, absorptivity and the like. In addition, terahertz waves have transient properties, and the terahertz photon energy is only in the order of millielectron volts and does not cause harmful ionization reaction to biological tissues, so that terahertz spectrum detection also has the characteristics of rapidness and no damage.
Glycerophospholipids (GP) and Triglycerides (TG) have been shown to be effective in monitoring spinal cord injury as biomarkers. Medical studies have shown that as the extent of spinal cord injury increases, the permeability of the Blood Spinal Cord Barrier (BSCB) increases and lipid molecules such as GP and TG are able to penetrate the blood spinal cord barrier increasing its concentration in the blood. Clinical medicine has also demonstrated that GP and TG molecule concentrations are positively correlated with the extent and time of spinal cord injury.
Thus, there is an urgent need for a new method for continuous monitoring of spinal cord injury pathology using terahertz spectroscopy to detect biomarker concentration changes.
Disclosure of Invention
In view of the above, the invention aims to provide a method for detecting the degree of spinal cord injury based on terahertz spectrum, which can realize the rapid, nondestructive and lower-cost effective detection of the degree of spinal cord injury and realize the effective detection and long-term monitoring of spinal cord injury through the detection of the content of lipid molecules TG and GP.
In order to achieve the above object, an embodiment of the present invention provides a device for detecting a degree of spinal cord injury based on terahertz spectrum, including the steps of:
Preparing a serum sample, scanning the serum sample by using an attenuated total reflection terahertz frequency domain spectrometer to obtain a terahertz frequency domain spectrum of the serum sample, preprocessing the terahertz frequency domain spectrum, calculating to obtain a refractive index spectrum of the serum sample, and constructing a refractive index spectrum data set;
measuring the blood lipid parameter concentration of an in-vitro serum sample by using a mass spectrometer, and corresponding the blood lipid parameter concentration to a refractive index spectrum in a refractive index data set to construct a database for guaranteeing the mapping relation between the refractive index spectrum and the blood lipid parameter concentration;
Performing principal component analysis on the refractive index spectrum in the refractive index data set to obtain a principal component analysis result, determining the blood lipid parameter concentration corresponding to the first principal component according to a database based on the first principal component contained in the principal component analysis result, and performing data fitting to obtain a detection model between the blood lipid parameter concentration and the first principal component;
Calculating a first main component of the refractive index of the terahertz spectrum corresponding to the blood sample to be detected with unknown spinal cord injury degree, predicting blood lipid parameter concentration based on the first main component based on the constructed detection model, and evaluating the spinal cord injury degree based on the blood lipid parameter concentration to realize the detection of the spinal cord injury degree based on the terahertz spectrum.
Preferably, the refractive index spectrum of the serum sample is obtained by calculating after preprocessing the terahertz frequency domain spectrum, which comprises the following steps:
Taking a terahertz frequency domain spectrum obtained when a serum sample to be measured is not placed as a reference spectrum, taking the terahertz frequency domain spectrum when the serum sample to be measured is placed as a sample spectrum, and obtaining amplitude attenuation and phase shift caused by the serum sample to be measured by using amplitude division and phase subtraction;
and calculating the refractive index spectrum of the serum sample to be measured through the Fresnel attenuation reflection law based on the amplitude attenuation and the phase shift.
Preferably, the principal component analysis of the refractive index spectrum in the refractive index dataset comprises:
and carrying out zero-mean matrix construction, covariance matrix calculation, eigenvalue and eigenvector calculation on the refractive index spectrum data, then arranging eigenvectors from large to small according to the eigenvalues, calculating the proportion of eigenvector accumulated variance interpretation, and then selecting each principal component according to the proportion to obtain a principal component analysis result.
Preferably, the blood lipid parameters include glycerophospholipids GP and triglycerides TG;
for glycerophospholipids GP, when fitting data to the first principal component, a linear equation is used for fitting.
Preferably, for the triglyceride TG, an exponential equation is used for fitting when fitting the data of the triglyceride TG to the first principal component.
Preferably, the coefficients of the corresponding equations are obtained by adopting a least square method in equation fitting, and a detection model consisting of the determined coefficients and the equations is obtained.
Preferably, the method further comprises evaluating the principal component analysis results using a confidence ellipse to distinguish between the corresponding lesion extent effects of the serum sample based on the principal component analysis results.
In order to achieve the above-mentioned purpose, the embodiment of the invention also provides a system for detecting the degree of spinal cord injury by terahertz spectroscopy, which comprises an attenuated total reflection terahertz frequency domain spectrometer, a mass spectrometer and a computing device;
The attenuated total reflection terahertz frequency domain spectrometer is used for scanning the serum sample to obtain a terahertz frequency domain spectrum of the serum sample;
The mass spectrometer is used for measuring the blood lipid parameter concentration of the in-vitro serum sample;
The calculation device is used for preprocessing the terahertz frequency domain spectrum, calculating to obtain a refractive index spectrum of a serum sample, constructing a refractive index spectrum data set, corresponding blood fat parameter concentration to the refractive index spectrum in the refractive index data set, constructing a database for guaranteeing the mapping relation between the refractive index spectrum and the blood fat parameter concentration, performing principal component analysis on the refractive index spectrum in the refractive index data set to obtain a principal component analysis result, determining the blood fat parameter concentration corresponding to the first principal component according to the database based on the first principal component contained in the principal component analysis result, performing data fitting to obtain a detection model between the blood fat parameter concentration and the first principal component, calculating the first principal component of the refractive index of the terahertz spectrum corresponding to the blood sample to be detected, predicting the blood fat parameter concentration based on the first principal component based on the constructed detection model, and estimating the spinal cord injury degree based on the blood fat parameter concentration to realize spinal cord injury degree detection based on the terahertz spectrum.
Preferably, the computing device is further configured to distinguish between the effects of the extent of damage corresponding to the serum sample based on the principal component analysis results by evaluating the principal component analysis results using a confidence ellipse.
Compared with the prior art, the invention has the beneficial effects that at least the following steps are included:
The invention solves the defects of long time consumption, high cost, potential radiation damage to human body and the like in the detection of spinal cord injury of patients in the prior art. Specifically, the invention carries out analysis based on terahertz frequency domain spectrum, reasonably designs a data analysis method to strengthen spectrum characteristics, calculates refractive index, and utilizes the relation between the refractive index and the absorption coefficient and blood lipid content to realize effective detection of sample blood lipid concentration, thereby finally achieving the aims of effective detection and long-term monitoring of spinal cord injury. Compared with the traditional detection technologies such as biochemical detection, imaging detection, electrophysiological detection and the like, the invention has the advantages of short detection time, low cost, no harmful ionization reaction to biological tissues, higher safety and detection efficiency.
Model prediction results show that based on a large-scale training data set, the identification effect of the first main component on the TG/GP content in serum is furthest mined, the refractive index characteristics of serum under different lipid concentrations are effectively utilized, and a good prediction effect is realized through a regression model. The correlation coefficient of the regression equation is larger than 0.94, and the average detection accuracy of the concentration is 94%, which indicates that the terahertz spectrum can accurately detect the blood lipid concentration change caused by spinal cord injury.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for detecting the degree of spinal cord injury based on terahertz spectroscopy;
FIG. 2 is a schematic diagram of an attenuated total reflection photoelectric terahertz frequency domain spectrometer system and a rat spinal cord injury model;
FIG. 3 is a graph showing the reflectance spectrum of ATR obtained by repeated measurement of a serum sample of a control group;
FIG. 4 is a box plot of ATR reflectance at 1.1 THz;
FIG. 5 is a serum refractive index profile;
FIG. 6 is a histogram of GP, TG concentration levels in serum samples measured by a mass spectrometer;
FIG. 7is a line graph of TG concentration levels in serum samples measured by mass spectrometry;
FIG. 8 is a line graph of GP concentration levels in serum samples measured by a mass spectrometer;
FIG. 9 is a graph of the eigenvalues of the first five Principal Components (PCs) and their cumulative contribution to the total variance of the data;
FIG. 10 is serum sample scores on the first 2 PCs;
FIG. 11 is a regression equation fit result image between GP/TG concentration and PC1 score;
Fig. 12 shows the distribution of the scattering points between the actual and calculated values of GP/TG concentration, and the prediction residual of GP/TG concentration.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the detailed description is presented by way of example only and is not intended to limit the scope of the invention.
Aiming at the problems in the prior detection technology, the invention establishes a detection model of serum sample GP and TG concentration by utilizing the positive correlation relation between GP and TG molecular concentration and spinal cord injury degree and combining a principal component analysis algorithm and a least squares algorithm based on terahertz spectrum so as to realize effective detection and long-term monitoring of spinal cord injury.
As shown in fig. 1 and fig. 2, the method for detecting the degree of spinal cord injury based on terahertz spectrum provided in the embodiment includes the following steps:
s1, preparing a serum sample, scanning the serum sample by using an attenuated total reflection terahertz frequency domain spectrometer to obtain a terahertz spectrum of the serum sample, preprocessing the terahertz spectrum, calculating to obtain a refractive index spectrum of the serum sample, and constructing a refractive index spectrum data set.
In the embodiment, experimental models with different spinal cord injury degrees are constructed, in-vitro serum samples at different spinal cord injury moments are extracted, and one part of each group of collected serum samples is used for model training and one part of each group of collected serum samples is used for model inspection. In order to eliminate the interference and influence of other factors, it is necessary to control the influence factors such as environment, experimental rats, etc. In the embodiment, all experimental rats are purchased from medical college in Zhejiang Hangzhou, china, all animal procedures used by the research institute are carried out according to the rules of Chinese animal welfare, and adult female Sprague-Dawley rats with weights of 200-220 g are uniformly fed in a regulated environment for control variable, and the temperature is kept at about 24 ℃ and the humidity is kept at about 40%. The living conditions of the rats followed a 12-hour light and dark cycle, with periodic changes of food, water and bedding.
In the examples, the T10 segment was selected as a site of spinal cord injury in rats for evaluation, and the control group used the same procedure as the experimental group, but was not subjected to spinal cord injury construction. The construction of the rat spinal cord injury experimental group specifically comprises the following steps:
The method comprises the steps of (1) injecting 1% (w/v) pentobarbital sodium into abdominal cavity of a rat to induce anesthesia, shaving at an operation incision and disinfecting with iodine, 2) cutting the skin of the rat, exposing fascia, lightly dissecting muscles, systematically separating the paraspinal tissues layer by layer after the muscles at two sides of the spinal column are cut, exposing a T10 section, performing laminectomy to show dura, and 3) conducting spinal cord injury induction on rats of an experimental group through constant pressure vascular clamp compression to induce spinal cord injury, wherein the spinal cord compression duration is changed to change the spinal cord injury degree, and specifically, the vascular clamp is used for compression for 30 or 120 seconds (15 g force, oscar, china). (4) After the operation, the muscle and skin were sutured layer by layer and disinfected with povidone-iodine. The body temperature is maintained after the operation, and the bladder is manually drained twice a day.
In the step, the models of different spinal cord injury degrees are realized by taking serum samples of different spinal cord injury times. Therefore, in order to study the pathological characteristics of different periods, after the spinal cord injury induction operation, rats in the experimental group are divided into five groups according to the serum sample extraction time, and the spinal cord injury time is respectively 6 hours, 1 day, 3 days, 7 days and 14 days for 3 rats in each group. The control group did not undergo spinal cord injury induction, and a total of 3 rats were treated.
The experimental models of different spinal cord injury degrees are divided by the time of spinal cord injury, and are specifically realized by extracting serum samples of different spinal cord injury times. After spinal cord injury induction is performed on experimental rats, spinal cord injury can be classified into an acute phase (< 12 hours), a subacute phase (12 hours to 2 days), a medium phase (2 days to 7 days) and a chronic phase (> 7 days) according to injury time. To investigate the pathological characteristics of the different periods, after the spinal cord injury induction procedure, rats of the experimental group were divided into five groups according to the serum sample extraction time, 3 rats of each group were respectively subjected to serum samples of spinal cord-pressed rats (experimental group) and non-spinal cord-injured rats (control group) at 6 hours, 1 day, 3 days, and 7 days, 14 days.
Specifically, the process of extracting serum samples of rats subjected to spinal cord compression (experimental group) and rats not subjected to spinal cord injury (control group) comprises (1) placing anesthetized rats in a supine position, dehairing and sterilizing the peripheral area of the spinal column, (2) obliquely inserting a blood taking needle into the thoracic cavity of the rats under the spinal column, collecting blood (4-5 ml) by using a blood taking tube (367955,BD Vacutainer) after the needle tip passes through the diaphragm and enters the ventricle, and (3) centrifuging (4000 g,15 min) the collected blood plasma to obtain serum samples (1-2 ml).
In an embodiment, after a serum sample is obtained, the serum sample is scanned by using an attenuated total reflection terahertz frequency domain spectrometer to obtain a terahertz frequency domain spectrum of the serum sample. The terahertz frequency-domain spectrum includes amplitude information and phase information. The photoelectric terahertz frequency domain spectrometer with attenuated total reflection carries out spectrum detection on a serum sample by utilizing evanescent waves generated by attenuated total reflection of terahertz waves on the surface of a Dove prism made of high-resistance silicon materials. The evanescent wave interacts with a sample placed on the surface of the attenuated total reflection prism to form a modulated terahertz signal carrying sample information, and the modulated terahertz signal is output. The terahertz photoconduction detection antenna further carries out coherent mixing on the modulated terahertz signal and the laser signal to generate a down-converted photocurrent signal, the down-converted photocurrent signal is amplified by a transimpedance amplifier and then is sampled by a digital signal processing unit, and the sampled oscillation photocurrent signal is subjected to Hilbert transformation to obtain a terahertz frequency domain spectrum containing amplitude and phase information.
Because the working range of the light sensor and the parasitic interference effect in the spectrometer are commonly existing at low frequency and no reliable terahertz data exists below 50 to 100GHz, the 300-1200GHz frequency band of the original frequency spectrum is selected as the frequency range of spectrum analysis, and the noise interference of the spectrum data is reduced by adopting a low smoothing technology.
Because the spectrometer system is easily interfered by external environment in the operation process, the same serum sample with each damage degree needs to be measured for multiple times to ensure the measurement precision and eliminate abnormal data, and the amplitude and the phase spectrum of terahertz waves when the sample of the blood sample to be measured is placed and the sample of the serum sample to be measured is not placed are respectively tested for each spectrum measurement of the serum sample. In this example, each serum sample was evaluated five times, and outliers were removed by the box plot method. Fig. 3 and 4 show attenuated total reflectance spectroscopy measurements of serum from healthy mice in the control group, and it can be seen that the spectrum of the fourth measurement is much larger in the high frequency region than the spectrum of the other four measurements. The box plot of the data at 1.1THz also shows that the fourth measurement far exceeds 1.5 times the quartile, indicating that it is an outlier of this type of data. And finally, carrying out average processing on the measurement data to obtain a more accurate measurement result.
In the embodiment, the terahertz frequency domain spectrum is further preprocessed, then the refractive index spectrum of the serum sample is obtained through calculation, and a refractive index spectrum data set is constructed. The specific pretreatment process is as follows:
(1) Taking a terahertz frequency domain spectrum obtained when a serum sample to be measured is not placed as a reference spectrum, taking the terahertz frequency domain spectrum when the serum sample to be measured is placed as a sample spectrum, and obtaining amplitude attenuation and phase shift caused by the serum sample to be measured by using amplitude division and phase subtraction;
The amplitude decay is calculated according to the formula:
Wherein R is the ratio of the total coefficient of the reference spectrum and the sample spectrum after attenuation, R sam (omega) is the total coefficient of attenuation of the sample spectrum, R ref (omega) is the total coefficient of attenuation of the reference spectrum, To place the spectral amplitude measured when the serum sample to be tested is placed,To measure the spectral amplitude without placing the serum sample to be measured,In order to avoid the photocurrent of absorption loss under ideal conditions, ω is the frequency of terahertz waves.
(2) Calculating the refractive index spectrum of the serum sample to be measured through the Fresnel attenuation reflection law based on the amplitude attenuation and the phase shift;
According to the fresnel equation for p-polarized radiation, r sam (ω) and r ref (ω) satisfy the following equations, respectively:
Wherein n prism =3.42 is the refractive index of the Dove prism of the spectrometer, n air =1 is the refractive index of air, θ=55.7 ° is the incident angle of the terahertz wave, Is the complex refractive index of serum sample n.
The complex refractive index of the serum sample n can be obtained by calculation of the formulas (2) and (3)Satisfies the following formula:
Finally, the expression of the refractive index n sample of the serum sample is obtained:
Wherein Rr ref represents Rxr ref, and Re [. Cndot ] represents an operation of taking the real part of the complex number in the brackets.
And (3) constructing a refractive index spectrum data set by calculating terahertz refractive index spectrums of all serum samples. The terahertz refractive index spectra obtained after pretreatment of the data in this example are shown in fig. 5, wherein Ctrl, 6H, 1D, 3D, 7D, and 14D represent serum samples collected 6 hours, 1 day, 3 days, 7 days, and 14 days after the sham operation control group and spinal cord injury, respectively. The hatched area indicates the standard error for each frequency bin. As the frequency increases, the refractive index of the serum gradually decreases. The difference between the low frequency groups is not obvious, but at 0.9-1.2 THz, the refractive index of the serum sample of the spinal cord injury group is obviously lower than that of the serum sample of the control group, and the refractive index is reduced along with the increase of the injury time. For serum samples taken at the same time point, the longer the spinal cord compression time, the smaller the refractive index. Furthermore, the spinal cord compression time has less effect on the refractive index of the blood sample than the injury time. The analysis shows that the terahertz spectrum can reflect the change of the spinal cord injury degree and time of the rat more sensitively.
S2, measuring the blood lipid parameter concentration of the isolated serum sample by using a mass spectrometer, and corresponding the blood lipid parameter concentration to the refractive index spectrum in the refractive index data set to construct a database for guaranteeing the mapping relation between the refractive index spectrum and the blood lipid parameter concentration.
In an embodiment, the actual blood lipid parameter concentration of the isolated serum sample is also measured by a mass spectrometer, and the blood lipid parameters comprise GP and TG. Specifically, the GP concentration and TG concentration are shown in fig. 6, and fig. 6 shows that the GP concentration and TG concentration are gradually increased with the increase of the damage time and the damage degree, and are inversely related to the terahertz refractive index spectrum. The embodiment also arranges the refractive index of the data set and blood lipid parameters (TG, GP) in one-to-one correspondence, namely, specifically, the experimental refractive index measured value and the medical actual blood lipid parameter value of the same serum sample are arranged into a group to construct a database, namely, each serum sample in the database corresponds to two types of data, namely, the refractive index measured value and the actual TG, GP concentration.
To more clearly observe the change in lipid concentration, a line graph of the change in GP concentration and TG concentration with time was also plotted, as shown in fig. 7 and 8. Fig. 7 shows that TG concentration in serum increases sharply in the acute phase of injury (within 24 hours), whereas TG concentration does not increase significantly in the subacute phase (1 to 7 days), and TG concentration increases significantly to 3 times 7 days on day 14. FIG. 8 shows that GP concentration also increases markedly in the acute phase, but then tends to rise slowly. As the injury time increased, the concentration difference between the 30s compression group and the 120s compression group gradually decreased, indicating that the GP concentration difference caused by the injury degree gradually decreased with the passage of time. The relationship between the blood lipid concentration and the terahertz spectrum characteristic is verified.
S3, carrying out principal component analysis on the refractive index spectrum in the refractive index data set to obtain a principal component analysis result, determining the blood fat parameter concentration corresponding to the first principal component according to the database based on the first principal component contained in the principal component analysis result, and carrying out data fitting to obtain a detection model between the blood fat parameter concentration and the first principal component.
In the embodiment, principal component analysis is performed on the sample refractive index spectrum, and key features in the spectrum data, namely, all principal components, are extracted through linear dimension reduction, so that principal component analysis results are obtained. The specific process is as follows:
(1) Constructing a zero-mean matrix;
The mean vector mu i for each observation (i.e. refractive index spectral data at different frequencies) is calculated, I is the index of the observation frequency, j is the index of the refractive index of the spectrum, x ji is the refractive index value of the terahertz frequency domain spectrum under the ith observation frequency, and the corresponding mean vector is subtracted from each column of the dataset to obtain a zero-mean matrix
(2) Covariance matrix calculation;
From the zero-mean matrix X, a covariance matrix Σ of refractive index spectrum data is calculated, expressed as:
(3) And performing eigenvalue decomposition on the covariance matrix to obtain eigenvalues and eigenvectors, arranging the eigenvectors from large to small according to the eigenvalues, calculating the proportion of the eigenvector accumulated variance interpretation, and selecting each principal component according to the proportion to obtain a principal component analysis result.
Specifically, the principal component analysis algorithm is realized by MATLAB software, and the effective dimension reduction is realized by taking the sample number n as 30 in the experiment. Fig. 9 shows the eigenvalues of the first five Principal Components (PC) and their cumulative contribution to the total variance of the data. The characteristic value rapidly decreases with increasing number of PCs, the first PC and the second PC account for 97.9% and 1.4% of the total variance of the serum spectrum data, respectively, while the other PCs account for less than 1% of the total variance of the data. This shows that the first two PCs already contain most of the spectral data features.
In an embodiment, based on the principal component analysis result, the PCA result is evaluated using the confidence ellipses to distinguish the effect of the degree of damage corresponding to the serum sample. Where confidence ellipses are determined using a set of given confidence levels, specifically, by projecting the error range of the data points onto a two-dimensional plane, an elliptical distribution is obtained. The calculation of confidence ellipses requires the use of error functions and normal distribution functions. Drawing confidence ellipses specifically comprises the following steps:
(1) The error range is determined, and the confidence interval is determined to be 95% in the invention. (2) Calculating ellipse parameters, wherein in the principal component space, an ellipse equation corresponding to the centralized data is that Wherein lambda 1 and lambda 2 are the magnitudes of the first and second eigenvalues, respectively, k is the confidence level, and when the confidence level is 95%, the k value is calculated to be 5.991 according to the chi-square distribution. (3) In order to restore the true confidence ellipse, the ellipse needs to be translated and rotated, and the confidence ellipses obtained by the spectrum data of the serum samples at different times of damage are drawn in the same coordinate system to obtain the final confidence ellipse.
Specifically, samples were divided into 6 groups according to the time of spinal cord injury, and confidence ellipses for each group of data were drawn according to 95% confidence intervals. Figure 10 shows the serum sample scores on the first 2 PCs. The scatter plot shows that six groups of samples have good clustering characteristics, particularly the first PC score for each group of data, which tends to increase with increasing time of injury. But the second set of PC scores did not differ significantly from group to group.
In the embodiment, based on the first principal component PC1 included in the principal component analysis result, the blood fat parameter concentration corresponding to the first principal component PC1 is determined according to the database, and data fitting is performed to obtain a detection model between the blood fat parameter concentration and the first principal component. Specifically, according to the scatter distribution between the GP/TG concentration and the PC1 score, a regression equation between the PC1 score and the GP and TG concentrations in serum is established by using a least square method, specifically, the specific distribution situation of data points is observed, the regression equation between the GP concentration and the PC1 score is primarily determined to be a linear equation y=b m*xi m+...+b1x+b0, and the regression equation between the TG concentration and the PC1 score is determined to be an exponential equation y=a 1ebx+a0. On this assumption, the establishment of the regression equation using the least squares algorithm includes the steps of:
the method comprises the steps of (1) establishing a data matrix by using a PC1 score, establishing an observation value vector by using a GP/TG concentration, simultaneously establishing corresponding coefficient vectors to be solved for beta GP and beta TG, (2) obtaining a coefficient vector by adopting a least square method through least square sum of errors, further obtaining a prediction model which is obtained through the mapping relation between the PC1 score and the GP/TG concentration, (3) calculating the associated number of the corresponding prediction models of the GP and the TG, namely R GP＝0.979,RTG =0.942, and drawing a regression equation between the GP/TG concentration and the PC1 score, (comprising a 95% confidence band and a 95% prediction band), as shown in fig. 11, (4) respectively calculating the calibration root mean square standard error RMSSEC GP＝0.70、RMSSECTG =2.23, the cross-validation root mean square standard error RMSSECV GP＝1.46、RMSSECVTG =7.61 and the prediction root mean square standard error RMSSEP GP＝1.20、RMSSEPTG =25.17 of the two prediction models, and drawing an image as shown in fig. 12. In fig. 12, (a) shows the distribution of the points between the actual and calculated values of the GP concentration, fig. 12, (b) shows the prediction residual of the GP concentration, fig. 12, (c) shows the distribution of the points between the actual and calculated values of the TG concentration, and fig. 12, (d) shows the prediction residual of the TG concentration.
The method comprises the steps of calibrating a Root Mean Square Standardized Error (RMSSEC) for evaluating the fitting degree of a model to training data, calculating the root mean square error between the prediction and the actual observation of a training set model, cross-verifying the Root Mean Square Standardized Error (RMSSECV) for evaluating the prediction performance of the model in the cross-verifying process, calculating the root mean square error between the prediction and the actual observation of a reserved verification set model, and calculating the root mean square error between the prediction and the actual observation of a new sample model, wherein the prediction Root Mean Square Standardized Error (RMSSEP) is used for evaluating the prediction capability of the model to the new sample.
S4, calculating a first main component of the refractive index of the terahertz spectrum corresponding to the blood sample to be detected with unknown spinal cord injury degree, predicting blood fat parameter concentration based on the first main component based on the constructed detection model, and estimating the spinal cord injury degree based on the blood fat parameter concentration to realize detection of the spinal cord injury degree based on the terahertz spectrum.
In the embodiment, the trained GP and TG detection models are used for detecting the detection set serving as an unknown serum sample, analysis results of blood lipid concentration are obtained, measured values and actual values of the GP and TG are compared, and the measurement accuracy of the models is verified. And the trained model is used for checking the checking set, and the average measurement precision of the model is 94%, so that the terahertz spectrum can accurately detect the blood lipid concentration change caused by spinal cord injury.
The embodiment also provides a spinal cord injury degree detection system based on terahertz spectrum, which comprises an attenuated total reflection terahertz frequency-domain spectrometer, a mass spectrometer and a computing device. The attenuated total reflection terahertz frequency domain spectrometer is used for scanning the serum sample to obtain the terahertz frequency domain spectrum of the serum sample. The mass spectrometer is used for measuring the blood lipid parameter concentration of the isolated serum sample. The computing device is mainly used for various analysis and calculation in the process of detecting the degree of the spinal cord injury, and specifically comprises various calculation in S1-S4 in the method so as to realize the detection of the degree of the spinal cord injury.
The computing device includes, at a hardware level, hardware required by other services such as internal buses, network interfaces, memory, and the like, in addition to the processor and the memory. The memory is a non-volatile memory, and the processor reads the corresponding computer program from the non-volatile memory into the memory and then runs the computer program to implement the various computing processes in S1-S4. Of course, other implementations, such as logic devices or combinations of hardware and software, are not excluded from the present invention, that is, the execution subject of the following processing flows is not limited to each logic unit, but may be hardware or logic devices.
The foregoing detailed description of the preferred embodiments and advantages of the invention will be appreciated that the foregoing description is merely illustrative of the presently preferred embodiments of the invention, and that no changes, additions, substitutions and equivalents of those embodiments are intended to be included within the scope of the invention.
Claims (9)
1. The method for detecting the spinal cord injury degree based on the terahertz spectrum is characterized by comprising the following steps of:
Preparing a serum sample, scanning the serum sample by using an attenuated total reflection terahertz frequency domain spectrometer to obtain a terahertz frequency domain spectrum of the serum sample, preprocessing the terahertz frequency domain spectrum, calculating to obtain a refractive index spectrum of the serum sample, and constructing a refractive index spectrum data set;
measuring the blood lipid parameter concentration of an in-vitro serum sample by using a mass spectrometer, and corresponding the blood lipid parameter concentration to a refractive index spectrum in a refractive index data set to construct a database for guaranteeing the mapping relation between the refractive index spectrum and the blood lipid parameter concentration;
Performing principal component analysis on the refractive index spectrum in the refractive index data set to obtain a principal component analysis result, determining the blood lipid parameter concentration corresponding to the first principal component according to a database based on the first principal component contained in the principal component analysis result, and performing data fitting to obtain a detection model between the blood lipid parameter concentration and the first principal component;
Calculating a first main component of the refractive index of the terahertz spectrum corresponding to the blood sample to be detected with unknown spinal cord injury degree, predicting blood lipid parameter concentration based on the first main component based on the constructed detection model, and evaluating the spinal cord injury degree based on the blood lipid parameter concentration to realize the detection of the spinal cord injury degree based on the terahertz spectrum.
2. The method for detecting the degree of spinal cord injury based on terahertz spectrum according to claim 1, wherein the refractive index spectrum of the serum sample is obtained by performing pretreatment on the terahertz frequency domain spectrum, and the method comprises the following steps:
Taking a terahertz frequency domain spectrum obtained when a serum sample to be measured is not placed as a reference spectrum, taking the terahertz frequency domain spectrum when the serum sample to be measured is placed as a sample spectrum, and obtaining amplitude attenuation and phase shift caused by the serum sample to be measured by using amplitude division and phase subtraction;
and calculating the refractive index spectrum of the serum sample to be measured through the Fresnel attenuation reflection law based on the amplitude attenuation and the phase shift.
3. The terahertz spectrum-based spinal cord injury level detection method according to claim 1, wherein the principal component analysis of the refractive index spectrum in the refractive index dataset comprises:
and carrying out zero-mean matrix construction, covariance matrix calculation, eigenvalue and eigenvector calculation on the refractive index spectrum data, then arranging eigenvectors from large to small according to the eigenvalues, calculating the proportion of eigenvector accumulated variance interpretation, and then selecting each principal component according to the proportion to obtain a principal component analysis result.
4. The method for detecting the degree of spinal cord injury based on terahertz spectrum according to claim 1, wherein the blood lipid parameters include glycerophospholipids GP and triglycerides TG;
for glycerophospholipids GP, when fitting data to the first principal component, a linear equation is used for fitting.
5. The method for detecting the degree of spinal cord injury based on terahertz spectrum according to claim 4, wherein when data fitting is performed on the triglyceride TG and the first main component with respect to the triglyceride TG, fitting is performed by using an exponential equation.
6. The method for detecting the degree of spinal cord injury based on terahertz spectrum according to claim 4, wherein coefficients of the corresponding equations are obtained by adopting a least square method to solve the equations during fitting, and a detection model consisting of the determined coefficients and the equations is obtained.
7. The method for detecting the degree of spinal cord injury based on terahertz spectrum of claim 1, further comprising evaluating the principal component analysis result by using a confidence ellipse based on the principal component analysis result to distinguish the effect of the degree of injury corresponding to the serum sample.
8. The system for detecting the degree of spinal cord injury by using the terahertz spectrum is characterized by comprising an attenuated total reflection terahertz frequency domain spectrometer, a mass spectrometer and a computing device;
The attenuated total reflection terahertz frequency domain spectrometer is used for scanning the serum sample to obtain a terahertz frequency domain spectrum of the serum sample;
The mass spectrometer is used for measuring the blood lipid parameter concentration of the in-vitro serum sample;
The calculation device is used for preprocessing the terahertz frequency domain spectrum, calculating to obtain a refractive index spectrum of a serum sample, constructing a refractive index spectrum data set, corresponding blood fat parameter concentration to the refractive index spectrum in the refractive index data set, constructing a database for guaranteeing the mapping relation between the refractive index spectrum and the blood fat parameter concentration, performing principal component analysis on the refractive index spectrum in the refractive index data set to obtain a principal component analysis result, determining the blood fat parameter concentration corresponding to the first principal component according to the database based on the first principal component contained in the principal component analysis result, performing data fitting to obtain a detection model between the blood fat parameter concentration and the first principal component, calculating the first principal component of the refractive index of the terahertz spectrum corresponding to the blood sample to be detected, predicting the blood fat parameter concentration based on the first principal component based on the constructed detection model, and estimating the spinal cord injury degree based on the blood fat parameter concentration to realize spinal cord injury degree detection based on the terahertz spectrum.
9. The terahertz spectrum spinal cord injury degree detection system is characterized in that the computing device is further used for distinguishing injury degree effects corresponding to serum samples based on principal component analysis results by using confidence ellipsometry to evaluate the principal component analysis results.
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