阅读说明：本技术 基于eeg和血清炎症因子分析脑损伤标志物的方法 (Method for analyzing brain injury marker based on EEG and serum inflammatory factor ) 是由 张立国 杨曼 金梅 刘强 李媛媛 马子荐 李义辉 胡林 于 2020-06-22 设计创作，主要内容包括：本发明提供一种基于EEG和血清炎症因子分析脑损伤标志物的方法,其过程包括：首先对脑电信号预处理,分别计算8组导联的慢波系数和近似熵的比值a,根据表达式Sum<Sub>p</Sub>＝a<Sub>1</Sub>+a<Sub>2</Sub>+…+a<Sub>8</Sub>,(p＝1、2)求出慢波系数Sum<Sub>1</Sub>值和近似熵的Sum<Sub>2</Sub>值,并根据加权平均法归一化为Sum值的范围；然后抽取受试者的空腹静脉血,采用酶联免疫吸附测定法检测血清炎症因子水平,根据加权平均法求解W值的范围,用于后续医生制定检测轻微脑损伤的方案。本发明将Sum值和W值相结合应用为轻微脑损伤的综合性标志物,并提出基于EEG和血清炎症因子计算Sum值和W值的方法,不仅为轻微脑损伤的后续检测方案提供了更可靠的数据信息,而且可以减少受试者家属的经济负担。(The invention provides a method for analyzing a brain injury marker based on EEG and serum inflammatory factors, which comprises the following steps: firstly, preprocessing an electroencephalogram signal, respectively calculating the ratio a of slow wave coefficients and approximate entropies of 8 groups of leads, and according to an expression Sum p ＝a 1 +a 2 +…+a 8 (p is 1, 2) finding the slow wave coefficient Sum 1 Sum of values and approximate entropy 2 The values are normalized to the range of Sum values according to a weighted average method; then extracting fasting venous blood of the subject, detecting the level of serum inflammatory factors by adopting an enzyme-linked immunosorbent assay method, and solving the range of the W value according to a weighted average method for a subsequent doctor to formulate a scheme for detecting mild brain injury. The invention combines and applies the Sum value and the W value as a comprehensive marker of mild brain injury, and provides a method for calculating the Sum value and the W value based on EEG and serum inflammatory factors, which not only provides more reliable data information for a subsequent detection scheme of mild brain injury, but also can reduce the economic burden of family members of a subject.)

1. A method for analyzing brain injury markers based on EEG and serum inflammatory factors, the method comprising the steps of:

step 1, collecting EEG signals of a subject in a quiet state and EEG signals of a subject in an excited state for 5 minutes respectively, recording the EEG signals in the two states, removing interference of power frequency signals, then performing discrete sequence wavelet transformation on EEG signals polluted by noise, then performing wavelet coefficient threshold processing, reconstructing EEG signals by processed coefficients, then performing independent component analysis by adopting a FastICA algorithm, listing each independent component, finding out artifact components and corresponding coefficients, further removing artifacts, and reconstructing the EEG signals to achieve the purpose of signal denoising;

step 2, utilizing the preprocessed electroencephalogram signals according toCalculating a slow wave coefficient SWC, wherein alpha, beta and theta are frequency band ranges, and a spectrum () function is used for calculating various spectrum functions and is suitable for time series analysis, and then according to an expression (3): ApEn ═ Φ^m(r)-Φ^m+1(r) calculating approximate entropy ApEn, wherein r is allowable deviation, m is vector dimension, phi in formula (3)^m(r) is a vector sequence { y (i) } average autocorrelation degree, and then the ratio a obtained by dividing the characteristic parameters of the right side lead of the slow wave coefficient and the approximate entropy by the characteristic parameters of the left side lead is respectively solved, and an expression (4) is solved according to the electroencephalogram characteristic parameters Sum: sum_p＝a₁+a₂+…+a₈(p is 1, 2) Sum for determining slow wave coefficient₁Sum of values and approximate entropy₂Value of a in expression (4)₁～a₈In the 1 st to 8 th symmetric lead groups, the characteristic parameter of the right lead is divided by the characteristic parameter of the left lead, and then, the expression (5) is obtained according to the weighted average method: sum 0.5. Sum₁+0.5·Sum₂Will slow wave coefficient Sum₁Sum of values and approximate entropy₂Values are normalized to a range of Sum values;

step 3, extracting 10mL of fasting venous blood of a subject, and detecting the level of serum inflammatory factors by adopting an enzyme-linked immunosorbent assay method;

step 4, solving the serum inflammatory factor level characteristic parameter W of 0.25W according to a weighted average method_IL-6+0.25W_IL-8+0.25W_CRP+0.25W_TNF-αNormalizing the four characteristic values of the serum inflammatory factor to be within the range of the W value, wherein W is_IL-6Is a characteristic parameter of interleukin-6, W_IL-8Is a characteristic parameter of interleukin-8, W_CRPIs the characteristic parameter of C-reactive protein, W_TNF-αIs the characteristic parameter of tumor necrosis factor-alpha.

2. The method for analyzing brain injury markers based on EEG and serum inflammatory factors according to claim 1, wherein said step 1 comprises the following steps:

step 11, obtaining stable data, and removing unstable data caused by external factors in the acquisition process;

step 12, removing power frequency interference of 50Hz by using an infinite impulse response digital filter in an EEGLAB electroencephalogram processing tool box, and then performing discrete sequence wavelet transformation on EEG signals polluted by noise to obtain wavelet coefficients with noise;

and step 13, performing wavelet coefficient threshold processing, reconstructing an EEG signal by the processed coefficient, then performing independent component analysis by adopting a FastICA algorithm, listing each independent component, finding out an artifact component and a corresponding coefficient, further removing the artifact, and reconstructing the EEG signal.

3. The method for analyzing brain injury markers based on EEG and serum inflammatory factors according to claim 1, wherein said step 2 comprises the following steps:

step 21, calculating a slow wave coefficient SWC:

the brain electrical signals are divided into 6 frequency bands: a first frequency band of 1.0 to 4.0Hz, a second frequency band of 4.1 to 8.0Hz, and a third frequency band of alpha₁8.1 to 10.0Hz, and a fourth frequency band of alpha₂10.1 to 13.0Hz, a fifth frequency band beta₁13.1 to 17.5Hz, sixth frequency band beta₂Defining a frequency spectrum characteristic parameter, namely a slow wave coefficient SWC as a power spectrum ratio (+ theta)/(alpha + beta) of a low frequency band (+ theta) and a high frequency band (alpha + beta), namely 17.6-30 Hz, whereinWherein α ═ α₁+α₂，β＝β₁+β₂Performing fast Fourier transform on the electroencephalogram data, calculating a power spectrum value of each frequency band, and then calculating slow wave coefficients of each lead according to definitions;

step 22, calculating approximate entropy:

adding a time window to an EEG signal, wherein the time of the selection window is 2s, N is 512, the approximate entropy value of each channel is based on the sampling points, the approximate entropy value of each sampling point is solved, then an approximate entropy waveform is drawn, a relatively stable part is selected in the waveform, the average value is solved, the average value is used as a corresponding approximate entropy characteristic parameter, and the solving process of the approximate entropy is as follows: composing the time series { x (i) } of length N into an m-dimensional vector y (i): y (i) { x (i), x (i +1), x (i +2), …, x (i + m-1) }, where i ranges from [1, N-m +1], and then, the maximum distance d between y (i) and y (j) is calculated [ y (i), y (j) ], i.e.: d [ y (i), y (j) ] | | | x (i + k-1) -x (j + k-1) | | k ═ 1,2, …, m, given an allowable deviation r >0, there is a probability that N-m +1 is equal to each i ≦ N ≦ m +1 of y (i)

The expression (1) reflects the probability that the distance between y (i) and y (j) in the m-dimensional modular expression in the sequence is less than r, m is 2, r is 0.1-0.2 times of the standard deviation of the original data, and then the probability that the distance between y (i) and y (j) in the m-dimensional modular expression in the sequence is less than r is obtainedTaking the logarithm, averaging, i.e.

From the above steps, phi can be obtained by the same method^m+1(r), finally using expression (3): ApEn ═ Φ^m(r)-Φ^m+1(r) calculating an approximate entropy;

step 23, calculating the symmetric lead characteristic parameter ratio a and Sum of the slow wave coefficient and the approximate entropy respectively_pValue, Sum_pThe sum of the characteristic parameter ratios of the 8 symmetrical lead groups is obtained.

4. The method for analyzing brain injury markers based on EEG and serum inflammatory factors according to claim 1, wherein 16 leads are divided into 8 groups of symmetrical leads, F7-F8, T3-T4, T5-T6, FP1-FP2, F3-F4, C3-C4, P3-P4, O1-O2, and then the pairs are divided intoDividing the characteristic parameter of the right lead in the lead group by the characteristic parameter of the left lead, wherein the ratio of the characteristic parameters is a in the formula (4)₁～a₈。

5. The method for analyzing brain injury markers based on EEG and serum inflammatory factors according to claim 1, wherein said step 3 comprises the following:

detecting the level of serum inflammatory factors including interleukin-6, interleukin-8, C-reactive protein and tumor necrosis factor-alpha in fasting venous blood of a subject by enzyme-linked immunosorbent assay, performing statistical analysis by using SPSS 20.0 software package, and making the counting data meet normal distribution and mean standard deviationThe representation is that t test is adopted, chi-square test is adopted for the measured data, and the difference has statistical significance when the probability P is less than 0.05.

6. The method for analyzing brain injury markers based on EEG and serum inflammatory factors according to claim 1, wherein said step 4 comprises:

according to the weighted average method, let each weight be 0.25, the sum of the weight functions be "1", each data is represented by "W", and the range of the W value of the normal human group obtained according to expression (6) is: 11.665-20.505; the mild groups were: 27.145-36.590

W＝0.25W_IL-6+0.25W_IL-8+0.25W_CRP+0.25W_TNF-α(6)

And substituting the data obtained by the subject into the expression (6) to obtain the W value of the subject.

Technical Field

The invention belongs to the field of medicine, and particularly relates to a method for analyzing a brain injury marker based on an EEG (electroencephalogram) and a serum inflammatory factor.

Background

Along with the development of society, the life of human beings is gradually improved, and the expression of the life prescription of people is also greatly changed, for example, the development of vehicles brings great convenience to people, but the probability of craniocerebral injury is increased to a certain extent due to the annual increase of the incidence rate of traffic accidents. Craniocerebral injury is an acute injury of the brain, and sometimes occurs due to external violence such as traffic accidents and mechanical trauma. The development and change of the craniocerebral injury are fast, if a subject cannot be diagnosed and treated in time, the prognosis is poor, the physical and mental health and even the life of the subject can be seriously influenced, and the slight Traumatic brain injury accounts for about 75 percent of all Traumatic Brain Injuries (TBI). In clinic, 15 to 30 percent of Mild Brain Injury (Mild Traumatic Brain Injury, MTBI) subjects can develop symptoms such as cognitive and sensory disorders after trauma; there are some subjects who will still have persistent postconcussion syndrome for months or years after trauma.

The main pathological change of MTBI subjects is bleeding, and bleeding foci occurring in MTBI are mostly dominated by intracranial microbleeding foci, which are currently mainly based on CT examination and conventional MRI, but mild brain injury may not find foci in all neuroimaging examinations, because current neuroimaging techniques have not been able to resolve some of the microstructures and foci. And CT and conventional MRI can only show the anatomical change of brain tissues, so that the diagnosis of MTBI and the like is greatly limited, and the diagnosis is often missed or the severity of brain trauma is judged to be too light. Mild brain injury has other diagnostic methods besides CT and conventional MRI, mainly including nerve function test, physical examination and medical history, but these methods are not very convenient and accurate, and after misdiagnosis, the method not only causes radioactive injury, but also increases the economic burden of the testee.

The human brain structure is symmetrical in structure and contralateral in function, so that the discrimination of the brain injury part based on the EEG (electroencephalogram) feature analysis has anatomical and physiological bases. When the brain is in a resting state, the electroencephalogram signals of the left and right symmetric brain areas have similarity, but when one side brain area is damaged, the similarity of the electroencephalogram signals of the damaged area and the non-damaged area at the opposite side symmetric position is reduced, the difference is increased, and the difference of the electroencephalogram signals can be represented through electroencephalogram signal characteristic parameters. In the prior art, an EEG-based analysis method is proposed, in which the ratio of characteristic parameters on two symmetrical sides of the brain is used as a unique marker for detecting mild brain injury, and a doctor can make a next detection scheme according to the range of the marker. However, this method is too single, and normalization is not performed in the aspect of data processing, so that reliability and accuracy need to be further improved, and the method is not very convenient to apply.

In summary, there is a need for a comprehensive marker for detecting mild and mild brain injuries, so as to reduce the examination cost, improve the reliability of data, provide a basis for a doctor to analyze and formulate a next detection scheme, and provide a corresponding calculation method to simplify the operation process.

Disclosure of Invention

The invention takes EEG and serum inflammatory factor level index representing craniocerebral injury as markers, and respectively carries out data normalization processing on the characteristic parameters of EEG signals and the characteristic parameters of serum inflammatory factor level to obtain the ranges of the characteristic parameters Sum value of EEG signals and the characteristic parameters W value of serum inflammatory factor level, thereby providing a new direction for marker selection and data calculation method of slight brain injury.

The invention provides a method for analyzing brain injury markers based on EEG and serum inflammatory factors, which comprises the following steps:

step 1, collecting EEG signals of a subject in a quiet state and EEG signals of a subject in an excited state for 5 minutes respectively, recording the EEG signals in the two states, removing interference of power frequency signals, then performing discrete sequence wavelet transformation on EEG signals polluted by noise, then performing wavelet coefficient threshold processing, reconstructing EEG signals by processed coefficients, then performing independent component analysis by adopting a FastICA algorithm, listing each independent component, finding out artifact components and corresponding coefficients, further removing artifacts, and reconstructing the EEG signals to achieve the purpose of signal denoising;

step 2, utilizing the preprocessed electroencephalogram signals according toCalculating a slow wave coefficient SWC, wherein alpha, beta and theta are frequency band ranges, and a spectrum () function is used for calculating various spectrum functions and is suitable for time series analysis, and then according to an expression (3): ApEn ═ Φ^m(r)-Φ^m+1(r) calculating approximate entropy ApEn, wherein r is allowable deviation, m is vector dimension, phi in formula (3)^m(r) is a vector sequence { y (i) } average autocorrelation degree, and then the ratio a obtained by dividing the characteristic parameters of the right side lead of the slow wave coefficient and the approximate entropy by the characteristic parameters of the left side lead is respectively solved, and an expression (4) is solved according to the electroencephalogram characteristic parameters Sum: sum_p＝a₁+a₂+…+a₈(p is 1, 2) Sum for determining slow wave coefficient₁Sum of values and approximate entropy₂Value of a in expression (4)₁～a₈In the 1 st to 8 th symmetric lead groups, the characteristic parameter of the right lead is divided by the characteristic parameter of the left lead, and then, the expression (5) is obtained according to the weighted average method: sum 0.5. Sum₁+0.5·Sum₂Will slow wave coefficient Sum₁Sum of values and approximate entropy₂Values are normalized to a range of Sum values;

step 3, extracting 10mL of fasting venous blood of a subject, and detecting the level of serum inflammatory factors by adopting an enzyme-linked immunosorbent assay method;

step 4, solving the serum inflammatory factor level characteristic parameter W of 0.25W according to a weighted average method_IL-6+0.25W_IL-8+0.25W_CRP+0.25W_TNF-αNormalizing the four characteristic values of the serum inflammatory factor to be within the range of the W value, wherein W is_IL-6Is a characteristic parameter of interleukin-6, W_IL-8Is a characteristic parameter of interleukin-8, W_CRPIs the characteristic parameter of C-reactive protein, W_TNF-αIs the characteristic parameter of tumor necrosis factor-alpha.

Preferably, the step 1 specifically comprises the following steps:

step 11, obtaining stable data, and removing unstable data caused by external factors in the acquisition process;

step 12, removing power frequency interference of 50Hz by using an infinite impulse response digital filter in an EEGLAB electroencephalogram processing tool box, and then performing discrete sequence wavelet transformation on EEG signals polluted by noise to obtain wavelet coefficients with noise;

and step 13, performing wavelet coefficient threshold processing, reconstructing an EEG signal by the processed coefficient, then performing independent component analysis by adopting a FastICA algorithm, listing each independent component, finding out an artifact component and a corresponding coefficient, further removing the artifact, and reconstructing the EEG signal.

Preferably, the step 2 specifically includes the following steps:

step 21, calculating a slow wave coefficient SWC:

the brain electrical signals are divided into 6 frequency bands: a first frequency band of 1.0 to 4.0Hz, a second frequency band of 4.1 to 8.0Hz, and a third frequency band of alpha₁8.1 to 10.0Hz, and a fourth frequency band of alpha₂10.1 to 13.0Hz, a fifth frequency band beta₁13.1 to 17.5Hz, sixth frequency band beta₂Defining a frequency spectrum characteristic parameter, namely a slow wave coefficient SWC as a power spectrum ratio (+ theta)/(alpha + beta) of a low frequency band (+ theta) and a high frequency band (alpha + beta), namely 17.6-30 Hz, whereinWherein α ═ α₁+α₂，β＝β₁+β₂Performing fast Fourier transform on the electroencephalogram data, calculating the power spectrum value of each frequency band, and then calculating according to the definitionSlow wave coefficients of the individual leads;

step 22, calculating approximate entropy:

adding a time window to an EEG signal, wherein the time of the selection window is 2s, N is 512, the approximate entropy value of each channel is based on the sampling points, the approximate entropy value of each sampling point is solved, then an approximate entropy waveform is drawn, a relatively stable part is selected in the waveform, the average value is solved, the average value is used as a corresponding approximate entropy characteristic parameter, and the solving process of the approximate entropy is as follows: composing the time series { x (i) } of length N into an m-dimensional vector y (i): y (i) { x (i), x (i +1), x (i +2), …, x (i + m-1) }, where i ranges from [1, N-m +1], and then, the maximum distance d between y (i) and y (j) is calculated [ y (i), y (j) ], i.e.: d [ y (i), y (j) ] | | | x (i + k-1) -x (j + k-1) | | k ═ 1,2, …, m, given an allowable deviation r >0, there is a probability that N-m +1 is equal to each i ≦ N ≦ m +1 of y (i)

The expression (1) reflects the probability that the distance between y (i) and y (j) in the m-dimensional modular expression in the sequence is less than r, m is 2, r is 0.1-0.2 times of the standard deviation of the original data, and then C is added_i ^m(r) taking the logarithm, averaging, i.e.

From the above steps, phi can be obtained by the same method^m+1(r), finally using expression (3): ApEn ═ Φ^m(r)-Φ^m+1(r) calculating an approximate entropy;

step 23, calculating the symmetric lead characteristic parameter ratio a and Sum of the slow wave coefficient and the approximate entropy respectively_pValue, Sum_pThe sum of the characteristic parameter ratios of the 8 symmetrical lead groups is obtained.

Preferably, 16 leads are divided into 8 groups of symmetric leads, F7-F8, T3-T4, T5-T6, FP1-FP2, F3-F4, C3-C4, P3-P4 and O1-O2, and then the characteristic parameters of the right leads in the symmetric lead group are divided by the characteristic parameters of the left leads, and the ratio is a in formula (4)₁～a₈。

Preferably, the step 3 comprises the following steps:

the level of serum inflammatory factors in fasting venous blood of a subject is detected by adopting an enzyme-linked immunosorbent assay method, wherein the serum inflammatory factors comprise interleukin-6, interleukin-8, C reactive protein and tumor necrosis factor-alpha, statistical analysis is carried out by applying an SPSS 20.0 software package, counting data conforms to normal distribution and is expressed by mean plus or minus standard deviation (x plus or minus s), t test is adopted, chi-square test is adopted for metering data, and the difference has statistical significance when the probability P is less than 0.05.

Preferably, the step 4 comprises:

according to the weighted average method, let each weight be 0.25, the sum of the weight functions be "1", each data is represented by "W", and the range of the W value of the normal human group obtained according to expression (6) is: 11.665-20.505; the mild groups were: 27.145-36.590

W＝0.25W_IL-6+0.25W_IL-8+0.25W_CRP+0.25W_TNF-α(6)

And substituting the data obtained by the subject into the expression (6) to obtain the W value of the subject.

Compared with the prior art, the invention has the following beneficial effects:

according to the method, the characteristic parameter approximate entropy, the ratio of slow wave coefficients and the Sum value are obtained through the electroencephalogram signals, and the W value after the level value normalization of the serum inflammatory factor is combined, so that a new marker is provided for medical workers to formulate a subsequent detection scheme, and a research basis is provided for clinical detection.

Drawings

FIG. 1 is a schematic diagram of a method of analyzing brain injury markers based on EEG and serum inflammatory factors according to the present invention; and

FIG. 2 is a distribution diagram of the characteristic parameter Sum value of the electroencephalogram signal.

Detailed Description

The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

It is to be noted that, unless otherwise specified, technical or scientific terms used herein shall have the ordinary meaning as understood by those skilled in the art to which the invention pertains.

The invention provides a method for analyzing brain injury markers based on EEG and serum inflammatory factors, the general flow chart of which is shown in figure 1, and the method comprises the following steps:

step 1, processing and analyzing the acquired electroencephalogram signals of a subject, and specifically comprises the following steps:

step 11, observing the stability of data, and manually removing unstable data caused by the influence of external factors in the acquisition process;

step 12, removing power frequency interference of 50Hz by using an infinite impulse response digital filter in an EEGLAB electroencephalogram processing tool box, and then performing discrete sequence wavelet transformation on EEG signals polluted by noise to obtain wavelet coefficients with noise;

and step 13, performing wavelet coefficient threshold processing, reconstructing an EEG signal by the processed coefficient, then performing independent component analysis by adopting a FastICA algorithm, listing each independent component, finding out an artifact component and a corresponding coefficient, further removing the artifact, and reconstructing the EEG signal.

Step 2, calculating characteristic parameters of the electroencephalogram signals, and specifically comprising the following steps;

step 21, calculating a slow wave coefficient: the brain electrical signal can be divided into 6 bands according to typical band division: a first frequency band of 1.0 to 4.0Hz, a second frequency band of 4.1 to 8.0Hz, and a third frequency band of alpha₁8.1 to 10.0Hz, and a fourth frequency band of alpha₂10.1 to 13.0Hz, a fifth frequency band beta₁13.1 to 17.5Hz, sixth frequency band beta₂The method is characterized in that a frequency spectrum characteristic parameter, namely a Slow Wave Coefficient (SWC), is defined as follows: the power spectrum ratio (+ θ)/(α + β) of the low frequency band (+ θ) to the high frequency band (α + β), i.e.:

wherein，α＝α₁+α₂，β＝β₁+β₂The spectrum () function is used to calculate various spectral functions, and is suitable for time series analysis. And (3) carrying out fast Fourier transform on the electroencephalogram data, calculating the power spectrum value of each frequency band, and then calculating the slow wave coefficient of each lead according to the definition.

Step 22, calculating approximate entropy: adding a time window to the EEG signal, wherein the selection window time is 2s (N is 512), the approximate entropy value of each channel is based on the sampling points, the approximate entropy value of each sampling point is obtained, then the approximate entropy waveform is drawn, a relatively stable part is selected in the waveform, the average value is obtained, and the average value is used as the corresponding approximate entropy characteristic parameter. The approximate entropy solving process is as follows: composing the time series { x (i) } of length N into an m-dimensional vector y (i): y (i) { x (i), x (i +1), x (i +2), …, x (i + m-1) }, wherein i ranges from [1, N-m +1]]. Then, calculating the maximum distance d [ y (i), y (j) ] between y (i) and y (j)]Namely: d [ y (i), y (j)]Max | | | x (i + k-1) -x (j + k-1) | | | k ═ 1,2, …, m, given an allowable deviation r>0, for each i ≦ N-m +1 of y (i), counting d [ y (i), y (j)]The number of r or less, the ratio of this number to the total number of vectors N-m +1 being expressed as

Then:

the expression (1) reflects the probability that the distance between y (i) and y (j) in the m-dimensional modular expression in the sequence is less than r, generally, m is 2, r is 0.1-0.2 times of the standard deviation of the original data, and then the m, the i and the r are comparedTaking logarithm and averaging, namely:

from the above steps, phi can be obtained by the same method^m+1(r), finally calculating the approximationEntropy-like:

ApEn＝Φ^m(r)-Φ^m+1(r) (3)

step 23, calculating the symmetric lead characteristic parameter ratio a and Sum of the slow wave coefficient and the approximate entropy respectively_pValue, Sum_pThe sum of the characteristic parameter ratios of the 8 symmetrical lead groups is obtained.

Firstly, 16 leads are divided into 8 groups of symmetrical leads, namely F7-F8, T3-T4, T5-T6, FP1-FP2, F3-F4, C3-C4, P3-P4 and O1-O2, then characteristic parameters of the right leads in the symmetrical lead group are divided by characteristic parameters of the left leads, and the ratio of the characteristic parameters is a_n(n-1, 2, …, 8) and then according to:

Sum_p＝a₁+a₂+…+a₈， (p＝1、2) (4)

respectively calculating Sum values of slow wave coefficient and approximate entropy to make Sum₁The value represents the slow wave coefficient Sum value, let Sum₂The value represents the Sum value of the approximate entropy, and the Sum value of the normalization result is calculated by expression (5) according to a weighted average method.

Sum＝0.5·Sum₁+0.5·Sum₂(5)

Step 24, performing tests according to the steps, and obtaining the distribution shown in fig. 2 by using statistical knowledge and clustering conditions, wherein diamonds represent the left side with brain injury, and squares represent the normal control group; triangles represent right side with brain damage. Experiments can obtain that the Sum values of the approximate entropy and the slow wave coefficient under normal conditions are uniformly distributed near 8, namely the Sum value distribution interval of the approximate entropy is 7.86-8.43, and the Sum value distribution interval of the slow wave coefficient is 7.26-8.63. Then, the range of Sum values is found according to expression (5): 7.56-8.53.

Step 3, detecting the level of serum inflammatory factors in venous blood of a subject, and specifically comprises the following steps:

step 31, firstly, determining the approximate range of the serum inflammatory factor level in mild brain injury, which comprises the following specific steps: subjects admitted for 48h were classified into mild groups (50 cases) according to the Grosso coma index (GCS) score of 13-15, and then 50 healthy subjects of the same period were selectedAs a control group. The control group was then assayed for serum inflammatory factor levels including interleukin-6 (inter-leukin-6, IL-6), interleukin-8 (inter-leukin-8, IL-8), C-reactive protein (CRP) and tumor necrosis factor-alpha (TNF-alpha) on the day and in the subjects' fasting venous blood using an enzyme-linked immunosorbent assay. Statistical analysis was performed using the SPSS 20.0 software package, and the count data was normalized to mean. + -. standard deviation

The representation is that t test is adopted, chi-square test is adopted for the measured data, and the difference has statistical significance when the probability P is less than 0.05. The results obtained are shown in table 1:

TABLE 1 comparison of serum inflammatory factor levels

Step 32, extracting 10mL of fasting venous blood of the next morning after the subject is admitted, and detecting the level of serum inflammatory factors including IL-6, IL-8, CRP and tumor necrosis factor-alpha (TNF-alpha) by enzyme-linked immunosorbent assay. The obtained IL-6, IL-8, CRP and TNF- α values were compared to those in Table 1.

Step 4, data processing and diagnosis: from the values in table 1, the respective weights are made 0.25 according to the weighted average method, the sum of the weight functions is "1", each data is represented by "W", and the range of the W value of the normal population obtained according to expression (6) is: 11.665-20.505; the mild groups were: 27.145-36.590

W＝0.25W_IL-6+0.25W_IL-8+0.25W_CRP+0.25W_TNF-α(6)

W_IL-6Is a characteristic parameter of interleukin-6, W_IL-8Is a characteristic parameter of interleukin-8, W_CRPIs the characteristic parameter of C-reactive protein, W_TNF-αIs the cause of tumor necrosisAnd (3) substituting the data obtained by the subject into the expression (6) according to the characteristic parameter of the sub-alpha, so as to obtain the W value of the subject.

After the test, the weighted average used in the steps 2 and 4 has the characteristics of additivity and independence, and satisfies the linear relation, which shows that after a certain evaluation object changes a single index, the evaluation change only depends on the change quantity of the index and is unrelated to other indexes, namely, the indexes are independent from each other.

Aiming at the problems of single quantity and poor reliability of the current clinical mild and mild brain injury detection markers, the method combines and applies the Sum value of the electroencephalogram signal characteristic parameter and the W value of the serum inflammatory factor level characteristic parameter as the comprehensive marker of the mild brain injury, and provides a method for calculating the Sum value and the W value based on the EEG and the serum inflammatory factor, thereby having great significance for the formulation of the subsequent detection scheme and having high clinical value.

The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments can still be modified, or some or all of the technical features thereof can be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.