CN113143292B - Brain injury marker analysis system based on EEG and serum inflammatory factor analysis - Google Patents
Brain injury marker analysis system based on EEG and serum inflammatory factor analysis Download PDFInfo
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Abstract
The invention provides a brain injury marker analysis system based on EEG and serum inflammatory factor analysis, which comprises: electroencephalogram signal pickup module and blood serumA detection module and a data processing module, the system being controlled in the following manner: firstly preprocessing an electroencephalogram signal, respectively calculating the ratio a of slow wave coefficients and approximate entropy of 8 groups of leads, and according to an expression Sum p ＝a 1 +a 2 +…+a 8 (p=1, 2) obtaining slow wave coefficient Sum 1 Sum of values and approximate entropy 2 Values and normalized to a range of Sum values according to a weighted average method; then, the fasting venous blood of the subject is extracted, the serum inflammatory factor level is detected by adopting an enzyme-linked immunosorbent assay, and the range of the W value is solved according to a weighted average method. The above data is used by the following physician to formulate a reference for detecting mild brain injury.
Description
Technical Field
The invention belongs to the field of medicine, and particularly relates to a brain injury marker analysis system based on EEG and serum inflammatory factor analysis.
Background
Along with the development of society, the life of human beings is gradually improved, and the life formula of people is also greatly changed, for example, the development of vehicles brings great convenience to us, but the probability of craniocerebral injury is increased to a certain extent due to the gradual rise of the occurrence rate of traffic accidents. Craniocerebral injury is an acute injury of the brain, sometimes caused by external violence such as traffic accidents, mechanical wounds and the like. The brain injury has rapid disease development and change, if the subject cannot be diagnosed and treated in time, the prognosis is poor, and the physical and mental health and even life of the subject can be seriously influenced, and researches show that the slightly traumatic brain injury accounts for about 75% of all traumatic brain injuries (Traumatic brain injuries, TBI). In clinic, 15 to 30 percent of subjects with slight brain injury (Mild Traumatic Brain Injury, MTBI) can have symptoms such as cognition, sensory disturbance and the like after trauma; some subjects will still have sustained post-concussion syndrome for months or years after trauma.
The major pathological changes in MTBI subjects are bleeding, and the foci occurring in MTBI are mostly intracranial micro-foci, currently based mainly on CT examination and conventional MRI, but mild brain lesions may not find lesions in all neuroimaging examinations, because current neuroimaging techniques have not reached the level of distinguishing between small structures and lesions. And CT and conventional MRI can show only anatomical changes of brain tissue, have great limitations on diagnosis of MTBI and the like, and often miss diagnosis or judge the severity of brain trauma to be too light. In addition to CT and conventional MRI, mild brain injury has other diagnostic methods, mainly including neurological tests, physical examination and medical history, but these methods are not very convenient or accurate, and can not only cause radioactive injury but also increase the economic burden of the subject after misdiagnosis.
The human brain structure is structurally symmetrical and functionally contralateral, so that discrimination of brain injury sites based on symmetric lead electroencephalogram (EEG) feature analysis is anatomical and physiological basis. When the brain is in a resting state, the brain electrical signals of the left and right symmetrical brain regions have similarity, but when one side brain region is damaged, the similarity of the brain electrical signals of the damaged region and the non-damaged region at the opposite side symmetrical position is reduced, the difference is increased, and the difference of the brain electrical signals can be characterized by the characteristic parameters of the brain electrical signals. There have been studies to propose an EEG-based analysis method, in which the ratio of the characteristic parameters of both sides of the brain symmetry is used as a unique marker for the detection of mild brain injury, and a doctor can formulate a next detection scheme according to the range of the marker. However, the method is too single, the normalization is not performed in the aspect of data processing, the reliability and the accuracy are required to be further improved, and the method is not very convenient to apply.
In summary, there is an urgent need for a comprehensive marker for detecting mild brain injury, so as to reduce the inspection cost and improve the reliability of data, provide a basis for a doctor to analyze and formulate a detection scheme of the next step, and provide a corresponding calculation method at the same time, so as to develop a set of system to reduce the working pressure of medical resources.
Disclosure of Invention
According to the invention, EEG and serum inflammatory factor level indexes representing craniocerebral injury are used as markers, and data normalization processing is carried out on characteristic parameters of an EEG signal and a plurality of characteristic parameters of serum inflammatory factor levels respectively to obtain a range of a Sum value of the characteristic parameters of the EEG signal and a W value of the characteristic parameters of the serum inflammatory factor level, so that a new direction is provided for the marker selection and data calculation method of slight brain injury.
The invention provides a brain injury marker analysis system based on EEG and serum inflammatory factor analysis, which comprises an electroencephalogram signal pickup module, a serum detection module and a data processing module,
the electroencephalogram signal pickup module is used for picking up electroencephalogram signals and transmitting data to the data processing module;
the serum detection module adopts the principle of enzyme-linked immunosorbent assay to measure the characteristic parameter W of interleukin-6 IL-6 Characteristic parameter W of interleukin-8 IL-8 Characteristic parameter W of C-reactive protein CRP Tumor necrosis factor-alpha W TNF-α Output to a data processing output module,
the data processing module processes and outputs all data;
the system is controlled in the following manner:
step 1, acquiring electroencephalogram signals in a quiet state and electroencephalogram signals in an excited state of a subject for 5 minutes, recording the electroencephalogram signals in the two states, performing discrete sequence wavelet transform on EEG signals polluted by noise after interference of power frequency signals is removed, performing wavelet coefficient threshold processing, reconstructing the EEG signals by the processed coefficients, performing independent component analysis by adopting a FastICA algorithm, listing each independent component, finding out artifact components and corresponding coefficients, removing the artifacts, reconstructing the EEG signals, and achieving the purpose of signal denoising;
Preferably, the step 1 specifically includes the following steps:
and 13, performing wavelet coefficient thresholding, reconstructing the EEG signal by the processed coefficient, 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 electroencephalogram signal is divided into 6 frequency bands: first frequency band delta=1.0-4.0 Hz, second frequency band theta=4.1-8.0 Hz, third frequency band alpha 1 =8.1 to 10.0Hz, the fourth frequency band being in the range α 2 =10.1 to 13.0Hz, fifth frequency band β 1 =13.1 to 17.5Hz, sixth band β 2 =17.6 to 30Hz, defining a spectral characteristic parameter, the slow wave coefficient SWC, as the power spectrum ratio (δ+θ)/(α+β) of the low frequency band (δ+θ) to the high frequency band (α+β), i.e
Wherein α=α 1 +α 2 ，β＝β 1 +β 2 Performing fast Fourier transform on the electroencephalogram data, calculating a power spectrum value of each frequency band, and then calculating a slow wave coefficient of each lead according to definition;
step 22, calculating approximate entropy:
adding a time window on an EEG signal, wherein the time of the selected window is 2s, N=512, the approximate entropy value of each channel is calculated according to sampling points, then an approximate entropy waveform is drawn, a relatively stable part is selected in the waveform, the average value is calculated, the average value is used as a corresponding approximate entropy characteristic parameter, and the approximate entropy is calculated by the following steps: the time sequence { x (i) } of length N is composed 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], then the maximum distance d [ y (i), y (j) ] between y (i) and y (j) is calculated, i.e.: d [ y (i), y (j) ]=max||x (i+k-1) -x (j+k-1) ||k=1, 2, …, m, given an allowable deviation r >0, there is a probability for each i.ltoreq.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 mode expression in the sequence is smaller than r, m is 2, r is 0.1-0.2 times of the standard deviation of the original data, and then
Taking logarithm and averaging, namely:
according to 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 p Value, sum p Characteristic parameter ratio for 8 symmetrical lead groupsAnd (3) sum of values.
Preferably, the 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, respectively, and then the characteristic parameter of the right lead in the symmetrical lead group is divided by the characteristic parameter of the left lead by a ratio of a in formula (4) 1 ～a 8 。
Preferably, the step 3 includes the following:
detecting serum inflammatory factor level in fasting venous blood of subject by enzyme-linked immunosorbent assay, including interleukin-6, interleukin-8, C-reactive protein and tumor necrosis factor-alpha, performing statistical analysis by SPSS 20.0 software package, and making the count data conform to normal distribution to average ± standard deviation
The method shows that the probability P is less than 0.05, which is statistically significant for the difference, by adopting t test and chi-square test for metering data.
Preferably, the step 4 includes:
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 normal person group W values obtained according to expression (6) is: 11.665-20.505; the mild group is: 27.145-36.590;
W＝0.25W IL-6 +0.25W IL-8 +0.25W CRP +0.25W TNF-α (6)
and then the data obtained by the subject is brought into expression (6), so that the W value of the subject can be obtained.
Compared with the prior art, the invention has the following beneficial effects:
the invention obtains the approximate entropy of characteristic parameters, the ratio of slow wave coefficients and the Sum value through the electroencephalogram signals, and combines the normalized W value of the serum inflammatory factor level value.
Drawings
FIG. 1 is a schematic diagram of a brain injury marker analysis system based on EEG and serum inflammatory factor analysis of the present invention;
fig. 2 is a graph showing a distribution of brain electrical signal characteristic parameter Sum values.
Detailed Description
The technical solutions 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 made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It is noted that unless otherwise indicated, technical or scientific terms used herein should be given the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.
The invention provides a brain injury marker analysis system based on EEG and serum inflammatory factors, which comprises an electroencephalogram signal pickup module, a serum detection module and a data processing module, wherein the electroencephalogram signal pickup module is used for picking up electroencephalogram signals and transmitting data to the data processing module;
the serum detection module adopts the principle of enzyme-linked immunosorbent assay to measure the characteristic parameter W of interleukin-6 IL-6 Characteristic parameter W of interleukin-8 IL-8 Characteristic parameter W of C-reactive protein CRP Tumor necrosis factor-alpha W TNF-α And outputting the data to a data processing and outputting module, wherein the data processing module processes and outputs all data.
The system is controlled in a general manner according to the flow chart, i.e. in the manner shown in fig. 1:
step 1, an electroencephalogram signal pickup module processes and analyzes an acquired electroencephalogram signal of a subject, and specifically comprises the following steps:
and 13, performing wavelet coefficient thresholding, reconstructing the EEG signal by the processed coefficient, 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 21, calculating a slow wave coefficient: the electroencephalogram signal can be divided into 6 bands according to a typical band division: first frequency band delta=1.0-4.0 Hz, second frequency band theta=4.1-8.0 Hz, third frequency band alpha 1 =8.1 to 10.0Hz, the fourth frequency band being in the range α 2 =10.1 to 13.0Hz, fifth frequency band β 1 =13.1 to 17.5Hz, sixth band β 2 =17.6 to 30Hz, defining spectral characteristic parameters-Slow Wave Coefficients (SWC) as: the power spectrum ratio (δ+θ)/(α+β) of the low frequency band (δ+θ) to the high frequency band (α+β), namely:
wherein α=α 1 +α 2 ，β＝β 1 +β 2 The spectrum () function is used to calculate various spectral functions, and is suitable for time series analysis. And performing fast Fourier transform on the electroencephalogram data, calculating a power spectrum value of each frequency band, and then calculating a slow wave coefficient of each lead according to definition.
Step 22, calculating approximate entropy: adding a time window to the EEG signal, selecting window time to be 2s (N=512), taking the approximate entropy value of each channel as a basis of sampling points, solving the approximate entropy value of each sampling point, drawing an approximate entropy waveform, selecting a relatively stable part from the waveform, and solving an average value of the relatively stable part, wherein the average value is used as a corresponding approximate entropy characteristic parameter. The approximate entropy solving process is as follows: the time sequence { x (i) } of length N is composed 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, the maximum distance d [ y (i), y (j) between y (i) and y (j) is calculated]The method comprises the following steps: d [ y (i), y (j)]=max||x (i+k-1) -x (j+k-1) ||k=1, 2, …, m, giving an allowable deviation r>0, for each of y (i), i.ltoreq.N-m+1 statistics d [ y (i), y (j)]The number of r is less than or equal to the ratio of the number to the total vector number N-m+1 is recorded as
Then:
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, typically, m is 2, r is 0.1 to 0.2 times the standard deviation of the original data, and then
Taking logarithm and averaging, namely:
according to the above steps, phi can be obtained by the same method m+1 (r) finally calculating the approximate entropy:
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 p Value, sum p Is the sum of the characteristic parameter ratios of 8 symmetrical lead groups.
First, dividing 16 leads into 8 groups of symmetrical leads, namely F7-F8, T3-T4, T5-T6, FP1-FP2, F3-F4, C3-C4, P3-P4 and O1-O2, dividing the characteristic parameter of the right lead in the symmetrical lead group by the characteristic parameter of the left lead, and using a for the ratio n (n=1, 2, …, 8), then according to:
Sum p ＝a 1 +a 2 +…+a 8 ，(p＝1、2) (4)
respectively calculating Sum values of slow wave coefficients and approximate entropy to enable Sum to 1 The value represents the slow wave coefficient Sum value, let Sum 2 The value represents the Sum value of the approximate entropy, and normalization is calculated from the expression (5) according to the weighted average methodAnd (5) obtaining a Sum value after the digestion.
Sum＝0.5·Sum 1 +0.5·Sum 2 (5)
Step 24, performing the test according to the steps, and obtaining the distribution shown in fig. 2 by using statistical knowledge and clustering conditions, wherein the diamond represents that the left side has brain injury, and the square represents a normal control group; triangles represent right side suffering from brain injury. The experiment shows that the approximate entropy and the Sum value of the slow wave coefficient under normal conditions are uniformly distributed near 8, namely the distribution interval of the approximate entropy Sum value is 7.86-8.43, and the distribution interval of the slow wave coefficient Sum value is 7.26-8.63. Then, the range of Sum values is derived from expression (5) as: 7.56 to 8.53.
step 31, firstly, determining that the serum inflammatory factor level is in the approximate range of mild brain injury, which comprises the following specific steps: subjects admitted for 48h were classified into mild groups (50 cases) according to glasgow coma index (GCS) score of 13 to 15, and then 50 contemporaneous physical examination healthy persons were selected as control groups. The level of serum inflammatory factors 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) in the fasting venous blood of the subject and on the current day of the control group were then examined using an ELISA assay. Statistical analysis using SPSS 20.0 software package, the count data was distributed in normal order to mean ± standard deviation
The method shows that the probability P is less than 0.05, which is statistically significant for the difference, by adopting t test and chi-square test for metering data. The results obtained are shown in Table 1:
TABLE 1 comparison of serum inflammatory factor levels
Step 32, 10mL of fasting venous blood of the morning after the subject is admitted is withdrawn, and serum inflammatory factor levels including IL-6, IL-8, CRP and tumor necrosis factor-alpha (tumor necrosisfactor-alpha, TNF-alpha) are detected using an ELISA assay. The IL-6, IL-8, CRP and tumor necrosis factor-alpha values obtained were compared with Table 1.
W＝0.25W IL-6 +0.25W IL-8 +0.25W CRP +0.25W TNF-α (6)
Wherein W is IL-6 Is characteristic parameter, W, of interleukin-6 IL-8 Is characteristic parameter, W, of interleukin-8 CRP Is characteristic parameter, W of C reaction protein TNF-α And (3) taking the data obtained by the subject as the characteristic parameters of the tumor necrosis factor-alpha and carrying the data into the expression (6) to obtain the W value of the subject.
Through inspection, the weighted averages used in the steps 2 and 4 have the characteristics of additivity and independence, and satisfy a linear relation, which indicates that after a certain evaluation object changes a single index, the change of the evaluation is only dependent on the change amount of the index, and is independent of other indexes, namely, each index is mutually independent.
Aiming at the problems of single quantity and low reliability of the existing clinical mild brain injury detection markers, the method combines and applies the combination of the Sum value of the brain signal characteristic parameter and the W value of the serum inflammatory factor level characteristic parameter to be 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, which has great significance for the establishment of a subsequent detection scheme and has high clinical value.
The embodiments described above are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the previous embodiment can be modified or some or all of the technical features can be replaced equivalently; such modifications and substitutions do not depart from the spirit of the invention.
Claims (4)
1. A brain injury marker analysis system based on EEG and serum inflammatory factor analysis is characterized by comprising an electroencephalogram signal pickup module, a serum detection module and a data processing module,
the electroencephalogram signal pickup module is used for picking up electroencephalogram signals and transmitting data to the data processing module;
the serum detection module adopts the principle of enzyme-linked immunosorbent assay to measure the characteristic parameter W of interleukin-6 IL-6 Characteristic parameter W of interleukin-8 IL-8 Characteristic parameter W of C-reactive protein CRP Tumor necrosis factor-alpha W TNF-α Output to the data processing module,
the data processing module processes all data and outputs a result;
the system is controlled in the following manner:
step 1, an electroencephalogram signal pickup module collects electroencephalogram signals in a quiet state and electroencephalogram signals in an excited state of a subject for 5 minutes respectively, a data processing module records the electroencephalogram signals in the two states, after interference of power frequency signals is removed, discrete sequence wavelet transformation is carried out on EEG signals polluted by noise, then wavelet coefficient threshold processing is carried out, the processed coefficients are used for reconstructing EEG signals, then independent component analysis is carried out by adopting a FastICA algorithm, each independent component is listed, artifact components and corresponding coefficients are found out, artifacts are removed, and the EEG signals are reconstructed, so that the purpose of signal denoising is achieved;
step 2, the data processing module uses the preprocessed electroencephalogram signals according to the following steps
Calculating a slow wave coefficient SWC, wherein alpha, beta, delta and theta are all frequency band ranges, and a spectrum () function is used for calculating various spectrum functions, is suitable for analysis of time sequences, and is further expressed according to an expression (3): apen=Φ m (r)-Φ m+1 (r) calculating an approximate entropy ApEn, where r is the allowable deviation, m is the vector dimension, Φ in formula (3) m (r) is the average autocorrelation of the vector sequence { y (i) }, and then the ratio a of the characteristic parameters of the right lead divided by the characteristic parameters of the left lead of the slow wave coefficient and the approximate entropy is calculated respectively, and according to the calculation expression (4) of the electroencephalogram characteristic parameter Sum: sum (Sum) p ＝a 1 +a 2 +…+a 8 Sum for obtaining slow wave coefficients (p=1, 2) 1 Sum of values and approximate entropy 2 Value, a in expression (4) 1 ～a 8 In the symmetric lead groups of the 1 st group to the 8 th group respectively, the characteristic parameter of the right lead is divided by the characteristic parameter of the left lead, and then, according to the weighted average method expression (5): sum=0.5·sum 1 +0.5·Sum 2 Will slow wave coefficient Sum 1 Sum of values and approximate entropy 2 Normalizing the values to a range of Sum values;
step 3, a serum detection module detects the serum inflammatory factor level by using 10mL of fasting venous blood of a subject and an enzyme-linked immunosorbent assay, detects the serum inflammatory factor level in the fasting venous blood of the subject by using the enzyme-linked immunosorbent assay, and carries out statistical analysis by using an SPSS 20.0 software package, wherein the counting data accords with normal distribution to average number +/-standard deviation
The method is characterized in that t test is adopted, chi-square test is adopted for metering data, and the probability P is less than 0.05, so that the statistical significance is achieved for the difference;
step 4, solving characteristic parameters W=0.25W of serum inflammatory factor level according to a weighted average method IL-6 +0.25W IL-8 +0.25W CRP +0.25W TNF-α Normalizing four characteristic values of serum inflammatory factors into a range of W values,
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 normal person group W values obtained according to the expression (6) is: 11.665-20.505; the mild group is: 27.145-36.590,
W＝0.25W IL-6 +0.25W IL-8 +0.25W CRP +0.25W TNF-α (6)
and then the data obtained by the subject is brought into expression (6), so that the W value of the subject can be obtained.
2. The brain injury marker analysis system according to claim 1, wherein said step 1 specifically comprises the steps of:
step 11, obtaining stable data, and removing unstable data caused by external factors in the acquisition process;
step 12, removing 50Hz power frequency interference by using an infinite impulse response digital filter in an EEGLAB electroencephalogram processing tool box, and performing discrete sequence wavelet transformation on EEG signals polluted by noise to obtain wavelet coefficients with noise;
and 13, performing wavelet coefficient thresholding, reconstructing the EEG signal by the processed coefficient, 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 brain injury marker analysis system according to claim 1, wherein said step 2 specifically comprises the steps of:
step 21, calculating a slow wave coefficient SWC:
the electroencephalogram signal is divided into 6 frequency bands: first frequency band delta=1.0-4.0 Hz, second frequency band theta=4.1-8.0 Hz, third frequency band alpha 1 =8.1 to 10.0Hz, the fourth frequency band being in the range α 2 =10.1 to 13.0Hz, fifth frequency band β 1 =13.1 to 17.5Hz, sixth band β 2 =17.6 to 30Hz, defining the spectral characteristic parameter-slow wave coefficient SWC as the power spectrum ratio of the low frequency band (δ+θ) to the high frequency band (α+β)The value (delta+theta)/(alpha+beta), i.e
Wherein α=α 1 +α 2 ，β＝β 1 +β 2 Performing fast Fourier transform on the electroencephalogram data, calculating a power spectrum value of each frequency band, and then calculating a slow wave coefficient of each lead according to definition;
step 22, calculating approximate entropy:
adding a time window on an EEG signal, wherein the time of the selected window is 2s, N=512, the approximate entropy value of each channel is calculated according to sampling points, then an approximate entropy waveform is drawn, a relatively stable part is selected in the waveform, the average value is calculated, the average value is used as a corresponding approximate entropy characteristic parameter, and the approximate entropy is calculated by the following steps: the time sequence { x (i) } of length N is composed 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], then the maximum distance d [ y (i), y (j) ] between y (i) and y (j) is calculated, i.e.: d [ y (i), y (j) ]=max||x (i+k-1) -x (j+k-1) ||k=1, 2, …, m, given an allowable deviation r >0, there is a probability for each i.ltoreq.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 mode expression in the sequence is smaller than r, m is 2, r is 0.1-0.2 times of the standard deviation of the original data, and then
Taking logarithm and averaging, namely:
according to 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) calculation ofApproximate entropy;
step 23, calculating the symmetric lead characteristic parameter ratio a and Sum of the slow wave coefficient and the approximate entropy respectively p Value, sum p Is the sum of the characteristic parameter ratios of 8 symmetrical lead groups.
4. The brain injury marker analysis system according to claim 1, wherein the brain signal pickup module divides the 16 leads into 8 groups of symmetrical leads, which are F7-F8, T3-T4, T5-T6, FP1-FP2, F3-F4, C3-C4, P3-P4, O1-O2, respectively, and divides the characteristic parameter of the right lead in the symmetrical lead group by the characteristic parameter of the left lead, which is a in formula (4) 1 ～a 8 。
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