Pain quantitative evaluation method and system based on electroencephalogram nonlinear entropy analysis

Abstract

The invention provides a pain quantitative evaluation method and system based on electroencephalogram nonlinear entropy analysis, and belongs to the field of electroencephalogram data analysis, and the method comprises the steps: S1, carrying out data processing on electroencephalogram data collected in a database, and setting a threshold value; s2, performing interval cutting on the processed electroencephalogram data, wherein the data cutting segment length is m; s3, calculating feature change in each interval, obtaining a feature value through cross-interval adaptive quantization processing, and averaging to obtain an average dynamic response distance, namely an interval feature value; s4, obtaining an average dynamic response distance, namely an interval characteristic value, of which the data cutting segment length is m + 1; and S5, adding the average characteristic values of the data segment length m and the data segment length m + 1, and averaging to obtain a pain quantitative evaluation index. And S6, quantifying the pain based on the pain quantitative evaluation index obtained in the step S5. According to the method, through cross-interval self-adaptive analysis and a multi-scale feature mining technology, nonlinear features in the electroencephalogram signals are accurately extracted to carry out pain quantitative evaluation.

Description

Technical Field

[0001] The present invention relates to the field of electroencephalogram (EEG) data analysis, and in particular to a method and system for quantitatively evaluating pain based on EEG nonlinear entropy analysis. Background Art

[0002] Currently, there are still many technical challenges in the quantitative assessment of pain, especially in accurately quantifying the differences in pain perception between different individuals. In recent years, electroencephalogram (EEG) analysis has gradually become an important tool in the field of pain assessment due to its non-invasiveness and real-time nature.

[0003] However, due to the complexity and nonlinear characteristics of EEG signals, traditional linear analysis methods often fail to fully capture subtle changes in pain states. Therefore, there is an urgent need for an advanced analysis method that can utilize nonlinear feature extraction to improve the accuracy of pain assessment and sensitivity to individual differences. Summary of the invention

[0004] In order to solve the deficiencies of the above-mentioned prior art, the purpose of the present invention is to provide a pain quantification assessment method and system based on EEG nonlinear entropy analysis, which performs deep feature extraction and quantitative analysis on EEG signals, identifies complex dynamic feature changes in signals, and accurately describes the state of pain perception. Based on the nonlinear feature extraction of EEG signals, this method can more keenly capture the EEG activity pattern of individuals, effectively improve the ability to quantify and assess the degree of pain, and overcome the limitations of traditional technologies in processing complex EEG signals.

[0005] Specifically, the present invention provides a method for quantitatively evaluating postoperative pain based on nonlinear characteristics of electroencephalogram (EEG), which comprises the following steps:

[0006] S1, process the EEG data collected in the database and set the data threshold r;

[0007] S2. Perform interval cutting on the processed EEG data. The data cutting segment length is m, specifically:

[0008] Assume that the original data sequence in each data interval is x(1), x(2), ..., x(N), with a total of N data points. After cutting, each data interval forms an m-dimensional vector in sequence, that is:

[0009] X(i)=[x(i),x(i+1),...,x(i+m-1)], i=1,2,...,N-m+1

[0010] Where X(i) is an m-dimensional vector composed of N data points in order, and i is the i-th data interval;

[0011] S3, calculating the characteristic change in each data interval, and obtaining the dynamic response distance through cross-interval adaptive quantization processing, averaging the dynamic response distances to obtain the average dynamic response distance, i.e., the interval characteristic value, specifically including the following sub-steps:

[0012] S31, calculating the extreme difference between X(i) and X(j), where X(j) is any data interval j outside the i-th data interval;

[0013] S32. Calculate the dynamic response distance between the two based on the extreme difference and define it as d ij The greater the extreme difference between the two, the closer the dynamic response distance is to 1, indicating that the time dimension is more complicated. The specific calculation process is as follows:

[0014] Δ1=maxX(i)-minX(j)

[0015] Δ2=maxX(j)-minX(i)

[0016]

[0017] Where Δ1 is the difference between the maximum value of X(i) and the minimum value of X(j), Δ2 is the difference between the maximum value of X(j) and the minimum value of X(i), max(Δ1,Δ2) is the extreme difference between X(i) and X(j), d ij is the dynamic response distance between the two; r is the data threshold;

[0018] S33, taking the average value of the dynamic response distances of all data intervals except X(i) to obtain the average dynamic response distance of the data cutting segment length m, i.e., the interval characteristic value:

[0019]

[0020] S4, adjust the data cutting segment length in step S2 to m+1, repeat steps S2-S3, and obtain the average dynamic response distance of the data cutting segment length m+1, that is, the interval characteristic value:

[0021]

[0022] S5. Add the average eigenvalues ​​of the data segment length m and the data segment length m+1 and average them to obtain the pain quantitative evaluation index. After processing, the pain quantitative evaluation index is within the range of [0 1]. The specific formula is as follows:

[0023]

[0024] Among them, MSIE is a quantitative assessment index for pain;

[0025] S6. Quantify the pain based on the pain quantification evaluation index obtained in step S5. The closer the pain quantification evaluation index is to 1, the higher the pain level is.

[0026] Preferably, the data processing in step S1 is specifically as follows: an adaptive notch filter is used to suppress 50 Hz power frequency noise, and a signal in a frequency band of 0.1-45 Hz is extracted and denoised using a denoising method based on wavelet transform.

[0027] Preferably, the database in step S1 is a database formed by collecting real-time EEG data of the person suffering from pain.

[0028] Preferably, in step S6, pain is divided into three levels: when MSIE is greater than 0.8, it is assessed as severe pain; when MSIE is between 0.75 and 0.8, it is defined as mild pain; and when MSIE is less than 0.75, it is assessed as no pain.

[0029]

[0030] Among them, P(MSIE) is the pain level.

[0031] Preferably, the data threshold in step S1 is a multiple of the data standard deviation.

[0032] On the other hand, the present invention provides a pain quantification assessment method and system based on EEG nonlinear entropy analysis, which includes a data processing unit, a data cutting unit, an interval eigenvalue calculation unit, a pain quantification assessment index calculation unit, and a pain level quantification unit;

[0033] The data processing unit is used to process the EEG data collected in the database and set the data threshold;

[0034] The data cutting unit performs interval cutting on the processed EEG data, and the data cutting segment lengths are m and m+1;

[0035] The interval characteristic value calculation unit calculates the interval characteristic values ​​of the data cutting segment lengths m and m+1 respectively;

[0036] The pain quantitative evaluation index calculation unit is used to add the average characteristic values ​​of the data cutting segment length m and the data cutting segment length m+1 and average the average value to obtain the pain quantitative evaluation index, and the pain quantitative evaluation index after processing is within the range of

[01] ;

[0037] The pain level quantification unit quantifies the pain based on the pain quantification evaluation index. The closer the pain quantification evaluation index is to 1, the higher the pain level is.

[0038] Preferably, the interval characteristic value calculation unit calculates the characteristic change in each data interval, obtains the dynamic response distance through cross-interval adaptive quantization processing, and averages the dynamic response distance to obtain the average dynamic response distance, i.e., the interval characteristic value, which specifically includes the following sub-steps:

[0039] Calculate the extreme difference between X(i) and X(j), where X(j) is any data interval j outside the i-th data interval;

[0040] The dynamic response distance between the two is calculated based on the extreme difference and defined as d ij The greater the extreme difference between the two, the closer the dynamic response distance is to 1, indicating that the time dimension is more complicated. The specific calculation process is as follows:

[0041] Δ1=maxX(i)-minX(j)

[0042] Δ2=maxX(j)-minX(i)

[0043]

[0044] Where Δ1 is the difference between the maximum value of X(i) and the minimum value of X(j), Δ2 is the difference between the maximum value of X(j) and the minimum value of X(i), max(Δ1,Δ2) is the extreme difference between X(i) and X(j), d ij is the dynamic response distance between the two; r is the data threshold;

[0045] The average dynamic response distance of all data intervals except X(i) is taken to obtain the average dynamic response distance of the data cutting segment length m, that is, the interval characteristic value is:

[0046]

[0047] The same steps are used to obtain the average dynamic response distance of the data cutting segment length m+1, that is, the interval characteristic value:

[0048]

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] (1) This paper proposes an advanced feature extraction and quantitative analysis method based on EEG signals. Through cross-interval adaptive analysis and multi-scale feature mining technology, the nonlinear features in EEG signals are accurately extracted. This method significantly improves the resolution and accuracy of pain state assessment, makes signal processing more sensitive, and can more effectively capture subtle changes in pain perception between individuals.

[0051] (2) The present invention proposes a new algorithm for pain assessment, which can quickly quantify the differences in pain perception among different individuals and significantly improve the sensitivity of EEG signal analysis in pain monitoring. The present invention optimizes the EEG signal feature extraction model and has important scientific research and clinical application value in consciousness state assessment, pain monitoring, and diagnosis and intervention of neurological diseases. In a clinical anesthesia environment, this technology can more accurately monitor changes in brain neural activity under different states, greatly improving the sensitivity and reliability of pain monitoring.

[0052] (3) The present invention proposes a postoperative pain quantification assessment system based on the nonlinear characteristics of EEG. The system can process and calculate the database of collected EEG data to obtain a pain quantification assessment index that characterizes the pain level. The pain level can be accurately quantified by the pain quantification assessment index. The system can more accurately monitor the changes in brain nerve activity under different states and ensure the sensitivity and reliability of pain monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 is a flow chart of the method of the present invention;

[0054] Figure 2 To calculate the comparison chart of different pain intensities with the baseline of the patient using the method of the present invention;

[0055] Figure 3 It is a schematic block diagram of the structure of the present invention. DETAILED DESCRIPTION

[0056] Hereinafter, embodiments of the present invention will be described with reference to the drawings.

[0057] The present invention provides a method for quantitatively evaluating postoperative pain based on nonlinear characteristics of EEG. Figure 1 As shown, it includes the following steps:

[0058] S1. Process the EEG data collected in the database and set the data threshold r, where r is a multiple of the data standard deviation. In a specific application, the EEG data in the database is collected from the EEG data of the postoperative patient and stored in the database, and then directly called in the database, and the called EEG data is subjected to noise reduction and other processing to obtain the noise-reduced EEG data.

[0059] S2. Perform interval cutting on the processed EEG data. The data cutting segment length is m, specifically:

[0060] Assume that the original data sequence in each data interval is x(1), x(2), ..., x(N), with a total of N data points. After cutting, each data interval forms an m-dimensional vector in sequence, that is:

[0061] X(i)=[x(i),x(i+1),...,x(i+m-1)], i=1,2,...,N-m+1

[0062] Among them, X(i) is an m-dimensional vector composed of N data points in sequence, and i is the i-th data interval.

[0063] S3, calculating the characteristic change in each data interval, and obtaining the dynamic response distance through cross-interval adaptive quantization processing, averaging the dynamic response distances to obtain the average dynamic response distance, i.e., the interval characteristic value, specifically including the following sub-steps:

[0064] S31. Calculate the extreme difference between X(i) and X(j), where X(j) is any data interval j outside the i-th data interval.

[0065] S32. Calculate the dynamic response distance between the two based on the extreme difference and define it as d ij The greater the extreme difference between the two, the closer the dynamic response distance is to 1, indicating that the time dimension is more complicated. The specific calculation process is as follows:

[0066] Δ1=maxX(i)-minX(j)

[0067] Δ2=maxX(j)-minX(i)

[0068]

[0069] Where Δ1 is the difference between the maximum value of X(i) and the minimum value of X(j), Δ2 is the difference between the maximum value of X(j) and the minimum value of X(i), max(Δ1,Δ2) is the extreme difference between X(i) and X(j), d ij is the dynamic response distance between the two; r is the data threshold.

[0070] S33, taking the average value of the dynamic response distances of all data intervals except X(i) to obtain the average dynamic response distance of the data cutting segment length m, i.e., the interval characteristic value:

[0071]

[0072] S4. Adjust the data cutting segment length in step S2 to m+1, repeat steps S2-S3, and obtain the average dynamic response distance, i.e., the interval characteristic value, of the data cutting segment length m+1.

[0073]

[0074] The process of calculating the average dynamic response distance of the data cutting segment length m+1 is the same as the process of calculating the average dynamic response distance of the data cutting segment length m+1, only the segmentation interval is replaced from m to m+1, and the rest of the steps are the same.

[0075] S5. Add the average eigenvalues ​​of the data segment length m and the data segment length m+1 and average them to obtain the pain quantitative evaluation index. After processing, the pain quantitative evaluation index is within the range of [0 1]. The specific formula is as follows:

[0076]

[0077] Among them, MSIE is a pain quantitative evaluation index. Through the pain quantitative evaluation index MSIE, the pain level can be clearly divided and the specific pain level can be obtained.

[0078] S6. Quantify the pain based on the pain quantitative evaluation index obtained in step S5. The closer the pain quantitative evaluation index is to 1, the higher the pain level is. In a specific application, pain is divided into three levels. When MSIE is greater than 0.8, it is evaluated as severe pain; MSIE between 0.75 and 0.8 is defined as mild pain; MSIE less than 0.75 is evaluated as no pain.

[0079]

[0080] Among them, P(MSIE) is the pain level.

[0081] On the other hand, the present invention provides a postoperative pain quantitative assessment system based on a postoperative pain quantitative assessment method of EEG nonlinear characteristics, such as Figure 3 As shown, it includes a data processing unit 1, a data cutting unit 2, an interval characteristic value calculation unit 3, a pain quantitative evaluation index calculation unit 4 and a pain level quantification unit 5.

[0082] The data processing unit 1 is used to process the EEG data collected in the database and set data thresholds.

[0083] The data cutting unit 2 performs interval cutting on the processed EEG data, and the data cutting segment lengths are m and m+1.

[0084] The interval feature value calculation unit 3 calculates the interval feature values ​​of the data segment lengths m and m+1 respectively.

[0085] The pain quantitative evaluation index calculation unit 4 is used to add the average characteristic values ​​of the data cutting segment length m and the data cutting segment length m+1 and average the average value to obtain the pain quantitative evaluation index. After processing, the pain quantitative evaluation index is within the range of [0 1].

[0086] The pain level quantification unit 5 quantifies the pain based on the pain quantification evaluation index. The closer the pain quantification evaluation index is to 1, the higher the pain level is.

[0087] The interval characteristic value calculation unit 3 calculates the characteristic change in each data interval, and obtains the dynamic response distance through cross-interval adaptive quantization processing, and averages the dynamic response distance to obtain the average dynamic response distance, i.e., the interval characteristic value, which specifically includes the following sub-steps:

[0088] Calculate the extreme difference between X(i) and X(j), where X(j) is any data interval j outside the i-th data interval.

[0089] The dynamic response distance between the two is calculated based on the extreme difference and defined as d ij The greater the extreme difference between the two, the closer the dynamic response distance is to 1, indicating that the time dimension is more complicated. The specific calculation process is as follows:

[0090] Δ1=maxX(i)-minX(j)

[0091] Δ2=maxX(j)-minX(i)

[0092]

[0093] Where Δ1 is the difference between the maximum value of X(i) and the minimum value of X(j), Δ2 is the difference between the maximum value of X(j) and the minimum value of X(i), max(Δ1,Δ2) is the extreme difference between X(i) and X(j), d ij is the dynamic response distance between the two; r is the data threshold.

[0094] The average dynamic response distance of all data intervals except X(i) is taken to obtain the average dynamic response distance of the data cutting segment length m, that is, the interval characteristic value is:

[0095]

[0096] The same steps are used to obtain the average dynamic response distance of the data cutting segment length m+1, that is, the interval characteristic value:

[0097] Specific embodiments

[0099] The present invention provides a method and system for quantitatively evaluating pain based on nonlinear entropy analysis of electroencephalograms. Figure 1 As shown, the following steps are included:

[0100] S1. Preprocess the original EEG signal and set the analysis threshold.

[0101] Considering that EEG signals are susceptible to interference from head movement, electromyography, electrooculography, and power frequency noise, this study first used an adaptive notch filter to suppress 50Hz power frequency noise. The eegfilt.m function in the EEGLAB tool was used to extract signals in the 0.1-45Hz frequency band to retain effective information related to pain assessment. A denoising method based on wavelet transform was used to effectively remove muscle and electrooculography artifacts in the awake and conscious state, and to determine the adaptive threshold required for subsequent analysis.

[0102] S2. Divide the preprocessed signal into interval data segments of length m.

[0103] The collected EEG data are discrete data points. After denoising the original data, the data is segmented into multidimensional vectors. Suppose the original data sequence is x(1), x(2), ..., x(N), with a total of N data points.

[0104] In order to form an m-dimensional vector, that is:

[0105] X(i)=[x(i),x(i+1),...,x(i+m-1)], i=1,2,...,N-m+1.

[0106] S3. Calculate the feature change in each interval, obtain the feature value through cross-interval adaptive quantization processing, and take the average.

[0107] The calculation of feature changes is the core of this algorithm. This algorithm calculates the feature differences between elements in the data segment and uses fuzzification technology to smooth and quantify these differences, thereby improving the accuracy of feature extraction. Different from the traditional nonlinear entropy algorithm, this algorithm processes the data differences within the interval through a fuzzy function to capture the dynamic characteristics of small changes in the signal. The specific formula is as follows:

[0108] First, the extreme difference between X(i) and X(j) is calculated to compare their maximum numerical changes. Then, the dynamic response distance between the two is calculated based on the extreme difference and defined as d ij The greater the extreme difference between the two, the closer the dynamic response distance is to 1. The calculation process is as follows:

[0109] Δ1=maxX(i)-minX(j)

[0110] Δ2=maxX(j)-minX(i)

[0111]

[0112] After that, the average dynamic response distance is obtained by taking the average of all dynamic response ranges except itself:

[0113]

[0114] S4. Adjust the interval length to m+1 and repeat the feature calculation process.

[0115] The original data is still in the sequence x(1), x(2), ..., x(N), with a total of N data points.

[0116] In order to form an m+1 dimensional vector, that is:

[0117] X(i)=[x(i),x(i+1),...,x(i+m)], i=1,2,...,Nm.

[0118] Then, the dynamic response range is calculated for all vectors except itself, and the sum is taken to get the average dynamic response distance with a data length of m+1. Assuming that the two different vectors are X(i) and X(j), the specific calculation results are as follows:

[0119] First, find the extreme difference between the two and calculate the dynamic response distance between the two. The same threshold as m is also applied when calculating the dynamic response distance.

[0120] Δ1=maxX(i)-minX(j)

[0121] Δ2=maxX(j)-minX(i)

[0122]

[0123] After that, the average dynamic response distance is obtained by taking the average of all dynamic response ranges except itself:

[0124]

[0125] S5. Calculate the average value of the intervals with lengths m and m+1, and record it as the final indicator.

[0126] After the above steps, the average value of the final results φ(m,r) and φ(m+1,r) is used to obtain the final pain quantitative evaluation index:

[0127]

[0128] S6. Quantify the pain based on the pain quantitative evaluation index obtained in step S5. The closer the pain quantitative evaluation index is to 1, the higher the pain level is. The pain is divided into three levels. When the MSIE is greater than 0.8, it is evaluated as severe pain; when the MSIE is between 0.75 and 0.8, it is defined as mild pain; when the MSIE is less than 0.75, it is evaluated as no pain.

[0129]

[0130] Among them, P(MSIE) is the pain level.

[0131] In this embodiment, the data from two patient databases are processed and calculated respectively, and the MSIE index data of pain patient No. 1 is finally obtained to be 0.75, and the MSIE index data of pain patient No. 2 is greater than 0.8. Therefore, the final pain levels of pain patient No. 1 and pain patient No. 2 are mild pain and severe pain, respectively. This can clearly and accurately quantify the pain, ensure the accuracy of quantification, and better evaluate the pain of postoperative patients.

[0132] Since in calculating the dynamic response distance d ij The result will definitely fall within the range of [0 1], so the final range of the pain quantification evaluation index will also fall within the range of [0 1]. It is convenient for comparison with other data. By calculating the extreme differences between signals and smoothing these differences using fuzzification technology, MSIE provides a deeper signal analysis capability in multi-scale space. It is particularly suitable for analyzing multi-dimensional signals in complex systems and helps to reveal their deep dynamic behavior.

[0133] In this application, a new pain quantification assessment method and system based on EEG nonlinear entropy analysis is proposed. The algorithm is based on the characteristic measurement between state vectors in the reconstructed multidimensional phase space to calculate their similarity. Unlike the maximum distance measurement used in traditional methods, the present invention introduces the concept of a cross-interval adaptive range and normalizes it. This innovative measurement method can more accurately capture the similarity characteristics between state vectors, thereby improving the sensitivity to complex signals, and ultimately forming the proposed pain quantification assessment algorithm.

[0134] The results calculated by the method of the present invention are as follows: Figure 2 The results show that: through the above test, the statistical results calculated using the method of the present invention are effective. Figure 2 As shown in the figure, the EEG data of two patients were collected before surgery and put into the database and processed as the baseline. Then the pain quantitative evaluation index was calculated through the above steps. After that, the pain degree was judged according to the patient's self-report and numerical score after surgery. One patient was in severe pain and the other was in mild pain. After that, EEG was collected and the pain quantitative evaluation was calculated through the above steps to compare with the EEG data collected before surgery. It can be observed from the pain quantitative evaluation index that the value is the lowest in the baseline state and is similar for the two patients; compared with severe pain, mild pain, and preoperative data, the lower the entropy value, the lower the pain level; the larger the entropy value, the more complex the sequence, and the more severe the pain level.

[0135] Accurate assessment of postoperative pain: Pain management is an indispensable part of surgical operations. Traditional pain assessment methods usually rely on patients' self-reports, such as visual analog scales (VAS) or numerical scores, but these methods are highly subjective and have large individual differences, making it difficult to assess patients' pain levels in real time and objectively. The pain quantification assessment method based on EEG nonlinear entropy analysis of the present invention can provide a more objective and immediate pain assessment by analyzing the complexity and nonlinear characteristics of patients' postoperative EEG signals.

[0136] In specific applications, the method of the present invention can detect the dynamic changes of the patient's EEG activity by analyzing nonlinear features such as nonlinear entropy in EEG signals, thereby inferring the patient's pain state. For example, in the postoperative awake state, the patient's EEG activity shows changes in specific frequency bands and signal complexity that are closely related to pain. Through this quantification method, clinicians can evaluate the patient's pain level in real time, judge the effectiveness of analgesic treatment, and make corresponding adjustments. Compared with traditional self-reported pain assessment methods, this method is more objective and can be quantified more accurately, and is particularly suitable for patients who do not have the ability to express themselves, such as patients in the anesthesia recovery period or children.

[0137] The embodiments described above are only descriptions of the preferred implementation modes of the present invention, and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A pain quantitative assessment method based on EEG nonlinear entropy analysis, characterized by: It includes the following steps: S1, process the EEG data collected in the database and set the data threshold r, where r is a multiple of the data standard deviation; S2. Perform interval cutting on the processed EEG data. The data cutting segment length is m, specifically: Assume that the original data sequence in each data interval is x(1), x(2), ..., x(N), with a total of N data points. After cutting, each data interval forms an m-dimensional vector in sequence, that is: X(i)=[x(i),x(i+1),...,x(i+m-1)], i=1,2,...,N-m+1 Where X(i) is an m-dimensional vector composed of N data points in order, and i is the i-th data interval; S3, calculating the characteristic change in each data interval, and obtaining the dynamic response distance through cross-interval adaptive quantization processing, averaging the dynamic response distances to obtain the average dynamic response distance, i.e., the interval characteristic value, specifically including the following sub-steps: S31, calculating the extreme difference between X(i) and X(j), where X(j) is any data interval j outside the i-th data interval; S32. Calculate the dynamic response distance between the two based on the extreme difference and define it as d ij The greater the extreme difference between the two, the closer the dynamic response distance is to 1, indicating that the time dimension is more complicated. The specific calculation process is as follows: Δ1=maxX(i)-minX(j) Δ2=maxX(j)-minX(i) Where Δ1 is the difference between the maximum value of X(i) and the minimum value of X(j), Δ2 is the difference between the maximum value of X(j) and the minimum value of X(i), max(Δ1,Δ2) is the extreme difference between X(i) and X(j), d ij is the dynamic response distance between the two; r is the data threshold; S33, taking the average value of the dynamic response distances of all data intervals except X(i) to obtain the average dynamic response distance of the data cutting segment length m, i.e., the interval characteristic value: S4, adjust the data cutting segment length in step S2 to m+1, repeat steps S2-S3, and obtain the average dynamic response distance of the data cutting segment length m+1, that is, the interval characteristic value: S5. Add the average eigenvalues ​​of the data segment length m and the data segment length m+1 and average them to obtain a pain quantitative evaluation index. The obtained pain quantitative evaluation index is within the range of [0 1]. The specific formula is as follows: Among them, MSIE is a quantitative assessment index for pain; S6. Quantify the pain based on the pain quantification evaluation index obtained in step S5. The closer the pain quantification evaluation index is to 1, the higher the pain level is.

2. The pain quantification assessment method based on EEG nonlinear entropy analysis according to claim 1 is characterized by: The data processing in step S1 is specifically as follows: an adaptive notch filter is used to suppress the 50 Hz power frequency noise, and a signal in the 0.1-45 Hz frequency band is extracted and denoised using a denoising method based on wavelet transform.

3. The method for quantitatively evaluating pain based on EEG nonlinear entropy analysis according to claim 1, characterized in that: The database in step S1 is a database formed by collecting real-time EEG data of the pain patient.

4. The method for quantitatively evaluating pain based on EEG nonlinear entropy analysis according to claim 1, characterized in that: In step S6, pain is divided into three levels, and when MSIE is greater than 0.8, it is assessed as severe pain; An MSIE between 0.75 and 0.8 was defined as mild pain; an MSIE less than 0.75 was rated as no pain; Among them, P(MSIE) is the pain level.

5. The method for quantitatively evaluating pain based on EEG nonlinear entropy analysis according to claim 1, characterized in that: The data threshold in step S1 is a multiple of the data standard deviation.

6. An evaluation system for the pain quantification evaluation method based on EEG nonlinear entropy analysis as claimed in claim 1, characterized in that: It includes a data processing unit, a data cutting unit, an interval characteristic value calculation unit, a pain quantitative evaluation index calculation unit and a pain level quantification unit; The data processing unit is used to process the EEG data collected in the database and set the data threshold; The data cutting unit performs interval cutting on the processed EEG data, and the data cutting segment lengths are m and m+1; The interval characteristic value calculation unit calculates the interval characteristic values ​​of the data cutting segment lengths m and m+1 respectively; The pain quantitative evaluation index calculation unit is used to add the average characteristic values ​​of the data cutting segment length m and the data cutting segment length m+1 and average the average value to obtain the pain quantitative evaluation index, and the pain quantitative evaluation index after processing is within the range of [0 1]; The pain level quantification unit quantifies the pain based on the pain quantification evaluation index. The closer the pain quantification evaluation index is to 1, the higher the pain level is.

7. The evaluation system of the pain quantification evaluation method based on EEG nonlinear entropy analysis according to claim 6 is characterized by: The interval characteristic value calculation unit calculates the characteristic change in each data interval, and obtains the dynamic response distance through cross-interval adaptive quantization processing, and averages the dynamic response distance to obtain the average dynamic response distance, i.e., the interval characteristic value, which specifically includes the following sub-steps: Calculate the extreme difference between X(i) and X(j), where X(j) is any data interval j outside the i-th data interval; The dynamic response distance between the two is calculated based on the extreme difference and defined as d ij The greater the extreme difference between the two, the closer the dynamic response distance is to 1, indicating that the time dimension is more complicated. The specific calculation process is as follows: Δ1=maxX(i)-minX(j) Δ2=maxX(j)-minX(i) Where Δ1 is the difference between the maximum value of X(i) and the minimum value of X(j), Δ2 is the difference between the maximum value of X(j) and the minimum value of X(i), max(Δ1,Δ2) is the extreme difference between X(i) and X(j), d ij is the dynamic response distance between the two; r is the data threshold; The average dynamic response distance of all data intervals except X(i) is taken to obtain the average dynamic response distance of the data cutting segment length m, that is, the interval characteristic value is: The same steps are used to obtain the average dynamic response distance of the data cutting segment length m+1, that is, the interval characteristic value:

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