Pain quantitative assessment method and system based on EEG nonlinear entropy analysis

Abstract

The present invention provides a pain quantification assessment method and system based on EEG nonlinear entropy analysis, which belongs to the field of EEG data analysis. The method includes: S1 processing EEG data collected in a database and setting a threshold; S2 performing interval cutting on the processed EEG data, with the data cutting segment length being m; S3 calculating feature changes in each interval, and obtaining feature values ​​through cross-interval adaptive quantization processing, averaging to obtain an average dynamic response distance, i.e., an interval feature value; S4 obtaining an average dynamic response distance, i.e., an interval feature value, with a data cutting segment length of m+1; S5 adding and averaging the average feature values ​​of data segments with a length of m and a data segment length of m+1 to obtain a pain quantification assessment index. S6 quantifying pain based on the pain quantification assessment index obtained in step S5. The method of the present invention uses cross-interval adaptive analysis and multi-scale feature mining technology to accurately extract nonlinear features from EEG signals for pain quantification assessment.

Description

Technical Field

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

[0002] Currently, quantifying pain still faces many technical challenges, especially in accurately quantifying differences in pain perception between individuals. Electroencephalogram (EEG) analysis, due to its non-invasive and real-time nature, has become an important tool in pain assessment in recent years.

[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 advanced analysis methods that can exploit nonlinear features to improve the accuracy of pain assessment and its sensitivity to individual differences. Summary of the Invention

[0004] To address the shortcomings of the aforementioned prior art, the present invention aims to provide a method and system for quantifying pain based on nonlinear EEG entropy analysis. This method performs deep feature extraction and quantitative analysis of EEG signals, identifying complex dynamic feature changes in the signals and accurately characterizing pain perception. Based on nonlinear feature extraction of EEG signals, this method can more sensitively capture individual EEG activity patterns, effectively improving the ability to quantify and assess pain severity, and overcoming the limitations of traditional techniques in processing complex EEG signals.

[0005] Specifically, the present invention provides a method for quantitatively assessing postoperative pain based on nonlinear characteristics of 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 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 sequence, and i is the i-th data interval;

[0011] S3. Calculate the feature change in each data interval, and obtain the dynamic response distance through cross-interval adaptive quantization processing. Average the dynamic response distances to obtain the average dynamic response distance, i.e., the interval feature value. This specifically includes the following sub-steps:

[0012] 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;

[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 complex. 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. Take 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 segment with a length of m, that is, the interval characteristic value:

[0019]

[0020] S4. Adjust the data segment length in step S2 to m+1, repeat steps S2-S3, and obtain the average dynamic response distance of the data segment length m+1, i.e., 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.

[0026] Preferably, the data processing in step S1 is specifically as follows: using an adaptive notch filter to suppress 50 Hz power frequency noise, extracting a signal in the 0.1-45 Hz frequency band and denoising it 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 the MSIE is greater than 0.8, it is assessed as severe pain; when the MSIE is between 0.75 and 0.8, it is defined as mild pain; and when the 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 data thresholds;

[0034] The data cutting unit cuts the processed EEG data into intervals, and the data cutting segments are of length m and m+1;

[0035] The interval eigenvalue calculation unit calculates the interval eigenvalues ​​of the data cutting segments with lengths of m and m+1 respectively;

[0036] The pain quantification evaluation index calculation unit is used to add the average eigenvalues ​​of the data cutting segment length m and the data cutting segment length m+1 and average the average value to obtain the pain quantification evaluation index. After processing, the pain quantification evaluation index 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.

[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 distances 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 complex. 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 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 segment with a length of 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, it achieves accurate extraction of nonlinear features in EEG signals. This method significantly improves the resolution and accuracy of pain state assessment, making signal processing more sensitive and able to more effectively capture subtle changes in pain perception between individuals.

[0051] (2) The present invention proposes a new algorithm for pain assessment that 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 the assessment of consciousness state, pain monitoring, and the diagnosis and intervention of neurological diseases. In the 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 represents the pain level. The pain quantification assessment index accurately quantifies the pain level. The system can more accurately monitor the changes in brain neural 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 The method of the present invention is used to calculate the comparison chart of different pain intensities with the baseline;

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

[0056] Hereinafter, embodiments of the present invention will be described with reference to the accompanying 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 a 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 postoperative patients and stored in the database. The data is then directly retrieved from the database and subjected to noise reduction and other processing to obtain noise-reduced EEG data.

[0059] S2. Perform interval cutting on the processed EEG data. The data 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. Calculate the feature change in each data interval, and obtain the dynamic response distance through cross-interval adaptive quantization processing. Average the dynamic response distances to obtain the average dynamic response distance, i.e., the interval feature value. This specifically includes 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 complex. 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. Take 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 segment with a length of m, that is, the interval characteristic value:

[0071]

[0072] S4. Adjust the data 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 segment length m+1.

[0073]

[0074] The process of calculating the average dynamic response distance of data segmentation with a length of m+1 is the same as that of calculating the average dynamic response distance of data segmentation with a length of 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] The MSIE is a pain quantification evaluation index that can be used to clearly classify pain levels and obtain a specific pain level.

[0078] 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. In a specific application, pain is divided into three levels: when the MSIE is greater than 0.8, it is assessed as severe pain; when the MSIE is between 0.75 and 0.8, it is defined as mild pain; and when the MSIE is less than 0.75, it is assessed 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 nonlinear characteristics of EEG, 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 cuts the processed EEG data into intervals, and the lengths of the data cutting segments 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 quantification evaluation index calculation unit 4 is used to add the average eigenvalues ​​of the data cutting segment length m and the data cutting segment length m+1 and average the average value to obtain the pain quantification evaluation index. After processing, the pain quantification 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.

[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 complex. 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 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 segment with a length of m+1, that is, the interval characteristic value:

[0097] Specific embodiments

[0099] The present invention provides a method and system for quantitatively assessing pain based on EEG nonlinear entropy analysis, such as Figure 1 As shown, the following steps are included:

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

[0101] Considering the susceptibility of EEG signals to interference from head movement, electromyography, electrooculography, and power-frequency noise, this study first employed an adaptive notch filter to suppress 50 Hz power-frequency noise. The eegfilt.m function in the EEGLAB tool was used to extract the signal in the 0.1-45 Hz frequency band to preserve information relevant to pain assessment. A wavelet-based denoising method was then used to effectively remove muscle and oculoculographic artifacts during wakefulness and conscious states, 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 is a discrete data point. After noise reduction, the raw data is segmented into multidimensional vectors. Suppose the raw 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] Calculating feature changes is the core of this algorithm. This algorithm calculates the feature differences between elements within a data segment and uses fuzzification techniques to smooth and quantify these differences, thereby improving the accuracy of feature extraction. Unlike traditional nonlinear entropy algorithms, this algorithm processes the data differences within the interval through a fuzzy function to capture the dynamic characteristics of subtle 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 of all vectors except itself is calculated, and the sum and average are taken to obtain 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 them. 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 of the characteristic values ​​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 quantification index obtained in step S5. The closer the pain quantification index is to 1, the higher the pain level. Pain is categorized into three levels: a MSIE greater than 0.8 is considered severe pain; an MSIE between 0.75 and 0.8 is considered mild pain; and an MSIE less than 0.75 is considered no pain.

[0129]

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

[0131] In this embodiment, data from two patient databases are used for processing and calculation respectively. The final MSIE index data of pain patient No. 1 is 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 ultimately fall within the range [0 1], so the final pain quantification index will also fall within the range [0 1]. This facilitates comparison with other data. By calculating extreme differences between signals and smoothing these differences using fuzzification techniques, MSIE provides deeper signal analysis capabilities in multi-scale space. It is particularly suitable for analyzing multidimensional signals in complex systems, helping to reveal their underlying 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 a characteristic metric between state vectors in a reconstructed multidimensional phase space to calculate their similarity. Unlike the maximum distance metric used in traditional methods, the present invention introduces the concept of a cross-interval adaptive range and normalizes it. This innovative metric can more accurately capture the similarity characteristics between state vectors, thereby improving sensitivity to complex signals and ultimately forming the proposed pain quantification assessment algorithm.

[0134] The results obtained by the method of the present invention are statistically analyzed by the frontal lobe EEG for pain. 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, EEG data from two patients were collected before surgery, stored in a database, and processed as a baseline. Pain quantification was then calculated using the aforementioned steps. Postoperatively, pain severity was determined based on patient self-reports and numerical ratings, with one patient receiving severe pain and the other receiving mild pain. EEG data were then collected and the pain quantification was calculated using the aforementioned steps for comparison with the preoperative EEG data. The pain quantification metrics were lowest at baseline and similar between the two patients. Comparing the data between severe pain, mild pain, and preoperative data, lower entropy values ​​indicated lower pain levels. Higher entropy values ​​indicated more complex sequences and increased pain severity.

[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 present invention's pain quantification assessment method based on EEG nonlinear entropy analysis 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 awake state after surgery, 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 merely descriptions of preferred implementations of the present invention and are not intended to limit the scope of the present invention. Without departing from the 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 fall within the scope of protection determined by the claims of the present invention.

Claims

1. A method for quantitatively assessing pain based on nonlinear entropy analysis of EEG, 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 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 sequence, and i is the i-th data interval; S3. Calculate the feature change in each data interval, and obtain the dynamic response distance through cross-interval adaptive quantization processing. Average the dynamic response distances to obtain the average dynamic response distance, i.e., the interval feature value. This specifically includes the following sub-steps: 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; 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 complex. 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. Take 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 segment with a length of m, that is, the interval characteristic value: S4. Adjust the data segment length in step S2 to m+1, repeat steps S2-S3, and obtain the average dynamic response distance of the data segment length m+1, i.e., the interval characteristic value: S5. Add the average eigenvalues ​​of the data segment length m and the data segment length m+1 and calculate the average value to obtain a pain quantification evaluation index. The obtained pain quantification 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.

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

3. The method for quantitatively assessing 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 person suffering from pain.

4. The method for quantitatively assessing 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 the 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 assessing 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 according to 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 data thresholds; The data cutting unit cuts the processed EEG data into intervals, and the data cutting segments are of length m and m+1; The interval eigenvalue calculation unit calculates the interval eigenvalues ​​of the data cutting segments with lengths of m and m+1 respectively; The pain quantification evaluation index calculation unit is used to add the average eigenvalues ​​of the data cutting segment length m and the data cutting segment length m+1 and average the eigenvalues ​​to obtain the pain quantification evaluation index. After processing, the pain quantification evaluation index 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.

7. The evaluation system for the pain quantification evaluation method based on EEG nonlinear entropy analysis according to claim 6, characterized in that: The interval eigenvalue calculation unit calculates the characteristic change in each data interval, and obtains the dynamic response distance through cross-interval adaptive quantization processing. The dynamic response distances are averaged to obtain the average dynamic response distance, i.e., the interval eigenvalue. Specifically, the following sub-steps are included: 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 complex. 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 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 segment with a length of m+1, that is, the interval characteristic value:

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