Information interdependence anesthesia depth estimation method based on Laplacian kernel function

Abstract

The invention belongs to the technical field of electroencephalogram information application, and particularly relates to an information interdependence anesthesia depth estimation method based on a Laplacian kernel function, which comprises the following steps: S1, acquiring dual-channel electroencephalogram data in an anesthesia state in a database, and preprocessing the electroencephalogram data; s2, calculating information interdependence of the dual-channel electroencephalogram data by using a Laplacian kernel function; s3, calculating a dual-channel Laplacian kernel function according to the time sequence electroencephalogram data points; s4, calculating the information interdependence probability density of the two-channel Laplacian kernel function of the time sequence electroencephalogram data points; and S5, evaluating the anesthesia depth by using the information interdependence of the Laplacian kernel function of the electroencephalogram data points. According to the method, the Laplace kernel function is used for carrying out probability estimation instead of probability density in information interdependence, the calculated information interdependence evaluates the anesthesia depth, and the problem that traditional anesthesia depth monitoring cannot accurately track and monitor different age groups is solved.

Description

Technical Field

[0001] The invention belongs to the technical field of electroencephalogram information application, and in particular relates to an information interdependence anesthesia depth estimation method based on Laplace kernel function. Background Art

[0002] General anesthesia (GA) is a medical technology widely used in clinical surgery. It can make patients enter an unconscious state during surgery, while achieving the effects of amnesia, analgesia, muscle relaxation and maintaining physiological function stability. This state can effectively ensure the safety of surgeons when performing various operations, make surgical operations smoother, and reduce the risk of intraoperative and postoperative complications. By precisely controlling the type and dosage of anesthetic drugs, doctors can ensure that patients do not feel pain during surgery and can quickly regain consciousness after surgery. However, there is still no recognized anesthesia indicator algorithm for all age groups.

[0003] Long-term research on EEG signals has found that brain degeneration in the elderly is a complex process, which is mainly manifested in the reduction of brain volume, thinning of the cortex and degeneration of white matter. At the pathophysiological level, this degeneration process is closely related to the atrophy of neurons, the degeneration of dendrites and the damage of white matter. The information exchange between the bilateral electroencephalograms (EEG) of the frontal lobe can reflect the changes in the level of consciousness. At present, the research on the EEG characteristics of people of different age groups under general anesthesia is still immature. Therefore, the present invention has developed an anesthesia depth index suitable for EEG data of all age groups, using bilateral EEG signals of the frontal lobe, based on the information interdependence analysis of the Laplace kernel function, which has a good application prospect, can effectively quantify the level of consciousness, and provides a new perspective for monitoring and evaluating the depth of anesthesia.

[0004] In EEG signal analysis, information interdependence is a nonlinear measurement method based on information theory. It is used to estimate the degree of interdependence between two random variables. It can be used to quantify the functional connection strength between EEG data collected by different electrode channels, thereby revealing the dynamic characteristics of the brain network. It has practical physiological significance for the assessment of anesthesia depth. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides an anesthesia depth estimation method based on information interdependence of the Laplace kernel function. The Laplace kernel function is used for probability estimation to replace the probability density in information interdependence. The calculated information interdependence is used to evaluate the anesthesia depth, which greatly reduces the problem that traditional anesthesia depth monitoring is difficult to accurately track and monitor for different age groups.

[0006] To achieve the above object, the present invention discloses the following technical solution:

[0007] An information interdependence anesthesia depth estimation method based on Laplace kernel function, comprising:

[0008] S1: Obtain dual-channel EEG data under anesthesia in the database and preprocess the EEG data;

[0009] S11: The dual-channel EEG data is collected by the anesthesia monitor and stored in the database, and the EEG data and characteristic parameter information under anesthesia during clinical surgery are extracted, including weight parameters, gender parameters and age parameters; the EEG data are divided into three levels according to the age parameters;

[0010] S12: marking the anesthesia state of the EEG data and segmenting the data to complete the preprocessing operation;

[0011] S13: Construct the first time series EEG data X=(x1,…x2) according to the dual-channel EEG data collected by the anesthesia monitor in step S11. i ...,x n ) and the second time series EEG data Y=(y1,...y j ...,y n ); get the time series EEG data points (x i ,y j );

[0012] S2: Use Laplace kernel function to calculate the information interdependence of dual-channel EEG data;

[0013] S21: Calculate the first Manhattan distance d1 of the first time series EEG data X and the second Manhattan distance d2 of the second time series EEG data Y in step S13;

[0014] S22: Calculate the Laplace kernel function K(x) of the first time series EEG data X at the i-th second. i )for:

[0015]

[0016] Among them, K(x i ) is the first time series EEG data x of the ith second i Laplace kernel function of x i is the first time series EEG data of the ith second; h is the bandwidth of the Laplace kernel function; i is the first time series number; j is the second time series number; n is the length of the EEG data; e is a natural constant;

[0017] Calculate the Laplace kernel function K(y) of the second time series EEG data Y at the jth second j )for:

[0018]

[0019] Among them, K(y j ) is the second time series EEG data y at the jth second j Laplace kernel function of y j is the second time series EEG data of the jth second;

[0020] S23: The bandwidth h of the Laplace kernel function is calculated by the formula:

[0021]

[0022] Among them, σ is the sample error of time series EEG data; IQR is the interquartile range of time series EEG data; min is the minimum value function;

[0023] S3: Calculate the dual-channel Laplace kernel function according to the time series EEG data points obtained in step S13;

[0024] For the time series EEG data points (x i ,y j ) The first Manhattan distance d1 and the second Manhattan distance d2 in step S21 are indexed and added, and the dual-channel Laplace kernel function of the time series EEG data points is obtained by cumulative calculation:

[0025]

[0026] Among them, K(x i ,y i ) is the dual-channel Laplace kernel function of the time series EEG data points;

[0027] S4: Calculate the information interdependence probability density of the dual-channel Laplace kernel function of the time series EEG data points in step S3;

[0028] S41: The dual-channel Laplace kernel function K(x i ,y i ) is estimated as the joint probability density f(x, y) of the first time series EEG data and the second time series EEG data; the Laplace kernel function K(x i ) is estimated as the edge probability density f(x) of the first time series EEG data; the Laplace kernel function K(y i ) is estimated as the marginal probability density f(y) of the second time series EEG data;

[0029] S42: The information interdependence between the first time series EEG data X and the second time series EEG data Y is obtained as follows:

[0030]

[0031] Wherein, I(X;Y) is the information interdependence between the first time series EEG data X and the second time series EEG data Y; f(x,y) is the joint probability density of the first time series EEG data and the second time series EEG data; f(x) is the marginal probability density of the first time series EEG data; f(y) is the marginal probability density of the second time series EEG data; dx is the differential of the first time series EEG data; dy is the differential of the second time series EEG data; log is the logarithmic function;

[0032] S43: The information interdependence of the Laplace kernel function of the EEG data points is obtained by cumulative calculation:

[0033]

[0034] Among them, I L (X; Y) is the information interdependence of the Laplace kernel function;

[0035] S5: Estimation of anesthetic depth using information interdependence of the Laplace kernel function of EEG data points;

[0036] Obtain the information interdependence I of the Laplace kernel function obtained in step S4 L (X; Y) is set as the anesthesia depth estimation parameter; the change curves of the anesthesia depth estimation parameters in the pre-anesthesia stage, the middle anesthesia stage and the anesthesia recovery stage are plotted respectively, the EEG data caused by the actual anesthesia process and the change trend of the anesthesia depth estimation parameters are identified and compared, and the anesthesia depth estimation is completed.

[0037] Preferably, in step S11, the EEG data are divided into three levels according to the age parameter, specifically: first-level age parameter EEG data, second-level age parameter EEG data and third-level age parameter EEG data.

[0038] Preferably, in step S12, the anesthetic state of the EEG data is marked and the data is segmented to complete the preprocessing operation, specifically: the anesthetic state of the EEG data is marked and segmented according to the records in the database, and 180 seconds of EEG data are taken for each pre-anesthesia stage, mid-anesthesia stage, and anesthesia recovery stage; all segmented EEG data are preprocessed, calculated based on a window length of 10 seconds, the window overlap rate is 75%, and the sampling frequency of the EEG data is 128HZ.

[0039] Preferably, the first Manhattan distance d1 in step S21 is obtained by i Manhattan distance d1 i Composition:

[0040] d1 i =|x i -x j |;

[0041] Among them, d1 i is the Manhattan distance of the EEG data at the ith second; x i is the first time series EEG data of the ith second; x j is the first time series EEG data of the jth second.

[0042] Preferably, the second Manhattan distance d2 in step S22 is obtained by the second time series EEG data y j Manhattan distance d2 j Composition:

[0043] d2 j =|y i -y j |;

[0044] Among them, d2 j is the EEG data y at the jth second j Manhattan distance of y i is the second time series EEG data of the ith second; j is the second time series EEG data of the jth second.

[0045] Preferably, the joint probability density f(x, y) of the first time series EEG data and the second time series EEG data in step S4 is:

[0046]

[0047] Wherein, f(x, y) is the joint probability density of the first time series EEG data and the second time series EEG data.

[0048] Preferably, the edge probability density f(x) of the first time series EEG data in step S4 is:

[0049]

[0050] Wherein, f(x) is the marginal probability density of the first time series EEG data.

[0051] Preferably, the edge probability density f(y) of the second time series EEG data in step S4 is:

[0052]

[0053] Wherein, f(y) is the marginal probability density of the second time series EEG data.

[0054] Preferably, in step S5, the information interdependence of the Laplace kernel function I LThe change of (X; Y) is set as the parameter of anesthesia depth; the information interdependence of the estimated Laplace kernel function during EEG tracking is plotted. L (X; Y) to observe the information interdependence between the EEG activity induced during anesthesia and the proposed Laplace kernel function I L The (X; Y) changing trend can be used to estimate the depth of anesthesia.

[0055] Compared with the prior art, the present invention has the following beneficial effects:

[0056] (1) The present invention adopts a physiologically inspired EEG data analysis method, uses the Laplace kernel function to perform probability estimation to replace the probability density in information interdependence, and calculates the information interdependence to evaluate the anesthesia depth, thereby reducing the problem that traditional anesthesia depth monitoring cannot track and monitor different age groups well.

[0057] (2) The present invention provides more options for anesthesia depth assessment in parameter settings of different bandwidths, which can be appropriately adjusted according to needs and is applicable to various age groups, especially in distinguishing different anesthesia consciousness states in adults and the elderly.

[0058] (3) The present invention uses kernel functions to estimate probability instead of directly calculating probability using traditional information theory. In theory, the data can be mapped into an infinite multidimensional space. Therefore, the present invention has good robustness and adaptability in complex situations with large amounts of data. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 It is a control block diagram of the information interdependence anesthesia depth estimation method based on Laplace kernel function of the present invention;

[0060] Figure 2 It is the time-frequency diagram of EEG data of the youth group case of the present invention;

[0061] Figure 3 A bispectral index curve diagram of EEG data of a youth group case of the present invention;

[0062] Figure 4 The information interdependence curve diagram of the EEG data of the youth group case of the present invention;

[0063] Figure 5 It is the time-frequency diagram of the EEG data of the middle-aged group case of the present invention;

[0064] Figure 6 The bispectral index curve of the EEG data of the middle-aged group case of the present invention;

[0065] Figure 7 It is the information interdependence curve diagram of the EEG data of the middle-aged group case of the present invention;

[0066] Figure 8 It is a time-frequency diagram of EEG data of the elderly group case of the present invention;

[0067] Fig. 9 The bispectral index curve of the EEG data of the elderly group case of the present invention;

[0068] Fig.10 This is a graph showing the information interdependence of EEG data of the elderly group of cases of the present invention;

[0069] Fig.11 It is a violin plot of information interdependence parameters under different consciousness states for three age groups of the present invention. DETAILED DESCRIPTION

[0070] The exemplary embodiments, features and aspects of the present invention will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise specified.

[0071] The present invention provides an information interdependence anesthesia depth estimation method based on Laplace kernel function, such as Figure 1 As shown, dual-channel EEG data under anesthesia in the database is obtained, and the EEG data is preprocessed; the information interdependence of the dual-channel EEG data is calculated using the Laplace kernel function; the dual-channel Laplace kernel function is calculated according to the time series EEG data points; the information interdependence probability density of the dual-channel Laplace kernel function of the time series EEG data points is calculated; the information interdependence of the Laplace kernel function of the EEG data points is used to evaluate the depth of anesthesia; which includes:

[0072] Step S1: Obtain dual-channel EEG data under anesthesia in the database and preprocess the EEG data.

[0073] Step S11: The anesthesia monitor collects dual-channel EEG data and saves it in a database, extracting EEG data and characteristic parameter information under anesthesia during clinical surgery, including weight parameters, gender parameters and age parameters; the EEG data is divided into three levels according to the age parameter, specifically: level one age parameter EEG data, the age parameter is 18-40; level two age parameter EEG data, the age parameter is 40-65; level three age parameter EEG data, the age parameter is greater than 65.

[0074] Step S12: Mark the anesthetic state of the EEG data and segment the data to complete the preprocessing operation, specifically: mark and segment the anesthetic state of the EEG data according to the records in the database, and take 180 seconds of EEG data for each pre-anesthesia stage, mid-anesthesia stage, and anesthesia recovery stage; perform EEG preprocessing on all segmented EEG data, based on a window length of 10 seconds, with a window overlap rate of 75%, and the sampling frequency of the EEG data is 128HZ.

[0075] Step S13: Construct the first time series EEG data X=(x1,...x i ...,x n ) and the second time series EEG data Y=(y1,...y j ...,y n ), the length of EEG data n is 1280; get the time series EEG data points (x i ,y j ).

[0076] Step S2: Calculate the information interdependence of dual-channel EEG data using the Laplace kernel function.

[0077] Step S21: Calculate the first Manhattan distance d1 of the first time series EEG data X and the second Manhattan distance d2 of the second time series EEG data Y in step S13. The embodiment of the present invention does not use the Gaussian kernel function based on normal distribution, which is the most commonly used in kernel probability estimation, but uses the Laplace kernel function that reduces the influence of parameters. Since the Manhattan distance is calculated to eliminate the interference caused by some noise, the Laplace kernel function has good robustness to noise.

[0078] The first Manhattan distance d1 is calculated by the first time series EEG data x i The Manhattan distance and d1 i Composition:

[0079]

[0080] Among them, d1 i is the Manhattan distance of the EEG data at the ith second; x i is the first time series EEG data of the ith second; x j is the first time series EEG data of the jth second; n is the length of the EEG data; i is the first time series number; j is the second time series number.

[0081] The second Manhattan distance d2 is calculated by the second time series EEG data y j Manhattan distance and d2 j Composition:

[0082]

[0083] Among them, d2 j is the EEG data y at the jth second j Manhattan distance of y i is the second time series EEG data of the ith second; j is the second time series EEG data of the jth second.

[0084] Step S22: Calculate the Laplace kernel function K(x i )for:

[0085]

[0086] Among them, K(x i ) is the first time series EEG data x of the ith second i Laplace kernel function of x i is the first time series EEG data of the ith second; h is the bandwidth of the Laplace kernel function; e is a natural constant.

[0087] Calculate the Laplace kernel function K(y) of the second time series EEG data Y at the jth second j )for:

[0088]

[0089] Among them, K(y j ) is the second time series EEG data y at the jth second j Laplace kernel function of y j is the second time series EEG data of the jth second.

[0090] Step S23: Calculate the bandwidth h of the Laplace kernel function by the formula:

[0091]

[0092] Among them, σ is the sample error of time series EEG data; IQR is the interquartile range of time series EEG data; min is the minimum value function.

[0093] In the embodiment of the present invention, the bandwidth h of the optimal Laplace kernel function obtained through cross-validation is 0.4. Due to the characteristics of its exponential term, the Laplace kernel function is highly sensitive to neighboring points and can quickly reduce the influence of distant points.

[0094] Step S3: Calculate the dual-channel Laplace kernel function according to the time series EEG data points obtained in step S13.

[0095] For the time series EEG data points (x i ,y j ) The first Manhattan distance d1 and the second Manhattan distance d2 in step S21 are indexed and added, and the dual-channel Laplace kernel function of the time series EEG data points is obtained by cumulative calculation:

[0096]

[0097] Among them, K(x i ,y i ) is the dual-channel Laplace kernel function of the time series EEG data points; d1 is the first Manhattan distance; d2 is the second Manhattan distance.

[0098] Step S4: Calculate the information interdependence probability density of the dual-channel Laplace kernel function of the time series EEG data points in step S3.

[0099] Step S41: The dual-channel Laplace kernel function K(x i ,y i ) is estimated as the joint probability density f(x, y) of the first time series EEG data and the second time series EEG data; the Laplace kernel function K(x i ) is estimated as the edge probability density f(x) of the first time series EEG data; the Laplace kernel function K(y i ) is estimated as the marginal probability density f(y) of the second time series EEG data.

[0100] Step S42: The information interdependence between the first time series EEG data X and the second time series EEG data Y is obtained as follows:

[0101]

[0102] Wherein, I(X;Y) is the information interdependence between the first time series EEG data X and the second time series EEG data Y; f(x,y) is the joint probability density of the first time series EEG data and the second time series EEG data; f(x) is the marginal probability density of the first time series EEG data; f(y) is the marginal probability density of the second time series EEG data; dx is the differential of the first time series EEG data; dy is the differential of the second time series EEG data; log is the logarithmic function.

[0103] The joint probability density f(x, y) of the first time series EEG data and the second time series EEG data is:

[0104]

[0105] Wherein, f(x, y) is the joint probability density of the first time series EEG data and the second time series EEG data.

[0106] The marginal probability density f(x) of the first time series EEG data is:

[0107]

[0108] Wherein, f(x) is the marginal probability density of the first time series EEG data.

[0109] The marginal probability density f(y) of the second time series EEG data is:

[0110]

[0111] Wherein, f(y) is the marginal probability density of the second time series EEG data.

[0112] Step S43: The information interdependence of the Laplace kernel function of the EEG data points is obtained by cumulative calculation:

[0113]

[0114] Among them, I L (X; Y) is the information interdependence of the Laplace kernel function.

[0115] Step S5: Use the information interdependence of the Laplace kernel function of the EEG data points to evaluate the depth of anesthesia; obtain the information interdependence I of the Laplace kernel function obtained in step S4 L (X; Y), set as the anesthesia depth estimation parameters; plot the information interdependence of the estimated Laplace kernel function during EEG tracking I L (X; Y) to observe the information interdependence between the EEG activity induced during anesthesia and the proposed Laplace kernel function I L (X; Y) change trend, draw the change curves of the anesthesia depth estimation parameters in the pre-anesthesia stage, mid-anesthesia stage and anesthesia recovery stage respectively, identify and compare the EEG data caused by the actual anesthesia process with the change trend of the anesthesia depth estimation parameters, and complete the estimation of the anesthesia depth.

[0116] The analysis results of the EEG data of the youth group case in the embodiment of the present invention are as follows: Figure 2 The time-frequency diagram of the EEG data of the youth group case of the present invention is shown. It can be seen that after anesthesia, there is a significant increase in energy at 10 Hz, which represents a decrease in the level of consciousness; Figure 3 The bispectral index curve of the EEG data of the youth group case of the present invention can be seen to have consistent changes with the time-frequency diagram. The bispectral index decreases after anesthesia and remains at a low level during the unconscious period, and gradually increases during the recovery period. Figure 4 This is the information interdependence curve diagram of the EEG data of the youth group case of the present invention. It can be seen that the information dependence decreases after anesthesia and remains at a low level during the unconscious period, and gradually increases during the recovery period, which can well detect the consciousness state of the youth group.

[0117] The analysis results of the EEG data of the middle-aged group of cases in the embodiment of the present invention are as follows: Figure 5 The time-frequency diagram of the EEG data of the youth group case of the present invention is shown. It can be seen that after anesthesia, there is a significant increase in energy at 10 Hz, which represents a decrease in the level of consciousness; Figure 6The bispectral index curve of the EEG data of the youth group case of the present invention is shown in FIG. 1 . The bispectral index decreases after anesthesia and remains at a low level during the unconscious period, and gradually increases during the recovery period; Figure 7 This is the information interdependence curve diagram of the EEG data of the youth group case of the present invention. It can be seen that the information dependence decreases after anesthesia and remains at a low level during the unconscious period, and gradually increases during the recovery period, which can well detect the consciousness state of the middle-aged group.

[0118] The analysis results of EEG data of the elderly group cases in the embodiment of the present invention are as follows: Figure 8 The figure shows the time-frequency diagram of the EEG data of the youth group case of the present invention. Compared with the unconscious state, the alpha frequency band of about 10 Hz increases, indicating that the level of consciousness decreases. Fig. 9 The bispectral index curve of the EEG data of the youth group case of the present invention is shown in FIG. 1 . The bispectral index decreases after anesthesia and remains at a low level during the unconscious period, and gradually increases during the recovery period; Fig.10 This is the information interdependence curve diagram of the EEG data of the youth group case of the present invention. It can be seen that the information dependence decreases after anesthesia and remains at a low level during the unconscious period, and gradually increases during the recovery period, which can well detect the consciousness state of the elderly group.

[0119] like Fig.11 This is a violin plot of the information interdependence parameters under different states of consciousness for three age groups of the present invention, showing the group level changes of the present invention under different states of consciousness for three age groups. It can be seen that in the youth group and the middle-aged group, the information dependence has a significant decrease and then increase from wakefulness to anesthesia and then to recovery, and there is also such a change trend in the elderly group.

[0120] The embodiments of the present invention have the following beneficial effects: the embodiments of the present invention adopt an EEG data analysis method inspired by physiology, use the Laplace kernel function to perform probability estimation to replace the probability density in information interdependence, and the calculated information interdependence evaluates the depth of anesthesia, which greatly reduces the problem that traditional anesthesia depth monitoring cannot track and monitor different age groups well; for anesthesia depth assessment, more options are provided in parameter settings of different bandwidths, which can be adjusted appropriately according to needs, and are suitable for a variety of age groups, especially in distinguishing different anesthesia consciousness states in adults and the elderly. The analysis of anesthesia EEG data of young, middle-aged and elderly people proves that the method can better meet actual needs and obtain correct analysis results; the kernel function is used to estimate probability instead of directly calculating probability using traditional information theory, and theoretically the data can be mapped to an infinite multidimensional space, so it has good robustness and adaptability in complex situations with large amounts of data.

[0121] 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 method for estimating anesthesia depth based on information interdependence of Laplace kernel function, characterized in that: It includes: S1: Obtain dual-channel EEG data under anesthesia in the database and preprocess the EEG data; S11: The dual-channel EEG data is collected by the anesthesia monitor and stored in the database, and the EEG data and characteristic parameter information under anesthesia during clinical surgery are extracted, including weight parameters, gender parameters and age parameters; the EEG data are divided into three levels according to the age parameters; S12: marking the anesthesia state of the EEG data and segmenting the data to complete the preprocessing operation; S13: Construct the first time series EEG data X=(x1, ..x i …,x n ) and the second time series EEG data Y=(y1,…y j …,y n ); get the time series EEG data points (x i ,y j ); S2: Use Laplace kernel function to calculate the information interdependence of dual-channel EEG data; S21: Calculate the first Manhattan distance d1 of the first time series EEG data X and the second Manhattan distance d2 of the second time series EEG data Y in step S13; S22: Calculate the Laplace kernel function K(x) of the first time series EEG data X at the i-th second. i )for: Among them, K(x i ) is the first time series EEG data x of the ith second i Laplace kernel function of x i is the first time series EEG data of the ith second; h is the bandwidth of the Laplace kernel function; i is the first time series number; j is the second time series number; n is the length of the EEG data; e is a natural constant; Calculate the Laplace kernel function K(y) of the second time series EEG data Y at the jth second j )for: Among them, K(y j ) is the second time series EEG data y at the jth second j Laplace kernel function of y j is the second time series EEG data of the jth second; S23: The bandwidth h of the Laplace kernel function is calculated by the formula: Among them, σ is the sample error of time series EEG data; IQR is the interquartile range of time series EEG data; min is the minimum value function; n is the length of EEG data; S3: Calculate the dual-channel Laplace kernel function according to the time series EEG data points obtained in step S13; For the time series EEG data points (x i ,y j ) The first Manhattan distance d1 and the second Manhattan distance d2 in step S21 are indexed and added, and the dual-channel Laplace kernel function of the time series EEG data points is obtained by cumulative calculation: Among them, K(x i ,y i ) is the dual-channel Laplace kernel function of the time series EEG data points; S4: Calculate the information interdependence probability density of the dual-channel Laplace kernel function of the time series EEG data points in step S3; S41: The dual-channel Laplace kernel function K(x i ,y i ) is estimated as the joint probability density f(x, y) of the first time series EEG data and the second time series EEG data; the Laplace kernel function K(x i ) is estimated as the edge probability density f(x) of the first time series EEG data; the Laplace kernel function K(y i ) is estimated as the marginal probability density f(y) of the second time series EEG data; S42: The information interdependence between the first time series EEG data X and the second time series EEG data Y is obtained as follows: Wherein, I(X;Y) is the information interdependence between the first time series EEG data X and the second time series EEG data Y; f(x,y) is the joint probability density of the first time series EEG data and the second time series EEG data; f(x) is the marginal probability density of the first time series EEG data; f(y) is the marginal probability density of the second time series EEG data; dx is the differential of the first time series EEG data; dy is the differential of the second time series EEG data; log is the logarithmic function; S43: The information interdependence of the Laplace kernel function of the EEG data points is obtained by cumulative calculation: Among them, I L (X; Y) is the information interdependence of the Laplace kernel function; S5: Estimation of anesthetic depth using information interdependence of the Laplace kernel function of EEG data points; Obtain the information interdependence I of the Laplace kernel function obtained in step S4 L (X; Y) is set as the anesthesia depth estimation parameter; the change curves of the anesthesia depth estimation parameters in the pre-anesthesia stage, the middle anesthesia stage and the anesthesia recovery stage are plotted respectively, the EEG data caused by the actual anesthesia process and the change trend of the anesthesia depth estimation parameters are identified and compared, and the anesthesia depth estimation is completed.

2. The method for estimating anesthesia depth based on information interdependence of Laplace kernel function according to claim 1, characterized in that: In step S11, the EEG data are divided into three levels according to the age parameter, specifically: first-level age parameter EEG data, second-level age parameter EEG data and third-level age parameter EEG data.

3. The method for estimating anesthesia depth based on information interdependence of Laplace kernel function according to claim 1, characterized in that: In step S12, the anesthetic state of the EEG data is marked and the data is segmented to complete the preprocessing operation, specifically: the anesthetic state of the EEG data is marked and segmented according to the records in the database, and 180 seconds of EEG data are taken for each pre-anesthesia stage, mid-anesthesia stage, and anesthesia recovery stage; all segmented EEG data are preprocessed, calculated based on a window length of 10 seconds, the window overlap rate is 75%, and the sampling frequency of the EEG data is 128HZ.

4. The method for estimating anesthesia depth based on information interdependence of Laplace kernel function according to claim 1, characterized in that: In step S21, the first Manhattan distance d1 is obtained by calculating the first time series EEG data x i Manhattan distance d1 i Composition: d1 i =|x i -x j Among them, d1 i is the Manhattan distance of the EEG data at the ith second; x i is the first time series EEG data of the ith second; x j is the first time series EEG data of the jth second.

5. The method for estimating anesthesia depth based on information interdependence of Laplace kernel function according to claim 1, characterized in that: In step S22, the second Manhattan distance d2 is calculated by the second time series EEG data y j Manhattan distance d2 j Composition: d2 j =|y i -y j Among them, d2 j is the EEG data y at the jth second j Manhattan distance of y i is the second time series EEG data of the ith second; j is the second time series EEG data of the jth second.

6. The method for estimating anesthesia depth based on information interdependence of Laplace kernel function according to claim 1, characterized in that: The joint probability density f(x, y) of the first time series EEG data and the second time series EEG data in step S4 is: Wherein, f(x, y) is the joint probability density of the first time series EEG data and the second time series EEG data.

7. The method for estimating anesthesia depth based on information interdependence of Laplace kernel function according to claim 1, characterized in that: The edge probability density f(x) of the first time series EEG data in step S4 is: Wherein, f(x) is the marginal probability density of the first time series EEG data.

8. The method for estimating anesthesia depth based on information interdependence of Laplace kernel function according to claim 1, characterized in that: The edge probability density f(y) of the second time series EEG data in step S4 is: Wherein, f(y) is the marginal probability density of the second time series EEG data.

9. The method for estimating anesthesia depth based on information interdependence of Laplace kernel function according to claim 1, characterized in that: In step S5, the information interdependence of the Laplace kernel function I L The change of (X; Y) is set as the parameter of anesthesia depth; the information interdependence of the estimated Laplace kernel function during EEG tracking is plotted. L (X; Y) to observe the information interdependence between the EEG activity induced during anesthesia and the proposed Laplace kernel function I L The (X; Y) changing trend can be used to estimate the depth of anesthesia.

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