Information interdependence-based anesthesia depth estimation method based on laplacian kernel function
By using an information interdependence method based on the Laplace kernel function, the accuracy problem of anesthesia depth monitoring in different age groups was solved, especially in the elderly population, achieving more accurate assessment and monitoring of anesthesia depth.
Patent Information
- Application Number
- CN202510093475.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2025-01-21
- Publication Date
- 2026-05-15
- Estimated Expiration
- 2045-01-21
AI Technical Summary
Existing technologies lack algorithms for monitoring the depth of anesthesia applicable to different age groups, making it particularly difficult to accurately track changes in the depth of anesthesia in the elderly.
An information interdependency method based on the Laplace kernel function was adopted, and information interdependency was calculated to assess the depth of anesthesia by replacing the traditional probability density with probability estimation. Dual-channel EEG data was used to monitor the depth of anesthesia.
It improves the accuracy and adaptability of anesthesia depth monitoring, especially in different age groups, particularly the elderly, and can better track changes in anesthesia depth. It also provides more parameter setting options and adapts to complex situations with large amounts of data.
Smart Images
Figure CN120000166B_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of EEG information application technology, and specifically relates to an anesthesia depth estimation method based on information interdependence of the Laplace kernel function. Background Technology
[0002] General anesthesia (GA) is a widely used medical technique in clinical surgery. It induces unconsciousness in patients during surgery, achieving effects such as amnesia, analgesia, muscle relaxation, and maintenance of physiological stability. This state effectively ensures the safety of surgeons during various procedures, making surgical operations smoother and reducing 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 recover consciousness quickly afterward. However, there is still no universally accepted algorithm for anesthesia indicators applicable to all age groups.
[0003] Long-term research on electroencephalogram (EEG) signals has revealed that brain degeneration in the elderly is a complex process, primarily manifested as a decrease in brain volume, thinning of the cortex, and degeneration of white matter. At the pathophysiological level, this degenerative process is closely related to neuronal atrophy, dendritic degeneration, and white matter damage. Information exchange between bilateral prefrontal cortex EEGs can reflect changes in consciousness levels. Currently, research on the EEG characteristics of different age groups under general anesthesia is still incomplete. Therefore, this invention develops an anesthesia depth index applicable to EEG data from all age groups. Utilizing bilateral frontal cortex EEG signals and based on information interdependence analysis using the Laplace kernel function, it shows promising application prospects, effectively quantifying consciousness levels and providing a new perspective for monitoring and assessing anesthesia depth.
[0004] In EEG signal analysis, information interdependence is a nonlinear metric 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 connectivity strength between EEG data collected from different electrode channels, thereby revealing the dynamic characteristics of brain networks and having practical physiological significance for assessing the depth of anesthesia. Summary of the Invention
[0005] To address the shortcomings of existing technologies, this invention provides an anesthesia depth estimation method based on information interdependence using the Laplace kernel function. By employing the Laplace kernel function for probability estimation instead of the probability density in information interdependence, the calculated information interdependence is used to assess anesthesia depth, greatly reducing the problem of traditional anesthesia depth monitoring not being able to accurately track and monitor different age groups.
[0006] To achieve the above objectives, the present invention discloses the following technical solution:
[0007] A method for estimating anesthesia depth based on information interdependence using the Laplace kernel function, comprising:
[0008] S1: Obtain dual-channel EEG data under anesthesia from the database and perform EEG data preprocessing;
[0009] S11: Dual-channel EEG data is collected by an anesthesia monitor and stored in a database. 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 parameter.
[0010] S12: Mark the anesthesia state and segment the EEG data to complete the preprocessing operation;
[0011] S13: Construct the first time-series EEG data X = (x1, ... x1) based on 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 ); Obtain time-series EEG data points (x i y j );
[0012] S2: Using the 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 second i. i )for:
[0015]
[0016] Where K(x) i x represents the first time-series EEG data at second i. i The Laplace kernel function; x i denoted as , where is the first time-series EEG data at second i; 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; and e is the natural constant.
[0017] Calculate the Laplace kernel function K(y) of the second time series EEG data Y at second j. j )for:
[0018]
[0019] Wherein K(y) j y represents the second time-series EEG data at second j. j The Laplace kernel function; y j This represents the second time-series EEG data at the j-th second.
[0020] S23: The bandwidth h of the Laplace kernel function is calculated using the formula:
[0021]
[0022] Where σ is the sample error of the time-series EEG data; IQR is the interquartile range of the time-series EEG data; min is the minimum value function;
[0023] S3: Calculate the dual-channel Laplace kernel function based on the time-series EEG data points obtained in step S13;
[0024] For the time-series EEG data points (x) in step S13 i y j The first Manhattan distance d1 and the second Manhattan distance d2 in step S21 are exponentialized and added together. The cumulative calculation yields the dual-channel Laplace kernel function for the time-series EEG data points as follows:
[0025]
[0026] Where K(x) i y i ) represents the two-channel Laplace kernel function for time-series EEG data points;
[0027] S4: Calculate the information interdependence probability density of the two-channel Laplace kernel function of the time series EEG data points in step S3;
[0028] S41: The dual-channel Laplace kernel function K(x) from step S3... i y i The probability density f(x, y) of the first time series EEG data and the second time series EEG data is estimated; the Laplace kernel function K(x) in step S22 is then used to estimate the probability density f(x, y). i The marginal probability density f(x) of the first time series EEG data is estimated; the Laplace kernel function K(y) in step S22 is then used. i The marginal probability density f(y) of the second time series EEG data is estimated.
[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] Where I(X;Y) represents the information interdependence between the first time-series EEG data X and the second time-series EEG data Y; f(x,y) represents the joint probability density of the first time-series EEG data and the second time-series EEG data; f(x) represents the marginal probability density of the first time-series EEG data; f(y) represents the marginal probability density of the second time-series EEG data; dx represents the differential of the first time-series EEG data; dy represents the differential of the second time-series EEG data; and log represents the logarithmic function.
[0032] S43: The information interdependence of the Laplace kernel function of the accumulated EEG data points is:
[0033]
[0034] Among them, I L (X; Y) represents the informational interdependence of the Laplace kernel function;
[0035] S5: Assessing depth of anesthesia using the 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) are set as the parameters for estimating the depth of anesthesia. The curves of the change of the parameters for estimating the depth of anesthesia are plotted in the pre-anesthesia, mid-anesthesia, and anesthesia recovery stages. The EEG data and the change trend of the parameters for estimating the depth of anesthesia caused by the actual anesthesia process are identified and compared to complete the estimation of the depth of anesthesia.
[0037] Preferably, in step S11, the EEG data is divided into three levels according to the age parameter: Level 1 age parameter EEG data, Level 2 age parameter EEG data, and Level 3 age parameter EEG data.
[0038] Preferably, in step S12, the EEG data is marked with anesthesia status and segmented to complete the preprocessing operation. Specifically, the EEG data is marked and segmented with anesthesia status according to the records in the database. 180 seconds of EEG data are taken for each of the pre-anesthesia, mid-anesthesia, and anesthesia recovery stages. EEG preprocessing is performed on all segmented EEG data. The window overlap rate is calculated based on a 10-second window length, and the sampling frequency of the EEG data is 128 Hz.
[0039] Preferably, in step S21, the first Manhattan distance d1 is determined by the first time-series EEG data x. i Manhattan is far from D1 i Composition is:
[0040] d1 i =|x i -x j |;
[0041] Among them, d1 i x is the Manhattan distance of the EEG data at the i-th second; i x represents the first time-series EEG data at second i; j This represents the first time-series EEG data at second j.
[0042] Preferably, in step S22, the second Manhattan distance d2 is determined by the second time-series EEG data y. j Manhattan is far from d2 j Composition is:
[0043] d2 j =|y i -y j |;
[0044] Among them, d2 j For the EEG data at second j, y j Manhattan distance; y i y represents the second time-series EEG data at second i; j This represents the second time-series EEG data at second j.
[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] Where 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 marginal probability density f(x) of the first time-series EEG data in step S4 is:
[0049]
[0050] Where f(x) is the marginal probability density of the first time series EEG data.
[0051] Preferably, the marginal probability density f(y) of the second time-series EEG data in step S4 is:
[0052]
[0053] Where f(y) is the marginal probability density of the second time series EEG data.
[0054] Preferably, in step S5, the information interdependence I of the Laplace kernel function is... LThe changes in (X; Y) are set as parameters for the depth of anesthesia; the information interdependence I of estimating the Laplace kernel function during EEG tracking is plotted. L Changes in (X; Y) were observed to assess the information interdependence between the electroencephalographic activity induced during anesthesia and the proposed Laplace kernel function. L The changing trends of (X; Y) are then used to estimate the depth of anesthesia.
[0055] Compared with the prior art, the present invention has the following beneficial effects:
[0056] (1) This invention employs a physiologically inspired EEG data analysis method, using the Laplace kernel function for probability estimation to replace the probability density in information interdependence, and calculates the information interdependence to assess the depth of anesthesia, thus reducing the problem that traditional anesthesia depth monitoring cannot effectively track and monitor different age groups.
[0057] (2) This invention provides more options for parameter settings in different bandwidths for the assessment of anesthesia depth. It can be adjusted appropriately according to needs and is applicable to various age groups. It is especially important in distinguishing different states of anesthesia consciousness in adults and the elderly.
[0058] (3) This invention uses kernel function to estimate probability instead of directly calculating probability using traditional information theory. Theoretically, it can map data to an infinite multidimensional space, thus having good robustness and adaptability in complex situations with large amounts of data. Attached Figure Description
[0059] Figure 1 This is a control block diagram of the information interdependence-based anesthesia depth estimation method based on the Laplace kernel function of the present invention;
[0060] Figure 2 This is a time-frequency diagram of the electroencephalogram (EEG) data from the youth group cases of this invention;
[0061] Figure 3 This is a bispectral index curve of EEG data from the youth group cases of this invention;
[0062] Figure 4 This is an information interdependence curve of the EEG data from the youth group case of the present invention.
[0063] Figure 5 This is a time-frequency diagram of the electroencephalogram (EEG) data from the middle-aged group in this invention;
[0064] Figure 6 This is a bispectral index curve of EEG data from the middle-aged group in this invention.
[0065] Figure 7 This is an information interdependence curve of the EEG data of the middle-aged group in this invention;
[0066] Figure 8 This is a time-frequency diagram of the electroencephalogram (EEG) data from the elderly group in this invention;
[0067] Figure 9 This is a bispectral index curve of EEG data from the elderly group in this invention.
[0068] Figure 10 This is an information interdependence curve of EEG data from the elderly group in this invention;
[0069] Figure 11 This is a violin diagram illustrating the information interdependence parameters for three age groups under different states of consciousness, as presented in this invention. Detailed Implementation
[0070] Exemplary embodiments, features, and aspects of the present invention will now be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements that have the same or similar functions. Although various aspects of the embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.
[0071] This invention provides a method for estimating anesthesia depth based on information interdependence using the Laplace kernel function, such as... Figure 1 As shown, dual-channel EEG data under anesthesia are acquired from the database and 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 based on the time-series EEG data points; the information interdependence probability density of the dual-channel Laplace kernel function for the time-series EEG data points is calculated; and the depth of anesthesia is assessed using the information interdependence of the Laplace kernel function for the EEG data points. This includes:
[0072] Step S1: Obtain dual-channel EEG data under anesthesia from the database and perform preprocessing of the EEG data.
[0073] Step S11: The anesthesia monitor collects dual-channel EEG data and stores it in the database. 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: Level 1 age parameter EEG data, age parameter is 18-40; Level 2 age parameter EEG data, age parameter is 40-65; Level 3 age parameter EEG data, age parameter is greater than 65.
[0074] Step S12: Mark the anesthesia state and segment the EEG data to complete the preprocessing operation. Specifically, mark and segment the EEG data according to the records in the database, and take 180 seconds of EEG data for each of the pre-anesthesia, mid-anesthesia and anesthesia recovery stages; perform EEG preprocessing on all segmented EEG data, calculate the window overlap rate based on a 10-second window length, and the sampling frequency of the EEG data is 128 Hz.
[0075] Step S13: Construct the first time-series EEG data X = (x1,...x1) based on the dual-channel EEG data acquired by the anesthesia monitor in step S11. i ...,x n ) and the second time series EEG data Y=(y1,...y j ...,y n The length of the EEG data is n = 1280; the time-series EEG data points (x) are obtained. i y j ).
[0076] Step S2: Use the Laplace kernel function to calculate the information interdependence of the two-channel EEG data.
[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 from step S13. This embodiment of the invention does not use the Gaussian kernel function based on a normal distribution, which is most commonly used in kernel probability estimation, but instead selects the Laplace kernel function, which reduces the influence of parameters. Because calculating the Manhattan distance eliminates some of the interference from noise, the Laplace kernel function has good robustness to noise.
[0078] The first Manhattan distance d1 is derived from the first time-series EEG data x. i Manhattan distance and d1 i Composition is:
[0079]
[0080] Among them, d1 i x is the Manhattan distance of the EEG data at the i-th second; i x represents the first time-series EEG data at second i; j is the first time series EEG data at second j; 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 derived from the second time-series EEG data y. j Manhattan distance and d2 j Composition is:
[0082]
[0083] Among them, d2 j For the EEG data at second j, y j Manhattan distance; y i y represents the second time-series EEG data at second i; j This represents the second time-series EEG data at second j.
[0084] Step S22: Calculate the Laplace kernel function K(x) of the first time-series EEG data X at second i. i )for:
[0085]
[0086] Where K(x) i x represents the first time-series EEG data at second i. i The Laplace kernel function; x i denoted as , where is the first time-series EEG data at second i; h is the bandwidth of the Laplace kernel function; and e is the natural constant.
[0087] Calculate the Laplace kernel function K(y) of the second time series EEG data Y at second j. j )for:
[0088]
[0089] Wherein K(y) j y represents the second time-series EEG data at second j. j The Laplace kernel function; y j This represents the second time-series EEG data at second j.
[0090] Step S23: Calculate the bandwidth h of the Laplace kernel function using the formula:
[0091]
[0092] Where σ is the sample error of the time-series EEG data; IQR is the interquartile range of the time-series EEG data; and min is the minimum function.
[0093] In this embodiment of the invention, cross-validation yielded an optimal Laplace kernel function bandwidth h = 0.4. Due to the characteristics of its exponential term, the Laplace kernel function is highly sensitive to nearest neighbor points and its influence on distant points is rapidly reduced.
[0094] Step S3: Calculate the dual-channel Laplace kernel function based on the time-series EEG data points obtained in step S13.
[0095] For the time-series EEG data points (x) in step S13 i y j The first Manhattan distance d1 and the second Manhattan distance d2 in step S21 are exponentialized and added together. The cumulative calculation yields the dual-channel Laplace kernel function for the time-series EEG data points as follows:
[0096]
[0097] Where K(x) i y i ) represents the two-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 two-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) from step S3... i y i The probability density f(x, y) of the first time series EEG data and the second time series EEG data is estimated; the Laplace kernel function K(x) in step S22 is then used to estimate the probability density f(x, y). i The marginal probability density f(x) of the first time series EEG data is estimated; the Laplace kernel function K(y) in step S22 is then used. i The marginal probability density f(y) of the second time series EEG data is estimated.
[0100] Step S42: Obtain the information interdependence between the first time-series EEG data X and the second time-series EEG data Y as follows:
[0101]
[0102] Where I(X;Y) represents the information interdependence between the first time-series EEG data X and the second time-series EEG data Y; f(x,y) represents the joint probability density of the first time-series EEG data and the second time-series EEG data; f(x) represents the marginal probability density of the first time-series EEG data; f(y) represents the marginal probability density of the second time-series EEG data; dx represents the differential of the first time-series EEG data; dy represents the differential of the second time-series EEG data; and log represents 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] Where 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] Where 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] Where 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 accumulated EEG data points is calculated as follows:
[0113]
[0114] Among them, I L (X; Y) represents the information interdependence of the Laplace kernel function.
[0115] Step S5: Assess the depth of anesthesia using the information interdependence of the Laplace kernel function of the EEG data points; obtain the information interdependence I of the Laplace kernel function obtained in step S4. L (X; Y) are set as parameters for estimating the depth of anesthesia; the information interdependence I of estimating the Laplace kernel function during EEG tracking is plotted. L Changes in (X; Y) were observed to assess the information interdependence between the electroencephalographic activity induced during anesthesia and the proposed Laplace kernel function. L (X; Y) Change trends: Plot the change curves of the anesthesia depth estimation parameters in the pre-anesthesia, mid-anesthesia, and anesthesia recovery stages, respectively. Identify and compare the change trends of EEG data and anesthesia depth estimation parameters caused by actual anesthesia process, and complete the estimation of anesthesia depth.
[0116] The analysis results of the EEG data of the youth group in this embodiment of the invention are as follows: Figure 2 The image shown is a time-frequency plot of EEG data from the youth group of this invention. It can be seen that a significant increase in energy occurs at 10 Hz after anesthesia, representing a decrease in consciousness level; as shown... Figure 3 The bispectral index (BPI) curve of the EEG data from the youth group cases of this invention shows a consistent change with the time-frequency plot: the BPI decreases after anesthesia and remains at a low level during unconsciousness, gradually increasing during the recovery period; as shown in the figure. Figure 4 The information interdependence curve of the EEG data of the youth group in this invention shows that the information interdependence decreases after anesthesia and remains at a low level during unconsciousness, gradually increasing during the recovery period, which can effectively detect the consciousness state of the youth group.
[0117] The analysis results of the EEG data of the annual group cases in the embodiments of the present invention are as follows: Figure 5 The image shown is a time-frequency plot of EEG data from the youth group of this invention. It can be seen that a significant increase in energy occurs at 10 Hz after anesthesia, representing a decrease in consciousness level; as shown... Figure 6This is a bispectral index curve of EEG data from the youth group cases of this invention. The bispectral index decreases after anesthesia and remains at a low level during unconsciousness, gradually increasing during the recovery period; as shown... Figure 7 The information interdependence curve of the EEG data of the youth group in this invention shows that the information interdependence decreases after anesthesia and remains at a low level during unconsciousness, gradually increasing during the recovery period, which can effectively detect the state of consciousness of the middle-aged group.
[0118] The analysis results of EEG data from elderly cases in this embodiment of the invention are as follows: Figure 8 The image shown is a time-frequency diagram of EEG data from the youth group of this invention. Compared with the unconscious state and the state of consciousness and recovery, the alpha band around 10 Hz increases, indicating a decrease in the level of consciousness; as shown... Figure 9 This is a bispectral index curve of EEG data from the youth group cases of this invention. The bispectral index decreases after anesthesia and remains at a low level during unconsciousness, gradually increasing during the recovery period; as shown... Figure 10 The information interdependence curve of the EEG data of the youth group in this invention shows that the information interdependence decreases after anesthesia and remains at a low level during unconsciousness, gradually increasing during the recovery period, which can effectively detect the state of consciousness of the elderly group.
[0119] like Figure 11 The violin diagram shows the changes in information interdependence parameters of the present invention under different states of consciousness in three age groups. It shows that information interdependence decreases and then increases significantly from wakefulness to anesthesia and then to recovery in the youth and middle-aged groups. A similar trend is also observed in the elderly group.
[0120] The embodiments of this invention have the following beneficial effects: These embodiments employ a physiologically inspired EEG data analysis method, using the Laplace kernel function for probability estimation instead of probability density in information interdependence. The calculated information interdependence is used to assess anesthesia depth, greatly reducing the problem of traditional anesthesia depth monitoring failing to effectively track different age groups. For anesthesia depth assessment, more options are provided for parameter settings at different bandwidths, which can be adjusted appropriately according to needs. This method is applicable to various age groups, and is particularly significant in distinguishing different states of consciousness under anesthesia between adults and the elderly. Analysis of anesthesia EEG data from young, middle-aged, and elderly individuals demonstrates that this method can better meet practical needs and obtain accurate analytical results. Using kernel function probability estimation instead of directly calculating probabilities using traditional information theory theoretically maps data to an infinite multidimensional space, thus exhibiting good robustness and adaptability in complex situations with large datasets.
[0121] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.
Claims
1. A method for estimating anesthesia depth based on information interdependence using the Laplace kernel function, characterized in that, It includes: S1: Obtain dual-channel EEG data under anesthesia from the database and perform EEG data preprocessing; S11: The anesthesia monitor collects dual-channel EEG data, stores it in the database, and extracts EEG data and characteristic parameter information under anesthesia during clinical surgery, including weight parameters, gender parameters and age parameters; EEG data are divided into three levels based on age parameters; Level 1 age parameter EEG data, with age parameters ranging from 18 to 40; The secondary age parameter EEG data indicates an age range of 40-65. Level 3 age-parameter EEG data, age parameter greater than 65; S12: Mark the anesthesia state and segment the EEG data to complete the preprocessing operation; S13: Construct the first time-series EEG data based on the dual-channel EEG data collected by the anesthesia monitor in step S11. Second time series EEG data ; Obtain time-series EEG data points ; S2: Using the Laplace kernel function to calculate the information interdependence of dual-channel EEG data; S21: Calculate the first time-series EEG data in step S13 First Manhattan distance Second time series EEG data Second Manhattan distance ; S22: Calculate the first time-series EEG data In the Laplace kernel function for seconds for: ； in, For the first First time-series EEG data (seconds) The Laplace kernel function; For the first First-time-series EEG data in seconds; The bandwidth of the Laplace kernel function; The first time series number; Number the second time series; The length of the EEG data; It is a natural constant; Calculate the second time series EEG data In the Laplace kernel function of second-time series EEG data for: ； in, For the first Second time-series EEG data The Laplace kernel function; For the first Second-time series EEG data in seconds; S23: Calculate the bandwidth of the Laplace kernel function using the formula. for: ； in, This refers to the sample error in time-series EEG data; Interquartile range of time-series EEG data; To find the minimum value of the function; The length of the EEG data; S3: Calculate the dual-channel Laplace kernel function based on the time-series EEG data points obtained in step S13; For the time-series EEG data points in step S13 The first Manhattan distance in step S21 Second Manhattan distance The two-channel Laplace kernel function of the time-series EEG data points is obtained by exponentializing, summing, and accumulating the results: ； in, For time-series EEG data points, use the dual-channel Laplace kernel function; S4: Calculate the information interdependence probability density of the two-channel Laplace kernel function of the time series EEG data points in step S3; S41: The dual-channel Laplace kernel function from step S3... The estimated probability density is the joint probability density of the first time series EEG data and the second time series EEG data. ; the Laplace kernel function in step S22 Estimate the marginal probability density of the first time series EEG data ; the Laplace kernel function in step S22 Estimate the marginal probability density of the second time series EEG data ; S42: Obtain the first time-series EEG data Second time series EEG data The information interdependence between them is: ； in, First-time series EEG data Second time series EEG data Information interdependence between them; The joint probability density of the first time series EEG data and the second time series EEG data; This represents the marginal probability density of the first time-series EEG data; The marginal probability density of the second time series EEG data; Differentiate the first time-series EEG data; Differentiate the second time-series EEG data; It is a logarithmic function; S43: The information interdependence of the Laplace kernel function of the accumulated EEG data points is: ； in, Information interdependence of the Laplace kernel function; S5: Assessing depth of anesthesia using the information interdependence of the Laplace kernel function of EEG data points; Obtain the information interdependency of the Laplace kernel function obtained in step S4 The parameters are set as the anesthesia depth estimation parameters; the change curves of the anesthesia depth estimation parameters are plotted in the pre-anesthesia, mid-anesthesia, and anesthesia recovery stages, respectively; the change trends of EEG data and anesthesia depth estimation parameters caused by actual anesthesia are identified and compared, and the anesthesia depth is estimated. The information interdependence of the Laplace kernel function in step S5 The change is set as a parameter for the depth of anesthesia; the information interdependence of estimating the Laplace kernel function during EEG tracking is plotted. The changes were observed, and the information interdependence between the electroencephalographic activity induced during anesthesia and the proposed Laplace kernel function was investigated. The changing trend is then used to estimate the depth of anesthesia.
2. The method for estimating anesthesia depth based on information interdependence using the Laplace kernel function according to claim 1, characterized in that: In step S12, the EEG data is marked with anesthesia status and segmented to complete the preprocessing operation. Specifically, the EEG data is marked and segmented with anesthesia status according to the records in the database. 180 seconds of EEG data are taken for each of the pre-anesthesia, mid-anesthesia, and anesthesia recovery stages. EEG preprocessing is performed on all segmented EEG data. The window overlap rate is calculated based on a 10-second window length, and the sampling frequency of the EEG data is 128 Hz.
3. The anesthesia depth estimation method based on information interdependence using the Laplace kernel function according to claim 1, characterized in that: The first Manhattan distance in step S21 From first-time series EEG data Manhattan distance Composition is: ； in, For the first Manhattan distance of second-level EEG data; For the first First-time-series EEG data in seconds; For the first First-time-series EEG data in seconds.
4. The method for estimating anesthesia depth based on information interdependence using the Laplace kernel function according to claim 1, characterized in that: The second Manhattan distance in step S22 From the second time-series EEG data Manhattan distance Composition is: ； in, For the first Seconds of EEG data Manhattan distance; For the first Second-time series EEG data in seconds; For the first Second-time series EEG data.
5. The method for estimating anesthesia depth based on information interdependence using the Laplace kernel function according to claim 1, characterized in that: The joint probability density of the first time-series EEG data and the second time-series EEG data in step S4 for: ； in, The probability density is the joint probability density of the first time series EEG data and the second time series EEG data.
6. The method for estimating anesthesia depth based on information interdependence using the Laplace kernel function according to claim 1, characterized in that: Marginal probability density of the first time-series EEG data in step S4 for: ； in, This represents the marginal probability density of the first time-series EEG data.
7. The method for estimating anesthesia depth based on information interdependence using the Laplace kernel function according to claim 1, characterized in that: Marginal probability density of the second time-series EEG data in step S4 for: ； in, This represents the marginal probability density of the second time-series EEG data.