A method and system for analyzing depth of anesthesia

Abstract

The application discloses a method and system for analyzing anesthetic depth, which first sets a standard anesthetic depth analysis model, then acquires original physiological parameters, carries out abnormal value screening pretreatment, acquires an actual physiological parameter set, carries out correction calculation on the actual physiological parameter set, acquires a basic parameter set, finally outputs a current anesthetic state parameter according to the basic parameter set and the standard anesthetic depth analysis model, and simultaneously outputs a predicted anesthetic state parameter according to a plurality of basic parameter sets and the standard anesthetic depth analysis model. The application realizes automatic data acquisition and calculation, effectively reduces the workload of an anesthetist, and is favorable for improving anesthetic effect. Meanwhile, the application effectively reduces the dependence on personal experience through data quantitative analysis, improves the objectivity and accuracy of analysis results, and can also predict the change trend of anesthetic depth according to existing data, so that early intervention is facilitated, and the comprehensiveness of anesthetic depth monitoring is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of biological medicine, in particular to a depth of anesthesia analysis method and analysis system. BACKGROUND

[0002] Anesthesia is a necessary procedure for surgery. During the surgery, the anesthesiologist needs to observe the clinical manifestations of the patient regularly or irregularly, and then determines whether the current anesthesia state meets the requirements of the surgery in combination with his own experience. The above determination and its dependence on the working experience of the anesthesiologist are restricted by factors such as observation detail, and the determination of the anesthesia state of the patient is not objective enough. At the same time, it can only determine the anesthesia state at the current time, and cannot predict the changes in the anesthesia state of the patient, lacking comprehensive analysis and monitoring of the depth of anesthesia of the patient. SUMMARY

[0003] The main purpose of the present application is to provide a depth of anesthesia analysis method and analysis system, which aims to solve the defect that the depth of anesthesia cannot be comprehensively monitored in the prior art.

[0004] The above-mentioned purpose is achieved by the following technical solutions:

[0005] A depth of anesthesia analysis method, comprising the following steps:

[0006] Setting a standard anesthesia depth analysis model;

[0007] Obtaining original physiological parameters, pre-processing the original physiological parameters according to an abnormal value screening formula to obtain an actual physiological parameter set;

[0008] Correcting and calculating the actual physiological parameter set to obtain a basic parameter set;

[0009] Outputting a current anesthesia state parameter according to the basic parameter set and the standard anesthesia depth analysis model;

[0010] Outputting a predicted anesthesia state parameter according to a plurality of basic parameter sets and the standard anesthesia depth analysis model.

[0011] Optionally, the expression of the standard anesthesia depth analysis model is: wherein R represents a total anesthesia score, and k1-k5 are all constants.

[0012] Optionally, obtaining original physiological parameters, pre-processing the original physiological parameters according to an abnormal value screening formula to obtain an actual physiological parameter set, comprises the following steps:

[0013] Dividing each original physiological parameter into a plurality of original parameter sets I1,..., I i ,..., I aWhere 'a' represents the total number of physiological indicators;

[0014] Calculate the mean of each original parameter set; the expression for calculating the mean of the i-th type of physiological index is as follows: ,in Let j represent the actual physiological parameter numbered j in the i-th type of physiological index, and n represent the total number of samples of the i-th type of physiological index.

[0015] Calculate the standard deviation of each original parameter set; the expression for calculating the standard deviation of the i-th physiological index is as follows: ,in Let j represent the actual physiological parameter numbered j in the i-th type of physiological index, and n represent the total number of samples of the i-th type of physiological index.

[0016] Outlier screening was performed on each original parameter set using the mean, standard deviation, and outlier screening formulas to obtain the actual physiological parameter sets I'1, ..., I'. i ... I' a The expression for the outlier screening formula is as follows: , This represents the actual physiological parameter numbered t among the i-th physiological indicators after screening.

[0017] Optionally, the actual physiological parameter set is corrected and calculated to obtain a basic parameter set, including the following steps:

[0018] Calculate the arithmetic mean of each physiological indicator based on the actual set of physiological parameters;

[0019] Each physiological indicator was assigned a correction model;

[0020] The basic parameter set {V} is obtained by performing correction calculations based on the arithmetic mean and each correction model. ** 1, V ** 2, ..., V ** i , ..., V ** a}

[0021] Optionally, the expression for the arithmetic mean is: ,in T represents the actual physiological parameter t in the i-th physiological indicator, m represents the number of parameters in the i-th physiological indicator after outlier screening, and T represents the number of parameters in the i-th physiological indicator. i V represents the set of the i-th physiological index in the actual physiological parameter set, and its expression is {V}. i、1 V i、2 , ..., V i、t , ..., V i、m The corrected calculation expression for the model is as follows: wherein Xi represents a base value of the i th physiological index of the patient before surgery, Xi represents a reference value of the i th physiological index of the healthy population, Xi represents a correction coefficient of the i th physiological index of the patient.

[0022] Optionally, output a current anesthesia state parameter according to the base parameter set and the standard anesthesia depth analysis model;

[0023] Set a normalization model and an anesthesia total score calculation formula for each physiological index respectively;

[0024] Normalize the base parameter set according to the normalization model to obtain a normalized parameter value of each physiological index;

[0025] Calculate an anesthesia total score according to each normalized parameter value;

[0026] Output a current anesthesia state parameter according to the anesthesia total score and the standard anesthesia depth analysis model.

[0027] Optionally, the calculation expression of the normalization model is wherein L i and U i represent a lower limit value and an upper limit value of the i th physiological index in the target anesthesia state respectively, and the calculation expression of the anesthesia total score is wherein a represents a total number of physiological indexes, Xi represents a weight value of the i th physiological index, and satisfies .

[0028] Optionally, output a predicted anesthesia state parameter according to a plurality of base parameter sets and the standard anesthesia depth analysis model, including the following steps:

[0029] Obtain a plurality of base parameter sets, and generate a historical base parameter set according to a collection time of each base parameter set, wherein the expression of the historical base parameter set is { (V ** 1, V ** 2,..., V ** i ,..., V ** a ) -1 , (V ** 1, V ** 2,..., V ** i ,..., V ** a ) 0}, wherein 0 represents a current state, and -1 represents historical data of a sampling period with the shortest time interval from the current state;

[0030] Interpolation calculations are performed based on the historical baseline parameter set to generate a fitted prediction model;

[0031] Generate a set of basic prediction parameters {V} based on the fitted prediction model. 1，f V 2，f , ..., V i，f , ..., V a，f};

[0032] The prediction base parameter set is normalized according to the normalization model to obtain the prediction normalization parameters;

[0033] The total predicted anesthesia score is calculated based on the predicted normalization parameter values ​​described above.

[0034] Based on the predicted total anesthesia score and the standard anesthesia depth analysis model, the predicted anesthesia status parameters are output.

[0035] Optionally, an interpolation calculation is performed based on the historical baseline parameter set to generate a fitted prediction model, including the following steps:

[0036] The historical baseline parameter set is converted into several time-parameter sequence pairs {(V)} ** 1，-1 , t 1，-1 ), (V ** 1，0 , t 1，0 )},...,{(V ** i，-1 , t i，-1 ), (V ** i，0 , t i，0 )},...,{(V ** a，-1 , t a，-1 ), (V ** a，0 , t a，0 )};

[0037] Linear interpolation is performed on each of the aforementioned time-parameter sequences to obtain fitting curves for each physiological indicator; wherein the expression for the fitting curve is: ,in This represents the parameter value of the i-th physiological indicator at the predicted time point. Indicates the predicted time point;

[0038] The fitted curves are then integrated into a fitted prediction model.

[0039] Accordingly, this application also discloses an analysis system based on the above-mentioned anesthesia depth analysis method, including:

[0040] a data setting module configured to set a standard anesthetic depth analysis model;

[0041] a data preprocessing module configured to acquire actual physiological parameters, and preprocess the actual physiological parameters to obtain an actual physiological parameter set according to an outlier screening formula;

[0042] a data correction module configured to correct and calculate the actual physiological parameter set to obtain a basic parameter set;

[0043] a first analysis module configured to output a current anesthetic state parameter according to the basic parameter set and the standard anesthetic depth analysis model;

[0044] a second analysis module configured to output a predicted anesthetic state parameter according to a plurality of basic parameter sets and the standard anesthetic depth analysis model.

[0045] Compared with the prior art, the present application has the following beneficial effects:

[0046] The present application first sets a standard anesthetic depth analysis model, then acquires original physiological parameters and performs outlier screening preprocessing on the original physiological parameters to obtain an actual physiological parameter set, and then corrects and calculates the actual physiological parameter set to obtain a basic parameter set, and finally outputs a current anesthetic state parameter according to the basic parameter set and the standard anesthetic depth analysis model, and simultaneously outputs a predicted anesthetic state parameter according to a plurality of basic parameter sets and the standard anesthetic depth analysis model;

[0047] The present application quantitatively quantifies the target anesthetic state through a standard anesthetic depth analysis model, then acquires physiological parameters such as heart rate and blood pressure, first performs outlier screening on the physiological parameters, then combines data correction of the patient's own physical state to obtain a basic parameter set, and finally quantitatively calculates the deviation of the basic parameter set from the appropriate anesthetic state to output the corresponding anesthetic state;

[0048] Compared with the prior art, the present application realizes full-automatic data acquisition and calculation, which can effectively reduce the workload of anesthetists and improve the anesthetic effect of patients;

[0049] Secondly, the present application can effectively reduce the dependence on the personal experience of anesthetists in the analysis process through real-time data analysis, which is beneficial to improving the objectivity and accuracy of the analysis results; and can also reduce the threshold of anesthetic analysis;

[0050] Meanwhile, the present application can not only analyze the current anesthetic depth, but also predict future data according to the change trend of the existing data, and finally output a predicted anesthetic state parameter as a reference for the change trend of the anesthetic state of the patient, so as to reserve a certain advance for the advance intervention of anesthetists;

[0051] That is, the present application can quantitatively analyze and predict the depth of anesthesia at the current time and in the future for a certain time, thereby realizing full-time monitoring and analysis of the depth of anesthesia, and effectively improving the comprehensiveness of the monitoring of the depth of anesthesia.

[0052] Finally, the present application uses the measured parameters of a plurality of different physiological indicators as original parameters, removes part of the sampling abnormal values through screening, and simultaneously corrects the data in combination with the physiological conditions of the patient's underlying diseases to obtain a basic parameter set for actual depth analysis and prediction, thereby highly correlating the anesthesia analysis with the actual physical condition, excluding the influence of various interferences, and effectively improving the accuracy of the prediction. BRIEF DESCRIPTION OF DRAWINGS

[0053] Figure 1 A flowchart of an anesthesia depth analysis method provided for the embodiment 1 of the present application;

[0054] Figure 2 A structure diagram of an anesthesia depth analysis system provided for the embodiment 2 of the present application;

[0055] The object implementation, functional features and advantages of the present application will be further described with reference to the embodiments in combination with the accompanying drawings. DETAILED DESCRIPTION

[0056] The technical solutions in the embodiments of the present application will be clearly and completely described below in combination with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all the other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0057] It should be noted that all the directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present application are only used to explain the relative position relationship, movement condition, etc. between the components in a certain specific posture (as shown in the drawings), and if the specific posture changes, the directional indications also change accordingly.

[0058] In the present application, unless otherwise explicitly specified and limited, the terms "connection", "fixation", etc. should be understood in a broad sense, for example, "fixation" can be fixed connection, or detachable connection, or integral; can be mechanical connection, or electrical connection; can be direct connection, or indirect connection through an intermediate medium; can be the internal connection of two elements or the interaction relationship between two elements, unless otherwise explicitly limited. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0059] In addition, if the description of "first", "second" and the like is involved in the embodiments of the present application, the description of "first", "second" and the like is only for the purpose of description, and cannot be understood as indicating or implying the relative importance of the indicated technical features or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first", "second" can be explicitly or implicitly included at least one of the features. In addition, the meaning of "and / or" appearing throughout the text includes three parallel schemes, for example, "A and / or B" includes A scheme or B scheme or A and B scheme. In addition, the technical solutions of each embodiment can be combined with each other, but it must be based on the realization of the ordinary skilled in the art, when the combination of technical solutions appears contradictory or unachievable, it should be considered that the combination of technical solutions does not exist, nor within the scope of protection required by the present application.

[0060] Embodiment 1:

[0061] Referring to Figure 1 , the present embodiment is an optional embodiment of the present application, which discloses an anesthesia depth analysis method, comprising the following steps:

[0062] S1, setting a standard anesthesia depth analysis model;

[0063] The medical staff sets the standard anesthesia depth classification model according to the actual situation of the patient, or directly uses the pre-stored standard anesthesia depth analysis model. The expression of the standard anesthesia depth analysis model is: , wherein R represents the total anesthesia score, k1-k5 are all constants, and the above parameters need to be set or call the pre-set parameters;

[0064] It should be noted that the appropriate anesthesia state is the best anesthesia state expected to be reached, and the light anesthesia state and the slightly light anesthesia state are both the anesthesia state of the patient which is shallower than the best anesthesia state, close to the state of wakefulness, only the degree of wakefulness is different, and the patient is in the state of insufficient anesthesia; the slightly deep anesthesia state and the deep anesthesia state indicate that the anesthesia state is deeper than the best anesthesia state, and the patient is in the state of excessive anesthesia;

[0065] S2, obtaining the original physiological parameters, and pre-processing the original physiological parameters to obtain the actual physiological parameter set according to the abnormal value screening formula;

[0066] S21, dividing each original physiological parameter into a plurality of original parameter sets I1,..., I i ,..., I a ; wherein a represents the total number of physiological indexes;

[0067] All the original physiological parameters collected by the device are retrieved, classified according to the types of physiological indicators, and a plurality of original parameter sets I1,..., Iaare generated i ,..., Ia a ; wherein a represents the total number of physiological indicators;

[0068] For example, all the heart rate parameters are grouped into the same set to generate the original parameter set of heart rate; similarly, the parameters of respiratory rate are grouped into the same set;

[0069] That is, the general expression of the original parameter set is I i = {V i,1 , V i,2 , V i,3 ,..., V i,n}; The sampling period of different physiological indicators may be different due to different devices, thereby resulting in different number of elements in the original parameter set;

[0070] It should be noted that the specific types of physiological indicators can be specified by medical personnel, such as setting the physiological indicators as heart rate, respiratory rate, cerebral electrical double frequency index, and mean arterial pressure. If 4 physiological indicators are set as above, a = 4;

[0071] S22, the mean value of each original parameter set is calculated; wherein the calculation expression of the mean value of the i-th physiological indicator is , wherein represents the actual physiological parameter numbered j in the i-th physiological indicator, and n represents the total sampling number of the i-th physiological indicator;

[0072] The parameters of each original parameter set are retrieved and the mean value is calculated, wherein the calculation expression of the mean value of the i-th physiological indicator is , wherein represents the actual physiological parameter numbered j in the i-th physiological indicator, and n represents the total sampling number of the i-th physiological indicator;

[0073] Through the above calculation, the mean value of each physiological indicator can be obtained respectively;

[0074] S23, the standard deviation of each original parameter set is calculated; wherein the calculation expression of the standard deviation of the i-th physiological indicator is , wherein represents the actual physiological parameter numbered j in the i-th physiological indicator, and n represents the total sampling number of the i-th physiological indicator;

[0075] According to the calculation formula of the standard deviation, the standard deviation of each physiological indicator is calculated respectively;

[0076] S24, screening outliers for each original parameter set according to the mean, standard deviation and outlier screening formula, and obtaining actual physiological parameter sets I'1,..., I' i ,..., I' a ; wherein the expression of the outlier screening formula is: , denotes the actual physiological parameter numbered t in the i th physiological indicator after screening.

[0077] The mean and standard deviation of the same physiological indicator are called, and the screening parameter corresponding to the physiological indicator is calculated by combining the outlier screening formula, wherein the calculation expression of the outlier screening formula is: , denotes the actual physiological parameter numbered t in the i th physiological indicator after screening.

[0078] Then, all original physiological parameters corresponding to the physiological indicator are compared with the calculation results of the above-mentioned outlier screening formula respectively, and the parameters meeting the standard are reserved as actual physiological parameters, otherwise they are deleted as abnormal parameters.

[0079] Through the above-mentioned outlier screening, some parameters with large errors caused by measurement or instrument equipment measurement can be deleted, so as to avoid the influence on the analysis results and improve the reliability and accuracy of the final analysis results.

[0080] Repeat the above steps to complete the screening of all physiological indicators, and separate all the retained parameters according to the physiological indicator types to generate the actual physiological parameter set.

[0081] S3, correcting the actual physiological parameter set to obtain a basic parameter set;

[0082] S31, calculating the arithmetic mean of each physiological indicator according to the actual physiological parameter set;

[0083] Obtain the actual physiological parameter set, and calculate the arithmetic mean of each physiological indicator according to the actual physiological parameter set, wherein the expression of the arithmetic mean is , wherein denotes the actual physiological parameter numbered t in the i th physiological indicator, and m denotes the number of parameters of the i th physiological indicator after outlier screening. It should be noted that m satisfies m≤n.

[0084] T i denotes the i th physiological indicator in the actual physiological parameter set, and its expression is {V i、1 , V i、2 ,..., V i、t ,..., V i、m};

[0085] S32, set a correction model for each physiological index respectively;

[0086] The calculation expression of the correction model is wherein represents the basic value of the i-th physiological index of the patient before the operation, represents the reference value of the i-th physiological index of the healthy population, represents the correction coefficient of the i-th physiological index of the patient;

[0087] It should be noted that the above parameters are confirmed by medical staff through data collection or using preset parameters; if the patient does not have a basic disease corresponding to the physiological index, the corresponding correction coefficient is 0;

[0088] S33, respectively, according to the arithmetic mean and the correction model, the basic parameter set {V ** 1, V ** 2,..., V ** i ,..., V ** a} is obtained by correction calculation.

[0089] According to the correction model corresponding to each physiological index, the corresponding arithmetic mean is corrected, so as to obtain the basic parameter set {V ** 1, V ** 2,..., V ** i ,..., V ** a};

[0090] If the patient has a basic disease affecting the aforementioned physiological index, the influence of the basic disease on the normal range of physiological parameters can be effectively eliminated through the above correction, thereby improving the accuracy of the basic parameters.

[0091] S4, according to the basic parameter set and the standard anesthesia depth analysis model, output the current anesthesia state parameter;

[0092] S41, set a normalization model and an anesthesia total score calculation formula for each physiological index respectively;

[0093] The calculation expression of the normalization model is wherein L i and U i respectively represent the lower limit value and the upper limit value of the i-th physiological index in the target anesthesia state:

[0094] Because the units of each physiological index are completely different, it is necessary to comprehensively preprocess the above parameters through normalization processing, so as to ensure the uniformity of the data;

[0095] Secondly, in the above normalization model, L i and U i respectively represent the lower limit value and the upper limit value of the i-th physiological index in the target anesthesia state, and the above parameters need to be determined by medical staff or modified according to the actual situation of the patient;

[0096] In the above normalization model, below the lower limit value indicates that the anesthesia state is far from the standard, and above the upper limit value indicates that the anesthesia is excessive. The parameters of 0 and 10 can quickly obtain the calculation result. The processing of the parameters between the lower limit value and the upper limit value can effectively measure the deviation amplitude relative to the lower limit value, so as to objectively reflect the anesthesia state.

[0097] S42, normalizing the basic parameter set according to the normalization model to obtain the normalized parameter value of each physiological index;

[0098] The normalized parameter values {s1, s2,..., s i ,..., s a} of all physiological indexes can be obtained by combining the parameters in step S33 and the above normalization model;

[0099] S43, calculating the anesthesia total score according to each normalized parameter value;

[0100] The calculation expression of the anesthesia total score is : wherein a represents the total number of physiological indexes, wi represents the weight value of the i-th physiological index, and satisfies ;

[0101] The weight of each physiological index is flexibly adjusted and determined by medical staff according to the actual situation, and finally the final anesthesia total score is calculated by combining the calculation formula of the above anesthesia total score.

[0102] S44, outputting the current anesthesia state parameter according to the anesthesia total score and the standard anesthesia depth analysis model.

[0103] S5, outputting the predicted anesthesia state parameter according to a plurality of basic parameter sets and the standard anesthesia depth analysis model.

[0104] S51, obtaining a plurality of basic parameter sets, and generating a historical basic parameter set according to the collection time of each basic parameter set, wherein the expression of the historical basic parameter set is ** { (V ** 1, V ** 2,..., V i ** a ) -1 , (V ** 1, V** 2,...,V ** i 2,...,V ** a 0}, wherein 0 represents the current state, and -1 represents the historical data of the sampling period with the shortest time interval from the current state;

[0105] The historical data of the sampling period closest to the current time is called, and all the basic parameter sets of the current time are called, and the above two groups of parameters are output as the historical basic parameter set;

[0106] S52, interpolation calculation is performed according to the historical basic parameter set to generate a fitting prediction model;

[0107] S521, the historical basic parameter set is converted into a plurality of time-parameter sequence pairs { (V ** 1，-1 , t 1，-1 ), (V ** 1，0 , t 1，0 )},..., { (V ** i，-1 , t i，-1 ), (V ** i，0 , t i，0 )},..., { (V ** a，-1 , t a，-1 ), (V ** a，0 , t a，0 )} ;

[0108] First, the historical basic parameter set is paired to generate a plurality of time-parameter sequence pairs, wherein the expression of the time-parameter sequence pair is: { (V ** 1，-1 , t 1，-1 ), (V ** 1，0 , t 1，0 )},..., { (V ** i，-1 , t i，-1 ), (V ** i，0 , t i，0 )},..., { (V ** a，-1 , t a，-1 ), (V ** a，0 , t a，0 )} ;

[0109] S522, linear interpolation calculation is performed on each of the time-parameter sequence pairs to obtain a fitting curve of each physiological index respectively; wherein the expression of the fitting curve is , wherein represents the parameter value of the i-th physiological index at the prediction time point, represents the prediction time point.

[0110] Two sequence pairs belonging to the same physiological index in the above-mentioned time-parameter sequence pairs are selected, and the corresponding parameter is calculated according to the expression of the fitting curve according to the value of the parameter in the standard two-dimensional coordinate system, wherein the expression of the fitting curve is , wherein represents the parameter value of the i-th physiological index at the prediction time point, represents the prediction time point;

[0111] S523, the fitting curves are integrated into a fitting prediction model.

[0112] S53, a prediction basic parameter set {V 1，f , V 2，f ,..., V i，f ,..., V a，f} is generated according to the fitting prediction model;

[0113] The current time is delayed by one sampling period as a prediction time point, and then the corresponding prediction basic parameters are calculated according to the fitting curves of each physiological index respectively, and are combined into a prediction basic parameter set {V 1，f , V 2，f ,..., V i，f ,..., V a，f};

[0114] S54, the prediction basic parameter set is normalized according to the normalization model to obtain a prediction normalized parameter;

[0115] The same normalization model as step S41 is used to normalize each prediction basic parameter, thereby generating a prediction normalized parameter set;

[0116] S55, a prediction anesthesia total score is calculated according to each of the prediction normalized parameter values;

[0117] The same anesthesia total score calculation formula as step S43 is used to calculate the prediction anesthesia total score.

[0118] S56, a prediction anesthesia state parameter is output according to the prediction anesthesia total score and the standard anesthesia depth analysis model.

[0119] The calculated predicted anesthesia total score is substituted into a standard anesthesia depth analysis model to output a predicted anesthesia state parameter.

[0120] It should be noted that in the initial stage of anesthesia, the predicted anesthesia state parameter is generally not output due to the lack of sufficient historical parameters.

[0121] Compared with the prior art, the present application realizes full-automatic data acquisition and calculation, which can effectively reduce the workload of anesthetists and improve the anesthesia effect of patients.

[0122] Secondly, the present application can effectively reduce the dependence on the personal experience of anesthetists in the analysis process through real-time data analysis, which is beneficial to improve the objectivity and accuracy of the analysis results, and can also reduce the threshold of anesthesia analysis.

[0123] Meanwhile, the present application can not only analyze the current anesthesia depth, but also predict future data according to the change trend of existing data, and finally output a predicted anesthesia state parameter as a reference for the change trend of the anesthesia state of the patient, so as to reserve a certain advance for the advance intervention of anesthetists.

[0124] That is, the present application can quantitatively analyze and predict the anesthesia depth at the current time and within a certain time in the future, so as to realize full-period monitoring and analysis of anesthesia depth, and effectively improve the comprehensiveness of anesthesia depth monitoring.

[0125] Finally, the present application uses a plurality of different physiological indicators as measured parameters as original parameters, and through screening and removing part of the sampling abnormal values, the basic parameter set used for actual depth analysis and prediction is obtained by combining the physiological status of the patient's basic diseases and other physiological conditions, so as to highly correlate the anesthesia analysis with the actual physical condition, eliminate the influence of various interferences, and effectively improve the prediction accuracy.

[0126] Embodiment 2

[0127] Reference Figure 2 The present embodiment is another optional embodiment of the present application, which discloses an anesthesia depth analysis system, comprising a data setting module and a data preprocessing module, the data setting module is used for setting parameters of a standard anesthesia depth analysis model, and is also used for setting various fixed parameters in the calculation formula of the modified model, the normalized model and the anesthesia total score.

[0128] The output ends of the data setting module and the data preprocessing module are respectively connected with a data correction module, the output end of the data correction module is respectively connected with a first analysis module and a second analysis module, and the input ends of the first analysis module and the second analysis module are also respectively connected with the data setting module to call various required data models.

[0129] The preferred embodiments of the present application have been described above with the illustrated embodiments, and are not intended to limit the scope of patent protection for the present application. Any equivalent structures or equivalent processes made by using the contents of the present application specification and drawings, or directly or indirectly applied to other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method for analyzing the depth of anesthesia, characterized in that, Includes the following steps: Establish a standard anesthesia depth analysis model; Obtain the raw physiological parameters, and preprocess the raw physiological parameters according to the outlier screening formula to obtain the actual physiological parameter set; Calculate the arithmetic mean of each physiological indicator based on the actual set of physiological parameters; Correction models were established for each physiological indicator; the calculation expression for the correction model is as follows: ,in This represents the baseline value of the i-th physiological indicator of the patient before surgery. This represents the reference value for the i-th physiological indicator in healthy individuals. This represents the correction coefficient for the i-th physiological indicator of the patient; The basic parameter set {V} is obtained by performing correction calculations based on the arithmetic mean and each correction model. ** 1, V ** 2, ..., V ** i , ..., V ** a }; Normalization models and formulas for calculating the total anesthesia score were established for each physiological indicator. The basic parameter set is normalized according to the normalization model to obtain the normalized parameter values ​​of each physiological indicator; the calculation expression of the normalization model is as follows: L i and U i These represent the lower and upper limits of the i-th physiological indicator under the target anesthesia state, respectively: The total anesthesia score is calculated based on the values ​​of each normalized parameter; the expression for the total anesthesia score is as follows: Where 'a' represents the total number of physiological indicators. Let represent the weight value of the i-th physiological indicator, and satisfy . ; The current anesthesia status parameters are output based on the total anesthesia score and the standard anesthesia depth analysis model. Acquire several basic parameter sets, and generate historical basic parameter sets based on the acquisition time of each basic parameter set, wherein the expression of the historical basic parameter set is {(V ** 1, V ** 2, ..., V ** i , ..., V ** a ) -1 , (V ** 1, V ** 2, ..., V ** i , ..., V ** a )0}, where 0 represents the current state and -1 represents the historical data with the shortest sampling period from the current state; The historical baseline parameter set is converted into several time-parameter sequence pairs {(V)} ** 1，-1 , t 1，-1 ), (V ** 1，0 , t 1，0 )},...,{(V ** i，-1 , t i，-1 ), (V ** i，0 , t i，0 )},...,{(V ** a，-1 , t a，-1 ), (V ** a，0 , t a，0 )}; Linear interpolation is performed on each of the aforementioned time-parameter sequences to obtain fitting curves for each physiological indicator; wherein the expression for the fitting curve is: ,in This represents the parameter value of the i-th physiological indicator at the predicted time point. Indicates the predicted time point; The fitted curves are integrated into a fitted prediction model; Generate a set of basic prediction parameters {V} based on the fitted prediction model. 1，f V 2，f , ..., V i，f , ..., V a，f }; The prediction base parameter set is normalized according to the normalization model to obtain the prediction normalization parameters; The total predicted anesthesia score is calculated based on the predicted normalization parameter values ​​described above. Based on the predicted total anesthesia score and the standard anesthesia depth analysis model, the predicted anesthesia status parameters are output.

2. The method for analyzing the depth of anesthesia according to claim 1, characterized in that, The expression for the standard anesthesia depth analysis model is: , where R represents the total anesthesia score, and k1-k5 are all constants.

3. The method for analyzing the depth of anesthesia according to claim 1, characterized in that, The process of obtaining raw physiological parameters and preprocessing them according to an outlier screening formula to obtain the actual physiological parameter set includes the following steps: Based on the types of physiological indicators, the raw physiological parameters are divided into several raw parameter sets I1, ..., I2. i ... I a Where 'a' represents the total number of physiological indicators; Calculate the mean of each original parameter set; the expression for calculating the mean of the i-th type of physiological index is as follows: ,in Let j represent the actual physiological parameter numbered j in the i-th type of physiological index, and n represent the total number of samples of the i-th type of physiological index. Calculate the standard deviation of each original parameter set; the expression for calculating the standard deviation of the i-th physiological index is as follows: ,in Let j represent the actual physiological parameter numbered j in the i-th type of physiological index, and n represent the total number of samples of the i-th type of physiological index. Outlier screening was performed on each original parameter set using the mean, standard deviation, and outlier screening formulas to obtain the actual physiological parameter sets I'1, ..., I'. i ... I' a The expression for the outlier screening formula is as follows: , This represents the actual physiological parameter numbered t among the i-th physiological indicators after screening.

4. The method for analyzing the depth of anesthesia according to claim 1, characterized in that, The expression for the arithmetic mean is: ,in T represents the actual physiological parameter t in the i-th physiological indicator, m represents the number of parameters in the i-th physiological indicator after outlier screening, and T represents the number of parameters in the i-th physiological indicator. i V represents the set of the i-th physiological index in the actual physiological parameter set, and its expression is {V}. i、1 V i、2 , ..., V i、t , ..., V i、m } 5. An analysis system based on the anesthesia depth analysis method according to any one of claims 1-4, characterized in that, include: The data setting module is used to set up a standard anesthesia depth analysis model; The data preprocessing module is used to obtain actual physiological parameters and preprocess the actual physiological parameters according to the outlier screening formula to obtain the actual physiological parameter set. The data correction module is used to perform correction calculations on the actual physiological parameter set to obtain the basic parameter set; The first analysis module is used to output the current anesthesia status parameters based on the basic parameter set and the standard anesthesia depth analysis model. The second analysis module is used to output predicted anesthesia state parameters based on several basic parameter sets and the standard anesthesia depth analysis model.

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