A sensor-based anesthesia depth monitoring method and system

Abstract

The present invention discloses a sensor-based anesthesia depth monitoring method and system, and relates to the technical field of anesthesia depth monitoring. The present invention includes preoperative anesthesia preparation and intraoperative anesthesia monitoring. First, the preoperative preparation data of the target patient is analyzed to set an initial anesthesia plan. Secondly, the initial neural characteristic data of the target patient is collected and analyzed based on the initial anesthesia model to determine the timing of starting the operation. Then, during the operation, the intraoperative physiological characteristic data and intraoperative neural characteristic data of the target patient are collected, and based on the intraoperative anesthesia model, it is determined whether there is an anesthesia abnormality. If an anesthesia abnormality is found, a corresponding anesthesia drug supply change plan is set according to a drug replacement model. The method improves the effectiveness and safety of anesthesia by real-time monitoring of the anesthesia depth, discovering and processing abnormal conditions during the anesthesia process.

Description

Technical Field

[0001] The present invention relates to the technical field of anesthesia depth monitoring, and in particular to a sensor-based anesthesia depth monitoring method and system. Background Art

[0002] During surgery, anesthesiologists need to closely monitor the patient's vital signs and anesthesia depth to ensure that the operation proceeds smoothly and reduce the patient's risks. Therefore, doctors need an anesthesia depth monitoring method that can adjust the patient's anesthesia based on the monitoring results of the anesthesia depth monitor to improve the patient's safety and comfort during surgery.

[0003] Existing technology, such as the invention patent application with announcement number CN116982937A, discloses a perioperative anesthesia depth monitoring system based on deep learning, which relates to the field of anesthesia depth monitoring technology. The system includes an electroencephalogram analysis module, which is used to analyze the collected electroencephalogram, convert the electroencephalogram signal into a spectrum diagram, obtain the energy of different waves, and then calculate the power spectrum density index of the electroencephalogram through the energy of different waves; an electrocardiogram analysis module, which calculates the power spectrum density of the signal at each frequency point, obtains the energy of different frequency intervals, and finally calculates the power spectrum density index of the electrocardiogram; a deep learning model training module, which establishes a deep learning model and performs training to learn the nonlinear relationship between physiological signals and anesthesia depth, and generates an anesthesia depth score. The invention aims to monitor the patient's anesthesia depth and vital signs in real time, conduct a more comprehensive evaluation of the perioperative anesthesia depth, and ensure the safety and comfort of the patient during surgery.

[0004] The above scheme has at least the following deficiencies: 1. The above scheme does not analyze the monitoring data of the target patient in the preoperative preparation stage, and cannot remind the staff when the target patient's anesthesia level reaches the point where surgery can be performed, and cannot ensure timely surgery. Patients are prone to excessive anesthetics. On the one hand, excessive anesthetics cause excessive anesthesia, which increases the time for the patient's nerve recovery after surgery and reduces the patient's comfort during the recovery period after surgery. On the other hand, excessive anesthetics will damage the patient's physical health and reduce the safety of anesthesia.

[0005] 2. The above invention only gives a prompt when the anesthesia depth is too shallow or too deep, and does not control the anesthetic drugs according to the physiological signal data and the nerve response data. On the one hand, it reduces the timeliness of the replacement of anesthetic drugs, and on the other hand, it requires manual judgment, which reduces the validity of the data. At the same time, the above invention does not consider the patient's tolerance to the drug according to the physiological signal data. The drug may have caused abnormal physiological reactions before reaching deep anesthesia, and the drug cannot be replaced in time, which reduces safety. Summary of the invention

[0006] In view of the above-mentioned technical deficiencies, the object of the present invention is to provide a sensor-based anesthesia depth monitoring method and system.

[0007] To solve the above technical problems, the present invention adopts the following technical solution: The present invention provides a sensor-based anesthesia depth monitoring method, comprising the following steps: Step 1, preoperative anesthesia preparation: collect the preoperative preparation data of the target patient, analyze the preoperative preparation data of the target patient, obtain the various anesthesia methods of the target patient, and then set the initial anesthesia plan for the target patient according to the various anesthesia methods of the target patient and the historical anesthesia records in the database.

[0008] Step 2: Intraoperative anesthesia monitoring: Collect the target patient's initial neural characteristic data, analyze the target patient's initial neural characteristic data based on the anesthesia initial model, and determine the timing of starting the operation. During the operation, collect the target patient's intraoperative physiological characteristic data and intraoperative neural characteristic data, analyze the target patient's intraoperative physiological characteristic data and intraoperative neural characteristic data based on the intraoperative anesthesia model to determine whether the anesthesia is abnormal. If the anesthesia is abnormal, set up an anesthesia drug supply change plan based on the drug replacement model.

[0009] Preferably, the intraoperative physiological characteristic data and intraoperative neural characteristic data of the target patient are analyzed, and the specific analysis process is as follows: the intraoperative physiological characteristic data of the target patient include the numerical values ​​of each physiological parameter and the intraoperative forehead temperature, the intraoperative neural characteristic data of the target patient include the numerical values ​​of each intraoperative electroencephalogram parameter and the intraoperative nystagmus frequency, the intraoperative anesthesia model includes a physiological response model and a neural response model, the intraoperative physiological characteristic data of the target patient is input into the physiological response model, and the output result of the intraoperative physiological response level model of the target patient is obtained, and the output result includes the numerical values ​​of 0 and 1, the intraoperative physiological characteristic data of the target patient is input into the neural response model, and the output result of the neural response model of the target patient is obtained, and the output result includes the numerical values ​​of -1, 0 and 1.

[0010] If the output results of the target patient's neural response model and physiological response model are both 0, it indicates that the intraoperative anesthesia is normal.

[0011] If the output result of the target patient's neural response model or physiological response model is not 0, it indicates abnormal anesthesia during surgery. If the output result of the target patient's neural response model is -1, and the output result of the target patient's physiological response model is 0, it indicates that the amount of anesthetic is too low and the drug supply needs to be increased. If the output result of the target patient's neural response model is -1, and the output result of the target patient's physiological response model is 1, it indicates that the anesthetic is intolerant, prompting the staff to change the drug. If the output result of the target patient's neural response model is 1, it indicates that the anesthetic is overdosed and the drug supply needs to be reduced.

[0012] Preferably, the specific setting process of setting up the anesthetic drug supply change plan is as follows: the historical patient usage times and the average usage dosage of each drug for the target patient are obtained from the database, and the usage index of each drug for the target patient is obtained by substituting them into the calculation formula.

[0013] If the target patient's drug supply needs to be increased, the drug with the highest index will be used to increase the preset supply. If the target patient's drug supply needs to be reduced, the drug with the highest index will be used to reduce the preset supply.

[0014] If the target patient needs to replace the medicine, then according to the analysis method of the usage index of each medicine used by the target patient, the usage index of each to-be-used medicine for the target patient is obtained, the medicine used by the target patient with the minimum usage index is recorded as the medicine to be replaced, and the to-be-used medicine with the maximum usage index of the target patient is recorded as the preset medicine. At the same time, the usage data of each medicine used is obtained from the database, and the usage data of each medicine used includes the number of times each medicine is used together with the medicine to be replaced, and also includes the maximum dosage of each medicine when used together with the medicine to be replaced, and also includes the number of times each medicine is used together with the preset medicine, and also includes the maximum dosage of each medicine when used together with the preset medicine. The usage data of each medicine used is input into the medicine replacement model to obtain the output result of each medicine used, and the value of the output result includes -1, 0 and 1. Each medicine used with an output result of 1 is a positive medicine used, and each medicine used with an output result of -1 is a negative medicine used. The preset medicine is replaced with the medicine to be replaced, and each positive medicine is step-adjusted according to the preset medicine supply increase, and each negative medicine is step-adjusted according to the preset medicine supply decrease.

[0015] On the other hand, the present invention provides a sensor-based anesthesia depth monitoring system, comprising the following modules: a preoperative anesthesia preparation module, used to collect the preoperative preparation data of the target patient, analyze the preoperative preparation data of the target patient, obtain various anesthesia methods for the target patient, and then set the initial anesthesia plan for the target patient according to the various anesthesia methods of the target patient and the historical anesthesia records in the database.

[0016] The intraoperative anesthesia monitoring module is used to collect the initial neural characteristic data of the target patient, analyze the initial neural characteristic data of the target patient based on the initial anesthesia model, and determine the timing of starting the operation. During the operation, the intraoperative physiological characteristic data and intraoperative neural characteristic data of the target patient are collected, and the intraoperative physiological characteristic data and intraoperative neural characteristic data of the target patient are analyzed based on the intraoperative anesthesia model to determine whether the anesthesia is abnormal. When the anesthesia is abnormal, an anesthesia drug supply change plan is set.

[0017] The beneficial effects of the present invention are as follows: 1. The present invention first sets an initial anesthesia plan by analyzing the preoperative preparation data of the target patient, and then collects the initial neural characteristic data of the target patient, and analyzes it based on the initial anesthesia model to determine the timing of starting the operation. Then, during the operation, the intraoperative physiological characteristic data and intraoperative neural characteristic data of the target patient are collected, and based on the intraoperative anesthesia model, it is determined whether there is an anesthesia abnormality. If an anesthesia abnormality is found, a corresponding anesthesia drug supply change plan is set according to the drug replacement model. In the preoperative preparation stage, this method determines the anesthesia degree of the target patient by monitoring the initial neural characteristic data of the target patient, and then determines the timing of starting the operation. The operation can be performed in a timely manner, reducing the risk of preoperative anesthesia and improving the safety of deep anesthesia.

[0018] 2. The present invention monitors the physiological reaction data and neural reaction data of the target patient during the operation, can timely identify the current abnormal anesthesia, and judge the type of abnormal anesthesia by analyzing the physiological reaction data and neural reaction data of the target patient, so as to change the anesthesia drug supply. The present invention not only judges the depth of anesthesia by neural reaction data, and changes the drug supply when the anesthesia is too deep or too shallow, but also considers the patient's tolerance to the drug through physiological reaction data, and timely changes the drug when the target patient has an abnormal physiological reaction to a certain type of drug, thereby improving the safety of deep anesthesia and reducing the possibility of risks in the patient's postoperative recovery period.

[0019] 3. The present invention analyzes the drug usage data during the operation and replaces the drugs, which can meet the patient's demand for anesthetic drugs and reduce the possibility of adverse reactions during the operation. At the same time, the correlation between the drugs used is judged through the drug usage data, and the supply amount of each drug is adjusted in a step-by-step manner, which meets the demand for drug replacement, further improves the effectiveness of drug replacement, increases the effectiveness of monitoring data, and improves the safety of deep anesthesia. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0021] Figure 1 The present invention is a schematic flow chart of the steps for implementing the method.

[0022] Figure 2 It is a schematic diagram of the system structure connection of the present invention. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] according to Figure 1 As shown, the present invention provides a sensor-based anesthesia depth monitoring method, comprising the following steps: Step 1, preoperative anesthesia preparation: collecting the preoperative preparation data of the target patient, analyzing the preoperative preparation data of the target patient, obtaining various anesthesia methods for the target patient, and then setting the initial anesthesia plan for the target patient according to the various anesthesia methods of the target patient and the historical anesthesia records in the database.

[0025] In a specific embodiment, the preoperative preparation data of the target patient is collected, and the specific collection process is as follows: the preoperative preparation data of the target patient includes the basic physical data of the target patient, the abnormal physical data of the target patient and the surgical requirements of the target patient. The basic physical data of the target patient is input by the target patient, and the surgical requirements of the target patient are input by the staff. The values ​​of various physical parameters of the target patient are collected by medical testing instruments, and the intervals of abnormal values ​​of various physical parameters are obtained from the database. If the value of a physical parameter of the target patient belongs to the corresponding interval of abnormal values, it indicates that the physical parameter of the target patient is abnormal, so as to obtain various abnormal physical parameters, and the abnormal physical data of the target patient are the various abnormal physical parameters.

[0026] It should be noted that the target patient's surgical needs include but are not limited to general anesthesia or spinal anesthesia, medical testing instruments include but are not limited to blood pressure monitors and platelet monitors, and the target patient's various physical parameters include but are not limited to blood pressure and platelet concentration.

[0027] In a specific embodiment, the preoperative preparation data of the target patient is analyzed, and the specific analysis process is as follows: each anesthesia regimen corresponding to the surgical needs of the target patient is obtained from the database, and recorded as each preset available anesthesia regimen for the target patient. At the same time, each prohibited drug corresponding to the abnormal physical data of the target patient is obtained from the database. If a preset available anesthesia regimen for the target patient does not contain any prohibited drug, it indicates that the preset available anesthesia regimen for the target patient is the anesthesia regimen to be used, thereby obtaining each anesthesia regimen to be used for the target patient.

[0028] The basic physical data of the target patient include the target patient's age, gender, weight and height. All historical anesthesia patients in the same age range, gender, weight range and height range as the target patient are recorded as historical anesthesia patients of the same type as the target patient. The anesthesia methods of the same historical anesthesia patients are obtained from the database, and the historical usage times of each anesthesia method are statistically obtained. The usage times of each anesthesia method to be used for the target patient are obtained from the historical usage times of each anesthesia method. The anesthesia methods to be used for the target patient are sorted from most to least according to the usage times, and the first preset number of anesthesia methods to be used are selected and recorded as the anesthesia methods, thereby obtaining the anesthesia methods for the target patient.

[0029] In a specific embodiment, the initial anesthesia plan for the target patient is set, and the specific setting process is as follows: the anesthesia method used most times by the target patient's similar historical anesthesia patients among the anesthesia methods of the target patient is recorded as the initial anesthesia method, the anesthetic drug supply plan corresponding to the initial anesthesia method is obtained from the database and recorded as the initial anesthesia drug supply plan, each drug in the initial anesthesia drug supply plan is recorded as a used drug, and each drug in each anesthesia method is recorded as a standby drug, and the specificity index of each monitoring method for each used drug and each standby drug is obtained from the database, and the preset operation time is obtained according to the surgical requirements of the target patient, and the stability index of each monitoring method for the preset operation time is obtained from the database, and the specificity index of each monitoring method for each used drug, the specificity of each monitoring method for each standby drug, and the stability index of each monitoring method for the preset operation time are substituted into the monitoring priority index calculation formula:

[0030] In the monitoring method Priority index , is the number of the monitoring method, , , , and Monitoring methods Use of drugs Specificity index and monitoring methods For drugs to be used Specificity and monitoring method of preset operation duration The stability index, The number of the drug to be used. , , The number of the drug to be used. , , and are the preset standard specificity index and standard stability index, is the preset standard stability index, , and They are respectively the preset weighting factor of the specific index of the used drug, the weighting factor of the specific index of the drug to be used and the weighting factor of the stability index, , , , , the monitoring method with the largest priority index is recorded as the preset monitoring method.

[0031] It should be noted that the anesthesia drug delivery plan includes but is not limited to the dosage of each drug and the input rate of each drug. The stability index is the probability of normal collection of the monitoring method during the corresponding operation time. The lower the stability index, the more times the monitoring method has incorrect collection during the corresponding operation time. The specificity index represents the ability of the monitoring method to identify each drug. For example, the specificity index of propofol monitoring through vital sign monitoring is 0.1, while the specificity index of propofol monitoring through electroencephalogram monitoring is 1.9. The standard parameters , and All are set by staff, weight factors , and All are set by staff, e.g. 1.2 1.3 0.6 0.21 is 0.32 and It is 0.47.

[0032] The initial anesthesia plan for the target patient is: anesthetize the target patient through the initial anesthesia medication plan, and at the same time, monitor the anesthesia of the target patient through a preset monitoring method.

[0033] Step 2: Intraoperative anesthesia monitoring: Collect the target patient's initial neural characteristic data, analyze the target patient's initial neural characteristic data based on the anesthesia initial model, and determine the timing of starting the operation. During the operation, collect the target patient's intraoperative physiological characteristic data and intraoperative neural characteristic data, analyze the target patient's intraoperative physiological characteristic data and intraoperative neural characteristic data based on the intraoperative anesthesia model to determine whether the anesthesia is abnormal. If the anesthesia is abnormal, set up an anesthesia drug supply change plan based on the drug replacement model.

[0034] In a specific embodiment, the initial neural characteristic data of the target patient is collected, and the specific collection process is as follows: after the target patient receives anesthetic drugs, a multi-parameter EEG sensor is placed at the monitoring point of the target patient, and the brain wave image of the target patient is collected by the multi-parameter EEG sensor, and the initial EEG parameters are obtained from the brain wave image. At the same time, the eye video of the target patient is collected by a camera, and the initial nystagmus frequency is obtained from the eye video of the target patient by machine vision.

[0035] The dosage and usage rate of each drug are obtained from the initial anesthesia drug supply plan, and then the monitoring period corresponding to the dosage of each drug and the monitoring period corresponding to the usage rate of each drug are obtained from the database, and the shortest monitoring period is selected as the monitoring period for collecting the initial neural characteristic data of the target patient.

[0036] It should be noted that the neural response acquisition device is, for example, a multi-parameter EEG sensor or a brain wave sensor, and the EEG parameters are, for example, the average power and bispectral index of the characteristic waveform.

[0037] In a specific embodiment, the initial neural characteristic data of the target patient are analyzed, and the specific analysis process is as follows: the initial neural characteristic data of the target patient include initial electroencephalogram parameters and initial nystagmus frequency, the initial neural characteristic data of the target patient are input into a neural anesthesia model, and the output result of the initial neural anesthesia model of the target patient is obtained, and the output result includes values ​​of 0 and 1.

[0038] If the output result is 1, it indicates that the user's initial anesthesia is completed, and the drug supply is stopped, and the staff is prompted to start the operation. If the output result is 0, it indicates that the user's initial anesthesia is not completed, and the drug supply is carried out, and the total supply of each drug of the user is collected. If the current total supply of a certain drug reaches the upper limit of the initial anesthesia plan, the drug is supplied according to the preset supply increase corresponding to the drug type in the database.

[0039] It should be noted that when the target patient's anesthesia level reaches the point where surgery can be performed, the staff will be reminded to ensure timely surgery and timely cessation of the initial anesthesia plan, which increases the patient's comfort and safety during the recovery period after surgery.

[0040] In a specific embodiment, the anesthesia initial model expression is:

[0041] ;

[0042] in, The output of the initial neuroanesthesia model for the target patient, is the initial EEG parameter The numerical value of is the number of the EEG parameter, , , Preset EEG parameters The standard value of is the initial nystagmus frequency, is the preset standard nystagmus frequency, is the weight factor of the preset EEG parameter d, , , and are the preset EEG weight factor and nystagmus weight factor, respectively. , , , It is the preset standard initial anesthetic index.

[0043] It should be noted that the standard parameters , and The same as the above standard parameter setting process, weight factor , and The weight factor setting process is the same as above, for example 1.3 0.6 0.05, 0.4, is 0.6 and is 1.2.

[0044] In a specific embodiment, the intraoperative physiological characteristic data and intraoperative neural characteristic data of the target patient are collected, and the specific collection process is as follows: after the target patient undergoes surgery, a multi-parameter EEG sensor is placed at the monitoring point of the target patient, and the brain wave image of the target patient is collected by the multi-parameter EEG sensor, and each intraoperative EEG parameter is obtained from the brain wave image, and at the same time, the eye image of the target patient is collected by a camera, and the intraoperative nystagmus frequency of the target patient is identified from the eye image of the target patient, and at the same time, the intraoperative physiological characteristic data of the target patient is collected by a physiological response monitoring instrument.

[0045] It should be noted that the physiological response monitoring instruments include but are not limited to skin resistance detectors, finger arterial pressure detectors and thermometers, and the intraoperative physiological characteristic data include the values ​​of various physiological parameters and intraoperative forehead temperature, wherein the various physiological parameters include but are not limited to skin resistance and finger arterial pressure.

[0046] In a specific embodiment, the intraoperative physiological characteristic data and intraoperative neural characteristic data of the target patient are analyzed, and the specific analysis process is as follows: the intraoperative physiological characteristic data of the target patient include the numerical values ​​of each physiological parameter and the intraoperative forehead temperature, the intraoperative neural characteristic data of the target patient include the numerical values ​​of each intraoperative electroencephalogram parameter and the intraoperative nystagmus frequency, the intraoperative anesthesia model includes a physiological response model and a neural response model, the intraoperative physiological characteristic data of the target patient is input into the physiological response model, and the output result of the intraoperative physiological response level model of the target patient is obtained, and the output result includes the numerical values ​​of 0 and 1, the intraoperative physiological characteristic data of the target patient is input into the neural response model, and the output result of the neural response model of the target patient is obtained, and the output result includes the numerical values ​​of -1, 0 and 1.

[0047] If the output results of the target patient's neural response model and physiological response model are both 0, it indicates that the intraoperative anesthesia is normal.

[0048] If the output result of the target patient's neural response model or physiological response model is not 0, it indicates abnormal anesthesia during surgery. If the output result of the target patient's neural response model is -1, and the output result of the target patient's physiological response model is 0, it indicates that the amount of anesthetic is too low and the drug supply needs to be increased. If the output result of the target patient's neural response model is -1, and the output result of the target patient's physiological response model is 1, it indicates that the anesthetic is intolerant, prompting the staff to change the drug. If the output result of the target patient's neural response model is 1, it indicates that the anesthetic is overdosed and the drug supply needs to be reduced.

[0049] In a specific embodiment, the intraoperative anesthesia model is expressed as follows: the intraoperative anesthesia model includes a physiological response model and a neural response model, and the physiological response model is:

[0050] ;

[0051] in, is the output of the physiological response model, is the value of the physiological parameter e, is the forehead temperature during surgery, is the number of the physiological parameter, , , is the standard value of the preset physiological parameter e, is the weight factor of the preset physiological parameter e, , , The preset standard forehead temperature during surgery. and are the preset weight factors of physiological parameters and forehead temperature, , , , It is a preset standard physiological response index.

[0052] It should be noted that the standard parameters , and The same as the above standard parameter setting process, weight factor , and The weight factor setting process is the same as above, for example 1.3 36.5 0.13 0.35 is 0.65 and It is 20.46.

[0053] The neural response model is:

[0054] ;

[0055] in, is the output of the neural response model, is the value of intraoperative EEG parameter d, is the intraoperative nystagmus frequency, is the standard value of the preset EEG parameter d, is the weight factor of the preset EEG parameter d, , , is the preset standard nystagmus frequency, and are the preset EEG weight factor and nystagmus weight factor, respectively. , , , and They are respectively the lower and upper limits of the preset standard intraoperative anesthesia index range.

[0056] It should be noted that the standard parameters and The standard parameter setting process is the same as above, for example is 1.32 and It is 4.53.

[0057] In a specific embodiment, the setting of the anesthesia drug supply change plan is specifically set as follows: the historical patient usage times and the average usage dose of each drug used by the target patient are obtained from the database, and the drug usage index calculation formula is substituted:

[0058] Get the drugs used by the target patients Usage Index , The number of the drug to be used. , , and Drugs used by target patients History of patient use and medication use The average dose used, and They are the standard number of uses and the standard dosage. and are the weight factors of the preset number of times of use and the weight factors of the dosage used, , , .

[0059] It should be noted that the standard parameters and The same as the above standard parameter setting process, weight factor and The weight factor setting process is the same as above, for example 5.34, 0.71 is 0.12 and It is 0.88.

[0060] If the target patient's drug supply needs to be increased, the drug with the highest index will be used to increase the preset supply. If the target patient's drug supply needs to be reduced, the drug with the highest index will be used to reduce the preset supply.

[0061] If the target patient needs to replace the medicine, then according to the analysis method of the usage index of each medicine used by the target patient, the usage index of each to-be-used medicine for the target patient is obtained, the medicine used by the target patient with the minimum usage index is recorded as the medicine to be replaced, and the to-be-used medicine with the maximum usage index of the target patient is recorded as the preset medicine. At the same time, the usage data of each medicine used is obtained from the database, and the usage data of each medicine used includes the number of times each medicine is used together with the medicine to be replaced, and also includes the maximum dosage of each medicine when used together with the medicine to be replaced, and also includes the number of times each medicine is used together with the preset medicine, and also includes the maximum dosage of each medicine when used together with the preset medicine. The usage data of each medicine used is input into the medicine replacement model to obtain the output result of each medicine used, and the value of the output result includes -1, 0 and 1. Each medicine used with an output result of 1 is a positive medicine used, and each medicine used with an output result of -1 is a negative medicine used. The preset medicine is replaced with the medicine to be replaced, and each positive medicine is step-adjusted according to the preset medicine supply increase, and each negative medicine is step-adjusted according to the preset medicine supply decrease.

[0062] It should be noted that the preset increase and decrease in drug supply are set by the staff, such as 0.01 and 0.001.

[0063] In a specific embodiment, the drug replacement model expression is:

[0064] ;

[0065] in, For use of drugs The output result is, For use of drugs The number of times the drug is used together with the drug that needs to be replaced, For use of drugs Maximum dosage when used together with the drug to be replaced, For use of drugs The number of times the drug is used together with the intended drug. For use of drugs Maximum dosage when used with the intended medication. and They are the preset standard usage times and standard maximum dosage. and are the weight factors of the preset number of uses and the maximum dosage, respectively. , , , and They are respectively the lower and upper limits of the preset standard drug demand index range.

[0066] It should be noted that the standard parameters , , and The same as the above standard parameter setting process, weight factor and The weight factor setting process is the same as above, for example For 12, is 1.34, 0.35 0.65 is 4.36 and It is 8.24.

[0067] according to Figure 2 As shown, the present invention provides a sensor-based anesthesia depth monitoring system, including the following modules: a preoperative anesthesia preparation module, an intraoperative anesthesia monitoring module and a database.

[0068] The preoperative anesthesia preparation module is connected to the intraoperative anesthesia monitoring module and the database respectively.

[0069] The preoperative anesthesia preparation module is used to collect the preoperative preparation data of the target patient, analyze the preoperative preparation data of the target patient, obtain the various anesthesia methods of the target patient, and then set the initial anesthesia plan for the target patient according to the various anesthesia methods of the target patient and the historical anesthesia records in the database.

[0070] The intraoperative anesthesia monitoring module is used to collect the initial neural characteristic data of the target patient, analyze the initial neural characteristic data of the target patient based on the initial anesthesia model, and determine the timing of starting the operation. During the operation, the intraoperative physiological characteristic data and intraoperative neural characteristic data of the target patient are collected, and the intraoperative physiological characteristic data and intraoperative neural characteristic data of the target patient are analyzed based on the intraoperative anesthesia model to determine whether the anesthesia is abnormal. When the anesthesia is abnormal, an anesthesia drug supply change plan is set.

[0071] A database is used to store historical anesthesia records, intervals of abnormal values ​​of various body parameters, various anesthesia plans corresponding to the surgical needs of target patients, various prohibited drugs corresponding to abnormal body data of target patients, anesthesia methods for patients with the same historical anesthesia, anesthetic drug supply plans corresponding to initial anesthesia methods, specificity indexes of various monitoring methods for various drugs in use, specificity indexes of various monitoring methods for various drugs to be used, stability indexes of various monitoring methods for preset surgical durations, monitoring periods corresponding to the dosage of various drugs, monitoring periods corresponding to the usage rates of various drugs, preset drug supply increases corresponding to various drug types, the number of historical patient uses of various drugs used by target patients, the average dosage of various drugs used by target patients, and usage data of various drugs used.

[0072] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined in this specification, they shall all fall within the protection scope of the present invention.

Claims

1. An anesthesia depth monitoring system, characterized in that: Includes the following modules: The preoperative anesthesia preparation module is used to collect the preoperative preparation data of the target patient, analyze the preoperative preparation data of the target patient, obtain the various anesthesia methods of the target patient, and then set the initial anesthesia plan of the target patient according to the various anesthesia methods of the target patient and the historical anesthesia records in the database; The intraoperative anesthesia monitoring module is used to collect the initial neural characteristic data of the target patient, analyze the initial neural characteristic data of the target patient based on the initial anesthesia model, and determine the timing of starting the operation. During the operation, the intraoperative physiological characteristic data and intraoperative neural characteristic data of the target patient are collected, and the intraoperative physiological characteristic data and intraoperative neural characteristic data of the target patient are analyzed based on the intraoperative anesthesia model to determine whether the anesthesia is abnormal. If the anesthesia is abnormal, an anesthesia drug supply change plan is set; The initial anesthesia plan for the target patient is set up, including: The anesthetic method used most frequently by the target patient's similar historical anesthetic patients among the target patient's anesthetic methods is recorded as the initial anesthetic method, the anesthetic drug supply plan corresponding to the initial anesthetic method is obtained from the database and recorded as the initial anesthetic drug supply plan, each drug in the initial anesthetic drug supply plan is recorded as the used drug, and each drug in each anesthetic method is recorded as each standby drug, and the specificity index of each monitoring method for each used drug and each standby drug is obtained from the database. At the same time, the preset operation time is obtained according to the surgical needs of the target patient, and the stability index of each monitoring method for the preset operation time is obtained from the database, and the specificity index of each monitoring method for each used drug, the specificity of each monitoring method for each standby drug and the stability index of each monitoring method for the preset operation time are substituted into the monitoring priority index calculation formula: In the monitoring method Priority index , is the number of the monitoring method, , , , and Monitoring methods Use of drugs Specificity index and monitoring methods For drugs to be used Specificity and monitoring method of preset operation duration The stability index, The number of the drug to be used. , , The number of the drug to be used. , , and are the preset standard specificity index and standard stability index, is the preset standard stability index, , and They are respectively the preset weighting factors of the specific index of the used drug, the specific index of the drug to be used and the weighting factor of the stability index, , , , , the monitoring method with the largest priority index is recorded as the preset monitoring method.

2. The anesthesia depth monitoring system according to claim 1, characterized in that: The preoperative preparation data of the target patient is analyzed, and the specific analysis process is as follows: The preoperative preparation data of the target patient includes the basic physical data of the target patient, the abnormal physical data of the target patient and the surgical requirements of the target patient. The anesthetic schemes corresponding to the surgical requirements of the target patient are obtained from the database and recorded as the preset available anesthetic schemes of the target patient. Meanwhile, the prohibited drugs corresponding to the abnormal physical data of the target patient are obtained from the database. If a preset available anesthetic scheme of the target patient does not contain any prohibited drug, it indicates that the preset available anesthetic scheme of the target patient is the anesthetic scheme to be used, thereby obtaining the anesthetic schemes to be used of the target patient; The basic physical data of the target patient include the target patient's age, gender, weight and height. All historical anesthesia patients in the same age range, gender, weight range and height range as the target patient are recorded as historical anesthesia patients of the same type as the target patient. The anesthesia methods of the same historical anesthesia patients are obtained from the database, and the historical usage times of each anesthesia method are statistically obtained. The usage times of each anesthesia method to be used for the target patient are obtained from the historical usage times of each anesthesia method. The anesthesia methods to be used for the target patient are sorted from most to least according to the usage times, and the first preset number of anesthesia methods to be used are selected and recorded as the anesthesia methods, thereby obtaining the anesthesia methods for the target patient.

3. The anesthesia depth monitoring system according to claim 2, characterized in that: The initial anesthesia plan for the target patient is: anesthetize the target patient through the initial anesthesia medication plan, and at the same time, monitor the anesthesia of the target patient through a preset monitoring method.

4. The anesthesia depth monitoring system according to claim 3, characterized in that: The initial neural characteristic data of the target patient is analyzed, and the specific analysis process is as follows: The initial neural characteristic data of the target patient include initial electroencephalogram parameters and initial nystagmus frequency. The initial neural characteristic data of the target patient are input into the neural anesthesia model to obtain an output result of the initial neural anesthesia model of the target patient, and the output result includes values ​​of 0 and 1; If the output result is 1, it indicates that the user's initial anesthesia is completed, and the drug supply is stopped, and the staff is prompted to start the operation. If the output result is 0, it indicates that the user's initial anesthesia is not completed, and the drug supply is carried out, and the total supply of each drug of the user is collected. If the current total supply of a certain drug reaches the upper limit of the initial anesthesia plan, the drug is supplied according to the preset supply increase corresponding to the drug type in the database.

5. The anesthesia depth monitoring system according to claim 1, characterized in that: The intraoperative physiological characteristic data and intraoperative neural characteristic data of the target patient are analyzed, and the specific analysis process is as follows: The target patient's intraoperative physiological characteristic data include the values ​​of various physiological parameters and the intraoperative forehead temperature; the target patient's intraoperative neural characteristic data include the values ​​of various intraoperative electroencephalogram parameters and the intraoperative nystagmus frequency; the intraoperative anesthesia model includes a physiological response model and a neural response model; the target patient's intraoperative physiological characteristic data are input into the physiological response model to obtain the output result of the target patient's intraoperative physiological response grade model, and the output result includes the values ​​of 0 and 1; the target patient's intraoperative physiological characteristic data are input into the neural response model to obtain the output result of the target patient's neural response model, and the output result includes the values ​​of -1, 0 and 1; If the output results of the target patient's neural response model and physiological response model are both 0, it indicates that the intraoperative anesthesia is normal; If the output result of the target patient's neural response model or physiological response model is not 0, it indicates abnormal anesthesia during surgery. If the output result of the target patient's neural response model is -1, and the output result of the target patient's physiological response model is 0, it indicates that the amount of anesthetic is too low and the drug supply needs to be increased. If the output result of the target patient's neural response model is -1, and the output result of the target patient's physiological response model is 1, it indicates that the anesthetic is intolerant, prompting the staff to change the drug. If the output result of the target patient's neural response model is 1, it indicates that the anesthetic is overdosed and the drug supply needs to be reduced.

6. The anesthesia depth monitoring system according to claim 5, characterized in that: The intraoperative anesthesia model includes a physiological response model and a neural response model; wherein the physiological response model is: ； in, is the output of the physiological response model, is the value of the physiological parameter e, is the forehead temperature during the operation, e is the number of the physiological parameter, , , is the standard value of the preset physiological parameter e, is the weight factor of the preset physiological parameter e, , , The preset standard forehead temperature during surgery. and are the preset weight factors of physiological parameters and forehead temperature, , , , It is a preset standard physiological response index; The neural response model is: ； in, is the output of the neural response model, is the value of intraoperative EEG parameter d, is the intraoperative nystagmus frequency, is the standard value of the preset EEG parameter d, is the weight factor of the preset EEG parameter d, , , is the preset standard nystagmus frequency, and are the preset EEG weight factor and nystagmus weight factor, respectively. and They are respectively the lower and upper limits of the preset standard intraoperative anesthesia index range.

7. The anesthesia depth monitoring system according to claim 6, characterized in that: The specific setting process of setting the anesthesia drug supply change plan is as follows: Obtain the historical patient usage frequency and average usage dose of each drug used by the target patient from the database, substitute them into the calculation formula, and obtain the usage index of each drug used by the target patient; If the target patient's drug supply needs to be increased, the drug with the highest index will be used to increase the preset supply; if the target patient's drug supply needs to be reduced, the drug with the highest index will be used to reduce the preset supply; If the target patient needs to replace the medicine, then according to the analysis method of the usage index of each medicine used by the target patient, the usage index of each to-be-used medicine for the target patient is obtained, the medicine used by the target patient with the minimum usage index is recorded as the medicine to be replaced, and the to-be-used medicine with the maximum usage index of the target patient is recorded as the preset medicine. At the same time, the usage data of each medicine used is obtained from the database, and the usage data of each medicine used includes the number of times each medicine is used together with the medicine to be replaced, and also includes the maximum dosage of each medicine when used together with the medicine to be replaced, and also includes the number of times each medicine is used together with the preset medicine, and also includes the maximum dosage of each medicine when used together with the preset medicine. The usage data of each medicine used is input into the medicine replacement model to obtain the output result of each medicine used, and the value of the output result includes -1, 0 and 1. Each medicine used with an output result of 1 is a positive medicine used, and each medicine used with an output result of -1 is a negative medicine used. The preset medicine is replaced with the medicine to be replaced, and each positive medicine is step-adjusted according to the preset medicine supply increase, and each negative medicine is step-adjusted according to the preset medicine supply decrease.

8. The anesthesia depth monitoring system according to claim 7, characterized in that: The drug replacement model expression is: ； in, For use of drugs The output result is, The number of the drug to be used. , , For use of drugs The number of times the drug is used together with the drug that needs to be replaced, For use of drugs Maximum dosage when used together with the drug to be replaced, For use of drugs The number of times the drug is used together with the intended drug. For use of drugs Maximum dosage when used with the intended medication. and They are the preset standard usage times and standard maximum dosage. and are the weight factors of the preset number of uses and the maximum dosage, respectively. , , , and They are respectively the lower and upper limits of the preset standard drug demand index range.

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