Disclosure of Invention
The invention provides a closed-loop intelligent anesthesia control system, which reduces the working strength of anesthesiologists and furthest reduces the injuries of surgical patients caused by improper anesthesia dosage control by carrying out integrated intelligent control on vital signs of the acquired patients, injection anesthesia control and anesthesia states of the patients.
The invention provides a closed-loop intelligent anesthesia control system, which comprises:
The vital sign acquisition module is used for acquiring vital signs of a patient in real time to obtain vital sign information;
the information processing module is used for processing and analyzing the physical sign information and making an anesthesia injection strategy;
And the monitoring and adjusting module is used for acquiring the anesthesia state of the patient when the patient is injected according to the anesthesia injection strategy, and adjusting the anesthesia injection strategy according to the anesthesia state.
In one possible implementation of this method,
Further comprises: an injection module for injecting a patient according to the anesthesia injection strategy, comprising:
The injection control unit is used for determining the thrust of the injection cylinder according to the injection dosage and the injection speed in the anesthesia injection strategy and pushing the injection cylinder to inject the patient forwards;
The injection control unit is also used for controlling the injection cylinder to stop injecting when the thrust of the injection cylinder is larger than a preset thrust value, and re-determining the thrust of the injection cylinder according to the residual injection dosage and the injection speed after the injection stopping time is longer than the preset time so as to push the injection cylinder to inject the patient forwards;
The injection control unit is also used for determining the thrust of the injection cylinder according to the adjusted anesthesia injection strategy after the anesthesia injection strategy is adjusted, and pushing the injection cylinder to inject the patient forwards.
In one possible implementation of this method,
The sign acquisition module comprises:
the acquisition unit is used for acquiring different types of observation information of the patient;
The information extraction unit is used for extracting the characteristics of the observation information of different types to obtain characteristic data, and obtaining sign information by utilizing the characteristic data;
and the information processing unit is used for grouping and correlating the physical sign information according to the attribute of the physical sign information after normalizing the physical sign information to obtain physical sign description information.
In one possible implementation of this method,
The information processing module includes:
the information analysis unit is used for acquiring basic information of the height, weight, age, sex and medical history of the patient, and establishing a human anesthesia model of the patient based on the physical sign information and combining the basic information;
And the policy making unit is used for testing the human anesthesia model by utilizing the anesthetic based on the target anesthesia state to obtain the optimal injection dosage and injection speed of the anesthetic and making an anesthesia injection policy.
In one possible implementation of this method,
The information analysis unit includes:
The inquiring unit is used for acquiring basic information of the height, weight, age, sex and medical history of the patient and inquiring the historical disease information of the patient from a patient database according to the basic information;
The extraction unit is used for extracting anesthesia dosage information and anesthesia feedback information from the historical disorder information;
The determining unit is used for determining anesthesia termination conditions according to the anesthesia feedback information;
The building unit is used for building a human body model according to the basic information and the sign information of the patient, training the human body model by taking the anesthesia amount information and the anesthesia amount information as training parameters until the anesthesia termination condition is reached, and stopping training to obtain the human body anesthesia model.
In one possible implementation of this method,
The determination unit includes:
the relation establishing unit is used for determining a first corresponding relation between the anesthetic and the corresponding medicine dosage in each historical disease based on the anesthetic dosage information; establishing a second corresponding relation between the anesthesia dosage information and the anesthesia feedback information;
The flow establishing unit respectively takes the anesthetic and the corresponding medicine dosage and the anesthetic feedback information thereof as a first node, a second node and a third node, wherein the first node and the second node comprise a plurality of sub-nodes, the first node and the second node correspond to the anesthetic type and the medicine dosage, and a static anesthetic flow is established according to the first corresponding relation and the second corresponding relation;
The comparison unit is used for extracting each group of processes from the static anesthesia processes, inputting the processes into the longitudinal comparator and the transverse comparator respectively, and outputting a first comparison result and a second comparison result;
The effect analysis unit is used for obtaining a first anesthetic effect of a single anesthetic based on the first comparison result, obtaining second anesthetic effects of a plurality of anesthetics based on the second comparison result, performing effect superposition on the first anesthetic effects according to the anesthetic dosage in each group of processes to obtain a third anesthetic effect, comparing the third anesthetic effect with the second anesthetic effect, and determining superposition effects of different anesthetics;
The conversion unit is used for dynamically labeling sub-nodes under a first node and a second node of the static anesthesia process based on the first anesthesia effect to obtain a first label, dynamically labeling the first node and the second node of the static anesthesia process based on the superposition effect to obtain a second label, and converting the static anesthesia process into a dynamic anesthesia process based on the first label and the second label;
the condition determining unit is used for inputting a preset anesthesia state into a third node of the dynamic anesthesia process, starting the dynamic anesthesia process, obtaining multiple groups of result data of the first node and the second node, and setting anesthesia termination conditions according to the multiple groups of result data.
In one possible implementation of this method,
The anesthesia termination condition is a set of a plurality of different anesthetic drug action regimens upon reaching the preset anesthesia state.
In one possible implementation of this method,
The policy making unit includes:
A state determining unit for determining a target sleep state, a target muscle relaxation state, a target analgesia state based on the target anesthesia state;
The test unit is used for taking a target sleep state, a target muscle relaxation state and a target pain loss state as intermediate parameters, and inputting the existing anesthetic into the human anesthesia model to obtain a plurality of groups of qualified anesthesia schemes;
a screening unit for determining a standard dosage range of each anesthetic for the patient according to the height, weight, age and sex of the patient, and selecting a preferred anesthetic regimen satisfying the standard dosage range from the plurality of groups of qualified anesthetic regimens;
The screening unit is further used for selecting the lowest cost of the preferred anesthetic solution as an optimal anesthetic solution according to the medication dosage of the anesthetic in the preferred anesthetic solution by utilizing the cost of the anesthetic, and making an anesthetic injection strategy according to the injection dosage and the injection speed of the anesthetic in the optimal anesthetic solution.
In one possible implementation of this method,
The monitoring and adjusting module comprises:
the strategy analysis unit is used for determining an anesthetic type, an anesthetic dosage, an anesthetic injection speed and an anesthetic injection time interval based on the anesthetic injection strategy, dividing an anesthetic process into a plurality of injection phases based on the injection time interval, and setting time sequence parameters of an anesthetic state in the process of each phase based on the anesthetic type, the anesthetic dosage and the anesthetic injection speed under each phase;
wherein the time series parameters include sleep state parameters, muscle relaxation state parameters, and analgesia state parameters;
A monitoring parameter setting unit for setting a parameter comparison rule in the anesthesia process according to the time series parameter;
The injection monitoring unit is used for carrying out anesthesia injection on the patient by utilizing the anesthesia injection strategy, monitoring parameters in the anesthesia process to obtain brain wave parameters, muscle relaxation parameters and hemodynamic parameters which change along with time, and converting the brain wave parameters, the muscle relaxation parameters and the hemodynamic parameters into sleep state parameters, muscle relaxation state parameters and pain loss state parameters by utilizing preset conversion rules to obtain actual state parameters;
the comparison unit is used for comparing the actual state parameters obtained each time by utilizing a parameter comparison rule to obtain a real-time comparison result, wherein the real-time comparison result comprises a first result, a second result and a third result;
The result analysis unit is used for determining a main monitoring result according to the characteristics of each stage and judging whether the main monitoring result is in a preset difference range or not;
If yes, not adjusting the anesthesia injection strategy;
if the main monitoring result is larger than a preset difference range, reducing the anesthesia injection speed in the current stage;
if the main monitoring result is smaller than the preset difference range, increasing the dosage of the anesthetic injection medicine in the current stage;
The result analysis unit is further configured to obtain a real-time stage result from the real-time comparison result after the current stage is finished, where the real-time stage result includes a first stage result, a second stage result, and a third stage result, determine a main stage result according to the characteristics of each stage after finishing, and determine whether the main stage result is within a preset stage difference range;
If yes, not adjusting the anesthesia injection strategy;
Otherwise, returning to the current stage according to the main stage result, and performing injection compensation on the current stage;
judging whether the secondary stage result is in the secondary difference range of the preset stage or not;
If yes, not adjusting the anesthesia injection strategy;
otherwise, according to the secondary stage result, the anesthesia injection strategy of the next stage is adjusted before the next stage is not started.
In one possible implementation of this method,
The system also comprises a preoperative evaluation module, which is used for carrying out preoperative evaluation on the patient according to the specific condition of the patient, and comprises the following steps:
The information acquisition unit is used for acquiring specific information of the patient from a patient database according to the identity information of the patient;
a comprehensive evaluation unit for determining a comprehensive evaluation value of the patient according to the specific information of the patient;
And the strategy fine tuning unit is used for determining the fine tuning amplitude value of the anesthesia injection strategy according to the comprehensive evaluation value, and selecting a target fine tuning scheme from the fine tuning schemes according to the fine tuning amplitude value to carry out fine tuning on the anesthesia injection strategy.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, it being understood that the preferred embodiments described herein are for illustration and explanation of the present invention only, and are not intended to limit the present invention.
Example 1
Based on embodiment 1, an embodiment of the present invention provides a closed-loop intelligent anesthesia control system, as shown in fig. 1, including:
The vital sign acquisition module is used for acquiring vital signs of a patient in real time to obtain vital sign information;
the information processing module is used for processing and analyzing the physical sign information and making an anesthesia injection strategy;
And the monitoring and adjusting module is used for acquiring the anesthesia state of the patient when the patient is injected according to the anesthesia injection strategy, and adjusting the anesthesia injection strategy according to the anesthesia state.
In this embodiment, the physical sign information includes heart beat, arterial constriction, pain sensation, and the like.
In this embodiment, the anesthetic injection strategy includes anesthetic name, anesthetic dose, injection rate.
In this embodiment, the patient's anesthetic state includes a sleep state, a muscle relaxing state, and a pain relieving state.
The beneficial effects of above-mentioned design scheme are: the anesthesia injection strategy is formulated by collecting vital signs of the patient, then the patient is injected according to the anesthesia injection strategy, the anesthesia state of the patient is monitored in real time in the injection process, the anesthesia injection strategy is adjusted in turn, the integrated intelligent anesthesia control is adopted, the anesthesia injection of the patient is completed, the working intensity of anesthesiologists is reduced, the differential control can be carried out according to the drug resistance of different patients, and the damage to the surgical patient caused by improper anesthesia dosage control is reduced to the greatest extent.
Example 2
Based on embodiment 1, the embodiment of the invention provides a closed-loop intelligent anesthesia control system, which further comprises: as shown in fig. 2, the injection module for injecting a patient according to the anesthesia injection strategy includes:
The injection control unit is used for determining the thrust of the injection cylinder according to the injection dosage and the injection speed in the anesthesia injection strategy and pushing the injection cylinder to inject the patient forwards;
The injection control unit is also used for controlling the injection cylinder to stop injecting when the thrust of the injection cylinder is larger than a preset thrust value, and re-determining the thrust of the injection cylinder according to the residual injection dosage and the injection speed after the injection stopping time is longer than the preset time so as to push the injection cylinder to inject the patient forwards;
The injection control unit is also used for determining the thrust of the injection cylinder according to the adjusted anesthesia injection strategy after the anesthesia injection strategy is adjusted, and pushing the injection cylinder to inject the patient forwards.
In this embodiment, the thrust of the syringe can be controlled, for example, by controlling the rotational speed of the motor.
In this embodiment, when the thrust of the injection cylinder is greater than the preset thrust value, it indicates that the injection of the medicament into the patient reaches a certain level, and the injection needs to be suspended for dissolving and diffusing the medicament in the patient, so as to obtain the accurate anesthetic state of the patient, and be favorable for reasonably adjusting the injection strategy according to the absorption conditions of different patients on the medicament, and injecting the appropriate anesthetic medicament into the patient.
The beneficial effects of above-mentioned design scheme are: the injection process of the patient is controlled through the injection module, the injection is suspended after a certain medicine is injected, enough time is provided for the medicine to be dissolved and spread in the patient, the accurate anesthesia state of the patient is obtained, the injection strategy is reasonably adjusted according to the absorption condition of different patients on the medicine, the patient is injected with proper anesthetic, and the working intensity of anesthesiologists is reduced.
Example 3
Based on embodiment 1, the embodiment of the invention provides a closed-loop intelligent anesthesia control system, wherein the physical sign acquisition module comprises:
the acquisition unit is used for acquiring different types of observation information of the patient;
The information extraction unit is used for extracting the characteristics of the observation information of different types to obtain characteristic data, and obtaining sign information by utilizing the characteristic data;
and the information processing unit is used for grouping and correlating the physical sign information according to the attribute of the physical sign information after normalizing the physical sign information to obtain physical sign description information.
In this embodiment, the different types of observation information include temperature acquisition information, blood oxygen acquisition information, electrocardiographic acquisition information, and the like.
In the embodiment, the standard of the physical sign information can avoid deviation of the existence format, the recording time and the like of the physical sign information obtained by scattered acquisition, so that analysis of the physical sign information is facilitated, and an accurate anesthesia injection strategy is formulated.
In this embodiment, the physical sign information is grouped and associated, so that the physical sign information can be conveniently checked and used.
The beneficial effects of above-mentioned design scheme are: by standardizing, grouping and correlating the collected different physical sign information, the collection deviation is avoided, analysis of the physical sign information is facilitated, and an accurate anesthesia injection strategy is formulated.
Example 4
Based on embodiment 1, an embodiment of the present invention provides a closed-loop intelligent anesthesia control system, where the information processing module includes:
the information analysis unit is used for acquiring basic information of the height, weight, age, sex and medical history of the patient, and establishing a human anesthesia model of the patient based on the physical sign information and combining the basic information;
And the policy making unit is used for testing the human anesthesia model by utilizing the anesthetic based on the target anesthesia state to obtain the optimal injection dosage and injection speed of the anesthetic and making an anesthesia injection policy.
In this embodiment, the anesthesia model is created based on patient information to indicate patient physical conditions and to apply appropriate anesthesia injection strategies to the patient.
The beneficial effects of above-mentioned design scheme are: by establishing a human anesthesia model of the patient according to the information of the patient, a proper anesthesia injection strategy is formulated for the patient, the working intensity of anesthesiologists is reduced, and the accuracy of anesthetic injection dosage is improved.
Example 5
Based on embodiment 4, an embodiment of the present invention provides a closed-loop intelligent anesthesia control system, where the information analysis unit includes:
The inquiring unit is used for acquiring basic information of the height, weight, age, sex and medical history of the patient and inquiring the historical disease information of the patient from a patient database according to the basic information;
The extraction unit is used for extracting anesthesia dosage information and anesthesia feedback information from the historical disorder information;
The determining unit is used for determining anesthesia termination conditions according to the anesthesia feedback information;
The building unit is used for building a human body model according to the basic information and the sign information of the patient, training the human body model by taking the anesthesia amount information and the anesthesia amount information as training parameters until the anesthesia termination condition is reached, and stopping training to obtain the human body anesthesia model.
In this embodiment, the anesthesia termination condition is an amount of anesthesia that corresponds best to the patient's anesthesia as determined from the anesthesia feedback information.
The beneficial effects of above-mentioned design scheme are: by establishing a human anesthesia model specially aiming at the patient according to the historical anesthesia information of the patient, the conditions of the height, weight, age, sex, medical history, temperature, blood oxygen, electrocardio and the like of the patient are comprehensively considered, and the best anesthesia injection strategy is provided for the patient.
Example 6
Based on embodiment 5, an embodiment of the present invention provides a closed-loop intelligent anesthesia control system, where the determining unit includes:
the relation establishing unit is used for determining a first corresponding relation between the anesthetic and the corresponding medicine dosage in each historical disease based on the anesthetic dosage information; establishing a second corresponding relation between the anesthesia dosage information and the anesthesia feedback information;
The flow establishing unit respectively takes the anesthetic and the corresponding medicine dosage and the anesthetic feedback information thereof as a first node, a second node and a third node, wherein the first node and the second node comprise a plurality of sub-nodes, the first node and the second node correspond to the anesthetic type and the medicine dosage, and a static anesthetic flow is established according to the first corresponding relation and the second corresponding relation;
The comparison unit is used for extracting each group of processes from the static anesthesia processes, inputting the processes into the longitudinal comparator and the transverse comparator respectively, and outputting a first comparison result and a second comparison result;
The effect analysis unit is used for obtaining a first anesthetic effect of a single anesthetic based on the first comparison result, obtaining second anesthetic effects of a plurality of anesthetics based on the second comparison result, performing effect superposition on the first anesthetic effects according to the anesthetic dosage in each group of processes to obtain a third anesthetic effect, comparing the third anesthetic effect with the second anesthetic effect, and determining superposition effects of different anesthetics;
The conversion unit is used for dynamically labeling sub-nodes under a first node and a second node of the static anesthesia process based on the first anesthesia effect to obtain a first label, dynamically labeling the first node and the second node of the static anesthesia process based on the superposition effect to obtain a second label, and converting the static anesthesia process into a dynamic anesthesia process based on the first label and the second label;
the condition determining unit is used for inputting a preset anesthesia state into a third node of the dynamic anesthesia process, starting the dynamic anesthesia process, obtaining multiple groups of result data of the first node and the second node, and setting anesthesia termination conditions according to the multiple groups of result data.
In this embodiment, the static anesthesia flow is a relationship between the anesthetic drug, the corresponding drug dosage and the anesthesia feedback information established at the first node, the second node and the third node according to the first corresponding relationship and the second corresponding relationship, and the relationship between the drug dosage and the anesthesia feedback information can be intuitively displayed on the static anesthesia flow.
In this embodiment, the first comparison result is the effect of the amount of the single anesthetic drug on the anesthetic feedback information.
In this embodiment, the second comparison result is the effect of the combined actions of the plurality of anesthetic agents on the anesthetic feedback information.
In this embodiment, the third anesthetic effect is a superposition of anesthetic effects corresponding to the dosage of the single anesthetic, for example, the anesthetic effects of the a and B drugs are 3 and 5, respectively, and the third anesthetic effect is 8.
In this embodiment, the superposition effects of different anesthetic drugs, for example, the anesthetic effects of the a drug and the B drug are 3 and 5, respectively, and the third anesthetic effect is 8 when the drugs act respectively, and if the second anesthetic effect is 9 due to the mutual influence of the a drug and the B drug when the drugs act simultaneously, the superposition effect of the a drug and the B drug is +1.
In this embodiment, the first label is determined by a first anesthetic effect, and the unused anesthetic effect value corresponds to a different first label.
In this embodiment, the first label is determined by the superposition effect of different anesthetic drugs, the unused anesthetic effect value corresponds to a different second label, the dynamic labeling is performed according to different anesthetic drug types of the first node, if the first node is the drug a and the drug B, the corresponding second label is the label corresponding to the superposition effect of the drug a and the drug B, and if the first node is the drug a and the drug C, the corresponding second label is the label corresponding to the superposition effect of the drug a and the drug C.
In this embodiment, the dynamic anesthesia procedure can utilize the adjustment of the first label and the second label according to the types and the amounts of different anesthetic drugs to realize the corresponding anesthesia feedback information, i.e. the anesthesia effect, when any of the types and the amounts of different anesthetic drugs are different.
In this embodiment, the anesthesia termination condition utilizes a dynamic anesthesia procedure to obtain multiple groups of different types and amounts of anesthetic drugs to achieve drug collocation that satisfies a preset anesthetic state.
The beneficial effects of above-mentioned design scheme are: and determining anesthesia termination conditions according to the anesthesia feedback information to obtain the anesthesia effect of patients under different anesthesia drugs, determining a preset anesthesia state according to the anesthesia effect, setting the anesthesia termination conditions according to a plurality of different anesthesia drug action schemes, and providing a basis for establishing a human anesthesia model specially aiming at the patients, thereby providing a guarantee for the patients with the best anesthesia injection strategy.
Example 7
Based on embodiment 6, the embodiment of the invention provides a closed-loop intelligent anesthesia control system, wherein the anesthesia termination condition is a set of multiple different anesthetic drug action schemes when the preset anesthesia state is reached.
The beneficial effects of above-mentioned design scheme are: when the preset anesthetic state is reached according to the anesthetic effect, a plurality of different anesthetic drug action schemes are determined, anesthetic termination conditions are set, a basis is provided for establishing a human body anesthetic model specially aiming at the patient, and therefore a best anesthetic injection strategy is provided for the patient.
Example 8
Based on embodiment 4, an embodiment of the present invention provides a closed-loop intelligent anesthesia control system, where the policy making unit includes:
A state determining unit for determining a target sleep state, a target muscle relaxation state, a target analgesia state based on the target anesthesia state;
The test unit is used for taking a target sleep state, a target muscle relaxation state and a target pain loss state as intermediate parameters, and inputting the existing anesthetic into the human anesthesia model to obtain a plurality of groups of qualified anesthesia schemes;
a screening unit for determining a standard dosage range of each anesthetic for the patient according to the height, weight, age and sex of the patient, and selecting a preferred anesthetic regimen satisfying the standard dosage range from the plurality of groups of qualified anesthetic regimens;
The screening unit is further used for selecting the lowest cost of the preferred anesthetic solution as an optimal anesthetic solution according to the medication dosage of the anesthetic in the preferred anesthetic solution by utilizing the cost of the anesthetic, and making an anesthetic injection strategy according to the injection dosage and the injection speed of the anesthetic in the optimal anesthetic solution.
In this embodiment, the multiple groups of qualified anesthetic schemes are methods for using multiple anesthetic agents to meet a target sleep state, a target muscle relaxation state and a target pain loss state, the preferred anesthetic scheme is selected from the qualified anesthetic schemes according to specific physical conditions of patients, and specific conditions of the patients are selected, so that side effects of the anesthetic agents on the patients are minimized, and the optimal anesthetic scheme is a scheme with the lowest cost selected from the preferred anesthetic scheme, so that the anesthetic effect is ensured to reach the standard, and the cost of the patients is reduced on the premise of smaller side effects.
The beneficial effects of above-mentioned design scheme are: the human body anesthesia model is tested by utilizing the anesthetic based on the target anesthesia state, and the optimal injection dosage and injection speed of the anesthetic are obtained under the conditions of considering the anesthesia effect, side effect and cost, and an anesthesia injection strategy is formulated, so that the working intensity of anesthesiologists is reduced, the differential control can be performed according to the drug resistance of different patients, the injury to the surgical patients caused by improper anesthesia dosage control is reduced to the maximum extent, and the anesthesia cost is reduced.
Example 9
Based on embodiment 1, an embodiment of the present invention provides a closed-loop intelligent anesthesia control system, where the monitoring adjustment module includes:
the strategy analysis unit is used for determining an anesthetic type, an anesthetic dosage, an anesthetic injection speed and an anesthetic injection time interval based on the anesthetic injection strategy, dividing an anesthetic process into a plurality of injection phases based on the injection time interval, and setting time sequence parameters of an anesthetic state in the process of each phase based on the anesthetic type, the anesthetic dosage and the anesthetic injection speed under each phase;
wherein the time series parameters include sleep state parameters, muscle relaxation state parameters, and analgesia state parameters;
A monitoring parameter setting unit for setting a parameter comparison rule in the anesthesia process according to the time series parameter;
The injection monitoring unit is used for carrying out anesthesia injection on the patient by utilizing the anesthesia injection strategy, monitoring parameters in the anesthesia process to obtain brain wave parameters, muscle relaxation parameters and hemodynamic parameters which change along with time, and converting the brain wave parameters, the muscle relaxation parameters and the hemodynamic parameters into sleep state parameters, muscle relaxation state parameters and pain loss state parameters by utilizing preset conversion rules to obtain actual state parameters;
the comparison unit is used for comparing the actual state parameters obtained each time by utilizing a parameter comparison rule to obtain a real-time comparison result, wherein the real-time comparison result comprises a first result, a second result and a third result;
The result analysis unit is used for determining a main monitoring result according to the characteristics of each stage and judging whether the main monitoring result is in a preset difference range or not;
If yes, not adjusting the anesthesia injection strategy;
if the main monitoring result is larger than a preset difference range, reducing the anesthesia injection speed in the current stage;
if the main monitoring result is smaller than the preset difference range, increasing the dosage of the anesthetic injection medicine in the current stage;
The result analysis unit is further configured to obtain a real-time stage result from the real-time comparison result after the current stage is finished, where the real-time stage result includes a first stage result, a second stage result, and a third stage result, determine a main stage result according to the characteristics of each stage after finishing, and determine whether the main stage result is within a preset stage difference range;
If yes, not adjusting the anesthesia injection strategy;
Otherwise, returning to the current stage according to the main stage result, and performing injection compensation on the current stage;
judging whether the secondary stage result is in the secondary difference range of the preset stage or not;
If yes, not adjusting the anesthesia injection strategy;
otherwise, according to the secondary stage result, the anesthesia injection strategy of the next stage is adjusted before the next stage is not started.
In this embodiment, since there are a plurality of types of anesthetic drugs to be injected during the anesthetic process, and the timing of the injection of the types of anesthetic drugs is also inconsistent, a plurality of injection phases, each of which is one of the main types of anesthetic drugs, are divided.
In this embodiment, the brain wave parameter, the muscle relaxation parameter, and the hemodynamic parameter are a sleep state parameter, a muscle relaxation state parameter, and a pain loss state parameter, respectively, where the hemodynamic parameter includes a heart rate, an arterial pressure parameter, and an oxygen consumption parameter.
In this embodiment, the first, second, and third results correspond to a sleep result, a muscle relaxation result, and a analgesia result, respectively.
In this embodiment, the primary monitoring result is determined according to the characteristics of each stage, in particular, according to the type of the injected medicine in the current stage, for example, the primary function of the type of the injected medicine is to selectively block the conduction of pain impulse, and the corresponding primary monitoring result is the third result, namely, the pain loss result.
In this embodiment, if the primary monitoring result is greater than the preset difference range, it indicates that the anesthetic result is advanced to the preset result, and at this time, the anesthetic result may be slowed down by properly reducing the anesthetic injection speed in the current stage according to the advanced amplitude.
In this embodiment, if the primary monitoring result is smaller than the preset difference range, it indicates that the anesthetic result at this time is behind the preset result, and at this time, the anesthetic result may be promoted by appropriately increasing the dosage of the anesthetic at the current stage according to the magnitude of the lag.
In this embodiment, the preset phase difference range is a range in which the anesthetic effect to be achieved as a whole is capable of fluctuating up and down after the current phase is ended.
In this embodiment, the first stage result, the second stage result, and the third stage result correspond to a sleep stage result, a muscle relaxation stage result, and a pain loss stage result, respectively.
In this embodiment, according to the characteristics after each stage is finished, the main stage result is determined according to the type of the injected medicine in the current stage, for example, the main function of the type of the injected medicine is to selectively block the conduction of the pain impulse, and the corresponding main stage result is the third stage result, namely the pain loss stage result, and the secondary stage result at this time is the first stage result, the second stage result, namely the sleep stage result and the muscle relaxation stage result.
In this embodiment, the main stage result is not within the preset stage difference range, which indicates that the anesthetic effect of the current stage does not reach the preset result requirement, and injection compensation needs to be performed on the current stage, and the specific injection compensation is determined according to the difference amplitude between the main stage result and the preset stage difference range.
In this embodiment, when the secondary stage result is not within the secondary difference range of the preset stage, it indicates that the primary anesthetic effect of the current stage reaches the preset result requirement, and the secondary anesthetic effect does not reach the preset result requirement. The anesthetic injection strategy of the next stage needs to be adjusted according to the influence condition of the next stage on the secondary anesthetic effect, for example, the muscle relaxation stage result of the secondary stage result does not meet the requirement, but the main acting muscle of the anesthetic of the next stage is relaxed, at this time, the anesthetic injection strategy of the next stage needs to be adjusted according to the difference between the muscle relaxation stage result and the preset result before the next stage is started, so as to compensate the anesthetic effect which does not meet the requirement in the current stage.
The beneficial effects of above-mentioned design scheme are: by monitoring the anesthesia process of the patient under the anesthesia injection strategy in real time and adjusting the anesthesia injection strategy in time according to the monitoring condition, the anesthesia injection strategy is controlled differently according to the drug resistance of the patient in the actual injection process, and the damage to the surgical patient caused by improper anesthesia drug quantity control is reduced to the maximum extent.
Example 10
Based on embodiment 1, an embodiment of the present invention provides a closed-loop intelligent anesthesia control system, further including, as shown in fig. 3, a preoperative evaluation module, configured to perform preoperative evaluation on a patient according to a specific situation of the patient, including:
The information acquisition unit is used for acquiring specific information of the patient from a patient database according to the identity information of the patient;
a comprehensive evaluation unit for determining a comprehensive evaluation value of the patient according to the specific information of the patient;
The calculation formula of the comprehensive evaluation value of the patient is as follows:
Wherein Q represents the comprehensive evaluation value of the patient, Q0 represents the average comprehensive evaluation value of the patient before the history anesthesia, K represents the history function evaluation value of the patient, sigma represents the influence value of the history symptoms of the patient on the physical function, tau represents the influence value of the existing symptoms of the patient on the physical function,Representing a current physical assessment of the patient, P representing an impact value of the patient's allergy history on the anesthetic effect;
The strategy fine tuning unit is used for determining a fine tuning amplitude value of the anesthesia injection strategy according to the comprehensive evaluation value, and selecting a target fine tuning scheme from fine tuning schemes according to the fine tuning amplitude value to carry out fine tuning on the anesthesia injection strategy;
The calculation formula of the anesthesia injection strategy fine adjustment amplitude value phi is as follows:
wherein va represents the average injection rate under the anesthesia injection strategy, vb represents the optimal average injection rate under the comprehensive evaluation of the patient, n represents the number of injected drugs under the anesthesia injection strategy that are associated with the patient's allergy history,For the dose of the ith injected drug associated with the patient's history of allergy under the anesthesia injection strategy,For the optimal dose of the ith injected medication associated with the patient's allergy history under the patient's comprehensive assessment.
In this embodiment, the comprehensive evaluation value of the patient is obtained based on the physical condition of the patient, and the better the physical condition of the patient, the larger the comprehensive evaluation value.
In this embodiment, the average integrated evaluation value of the patient's history before anesthesia is set according to the sex and age of the patient if the patient does not have a history of anesthesia.
In this embodiment, the value of the influence of the patient's history of the condition on the physical function is determined by the patient's history of the condition, which may be, for example, hypertension, arrhythmia, respiratory tract infection, stomach illness, kidney disease, etc., and even if healed, the patient's physical function is affected to some extent.
In this embodiment, the influence of the patient's allergy history on the anesthetic effect may be, for example, that if the anesthetic a is injected too much, the patient will be allergic, and thus the injection dose of the anesthetic a should be controlled.
In this embodiment, the larger the trim amplitude value, the larger the adjustment amplitude of the corresponding target trim scheme.
In this embodiment, for the formulaFor example, Q0＝0.8,Q0 may have a value range (0.6,1.5), k=0.8, K has a value range (0.6,1), σ has a value of 0.10, σ has a value range (0.01,0.20), τ=0.3, τ has a value range (0.1, 0.5), P has a value of 0.05, and P has a value range (0.01,0.10), q=1.05.
In this embodiment, for the formulaIt may for example be that,Φ=0.3.
The beneficial effects of above-mentioned design scheme are: according to the specific condition of the patient, preoperative evaluation is carried out on the patient, and according to the evaluation result, the anesthesia injection strategy is finely adjusted before the patient is injected, so that the anesthesia injection strategy is more in line with the physical condition of the patient, and the damage to the patient in operation caused by improper anesthesia dosage control is reduced to the greatest extent.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.