CN114225170A

CN114225170A - Intelligent anesthesia control method and system

Info

Publication number: CN114225170A
Application number: CN202111540177.7A
Authority: CN
Inventors: 曾睿峰; 秦乐; 上官王宁; 高叶
Original assignee: Second Affiliated Hospital and Yuying Childrens Hospital of Wenzhou Medical University
Current assignee: Second Affiliated Hospital and Yuying Childrens Hospital of Wenzhou Medical University
Priority date: 2021-12-16
Filing date: 2021-12-16
Publication date: 2022-03-25

Abstract

The embodiment of the disclosure provides an intelligent anesthesia control method and system, wherein the method comprises the following steps: acquiring the type of the operation; determining an optimal anesthesia depth according to the type of the operation and the progress of the operation, wherein the anesthesia depth comprises a waking period, an anesthesia period and a recovery period; acquiring vital sign information of a patient, wherein the vital sign information comprises information reflecting sedation depth, muscle relaxation degree and analgesia degree; establishing a mapping relation between the anesthesia depth and the vital sign information; and obtaining the type, the medicine dosage and the injection speed of the anesthetic required by the patient according to the type of the operation, the optimal anesthetic depth and the mapping relation. By the scheme, the safety and the automation level of the anesthesia process are improved.

Description

Intelligent anesthesia control method and system

Technical Field

The invention relates to the technical field of artificial intelligence, in particular to an intelligent anesthesia control method and system.

Background

Anesthesia is an essential key link in the clinical operation process, and improper anesthesia can cause adverse consequences such as awareness in the operation, postoperative pain, postoperative nausea and the like of a patient, and can cause death in severe cases.

Furthermore, the time for the deep anesthesia phase in surgery should be reduced as much as possible, since the deeper the anesthesia depth the greater the risk of the patient recovering.

In addition, different kinds of surgery (e.g., laparoscopic cholecystectomy) are standardized, but different stages require different anesthesia depths for the patient, and the individual stages require different levels of unconsciousness, painlessness, and muscle relaxation in response to the anesthesia.

Disclosure of Invention

In view of the above, embodiments of the present disclosure provide an intelligent anesthesia control method and system, which at least partially solve the problems in the prior art.

In a first aspect, an embodiment of the present disclosure provides an intelligent anesthesia control method, including:

acquiring the type of the operation;

determining an optimal anesthesia depth according to the type of the operation and the progress of the operation, wherein the anesthesia depth comprises a waking period, an anesthesia period and a recovery period;

acquiring vital sign information of a patient, wherein the vital sign information comprises information reflecting sedation depth, muscle relaxation degree and analgesia degree;

establishing a mapping relation between the anesthesia depth and the vital sign information; and

and obtaining the type, the medicine dosage and the injection speed of the anesthetic required by the patient according to the type of the operation, the optimal anesthetic depth and the mapping relation.

According to a specific implementation manner of the embodiment of the present disclosure, the determining an optimal anesthesia depth according to the type of the operation and the progress of the operation includes:

establishing a standardized procedure according to the type of the operation, wherein the standardized procedure comprises a plurality of stages and a standard time of each stage;

determining the optimal depth of anesthesia for each stage; and is

The method further comprises the following steps:

generating a warning message when the actual surgical procedure differs from the standardized surgical procedure by more than a predetermined value.

According to a specific implementation of an embodiment of the present disclosure, the information reflecting sedation depth includes a derived electroencephalogram and evoked potentials; the information reflecting the degree of relaxation of the muscle includes muscle strength; the information on the degree of analgesia of the response includes hemodynamic parameters, pupil size and manual and electrical guidance ability.

According to a specific implementation manner of the embodiment of the present disclosure, the establishing a mapping relationship between the anesthesia depth and the vital sign information includes:

acquiring electroencephalogram training data and vital sign information training data, wherein the electroencephalogram training data are electroencephalograms of a detector in the whole anesthesia period;

extracting a frequency spectrum characteristic diagram of the electroencephalogram signal training data, wherein the frequency spectrum characteristic diagram comprises a waking period frequency spectrogram, an anesthesia period spectrogram and a recovery period spectrogram;

inputting the frequency spectrum characteristic diagram into a convolutional neural network model to obtain a trained first convolutional neural network model;

inputting the vital sign information training data into a convolutional neural network model to obtain a trained second convolutional neural network model;

and connecting the second convolutional neural network model and the first convolutional neural network model in series to establish a mapping relation between the anesthesia depth and the vital sign information.

According to a specific implementation manner of the embodiment of the present disclosure, the concatenating the two convolutional neural network models and the first convolutional neural network model to establish a mapping relationship between an anesthesia depth and the vital sign information includes:

keeping two of the information reflecting the sedation depth, the muscle relaxation degree and the analgesia degree in the vital sign information unchanged, and gradually changing the parameters of at least one of the information to obtain the corresponding parameter threshold value when the first convolution neural network model output is changed.

According to a specific implementation manner of the embodiment of the present disclosure, inputting the spectrum feature map into a convolutional neural network model to obtain a trained first convolutional neural network model, including:

inputting the frequency spectrum characteristic diagram into a convolutional neural network model, and optimizing the initial weight in the convolutional neural network model by adopting a genetic algorithm to obtain a convolutional neural network optimization model;

and taking the frequency spectrum characteristic diagram as the input of the convolutional neural network optimization model, and adjusting the weight in the convolutional neural network optimization model by adopting a back propagation algorithm to obtain a trained first convolutional neural network model.

According to a specific implementation manner of the embodiment of the present disclosure, obtaining the anesthetic drug type, the drug dose and the injection speed required by the patient according to the type of the operation, the optimal anesthetic depth and the mapping relationship includes:

determining requirements of the waking period, the anesthesia period and the recovery period on the sedation depth, the muscle relaxation degree and the analgesia degree according to the type of the operation;

and adjusting the anesthetic drug type, the drug dosage and the injection speed according to the requirements and the parameter threshold.

In a second aspect, an embodiment of the present disclosure provides an intelligent anesthesia control system, which includes:

the operation type acquisition module is used for acquiring the type of an operation;

the optimal anesthesia depth determining module is used for determining the optimal anesthesia depth according to the type of the operation and the process of the operation, wherein the anesthesia depth comprises an awake period, an anesthesia period and a recovery period;

the vital sign information acquisition module is used for acquiring vital sign information of a patient, wherein the vital sign information comprises information of reaction sedation depth, muscle relaxation degree and analgesia degree;

the mapping establishing module is used for establishing a mapping relation between the anesthesia depth and the vital sign information; and

and the calculation module is used for obtaining the type, the medicine dosage and the injection speed of the anesthetic required by the patient according to the type of the operation, the optimal anesthetic depth and the mapping relation. .

In a third aspect, an embodiment of the present disclosure further provides an electronic device, where the electronic device includes:

at least one processor; and the number of the first and second groups,

a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the intelligent anesthesia control method of the first aspect or any implementation manner of the first aspect.

In a fourth aspect, the disclosed embodiments also provide a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the intelligent anesthesia control method in the first aspect or any implementation manner of the first aspect.

In a fifth aspect, the present disclosure also provides a computer program product including a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions that, when executed by a computer, cause the computer to perform the intelligent anesthesia control method in the first aspect or any implementation manner of the first aspect.

The intelligent anesthesia control method in the embodiment of the disclosure comprises the steps of obtaining the type of an operation; determining an optimal anesthesia depth according to the type of the operation and the progress of the operation, wherein the anesthesia depth comprises a waking period, an anesthesia period and a recovery period; acquiring vital sign information of a patient, wherein the vital sign information comprises information reflecting sedation depth, muscle relaxation degree and analgesia degree; establishing a mapping relation between the anesthesia depth and the vital sign information; and obtaining the type, the medicine dosage and the injection speed of the anesthetic required by the patient according to the type of the operation, the optimal anesthetic depth and the mapping relation. By the scheme, the safety and the automation level of the anesthesia process are improved.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings needed to be used in the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present disclosure, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.

Fig. 1 is a schematic flow chart of an intelligent anesthesia control method according to an embodiment of the present disclosure;

FIG. 2 is a graph of an overall spectral signature corresponding to an overall anesthesia session according to an embodiment of the present disclosure;

FIG. 3 is a quantity of information reflecting vital signs provided by embodiments of the present disclosure;

fig. 4 is a flowchart for establishing a mapping relationship between anesthesia depth and vital sign information according to an embodiment of the present disclosure;

FIG. 5 is electroencephalogram training data provided by an embodiment of the present disclosure; and is

FIG. 6 is a flow chart for deriving the type of anesthetic drug, the dosage of the drug, and the injection rate required by the patient according to an embodiment of the present disclosure;

fig. 7 is a block diagram of an intelligent anesthesia control system provided by an embodiment of the present disclosure.

Detailed Description

The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

The embodiments of the present disclosure are described below with specific examples, and other advantages and effects of the present disclosure will be readily apparent to those skilled in the art from the disclosure in the specification. It is to be understood that the described embodiments are merely illustrative of some, and not restrictive, of the embodiments of the disclosure. The disclosure may be embodied or carried out in various other specific embodiments, and various modifications and changes may be made in the details within the description without departing from the spirit of the disclosure. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.

It is noted that various aspects of the embodiments are described below within the scope of the appended claims. It should be apparent that the aspects described herein may be embodied in a wide variety of forms and that any specific structure and/or function described herein is merely illustrative. Based on the disclosure, one skilled in the art should appreciate that one aspect described herein may be implemented independently of any other aspects and that two or more of these aspects may be combined in various ways. For example, an apparatus may be implemented and/or a method practiced using any number of the aspects set forth herein. Additionally, such an apparatus may be implemented and/or such a method may be practiced using other structure and/or functionality in addition to one or more of the aspects set forth herein.

It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present disclosure, and the drawings only show the components related to the present disclosure rather than the number, shape and size of the components in actual implementation, and the type, amount and ratio of the components in actual implementation may be changed arbitrarily, and the layout of the components may be more complicated.

In addition, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects may be practiced without these specific details.

The embodiment of the disclosure provides an intelligent anesthesia control method. The intelligent anesthesia control method provided by the present embodiment may be executed by a computing device, which may be implemented as software, or implemented as a combination of software and hardware, and may be integrally provided in a server, a terminal device, or the like.

First, referring to fig. 1, an intelligent anesthesia control method provided by an embodiment of the present disclosure is described, which includes:

s100: the type of procedure is obtained. The type of surgery may be, for example, laparoscopic surgery, hepatobiliary surgery, a resection of a lesion, etc., which may be selected, for example, by a pull-down menu on the display. In this case, a database may be provided and various types of surgeries are contained in the database, and then at the start of the surgery, the selection of the surgery type is made through a pull-down menu.

S200: and determining the optimal anesthesia depth according to the type of the operation and the progress of the operation, wherein the anesthesia depth comprises a waking period, an anesthesia period and a recovery period.

For certain types of surgery, such as laparoscopic cholecystectomy, the procedure is standardized, for example, the main procedures include: establishing an operation hole, treating a gallbladder triangle, peeling the gallbladder and releasing carbon dioxide. During these several procedures, there is an optimal depth of anesthesia for each procedure, for example during the gallbladder removal phase, which may be required for anesthesia, and during the carbon dioxide release phase, which may be desirable for recovery.

That is, in the present embodiment, for each type of surgery, the ideal anesthetic state of the patient, i.e., which of the awake phase, the anesthetic phase, and the recovery phase, is set according to the different phases of the surgery. The ideal anesthetic state can be set by an expert in the industry.

In addition, the depth of anesthesia can be monitored by using Electroencephalogram (EEG), specifically, an overall spectral feature map corresponding to the entire anesthesia period can be obtained according to the EEG first, as shown in fig. 2; then, the overall spectral feature map is divided into spectral feature maps of different periods, where the spectral feature maps include a awake period spectral map, an anesthesia period spectral map, and a recovery period spectral map, as shown in fig. 2, where part (a) of fig. 2 is the awake period spectral map, part (b) of fig. 2 is the anesthesia period spectral map, and part (c) of fig. 2 is the recovery period spectral map.

In the embodiment of the invention, it is particularly advantageous to establish the corresponding relation between the anesthesia depth and the electroencephalogram signal, which has been recognized by multiple countries on one hand, and on the other hand, can prevent the relation between a single parameter and the anesthesia depth from being undetermined under the condition of multiple parameters.

S300: vital sign information of the patient is obtained, wherein the vital sign information comprises information reflecting sedation depth, muscle relaxation degree and analgesia degree.

In general, the depth of anesthesia can be described in a number of ways, the most important of which are the depth of sedation, which in turn can be described by 1) spontaneously derived electroencephalographic parameters, 2) evoked potentials, which can be auditory or any other form of sensory potential generated by the patient upon stimulation; the degree of muscle relaxation can be determined by 1) directly determining the strength of the muscle based on the patient's ability to comply with the command, 2) indirectly stimulating the motor nerve, and determining the contraction of the muscle through various routes. Common methods include direct measurement of force, acceleration of contraction, electronic measurement of muscle contraction, electronic measurement of movement, or sound caused by muscle relaxation; the degree of analgesia can be described by 1) hemodynamic parameters including heart rate, blood pressure, or derivatives, 2) the body's response to pain, such as changes in sweating, lacrimation, pupil size, and electrical conduction capabilities, and 3) response to deliberately evoked pain stimuli unrelated to surgery.

That is, in the disclosed embodiment, the above vital sign information can be obtained, so as to obtain the sedation depth, the muscle relaxation degree and the analgesia degree of the patient.

S400: and establishing a mapping relation between the anesthesia depth and the vital sign information.

After the optimal anesthesia depth at each stage of the operation is obtained through industry experience, the physiological parameters of the patient, such as blood pressure, need to be controlled more directly for intuitive control, and a mapping relation between the anesthesia depth and the vital sign information needs to be established for achieving the purpose.

As can be seen from the above, the depth of anesthesia can be measured by using a single parameter, i.e., the electroencephalogram signal, and the vital sign information includes a plurality of parameters, which means that a mapping relationship between the plurality of parameters and the single parameter needs to be established. In the disclosed embodiment, the mapping relationship may be established by a convolutional neural network, where the input to the neural network is vital sign information and the output is the depth of anesthesia, i.e., the awake period, the anesthesia period, and the recovery period.

The process of establishing the convolutional neural network generally includes acquiring training data and training a model, and the process is not described in detail.

S500: and obtaining the type, the medicine dosage and the injection speed of the anesthetic required by the patient according to the type of the operation, the optimal anesthetic depth and the mapping relation.

After the mapping relation between the anesthesia depth and the vital sign information is established, the proper type, dosage and injection speed of the anesthetic can be selected according to the optimal anesthesia depth corresponding to the stage of the operation, so that the vital sign information can be controlled at the target value.

In the embodiment of the disclosure, the anesthesia scheme suitable for the type of operation is selected according to the difference of the types of operations, so that the whole anesthesia process is more scientific, and the condition that a general anesthesia scheme is not suitable for isolation is avoided.

In addition, in order to ensure the safety of the anesthesia process, firstly, the anesthesia depth of the operation process needs to be ensured, for example, the anesthesia depth can not be in the recovery period during the operation process, and the anesthesia depth can not be in the anesthesia period during the recovery period, and the error can cause serious medical accidents. After determining the anesthesia depth of each stage of the operation according to the operation type, the safety of the anesthesia process can be ensured as a whole, however, in order to meet the requirements of different types of operations on the sedation depth, the muscle relaxation degree and the analgesia degree, in the embodiment of the disclosure, by adjusting the vital sign information reflecting the sedation depth, the muscle relaxation degree and the analgesia degree, the vital sign state more suitable for the current operation stage can be provided on the premise of ensuring the whole sedation depth.

For example, for laparoscopic cholecystectomy, a mild hypnotic level, a deep level of muscle relaxation and a deep level of analgesia are required, since mild hypnosis ensures rapid recovery after general anesthesia, deep analgesia ensures optimal stress suppression and deep core muscle relaxation, ensuring optimal conditions for operating abdominal surgery. In this case, the drug intake at the hypnotic level is reduced and the drug intake at the analgesic level and the muscle relaxation level is increased on the premise of satisfying the anesthesia depth requirements at each stage of the laparoscopic cholecystectomy procedure.

According to a specific implementation manner of the embodiment of the present disclosure, in the step S200 of determining the optimal anesthesia depth according to the type of the operation and the progress of the operation, a standardized operation progress may be first established according to the type of the operation, wherein the standardized operation progress includes a plurality of stages and a standard time of each stage. That is, for each type of surgery, not only the flow of its operation, but also the time taken for each flow is standardized, and the standardized surgical procedure can be established on the average of a single hospital, or can be determined by an industry medical professional. After the standardized procedure is determined, the optimal depth of anesthesia for each stage can be determined, which can be determined by expert knowledge, as described above. In the embodiment of the invention, the operation process is split, and the corresponding anesthesia depth is set for each stage, which is beneficial to minimizing the damage of the whole anesthesia process, is convenient for precipitating the knowledge of an expert in the industry into a standardized behavior standard, and can reduce the situation of low service level caused by the deviation of the individual experience knowledge of doctors.

In the case where a standardized surgical procedure is established, it is also possible to detect the actual surgical progress and to generate a warning message when the actual surgical progress differs from the standardized surgical progress by more than a predetermined value. For example, in the case of surgery, a single procedure that is too long may result in irreversible damage to the patient, or a lack of depth of anesthesia may result in additional distress to the patient, at which time a warning may be issued to enable the physician to take additional measures to compensate.

As shown in fig. 3, to improve the automation level of the overall system, in the disclosed embodiment, the information reflecting the depth of sedation includes a derivative electroencephalogram and evoked potentials; the information reflecting the degree of relaxation of the muscle includes muscle strength; the information on the degree of analgesia of the response includes hemodynamic parameters, pupil size and manual and electrical guidance ability. It will be appreciated that other parameters may be used to reflect the depth of sedation, the degree of muscle relaxation and the degree of analgesia.

After the detectable vital sign information is selected, a mapping relationship between the anesthesia depth and the vital sign information can be established. Specifically, as shown in fig. 4, the process of establishing the mapping relationship is as follows:

s401: acquiring electroencephalogram training data and vital sign information training data, wherein the electroencephalogram training data are electroencephalograms of a detector in the whole anesthesia period.

The electroencephalogram signal training data is as shown in fig. 5, and the vital sign information training data can be acquired at the same time as the electroencephalogram signal training data, so that a good correspondence relationship is established.

S402: and extracting a frequency spectrum characteristic diagram of the electroencephalogram signal training data, wherein the frequency spectrum characteristic diagram comprises a waking period frequency spectrogram, an anesthesia period spectrogram and a recovery period spectrogram. The frequency spectrum characteristic diagram of the electroencephalogram training data is shown in fig. 2, and is not described herein again.

S403: and inputting the frequency spectrum characteristic diagram into a convolutional neural network model to obtain a trained first convolutional neural network model.

Specifically, a convolutional neural network model can be constructed firstly, then initial weights of convolutional layers and full-connection layers in the convolutional neural network model are determined, in the invention, numbers in a small interval with a mean value of zero can be randomly selected as the initial weights, then, a frequency spectrum characteristic diagram can be used as the input of each convolutional neural network model in an initialization population, the fitness value of each convolutional neural network model in the initialization population is calculated, and whether the fitness value is smaller than a preset classification error value or whether the current iteration number is larger than or equal to a preset iteration number is judged; if so, determining the convolutional neural network model corresponding to the maximum fitness value as a convolutional neural network optimization model; and if not, carrying out selection, crossing and variation operations on the individuals in the initialized population, updating the initialized population and the current iteration times, returning to take the frequency spectrum characteristic diagram as the input of each convolutional neural network model in the initialized population, and calculating the fitness value of each convolutional neural network model in the initialized population individuals. In this embodiment, the classification error value represents the percentage of misclassified samples to the total number of input training samples. And finally, taking the frequency spectrum characteristic graph as the input of the convolutional neural network optimization model, and adjusting the weight in the convolutional neural network optimization model by adopting a back propagation algorithm to obtain the trained convolutional neural network optimization model.

S404: and inputting the vital sign information training data into a convolutional neural network model to obtain a trained second convolutional neural network model. The second convolutional neural network model may be obtained by a method similar to that for obtaining the first convolutional neural network model, and will not be described herein again.

S405: and connecting the second convolutional neural network model and the first convolutional neural network model in series to establish a mapping relation between the anesthesia depth and the vital sign information.

That is to say, in the original embodiment, the corresponding electroencephalogram signal is obtained through the vital sign information by the second convolutional neural network model, and the obtained electroencephalogram signal is input to the first convolutional neural network model, so that the anesthesia depth is obtained, and the mapping relationship between the anesthesia depth and the vital sign information is established.

Specifically, in the step of connecting the two convolutional neural network models and the first convolutional neural network model in series to establish the mapping relationship between the anesthesia depth and the vital sign information, two of the information reflecting the sedation depth, the muscle relaxation degree and the analgesia degree in the vital sign information may be kept unchanged, and the parameter of at least one of the information may be changed step by step to obtain the corresponding parameter threshold value when the output of the first convolutional neural network model is changed.

By fixing two of the depth of sedation, the degree of muscle relaxation and the degree of analgesia and varying the value of the third one, the effect of this variation on the depth of anesthesia can be obtained and threshold values for this variation are obtained for the wake period, the anesthesia period and the recovery period, so that the depth of anesthesia can be varied by controlling a single quantity.

In further embodiments, the obtained concatenated convolutional neural network model also needs to be calibrated. In particular, neural networks essentially classify inputs by probability. For example, for the second convolutional neural network model, the corresponding electroencephalogram signal is determined by inputting the vital sign information, and the actual output is a probability, for example, if the threshold is set to 80%, the awake period is determined if the probability of 80% is the awake period spectrogram. Similarly, the first convolutional neural network model also determines the corresponding depth of anesthesia by probability.

In the invention, in order to ensure the effectiveness of the constructed model, whether the probability product of the first neural network and the second neural network is greater than a preset value is also judged, the model is judged to be effective only if the product is greater than the preset value (80%), otherwise, the input of the second convolutional neural network model is continuously changed and only changed so as to meet the condition that the product is greater than the preset value (80%). The reason for only changing the second convolutional neural network is that the input of the second convolutional neural network is the source, and if the first convolutional neural network model is determined well, the first convolutional neural network is continuously modified, which will result in that the reason for the low confidence of the whole network cannot be judged.

Referring to fig. 6, according to a specific implementation manner of the embodiment of the present disclosure, deriving the type of anesthetic drug, the drug dose, and the injection speed required by the patient according to the type of the operation, the optimal anesthetic depth, and the mapping relationship includes:

s601: the requirements for depth of sedation, muscle relaxation and analgesia for the awake period, anesthesia period and recovery period are determined according to the type of surgery.

In particular, the requirements for depth of sedation, muscle relaxation and analgesia for various stages of the procedure may be accessed by experts in the field, for example for laparoscopic cholecystectomy, which requires a mild hypnotic level, a deep muscle relaxation level and a deep analgesia level, since mild hypnosis ensures rapid recovery after general anesthesia and deep analgesia ensures optimal stress suppression and deep core muscle relaxation, ensuring optimal conditions for operating abdominal surgery.

S602: and adjusting the anesthetic drug type, the drug dosage and the injection speed according to the requirements and the parameter threshold.

After the requirements for depth of sedation, degree of muscle relaxation, and degree of analgesia for each phase are determined, the anesthetic type, drug dose, and injection rate can be adjusted to achieve the goals. For example, for laparoscopic cholecystectomy, the drug intake at hypnotic level can be reduced and the drug intake at analgesic and muscle relaxation levels can be increased while meeting the requirement for anesthesia depth at various stages of the laparoscopic cholecystectomy procedure.

As described above, in the present invention, in the step of concatenating the two convolutional neural network models and the first convolutional neural network model to establish the mapping relationship between the anesthesia depth and the vital sign information, it is possible to keep two of the information reflecting the sedation depth, the muscle relaxation degree, and the analgesia degree in the vital sign information unchanged, and gradually change the parameter of at least one of them to obtain the corresponding parameter threshold value when the output of the first convolutional neural network model is changed.

Further, since an error in the sedation depth may cause an irrecoverable medical accident with less influence on the degree of muscle relaxation and analgesia, in actual practice, when adjusting the type of anesthetic, the dose of the drug and the injection speed to achieve the corresponding requirement of anesthesia, for the sedation depth, the lower threshold is selected so as to prevent over-anesthesia, and for the degree of muscle relaxation and analgesia, the upper threshold may be selected so as to ensure sufficient degree of muscle relaxation and analgesia.

Fig. 7 illustrates an intelligent anesthesia control system 700, comprising:

a surgery type obtaining module 701 for obtaining the type of the surgery;

an optimal anesthesia depth determination module 702, which determines an optimal anesthesia depth according to the type of the operation and the progress of the operation, wherein the anesthesia depth includes an awake period, an anesthesia period and a recovery period;

a vital sign information obtaining module 703 for obtaining vital sign information of the patient, wherein the vital sign information includes information of reaction sedation depth, muscle relaxation degree, and analgesia degree;

a mapping establishing module 704 for establishing a mapping relationship between the anesthesia depth and the vital sign information; and

the calculation module 705 obtains the type of anesthetic, the dosage of the drug and the injection speed required by the patient according to the type of the operation, the optimal anesthetic depth and the mapping relationship.

The system 700 may correspondingly execute the content in the above method embodiments, and details of the part not described in detail in this embodiment refer to the content described in the above method embodiments, which is not described herein again.

An embodiment of the present disclosure further provides an electronic device, including:

at least one processor; and the number of the first and second groups,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the intelligent anesthesia control method of the foregoing method embodiments.

The disclosed embodiments also provide a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the intelligent anesthesia control method in the aforementioned method embodiments.

The disclosed embodiments also provide a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, cause the computer to perform the intelligent anesthesia control method of the aforementioned method embodiments.

The electronic devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., car navigation terminals), and the like, and fixed terminals such as digital TVs, desktop computers, and the like.

An electronic device may include a processing means (e.g., a central processing unit, a graphics processor, etc.) that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM) or a program loaded from a storage means into a Random Access Memory (RAM). In the RAM, various programs and data necessary for the operation of the electronic apparatus are also stored. The processing device, the ROM, and the RAM are connected to each other by a bus. An input/output (I/O) interface is also connected to the bus.

Generally, the following devices may be connected to the I/O interface: input devices including, for example, touch screens, touch pads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, and the like; output devices including, for example, Liquid Crystal Displays (LCDs), speakers, vibrators, and the like; storage devices including, for example, magnetic tape, hard disk, etc.; and a communication device. The communication means may allow the electronic device to communicate wirelessly or by wire with other devices to exchange data. While the figures illustrate an electronic device having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided.

In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication means, or installed from a storage means, or installed from a ROM. The computer program, when executed by a processing device, performs the above-described functions defined in the methods of the embodiments of the present disclosure.

It should be noted that the computer readable medium in the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.

The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device.

The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: acquiring at least two internet protocol addresses; sending a node evaluation request comprising the at least two internet protocol addresses to node evaluation equipment, wherein the node evaluation equipment selects the internet protocol addresses from the at least two internet protocol addresses and returns the internet protocol addresses; receiving an internet protocol address returned by the node evaluation equipment; wherein the obtained internet protocol address indicates an edge node in the content distribution network.

Alternatively, the computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: receiving a node evaluation request comprising at least two internet protocol addresses; selecting an internet protocol address from the at least two internet protocol addresses; returning the selected internet protocol address; wherein the received internet protocol address indicates an edge node in the content distribution network.

Computer program code for carrying out operations for aspects of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The units described in the embodiments of the present disclosure may be implemented by software or hardware. Where the name of a unit does not in some cases constitute a limitation of the unit itself, for example, the first retrieving unit may also be described as a "unit for retrieving at least two internet protocol addresses".

It should be understood that portions of the present disclosure may be implemented in hardware, software, firmware, or a combination thereof.

The above description is only for the specific embodiments of the present disclosure, but the scope of the present disclosure is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present disclosure should be covered within the scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.

Claims

1. An intelligent anesthesia control method, comprising:

acquiring the type of the operation;

2. The intelligent anesthesia control method of claim 1, wherein said determining an optimal depth of anesthesia based on the type of surgery and the progress of the surgery comprises:

determining the optimal depth of anesthesia for each stage; and is

The method further comprises the following steps:

3. The intelligent anesthesia control of claim 1, wherein the information reflecting sedation depth comprises derived electroencephalograms and evoked potentials; the information reflecting the degree of relaxation of the muscle includes muscle strength; the information on the degree of analgesia of the response includes hemodynamic parameters, pupil size and manual and electrical guidance ability.

4. The intelligent anesthesia control method of claim 1, wherein the establishing of the mapping relationship between the anesthesia depth and the vital sign information comprises:

5. The intelligent anesthesia control method of claim 4, wherein said concatenating the two convolutional neural network models and the first convolutional neural network model to establish a mapping between anesthesia depth and the vital sign information comprises:

6. The intelligent anesthesia control method of claim 4, wherein inputting the spectral feature map into a convolutional neural network model to obtain a trained first convolutional neural network model comprises:

7. The intelligent anesthesia control method of claim 5, wherein deriving the type of anesthetic drug, the drug dose and the injection speed required by the patient according to the type of the operation, the optimal anesthesia depth and the mapping relationship comprises:

8. The intelligent anesthesia control method of claim 4, wherein the establishing of the mapping relationship between the anesthesia depth and the vital sign information further comprises:

and judging whether the probability product of the first neural network and the second neural network is larger than a preset value or not, if the product is smaller than the preset threshold value, continuously changing and only changing the input of the second convolutional neural network model until the condition that the product is larger than the preset value is met.

9. The intelligent anesthesia control method of claim 7, wherein said adjusting the anesthetic drug type, drug dose and injection speed according to the requirements and the parameter thresholds comprises: the lower threshold is chosen for the depth of sedation and the upper threshold is chosen for the degree of muscle relaxation and the degree of analgesia.

10. An intelligent anesthesia control system, the system comprising:

and the calculation module is used for obtaining the type, the medicine dosage and the injection speed of the anesthetic required by the patient according to the type of the operation, the optimal anesthetic depth and the mapping relation.