CN114177417A - Anesthesia target control intelligent infusion pump controller based on electroencephalogram parameter feedback - Google Patents
Anesthesia target control intelligent infusion pump controller based on electroencephalogram parameter feedback Download PDFInfo
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
- A61M5/00—Devices for bringing media into the body in a subcutaneous, intra-vascular or intramuscular way; Accessories therefor, e.g. filling or cleaning devices, arm-rests
- A61M5/14—Infusion devices, e.g. infusing by gravity; Blood infusion; Accessories therefor
- A61M5/142—Pressure infusion, e.g. using pumps
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
- A61M5/00—Devices for bringing media into the body in a subcutaneous, intra-vascular or intramuscular way; Accessories therefor, e.g. filling or cleaning devices, arm-rests
- A61M5/14—Infusion devices, e.g. infusing by gravity; Blood infusion; Accessories therefor
- A61M5/168—Means for controlling media flow to the body or for metering media to the body, e.g. drip meters, counters ; Monitoring media flow to the body
- A61M5/16804—Flow controllers
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
- G16H20/10—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
- G16H20/17—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients delivered via infusion or injection
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
- A61M5/00—Devices for bringing media into the body in a subcutaneous, intra-vascular or intramuscular way; Accessories therefor, e.g. filling or cleaning devices, arm-rests
- A61M5/14—Infusion devices, e.g. infusing by gravity; Blood infusion; Accessories therefor
- A61M5/142—Pressure infusion, e.g. using pumps
- A61M2005/14208—Pressure infusion, e.g. using pumps with a programmable infusion control system, characterised by the infusion program
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
- A61M2202/00—Special media to be introduced, removed or treated
- A61M2202/04—Liquids
- A61M2202/0468—Liquids non-physiological
- A61M2202/048—Anaesthetics
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
- A61M2205/00—General characteristics of the apparatus
- A61M2205/33—Controlling, regulating or measuring
- A61M2205/3331—Pressure; Flow
- A61M2205/3334—Measuring or controlling the flow rate
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
- A61M2230/00—Measuring parameters of the user
- A61M2230/08—Other bio-electrical signals
- A61M2230/10—Electroencephalographic signals
Abstract
The invention provides an anesthesia target control intelligent infusion pump controller based on electroencephalogram parameter feedback, which comprises: the data acquisition module is used for acquiring the electroencephalogram signal and the biological signal of the patient so as to obtain the electroencephalogram parameter and the physiological parameter of the patient; the intelligent processing module is used for generating a plurality of injection schemes based on the electroencephalogram parameters and the physiological parameters; and the control module is used for acquiring the optimal injection scheme according to the operation process, generating an infusion pump control scheme and controlling the infusion pump to inject the medicament to the patient. According to the invention, the electroencephalogram parameters and the biological parameters of the patient before the operation are taken, so that the critical points of the depth and the depth of sedation of the patient after the operation are judged, the anesthetic administration is standardized and normalized, and the brain inhibition degree can be timely adjusted according to the anesthetic, so that the pump speed of the administration of each pump is achieved, and the method is humanized and standardized.
Description
Technical Field
The invention relates to the technical field of medical anesthesia, in particular to an anesthesia target control intelligent infusion pump controller based on electroencephalogram parameter feedback.
Background
At present, ensuring that a patient can successfully receive surgical treatment under painless and safe conditions is a basic task of anesthesia clinic, but the task is only a part of the work content of modern anesthesia disciplines. The anesthesia work also comprises preparation and treatment before and after anesthesia, monitoring and treatment of critical patients, emergency resuscitation, pain treatment and the like. In order to complete clinical anesthesia, the basic theory of anesthesia and the skilled application of various anesthesia techniques are required to be mastered, and the characteristics of various surgical operations are required to be familiar.
At present, in clinical anesthesia operations, most of operations such as dosage, administration speed and the like are generally implemented by experience of an anesthesiologist, various parameter indexes and tolerance degrees of different patients adopt unified standards, and meanwhile, the operation is also influenced by human factors; in the anesthesia maintenance period, an anesthesiologist controls the anesthesia medication according to the vital sign parameters of a patient, although the anesthesia depth is closely related to the vital signs, the change of the vital signs of all surgical patients is caused by the anesthesia medication, so that the anesthesia can be only in a biological theory, and the quality of the surgical anesthesia is neglected to cause later damage to the patient; and the operation of anesthesia administration and the like can not be adapted to patients with various parameter indexes and tolerance degrees to the maximum extent easily, and the problems of wrong and missed links, improper dose control, non-humanized administration pump speed and the like are easily caused in the anesthesia operation.
Therefore, the technical scheme provides an anesthesia target control intelligent infusion pump controller based on electroencephalogram parameter feedback, an optimal injection scheme is generated by acquiring electroencephalogram parameters and biological parameters of a patient before an operation, anesthesia administration is standardized and normalized, the pump speed of each pump administration reaches the optimal requirement, and new people who just engage in anesthesia can also finish high-quality anesthesia; meanwhile, in the operation anesthesia, the vital signs of the patient are abnormal, and the system can timely give related medicines according to the degree of variation of different vital sign parameters, so that the vital sign parameters of the patient in the operation are always maintained in a normal range.
Disclosure of Invention
The invention provides an anesthesia target control intelligent infusion pump controller based on electroencephalogram parameter feedback, which generates an optimal injection scheme by acquiring electroencephalogram parameters and biological parameters of a patient before an operation, standardizes anesthesia administration, ensures that the pump speed of each pump for administration reaches the optimal requirement, and ensures that a new person just engaged in anesthesia can complete high-quality anesthesia.
The invention provides an anesthesia target control intelligent infusion pump controller based on electroencephalogram parameter feedback, which comprises:
the data acquisition module is used for acquiring the electroencephalogram signal and the biological signal of the patient so as to obtain the electroencephalogram parameter and the physiological parameter of the patient;
the intelligent processing module is used for generating a plurality of injection schemes based on the electroencephalogram parameters and the physiological parameters;
and the control module is used for acquiring the optimal injection scheme according to the operation process, generating an infusion pump control scheme and controlling the infusion pump to inject the medicament to the patient.
Preferably, the anesthesia target control intelligent infusion pump controller based on electroencephalogram parameter feedback, the infusion pump comprises: any one or more of a sedation injection pump, a shufen injection pump, a resifen injection pump, a muscle relaxation injection pump, a booster injection pump, a hypotensor injection pump, and a bradycardia injection pump.
Preferably, the anesthesia target control intelligent infusion pump controller based on electroencephalogram parameter feedback, the control module comprises:
the scheme receiving submodule is used for receiving a plurality of injection schemes generated by the intelligent processing module;
the scheme analysis submodule is used for analyzing the plurality of injection schemes, acquiring the optimal injection scheme and generating corresponding working instructions;
and the instruction issuing sub-module is used for controlling the infusion pump to execute corresponding operation based on the working instruction.
Preferably, the instruction issuing submodule is used for issuing a working instruction to the infusion pump, controlling a related sedation injection pump, a sufen injection pump, a resifen injection pump, a muscle relaxation injection pump, a pressure boosting injection pump, a pressure reducing injection pump and a bradycardia injection pump in the infusion pump, and injecting corresponding medicaments to a patient according to a specified time and a specified dosage.
Preferably, the anesthesia target control intelligent infusion pump controller based on electroencephalogram parameter feedback further comprises:
a case acquisition module for acquiring a case file of a patient from receiving treatment to ending treatment, comprising:
the case generation submodule is used for acquiring the real-time reaction of a patient after the medicament injection is carried out according to a control scheme and generating a case file by combining the body information related to the patient;
the case encryption submodule is used for naming the case files according to the names and the case characteristics of the patients, encrypting the named case files to obtain encrypted case files, inputting the corresponding relation between the secret key and the encrypted case files into a secret key corresponding library in a local client, and uploading the encrypted case files to a cloud database for storage;
and the information searching submodule is used for searching matched case files in the cloud database based on the patient name and the case characteristics, searching a decoding secret key corresponding to the encrypted case files in a secret key corresponding library of the local client according to the encrypted case files, and decrypting the encrypted case files to obtain the case files of the corresponding patients.
Preferably, the anesthesia target control intelligent infusion pump controller based on electroencephalogram parameter feedback, the data acquisition module further comprises:
the signal acquisition unit is used for acquiring an electroencephalogram signal of a patient before an operation;
the signal processing unit is used for amplifying the electroencephalogram signals to obtain amplified electroencephalogram signals;
inputting the obtained amplified electroencephalogram signal into a preset noise detection model, and outputting a noise signal type corresponding to the amplified electroencephalogram signal;
based on a preset noise signal type-filtering method correspondence table, acquiring a denoising method of the noise signal type, and performing corresponding filtering denoising on the amplified electroencephalogram signal to obtain a first denoised electroencephalogram signal;
acquiring a maximum peak point of the first de-noised electroencephalogram signal frequency, and judging whether the maximum peak point is within a preset range;
if so, outputting the first denoised electroencephalogram signal as a second denoised electroencephalogram signal;
if not, inputting the first de-noised electroencephalogram signal into the preset noise detection model, re-acquiring a corresponding de-noising method according to a preset noise signal type-filtering method correspondence table to perform corresponding filtering de-noising until the maximum peak point is within a preset range, and outputting a signal of which the maximum peak point is within the preset range as a second de-noised electroencephalogram signal;
the parameter acquisition unit is used for acquiring the singular spectrum of the second de-noised electroencephalogram signal, decomposing the singular spectrum and acquiring singular spectrum components of each order of the second de-noised electroencephalogram signal;
acquiring a corresponding second denoised electroencephalogram signal wave band based on the singular spectral components of each order of the second denoised electroencephalogram signal, and performing multi-layer wavelet transformation on the corresponding second denoised electroencephalogram signal wave band based on a preset wavelet function to obtain a wavelet coefficient corresponding to the second denoised electroencephalogram signal wave band;
acquiring wavelet coefficient difference values of every adjacent second denoised electroencephalogram signal wave band, acquiring standard deviations of all the wavelet coefficient difference values, and judging whether the standard deviations are within a preset range or not;
if so, outputting the second denoised electroencephalogram signal;
if not, calculating a compensation coefficient of a second de-noised electroencephalogram signal based on the preset wavelet function, performing compensation processing on the second de-noised electroencephalogram signal based on the compensation coefficient, performing preprocessing on the compensated second de-noised electroencephalogram signal until the standard deviation is within a preset range, and outputting the second de-noised electroencephalogram signal with the standard deviation within the preset range;
and inputting the output second de-noised electroencephalogram signal into a pre-established electroencephalogram characteristic detection model, outputting the current electroencephalogram characteristic and the corresponding current characteristic type, and obtaining electroencephalogram parameters.
Preferably, the anesthesia target control intelligent infusion pump controller based on electroencephalogram parameter feedback, the intelligent processing module further comprises:
the inhibition acquisition submodule is used for acquiring a first case related to the electroencephalogram parameters of the patient based on a big data platform to obtain a first case library;
the first obtaining submodule is used for obtaining complete information of a provider of each first case in the first case base, obtaining total evaluation numbers of the corresponding providers and calculating the credibility index of the provider corresponding to the first case;
removing a first case corresponding to a provider with a credibility index lower than a preset threshold value from the first case library to obtain a second case library, obtaining the inhibition degree of electroencephalogram parameters of each second case in the second case library on different types of narcotics, classifying the obtained second case library based on the narcotic type information, and determining the average value of the inhibition degrees of the same type of narcotics as the standard inhibition degree of the corresponding type of narcotics;
the establishing submodule is used for determining human body signals when different kinds of narcotics are injected based on the case parameters of the second case library, and establishing a human body bearing model of the same kind of narcotics by combining the standard inhibition degree;
the second acquisition sub-module is used for acquiring a current electroencephalogram corresponding to the electroencephalogram parameters of the current patient, dividing the current electroencephalogram into a plurality of sub-signals based on preset time nodes, acquiring a signal wave vector of each sub-signal, matching to obtain a mark value of each signal wave vector based on a vector matching database, and constructing to obtain a mark curve;
establishing an allowable maximum tolerance curve and an allowable minimum tolerance curve of the current patient under different kinds of narcotics based on the human body tolerance model and the marking curve;
the screening submodule is used for screening a first active point of a corresponding allowable maximum tolerance curve and a second active point of the allowable minimum tolerance curve under the same type of narcotic and taking the first active point and the second active point as corresponding anesthesia critical points;
and the scheme acquisition submodule is used for acquiring the standard injection dose of the current patient corresponding to different anesthesia critical points based on the anesthesia critical points corresponding to different types of anesthetics, and acquiring a plurality of injection schemes based on the optimal injection speed and the standard injection dose of the anesthesia critical points.
Preferably, the anesthesia target control intelligent infusion pump controller based on electroencephalogram parameter feedback, the scheme analysis submodule further comprises:
the scheme determining unit is used for acquiring a surgical procedure, matching injection schemes corresponding to different anesthetics based on the surgical procedure, and acquiring an injection scheme corresponding to the anesthetic with the highest matching degree as an optimal injection scheme;
and the instruction generating unit is used for acquiring the optimal time point when the patient reaches the anesthesia critical point based on the operation flow and the operation starting time, and generating a corresponding working instruction based on the optimal injection scheme and the optimal time point.
Preferably, the anesthesia target control intelligent infusion pump controller based on electroencephalogram parameter feedback further comprises:
the state monitoring module is used for detecting the sleep degree and the body state of a patient in real time in the operation process, and comprises:
the anesthesia state determining submodule is used for acquiring real-time electroencephalogram parameters and real-time biological parameters of a patient in real time when the infusion pump executes corresponding operation;
acquiring the sleep degree of the patient based on the real-time electroencephalogram parameters, and judging whether the sleep degree of the patient is within a preset range;
if so, continuing to inject the anesthetic according to the optimal injection scheme;
if the sleeping degree of the patient is smaller than the preset range, the amount of the anesthetic injection is increased;
and if the sleeping degree of the patient is larger than the preset range, reducing the amount of the anesthetic injection.
Preferably, the anesthesia target control intelligent infusion pump controller based on electroencephalogram parameter feedback is characterized in that the electroencephalogram signal acquisition submodule further comprises:
the signal detection unit is used for detecting the electroencephalogram signals of the patient collected by the signal collection unit, calculating the signal-to-noise ratio of the collected electroencephalogram signals of the patient, and judging whether the signal-to-noise ratio of the acquired electroencephalogram signals of the patient is smaller than a preset signal-to-noise ratio threshold value or not;
if not, judging that the obtained electroencephalogram signal of the patient is qualified;
if so, judging that the obtained electroencephalogram signals of the patient are unqualified and acquiring again.
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 hereof as well as the appended drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a flow chart of an intelligent anesthesia target-controlled infusion pump controller based on electroencephalogram parameter feedback in an embodiment of the present invention;
FIG. 2 is a flowchart of an intelligent anesthesia target-controlled infusion pump controller based on EEG parameter feedback according to another embodiment of the present invention;
FIG. 3 is a flowchart of an intelligent anesthesia target-controlled infusion pump controller based on EEG parameter feedback according to another embodiment of the present invention;
fig. 4 is a flowchart of an anesthesia target-controlled intelligent infusion pump controller based on electroencephalogram parameter feedback in another embodiment of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
An intelligent anesthesia target control infusion pump controller based on electroencephalogram parameter feedback according to an embodiment of the present invention is described below with reference to fig. 1 to 4.
Example 1:
as shown in fig. 1, an embodiment of the present invention provides an intelligent anesthesia target-controlled infusion pump controller based on electroencephalogram parameter feedback, including:
the data acquisition module is used for acquiring the electroencephalogram signal and the biological signal of the patient so as to obtain the electroencephalogram parameter and the physiological parameter of the patient;
the intelligent processing module is used for generating a plurality of injection schemes based on the electroencephalogram parameters and the physiological parameters;
and the control module is used for acquiring the optimal injection scheme according to the operation process, generating an infusion pump control scheme and controlling the infusion pump to inject the medicament to the patient.
In the embodiment, the electroencephalogram signals are the overall reflection of the electrophysiological activity of brain nerve cells on the surface of the cerebral cortex or scalp, and the electroencephalogram signals contain a large amount of physiological and disease information, so that the electroencephalogram signal processing in the aspect of clinical medicine can not only provide a diagnosis basis for certain brain diseases, but also provide an effective treatment means for certain brain diseases.
In this embodiment, the injection scheme is a plurality of schemes for injecting anesthetic into the patient, which are obtained according to the electroencephalogram parameter, the physiological parameter, and the type of anesthetic of the patient.
In this embodiment, the optimal injection protocol is the injection protocol with the injection speed and the time to achieve the preset anesthetic effect that best match the surgical procedure.
In this embodiment, the control scheme is a control scheme generated based on an optimal infusion scheme and capable of controlling the infusion pump to perform drug registration according to the optimal infusion scheme.
In this embodiment, the biological signal is an unstable natural signal emitted by a complex living body, and is different from a general signal in terms of the characteristics of the signal itself, the detection method, and the processing technique.
In this embodiment, the electroencephalogram parameters are parameters of electroencephalograms in the brain of the patient obtained from the electroencephalogram signal, such as wave bands, frequencies, and the like.
In this embodiment, the biological parameters are parameters of the patient's electrocardio, blood pressure, blood oxygen, and exhaled carbon dioxide obtained from the biological signals;
the beneficial effect of above-mentioned scheme: the scheme can obtain the optimal injection scheme by taking the electroencephalogram parameters and the biological parameters of the patient before the operation, realizes the standardized and standardized anesthesia administration according to the optimal injection scheme, enables the pump speed of each pump administration to reach, is humanized and standardized, and enables new people who just engage in anesthesia to complete high-quality anesthesia.
Example 2:
based on embodiment 1, the anesthesia target control intelligent infusion pump controller based on electroencephalogram parameter feedback comprises: any one or more of a sedation injection pump, a shufen injection pump, a resifen injection pump, a muscle relaxation injection pump, a booster injection pump, a hypotensor injection pump, and a bradycardia injection pump.
The beneficial effect of above-mentioned scheme: the scheme can meet the requirement of an intelligent anesthesia target control system by using a common injection pump without using a TCI pump; meanwhile, a sedation injection pump, a shufen injection pump, a resifen injection pump, a muscle relaxation injection pump, a booster injection pump, a depressor injection pump and a bradycardia injection pump can be controlled, corresponding medicaments can be injected in time when needed, the working strength of doctors can be reduced, and medical accidents are reduced.
Example 3:
based on embodiment 1, the control module includes:
the scheme receiving submodule is used for receiving a plurality of injection schemes generated by the intelligent processing module;
the scheme analysis submodule is used for analyzing the plurality of injection schemes, acquiring the optimal injection scheme and generating corresponding working instructions;
and the instruction issuing sub-module is used for controlling the infusion pump to execute corresponding operation based on the working instruction.
In this embodiment, the working instruction is a working instruction that is generated according to the optimal injection scheme and controls the infusion pump to execute the injection operation according to the optimal injection scheme, where the optimal injection scheme is obtained by analyzing the injection scheme.
The beneficial effect of above-mentioned scheme: the invention can analyze the received injection scheme to obtain the optimal injection scheme, and generates a working instruction according to the optimal injection scheme to control the infusion pump to perform the injection operation, thereby ensuring the accuracy of the injection operation.
Example 4:
based on embodiment 3, the anesthesia target control intelligent infusion pump controller based on electroencephalogram parameter feedback, the instruction issuing sub-module is used for issuing a working instruction to the infusion pump, controlling a related sedation injection pump, a shufen injection pump, a resifen injection pump, a muscle relaxation injection pump, a pressure boosting injection pump, a pressure reducing injection pump and a heart rate relieving injection pump in the infusion pump, and injecting corresponding medicaments to a patient according to the specified time and the specified dosage.
The beneficial effect of above-mentioned scheme: can be according to the demand, assign corresponding instruction to different syringe pumps, make each pump can standardize, standardize according to the demand and dose, can carry out differentiation control according to different patients' drug resistance, have very strong practicality.
Example 5:
based on embodiment 1, the anesthesia target control intelligent infusion pump controller based on electroencephalogram parameter feedback further comprises:
a case acquisition module for acquiring a case file of a patient from receiving treatment to ending treatment, comprising:
the case generation submodule is used for acquiring the real-time reaction of a patient after the medicament injection is carried out according to a control scheme and generating a case file by combining the body information related to the patient;
the case encryption submodule is used for naming the case files according to the names and the case characteristics of the patients, encrypting the named case files to obtain encrypted case files, inputting the corresponding relation between the secret key and the encrypted case files into a secret key corresponding library in a local client, and uploading the encrypted case files to a cloud database for storage;
and the information searching submodule is used for searching matched case files in the cloud database based on the patient name and the case characteristics, searching a decoding secret key corresponding to the encrypted case files in a secret key corresponding library of the local client according to the encrypted case files, and decrypting the encrypted case files to obtain the case files of the corresponding patients.
In this embodiment, the case file is a record of medical activities such as examination, diagnosis, treatment, etc. performed by medical staff for the occurrence, development, and outcome of a disease of a patient.
In this embodiment, the physical information is real-time physical health information and medical history of the patient;
the beneficial effect of above-mentioned scheme: the invention can record the reaction and body information of a patient after the patient injects a medicament according to a control scheme in real time, generate a case file, name the case file, encrypt and send the case file to the cloud database for storage, store a decoding key to the local client, when the case needs to be called, obtain a corresponding case in the cloud database according to the name and the characteristics of the patient, decrypt the case by the decoding key stored in the local client, store the case file to the cloud, facilitate the return at any time and any place, encrypt the case file, and ensure the privacy of the case file.
Example 6:
based on embodiment 1, as shown in fig. 2, the controller for controlling an intelligent infusion pump based on target anesthesia based on electroencephalogram parameter feedback, the data acquisition module further includes:
the signal acquisition unit is used for acquiring an electroencephalogram signal of a patient before an operation;
the signal processing unit is used for amplifying the electroencephalogram signals to obtain amplified electroencephalogram signals;
inputting the obtained amplified electroencephalogram signal into a preset noise detection model, and outputting a noise signal type corresponding to the amplified electroencephalogram signal;
based on a preset noise signal type-filtering method correspondence table, acquiring a denoising method of the noise signal type, and performing corresponding filtering denoising on the amplified electroencephalogram signal to obtain a first denoised electroencephalogram signal;
acquiring a maximum peak point of the first de-noised electroencephalogram signal frequency, and judging whether the maximum peak point is within a preset range;
if so, outputting the first denoised electroencephalogram signal as a second denoised electroencephalogram signal;
if not, inputting the first de-noised electroencephalogram signal into the preset noise detection model, re-acquiring a corresponding de-noising method according to a preset noise signal type-filtering method correspondence table to perform corresponding filtering de-noising until the maximum peak point is within a preset range, and outputting a signal of which the maximum peak point is within the preset range as a second de-noised electroencephalogram signal;
the parameter acquisition unit is used for acquiring the singular spectrum of the second de-noised electroencephalogram signal, decomposing the singular spectrum and acquiring singular spectrum components of each order of the second de-noised electroencephalogram signal;
acquiring a corresponding second denoised electroencephalogram signal wave band based on the singular spectral components of each order of the second denoised electroencephalogram signal, and performing multi-layer wavelet transformation on the corresponding second denoised electroencephalogram signal wave band based on a preset wavelet function to obtain a wavelet coefficient corresponding to the second denoised electroencephalogram signal wave band;
acquiring wavelet coefficient difference values of every adjacent second denoised electroencephalogram signal wave band, acquiring standard deviations of all the wavelet coefficient difference values, and judging whether the standard deviations are within a preset range or not;
if so, outputting the second denoised electroencephalogram signal;
if not, calculating a compensation coefficient of a second de-noised electroencephalogram signal based on the preset wavelet function, performing compensation processing on the second de-noised electroencephalogram signal based on the compensation coefficient, performing preprocessing on the compensated second de-noised electroencephalogram signal until the standard deviation is within a preset range, and outputting the second de-noised electroencephalogram signal with the standard deviation within the preset range;
and inputting the output second de-noised electroencephalogram signal into a pre-established electroencephalogram characteristic detection model, outputting the current electroencephalogram characteristic and the corresponding current characteristic type, and obtaining electroencephalogram parameters.
In this embodiment, the preset noise detection model is a pre-trained noise detection model, and can detect the noise type, which is obtained by training each type of noise sample in the internet database.
In this embodiment, the maximum peak point is a point corresponding to the maximum value of the first denoised electroencephalogram signal frequency.
In this embodiment, the singular spectrum is a trajectory matrix constructed according to the time sequence of the observed signal, and the trajectory matrix is decomposed and reconstructed to extract signals representing different components of the original time sequence, such as a long-term trend signal, a periodic signal, a noise signal, and the like, so as to analyze the structure of the time sequence and further predict the structure.
In this embodiment, the compensation coefficient is obtained according to the preset wavelet function, and has a gain effect on the second denoised electroencephalogram signal.
The beneficial effect of above-mentioned scheme: the electroencephalogram signal processing method and the electroencephalogram signal processing device can process the collected electroencephalogram signal, enable the processed de-noised electroencephalogram signal to meet the requirements, enable the de-noised electroencephalogram signal to meet the preset requirements, enable the de-noised electroencephalogram signal to be input into an electroencephalogram characteristic detection model when the de-noised electroencephalogram signal meets the preset requirements, enable electroencephalogram characteristics contained in the de-noised electroencephalogram signal to be accurately obtained and feature types corresponding to the electroencephalogram characteristics, and can effectively avoid the problem that errors occur in model detection results due to the fact that the electroencephalogram signal cannot meet the preset requirements.
Example 7:
based on embodiment 1, as shown in fig. 3, the intelligent anesthesia target control infusion pump controller based on electroencephalogram parameter feedback, the intelligent processing module further includes:
the inhibition acquisition submodule is used for acquiring a first case related to the electroencephalogram parameters of the patient based on a big data platform to obtain a first case library;
the first obtaining submodule is used for obtaining complete information of a provider of each first case in the first case base, obtaining total evaluation numbers of the corresponding providers and calculating the credibility index of the provider corresponding to the first case;
removing a first case corresponding to a provider with a credibility index lower than a preset threshold value from the first case library to obtain a second case library, obtaining the inhibition degree of electroencephalogram parameters of each second case in the second case library on different types of narcotics, classifying the obtained second case library based on the narcotic type information, and determining the average value of the inhibition degrees of the same type of narcotics as the standard inhibition degree of the corresponding type of narcotics;
the establishing submodule is used for determining human body signals when different kinds of narcotics are injected based on the case parameters of the second case library, and establishing a human body bearing model of the same kind of narcotics by combining the standard inhibition degree;
the second acquisition sub-module is used for acquiring a current electroencephalogram corresponding to the electroencephalogram parameters of the current patient, dividing the current electroencephalogram into a plurality of sub-signals based on preset time nodes, acquiring a signal wave vector of each sub-signal, matching to obtain a mark value of each signal wave vector based on a vector matching database, and constructing to obtain a mark curve;
establishing an allowable maximum tolerance curve and an allowable minimum tolerance curve of the current patient under different kinds of narcotics based on the human body tolerance model and the marking curve;
the screening submodule is used for screening a first active point of a corresponding allowable maximum tolerance curve and a second active point of the allowable minimum tolerance curve under the same type of narcotic and taking the first active point and the second active point as corresponding anesthesia critical points;
and the scheme acquisition submodule is used for acquiring the standard injection dose of the current patient corresponding to different anesthesia critical points based on the anesthesia critical points corresponding to different types of anesthetics, and acquiring a plurality of injection schemes based on the optimal injection speed and the standard injection dose of the anesthesia critical points.
In this example, the standard degree of inhibition is the degree of inhibition of the anesthetic effect of the patient himself to different kinds of narcotics;
in the embodiment, the first case library is a database for recording the suppression degree of case electroencephalogram parameters of case patients to narcotics;
in the embodiment, the credibility index is the effective degree of the information of the data provider, which is obtained by calculating according to the complete information of the provider and the total evaluation number of the corresponding provider in the internet;
in this embodiment, the anesthetic type information is to classify the anesthesia according to an index, such as: classified according to the anesthetic intoxication pharmaceutical manufacturers;
in this embodiment, the anesthesia critical point is a value corresponding to when the patient reaches a deep anesthesia state and a shallow anesthesia state, or a critical point when the patient is in different anesthesia states.
In this embodiment, the flag value is obtained by mapping each vector in the database, and the vector matching database includes: the signal wave vector and the corresponding sign value are included, and the signal wave vector can be obtained according to the peak value and the valley value of the signal.
In this embodiment, for example, different kinds of anesthesia are determined according to the concentration of anesthesia, and in this case, the anesthesia tolerance of the patient for different concentrations can be determined, so as to obtain corresponding maximum and minimum tolerance curves.
In this embodiment, the signal points corresponding to the maximum peak, the minimum peak, the maximum valley and the minimum valley in the maximum allowable tolerance curve can be regarded as the first active points, and the signal points corresponding to the maximum peak, the minimum peak, the maximum valley and the minimum valley in the minimum allowable tolerance curve can be regarded as the second active points.
In this embodiment, the flag curve is a curve that is formed on a time-series basis from a plurality of flag values.
The beneficial effect of above-mentioned scheme: the method can acquire the inhibition degree of the electroencephalogram parameters of the patient on the narcotic based on a big data platform to obtain the standard inhibition degree, and can judge the critical points of the sedation depth and the sedation depth of different surgical patients according to the human body signals when different kinds of narcotic are injected and by combining the standard inhibition degree, the human body bearing model of the same kind of narcotic is established, and the anesthesia critical points are acquired according to the electroencephalogram signals of the human body bearing model.
Example 8:
based on the embodiment 3, the controller for anesthesia target-controlled intelligent infusion pump based on electroencephalogram parameter feedback, the scheme analysis submodule further comprises:
the scheme determining unit is used for acquiring a surgical procedure, matching injection schemes corresponding to different anesthetics based on the surgical procedure, and acquiring an injection scheme corresponding to the anesthetic with the highest matching degree as an optimal injection scheme;
and the instruction generating unit is used for acquiring the optimal time point when the patient reaches the anesthesia critical point based on the operation flow and the operation starting time, and generating a corresponding working instruction based on the optimal injection scheme and the optimal time point.
In this embodiment, the operation procedure is an operation procedure after the start of the operation, which is established according to the patient's condition.
The beneficial effect of above-mentioned scheme: the anesthesia method and the anesthesia device can calculate the anesthesia scheme with the highest matching degree with the operation flow according to the operation flow of the patient to obtain the optimal anesthesia scheme, and appoint the time for starting the injection of the anesthetic according to the operation starting time and the operation flow to ensure that the patient can achieve the expected anesthesia effect during the operation and the normal operation of the operation.
Example 9:
based on embodiment 1, as shown in fig. 4, the controller for anesthesia target-controlled intelligent infusion pump based on electroencephalogram parameter feedback further includes:
the state monitoring module is used for detecting the sleep degree and the body state of a patient in real time in the operation process, and comprises:
the anesthesia state determining submodule is used for acquiring real-time electroencephalogram parameters and real-time biological parameters of a patient in real time when the infusion pump executes corresponding operation;
acquiring the sleep degree of the patient based on the real-time electroencephalogram parameters, and judging whether the sleep degree of the patient is within a preset range;
if so, continuing to inject the anesthetic according to the optimal injection scheme;
if the sleeping degree of the patient is smaller than the preset range, the amount of the anesthetic injection is increased;
and if the sleeping degree of the patient is larger than the preset range, reducing the amount of the anesthetic injection.
In this embodiment, the sleep level is a state of depth of sleep after the patient has been injected with an anesthetic.
The beneficial effect of above-mentioned scheme: the scheme can detect and analyze real-time electroencephalogram parameters and real-time biological parameters of a patient in real time after the operation starts, acquire the sleep degree of the patient, judge whether the sleep degree of the patient is within a preset range, and increase or reduce the anesthetic dose according to the judgment result.
Example 10:
based on embodiment 6, the controller for anesthesia target-controlled intelligent infusion pump based on electroencephalogram parameter feedback, the electroencephalogram signal acquisition sub-module further comprises:
the signal detection unit is used for detecting the electroencephalogram signals of the patient collected by the signal collection unit, calculating the signal-to-noise ratio of the collected electroencephalogram signals of the patient, and judging whether the signal-to-noise ratio of the acquired electroencephalogram signals of the patient is smaller than a preset signal-to-noise ratio threshold value or not;
if not, judging that the obtained electroencephalogram signal of the patient is qualified;
if so, judging that the obtained electroencephalogram signals of the patient are unqualified and acquiring again.
In this embodiment, when the electroencephalogram signal is acquired, the electroencephalogram signal is divided into a plurality of component signals;
acquiring sampling frequency when the electroencephalogram signals are acquired, total power of the acquired electroencephalogram signals, power of each component signal and carrier frequency of each component signal;
from the data obtained, the power W of the noise signal in the ith component signal can be calculatedZiThe calculation formula is as follows:
wherein, gamma is the roll-off coefficient of the ith component signal and is 0.34; wiIs the total power of the ith component signal; tau is the collection frequency when the electroencephalogram signals are collected; epsiloniThe carrier frequency of the ith component signal; theta is an error coefficient during signal acquisition and takes a value of 0.95; delta is the attenuation coefficient of the signal and takes the value of 0.96; wi is provided withRepresenting the effective power of the ith component signal;
according to the power W of the noise signal in the ith component signalZiCan calculate the ithSignal-to-noise ratio e of component signaliThe calculation formula is as follows:
wherein W is the total power of the acquired electroencephalogram signals; l is the number of component signals; Δ is an effective adjustment parameter of the average power corresponding to all the component signals, and the value is related to the average value of the powers of all the noise signals, that is, the larger the average value of the powers of all the noise signals is, the larger the corresponding effective adjustment parameter is, for example: the average of the power of all noise signals is 10db and the corresponding effective tuning parameter is also 10 db.
And comparing the signal-to-noise ratio of each obtained component signal with a preset signal-to-noise ratio threshold, and if the signal-to-noise ratio of the obtained component signal is smaller than the preset signal-to-noise ratio threshold, judging that the acquired electroencephalogram signal is unqualified.
The signal-to-noise ratio of each component signal can be accurately calculated according to the algorithm, so that the qualification of the acquired signal is judged according to the signal-to-noise ratio, and the influence on the analysis result due to unqualified signals in subsequent signal analysis is avoided.
The method can divide the acquired electroencephalogram signal into a plurality of component signals, judge whether the signal-to-noise ratio of each component signal is smaller than a preset signal-to-noise ratio threshold value or not, judge that the acquired electroencephalogram signal is unqualified when the component signal smaller than the preset signal-to-noise ratio threshold value exists, ensure the quality of the acquired electroencephalogram signal and ensure the correctness of electroencephalogram parameters.
The beneficial effect of above-mentioned scheme: the scheme can detect the signal-to-noise ratio of the acquired electroencephalogram signal in real time, and when the signal-to-noise ratio does not meet the preset requirement, the electroencephalogram signal is acquired again, so that the problem in subsequent processing caused by the abnormal acquired electroencephalogram signal is avoided.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (10)
1. The utility model provides an intelligent infusion pump controller of anesthesia target accuse based on brain electrical parameter feedback which characterized in that includes:
the data acquisition module is used for acquiring the electroencephalogram signal and the biological signal of the patient so as to obtain the electroencephalogram parameter and the physiological parameter of the patient;
the intelligent processing module is used for generating a plurality of injection schemes based on the electroencephalogram parameters and the physiological parameters;
and the control module is used for acquiring the optimal injection scheme according to the operation process, generating an infusion pump control scheme and controlling the infusion pump to inject the medicament to the patient.
2. The intelligent anesthesia target-controlled infusion pump controller based on electroencephalogram parameter feedback of claim 1, wherein the infusion pump comprises: any one or more of a sedation injection pump, a shufen injection pump, a resifen injection pump, a muscle relaxation injection pump, a booster injection pump, a hypotensor injection pump, and a bradycardia injection pump.
3. The intelligent anesthesia target-controlled infusion pump controller based on electroencephalogram parameter feedback of claim 1, wherein the control module comprises:
the scheme receiving submodule is used for receiving a plurality of injection schemes generated by the intelligent processing module;
the scheme analysis submodule is used for analyzing the plurality of injection schemes, acquiring the optimal injection scheme and generating corresponding working instructions;
and the instruction issuing sub-module is used for controlling the infusion pump to execute corresponding operation based on the working instruction.
4. The intelligent anesthesia target-controlled infusion pump controller based on electroencephalogram parameter feedback according to claim 3, wherein the instruction issuing sub-module is used for issuing a working instruction to the infusion pump, controlling a related sedation injection pump, a sufen injection pump, a resifen injection pump, a muscle relaxation injection pump, a booster injection pump or a depressor injection pump, and a bradycardia injection pump in the infusion pump, and injecting a corresponding medicament to the patient according to the specified time and the specified dosage.
5. The intelligent anesthesia target-controlled infusion pump controller based on electroencephalogram parameter feedback of claim 1, further comprising:
a case acquisition module for acquiring a case file of a patient from receiving treatment to ending treatment, comprising:
the case generation submodule is used for acquiring the real-time reaction of a patient after the medicament injection is carried out according to a control scheme and generating a case file by combining the body information related to the patient;
the case encryption submodule is used for naming the case files according to the names and the case characteristics of the patients, encrypting the named case files to obtain encrypted case files, inputting the corresponding relation between the secret key and the encrypted case files into a secret key corresponding library in a local client, and uploading the encrypted case files to a cloud database for storage;
and the information searching submodule is used for searching matched case files in the cloud database based on the patient name and the case characteristics, searching a decoding secret key corresponding to the encrypted case files in a secret key corresponding library of the local client according to the encrypted case files, and decrypting the encrypted case files to obtain the case files of the corresponding patients.
6. The intelligent anesthesia target-controlled infusion pump controller based on electroencephalogram parameter feedback of claim 1, wherein the data acquisition module further comprises:
the signal acquisition unit is used for acquiring an electroencephalogram signal of a patient before an operation;
the signal processing unit is used for amplifying the electroencephalogram signals to obtain amplified electroencephalogram signals;
inputting the obtained amplified electroencephalogram signal into a preset noise detection model, and outputting a noise signal type corresponding to the amplified electroencephalogram signal;
based on a preset noise signal type-filtering method correspondence table, acquiring a denoising method of the noise signal type, and performing corresponding filtering denoising on the amplified electroencephalogram signal to obtain a first denoised electroencephalogram signal;
acquiring a maximum peak point of the first de-noised electroencephalogram signal frequency, and judging whether the maximum peak point is within a preset range;
if so, outputting the first denoised electroencephalogram signal as a second denoised electroencephalogram signal;
if not, inputting the first de-noised electroencephalogram signal into the preset noise detection model, re-acquiring a corresponding de-noising method according to a preset noise signal type-filtering method correspondence table to perform corresponding filtering de-noising until the maximum peak point is within a preset range, and outputting a signal of which the maximum peak point is within the preset range as a second de-noised electroencephalogram signal;
the parameter acquisition unit is used for acquiring the singular spectrum of the second de-noised electroencephalogram signal, decomposing the singular spectrum and acquiring singular spectrum components of each order of the second de-noised electroencephalogram signal;
acquiring a corresponding second denoised electroencephalogram signal wave band based on the singular spectral components of each order of the second denoised electroencephalogram signal, and performing multi-layer wavelet transformation on the corresponding second denoised electroencephalogram signal wave band based on a preset wavelet function to obtain a wavelet coefficient corresponding to the second denoised electroencephalogram signal wave band;
acquiring wavelet coefficient difference values of every adjacent second denoised electroencephalogram signal wave band, acquiring standard deviations of all the wavelet coefficient difference values, and judging whether the standard deviations are within a preset range or not;
if so, outputting the second denoised electroencephalogram signal;
if not, calculating a compensation coefficient of a second de-noised electroencephalogram signal based on the preset wavelet function, performing compensation processing on the second de-noised electroencephalogram signal based on the compensation coefficient, performing preprocessing on the compensated second de-noised electroencephalogram signal until the standard deviation is within a preset range, and outputting the second de-noised electroencephalogram signal with the standard deviation within the preset range;
and inputting the output second de-noised electroencephalogram signal into a pre-established electroencephalogram characteristic detection model, outputting the current electroencephalogram characteristic and the corresponding current characteristic type, and obtaining electroencephalogram parameters.
7. The intelligent anesthesia target-controlled infusion pump controller based on electroencephalogram parameter feedback of claim 1, wherein the intelligent processing module further comprises:
the inhibition acquisition submodule is used for acquiring a first case related to the electroencephalogram parameters of the patient based on a big data platform to obtain a first case library;
the first obtaining submodule is used for obtaining complete information of a provider of each first case in the first case base, obtaining total evaluation numbers of the corresponding providers and calculating the credibility index of the provider corresponding to the first case;
removing a first case corresponding to a provider with a credibility index lower than a preset threshold value from the first case library to obtain a second case library, obtaining the inhibition degree of electroencephalogram parameters of each second case in the second case library on different types of narcotics, classifying the obtained second case library based on the narcotic type information, and determining the average value of the inhibition degrees of the same type of narcotics as the standard inhibition degree of the corresponding type of narcotics;
the establishing submodule is used for determining human body signals when different kinds of narcotics are injected based on the case parameters of the second case library, and establishing a human body bearing model of the same kind of narcotics by combining the standard inhibition degree;
the second acquisition sub-module is used for acquiring a current electroencephalogram corresponding to the electroencephalogram parameters of the current patient, dividing the current electroencephalogram into a plurality of sub-signals based on preset time nodes, acquiring a signal wave vector of each sub-signal, matching to obtain a mark value of each signal wave vector based on a vector matching database, and constructing to obtain a mark curve;
establishing an allowable maximum tolerance curve and an allowable minimum tolerance curve of the current patient under different kinds of narcotics based on the human body tolerance model and the marking curve;
the screening submodule is used for screening a first active point of a corresponding allowable maximum tolerance curve and a second active point of the allowable minimum tolerance curve under the same type of narcotic and taking the first active point and the second active point as corresponding anesthesia critical points;
and the scheme acquisition submodule is used for acquiring the standard injection dose of the current patient corresponding to different anesthesia critical points based on the anesthesia critical points corresponding to different types of anesthetics, and acquiring a plurality of injection schemes based on the optimal injection speed and the standard injection dose of the anesthesia critical points.
8. The intelligent anesthesia target-controlled infusion pump controller based on electroencephalogram parameter feedback of claim 3, wherein the scheme parsing submodule further comprises:
the scheme determining unit is used for acquiring a surgical procedure, matching injection schemes corresponding to different anesthetics based on the surgical procedure, and acquiring an injection scheme corresponding to the anesthetic with the highest matching degree as an optimal injection scheme;
and the instruction generating unit is used for acquiring the optimal time point when the patient reaches the anesthesia critical point based on the operation flow and the operation starting time, and generating a corresponding working instruction based on the optimal injection scheme and the optimal time point.
9. The intelligent anesthesia target-controlled infusion pump controller based on electroencephalogram parameter feedback of claim 1, further comprising:
the state monitoring module is used for detecting the sleep degree and the body state of a patient in real time in the operation process, and comprises:
the anesthesia state determining submodule is used for acquiring real-time electroencephalogram parameters and real-time biological parameters of a patient in real time when the infusion pump executes corresponding operation;
acquiring the sleep degree of the patient based on the real-time electroencephalogram parameters, and judging whether the sleep degree of the patient is within a preset range;
if so, continuing to inject the anesthetic according to the optimal injection scheme;
if the sleeping degree of the patient is smaller than the preset range, the amount of the anesthetic injection is increased;
and if the sleeping degree of the patient is larger than the preset range, reducing the amount of the anesthetic injection.
10. The intelligent anesthesia target-controlled infusion pump controller based on electroencephalogram parameter feedback of claim 6, wherein the electroencephalogram signal acquisition sub-module further comprises:
the signal detection unit is used for detecting the electroencephalogram signals of the patient collected by the signal collection unit, calculating the signal-to-noise ratio of the collected electroencephalogram signals of the patient, and judging whether the signal-to-noise ratio of the acquired electroencephalogram signals of the patient is smaller than a preset signal-to-noise ratio threshold value or not;
if not, judging that the obtained electroencephalogram signal of the patient is qualified;
if so, judging that the obtained electroencephalogram signals of the patient are unqualified and acquiring again.
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| CN116616794A (en) * | 2023-05-18 | 2023-08-22 | 中国人民解放军海军特色医学中心 | Underwater operator fatigue adjustment method and system based on electroencephalogram signals |
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| CN116616794A (en) * | 2023-05-18 | 2023-08-22 | 中国人民解放军海军特色医学中心 | Underwater operator fatigue adjustment method and system based on electroencephalogram signals |
| CN116746927A (en) * | 2023-05-18 | 2023-09-15 | 中国人民解放军海军特色医学中心 | Method and system for adjusting states of underwater operators during underwater operation of closed cabin |
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