CN113729730A

CN113729730A - Anesthesia state monitoring system based on electroencephalogram analysis technology

Info

Publication number: CN113729730A
Application number: CN202111033962.3A
Authority: CN
Inventors: 吕华伟
Original assignee: Henan Anything Technology Development Co ltd
Current assignee: Henan Anything Technology Development Co ltd
Priority date: 2021-09-03
Filing date: 2021-09-03
Publication date: 2021-12-03

Abstract

The invention discloses an anesthesia state monitoring system based on an electroencephalogram analysis technology, which comprises an EEG acquisition device, a central processing unit and a background analysis terminal, wherein the EEG acquisition device is used for acquiring and preprocessing electroencephalograms under the anesthesia state of a patient, the central processing unit is used for processing and normalizing the acquired electroencephalograms under the anesthesia state of the patient, and the background analysis terminal is used for performing core algorithm analysis processing on the preprocessed and normalized electroencephalograms of the patient; the EEG acquisition equipment comprises an electroencephalogram acquisition patch, wherein an electroencephalogram acquisition device for acquiring electroencephalogram signals is fixedly mounted on the electroencephalogram acquisition patch, a conductive silica gel layer is further arranged on the outer side of the electroencephalogram acquisition patch, and a PET (polyethylene terephthalate) protective film is further bonded on the outer side of the conductive silica gel layer; the system combines the trend analysis of quantitative indexes and the anesthesia depth monitoring of electroencephalogram real-time spectrogram analysis, measures and describes the spontaneous or induced rhythmic bioelectricity activity of the frontal cortex to monitor the depth of the unconsciousness of the patient under anesthesia, and improves the high efficiency and the safety of the system for monitoring the anesthesia use.

Description

Anesthesia state monitoring system based on electroencephalogram analysis technology

Technical Field

The invention belongs to the technical field of electroencephalogram analysis and anesthesia state monitoring, and particularly relates to an anesthesia state monitoring system based on an electroencephalogram analysis technology.

Background

The level of depth of General Anesthesia (GA) is required to be suitable for an individual patient undergoing surgery, and anesthesia-related complications may increase if the anesthesia is deeper than needed to maintain the patient unconscious, such as postoperative nausea, vomiting, cognitive dysfunction, etc.; if the depth of anesthesia is too shallow, the patient may not be completely unconscious, and there is a risk that intraoperative awareness will occur; therefore, the optimal dosage of drug administration is very important for realizing ideal and effective analgesia, unconsciousness and immobility so as to reduce the potential negative effects of insufficient dosage or overdose dosage, in the traditional anesthesia technology, an anesthesiologist mainly judges the anesthesia depth and adjusts the anesthesia drug by observing clinical signs such as blood pressure, heart rate, respiration, sweating, pupillary reflex, pulse blood oxygen saturation, lacrimation, eyeball movement, facial expression and the like, however, the analysis of the signs becomes difficult and unreliable by using drugs such as muscle relaxant, vasodilator and the like, the anesthesia depth level can not be completely mastered by simple clinical observation, furthermore, in the traditional electroencephalogram patch under the anesthesia state, the traditional electroencephalogram patch is completed by adopting a conventional pressure adsorption patch and medical detection gel application, the operation is complicated, and a set of equipment is shared by multiple persons, the sterilization and disinfection and the environmental sanitation are difficult to guarantee, and the experience of detection is very poor for a patient, so that an anesthesia state monitoring system based on an electroencephalogram analysis technology is needed.

Disclosure of Invention

The invention aims to provide an anesthesia state monitoring system based on an electroencephalogram analysis technology to solve the existing problems, the system combines trend analysis of quantitative indexes and anesthesia depth monitoring of electroencephalogram real-time spectrogram analysis to measure and describe spontaneous or induced rhythmic bioelectricity activity of frontal cortex so as to monitor the depth of an unconscious state of a patient under anesthesia, the high efficiency and safety of the system for anesthesia use monitoring are greatly improved, the system is used in a disposable patch mode, the environmental sanitation of the system is improved, and further the experience of the patient and the working efficiency of medical staff are greatly improved.

The technical scheme adopted by the invention is as follows:

an anesthesia state monitoring system based on an electroencephalogram analysis technology comprises an EEG acquisition device, a central processing unit and a background analysis terminal, wherein the EEG acquisition device is used for acquiring and preprocessing electroencephalograms of a patient in an anesthesia state, the central processing unit is used for processing and normalizing the acquired electroencephalograms of the patient in the anesthesia state, and the background analysis terminal is used for performing core algorithm analysis processing on the preprocessed electroencephalograms of the patient;

the EEG acquisition equipment comprises an EEG acquisition patch attached to the forehead of a patient, an EEG acquisition device for acquiring brain wave signals is fixedly mounted in the EEG acquisition patch, a conductive silica gel layer is further arranged on the outer side of the EEG acquisition patch, a PET protective film is further bonded on the outer side of the conductive silica gel layer, one end of the EEG acquisition device penetrates through the conductive silica gel layer to be in contact with the forehead of the patient, and the other end of the EEG acquisition device is connected with the central processing unit through a lead, so that skin contact and signal acquisition are facilitated;

the central processing unit comprises a signal receiver for receiving electroencephalogram signal data, a preamplifier for performing primary signal processing and amplification on electroencephalogram signal original data, a secondary amplifier for performing secondary amplification processing on electroencephalogram signal data, a filter for performing filtering processing on acquired electroencephalogram signals, a digital-to-analog converter for performing digital-to-analog conversion processing on electroencephalogram signals, an optical coupler signal isolator for performing initial optical coupler signal isolation processing on electroencephalogram signals and a network transmission module for transmitting the electroencephalogram signal data after preprocessing and normalization, and the central processing unit is integrally installed in the EEG acquisition equipment;

the background analysis terminal comprises a signal receiving module for receiving a central processing unit to preprocess a regular brain wave signal, an algorithm analysis module for performing core algorithm processing on each parameter of the obtained preprocessed brain wave signal, an anesthesia consciousness classification module for performing index conversion on a reference value based on a processing result of the algorithm analysis module, a display module for visually displaying acquired brain wave signal data and algorithm analysis data and a data storage module for storing the acquired brain wave signal data and the algorithm analysis data;

the anesthesia consciousness classification module comprises an anesthesia state index module for judging and classifying result values after algorithm analysis processing is carried out on electroencephalogram data of a patient and an anesthesia consciousness classification module for classifying, judging and grading the result values based on the anesthesia state index module for monitoring algorithm analysis processing under the anesthesia state of the patient;

the display module comprises an electroencephalogram oscillogram module for visually displaying the fluctuation of the electroencephalogram signal of the patient under the anesthesia state, an anesthesia depth trend graph module for displaying the fluctuation of consciousness after being processed by an analytic algorithm when the patient is in the anesthesia state, an electromyogram index oscillogram module for displaying the body performance of the patient under the anesthesia state, an anesthesia index dendrogram module for visually displaying the index of the anesthesia state of the patient under the anesthesia state and an anesthesia consciousness dendrogram module for displaying the index of the anesthesia consciousness of the patient under the anesthesia state.

Preferably, the electroencephalogram collection patch is of a strip-shaped long-strip-shaped structure, and the part, which is located on the electroencephalogram collector, of the electroencephalogram collection patch is of any one of a circular structure, a triangular structure, a square structure and a heart-shaped structure.

Preferably, the number of the electroencephalogram collectors on the electroencephalogram collecting patch is M, and M is more than or equal to 2.

Further preferably, a miniature lithium power supply is further arranged in the electroencephalogram acquisition patch, and the miniature lithium power supply is respectively electrically connected with the electroencephalogram acquisition device and the central processing unit.

Preferably, the network transmission module adopts a 5G network transmission protocol.

Preferably, the anesthesia state index module comprises a first-level index module, a second-level index module, a third-level index module and a fourth-level index module, and the BIS reference value of the first-level index module is 85-100; the BIS reference value of the secondary index module is 65-84; the BIS reference value of the tertiary index module is 40-64; the BIS reference value of the four-level index module is 0-39.

Further preferably, the anesthesia awareness grading module connected to the anesthesia status index module comprises a normal awareness level corresponding to the primary index module, a sedation awareness level corresponding to the secondary index module, an anesthesia awareness level corresponding to the tertiary index module, and an over anesthesia awareness level corresponding to the quaternary index module.

Preferably, the background analysis terminal is installed on an external network display terminal in an APP manner.

Further preferably, the network display terminal is any one of a computer and a mobile network device.

In summary, due to the adoption of the technical scheme, the invention has the beneficial effects that:

1. according to the anesthesia depth monitoring system, the EEG acquisition equipment and the background analysis terminal are arranged, the spontaneous or induced rhythmic bioelectricity activity of the frontal cortex is measured and described by combining the trend analysis of quantitative indexes and the anesthesia depth monitoring of electroencephalogram real-time spectrogram analysis, so that the unconsciousness depth of a patient under anesthesia is monitored, the high efficiency and the safety of the system for anesthesia use monitoring are greatly improved, the electroencephalogram acquisition patch is specifically arranged as a signal monitoring carrier, a plurality of electroencephalogram acquisition device parts with special structures are uniformly distributed and used for acquiring electroencephalogram signals, and a clean self-adhesive medical conductive silica gel layer is arranged on the outer side of the electroencephalogram acquisition patch, so that the environmental sanitation of the system is improved, and the experience of the patient and the working efficiency of medical workers are greatly improved;

2. in the invention, through arranging the EEG collecting patch, the conductive silica gel layer and the PET protective film, the convenient and disposable use of EEG collecting equipment which is directly contacted with the skin of a human body is realized, the arrangement part of the EEG collector adopts a special appearance structure design, has good marking property and identification property, the EEG signal collection and the pretreatment of a central processing unit are integrated into a regular and integrated structure, the outermost PET protective film is torn off when in use, and a micro lithium power supply is arranged to supply power to the EEG collecting equipment, so that the invention is reasonable and effective, is convenient for improving the accuracy of signal collection, the integral EEG collecting equipment is sleeved for disposable use, is safe and sanitary, prevents cross infection of epidemic diseases, the conductive silica gel is continuously extruded or compression-molded silicon rubber doped with special conductive particles, has moderate hardness, high conductivity and water vapor sealing effect, and can improve the electric field distribution of an insulating shielding layer, the damage of insulation is reduced, and the stability of signal acquisition data can be effectively ensured by the conductive silica gel bonded electroencephalogram collector;

3. in the invention, the preprocessing and the regularization processing of brain electrical signal collection under the anesthesia state of a patient are realized by arranging a central processing unit, specifically arranging a signal receiver for receiving brain electrical wave signal data collected by a brain electrical collector, arranging a preamplifier for processing and amplifying primary signals of brain electrical original data, arranging a secondary amplifier for performing secondary amplification processing on the brain electrical signal data, arranging a filter for filtering the collected brain electrical signal, arranging a digital-to-analog converter for performing digital-to-analog conversion processing on the brain electrical signal, arranging an optical coupling signal isolator for performing initial optical coupling signal isolation processing on the brain electrical signal, arranging a network transmission module for transmitting the brain electrical signal data after being preprocessed and regularized, and integrating the central processing unit into an EEG collecting device, the signal acquisition fixing and the data transmission of the patient are convenient to realize, and the loss of cable transmission and the trouble of plugging are reduced;

4. in the invention, by arranging the background analysis terminal, the real-time monitoring, the analytic processing of the parameter algorithm and the grading judgment of the anesthesia depth index of the patient in the anesthesia state are realized, the anesthesia use condition of the patient is reasonably and efficiently ensured, the real-time understanding of the anesthesia state of the patient and the reasonability of anesthesia medication of the medical staff are also met, the resources are saved, and the safety is ensured; the method comprises the steps of acquiring corresponding electroencephalogram data parameters by receiving electroencephalogram signals which are preprocessed and normalized by a central processing unit, carrying out core algorithm processing on the acquired parameters of the preprocessed electroencephalogram signals to obtain result reference values, then carrying out index conversion on the result reference values processed by an algorithm analysis module to realize anesthesia consciousness classification of anesthesia depth of a patient, and simultaneously realizing visual display of the acquired electroencephalogram signal data and the algorithm analysis data, such as an electroencephalogram, an anesthesia depth trend graph, an electromyogram, an anesthesia index tree graph and an anesthesia consciousness tree graph, and then recording and storing all data to facilitate calling and checking;

5. in the invention, EEG signal feature extraction is to use EEG signal as source signal, determine various parameters and use them as vector to form feature vector characterizing signal feature, the feature parameter mainly includes two categories of time domain signal (such as amplitude) and frequency domain signal (such as frequency), the frequency domain analysis method is mainly based on power and coherence of each frequency band of EEG signal, and the method is based on the assumption that EEG signal has stable characteristic, in EEG signal research, the commonly used frequency domain analysis method includes power spectrum estimation (direct method and indirect method) time domain analysis method which mainly analyzes the geometric properties of EEG waveform, such as amplitude, mean value, variance, skewness, kurtosis, etc., the microcomputer carries out fast Fourier transform on the original EEG to generate information of power, frequency and phase, synthesizes power and frequency, and eliminates influence of phase information, the data generated by the method comprises bispectrum analysis and traditional frequency-power analysis, the bispectrum analysis and the traditional frequency-power analysis are quantitatively processed to obtain bis anesthesia state indexes, and the secondary nonlinear characteristics of signals and the degree of deviation from normal distribution are determined by analyzing the phase coupling relation among all components of the electroencephalogram.

Drawings

FIG. 1 is a block diagram of the overall structural connection principle of the present invention;

FIG. 2 is a top view block diagram of the EEG acquisition apparatus of the present invention;

fig. 3 is a hierarchy distribution diagram of an EEG acquisition device of the invention.

The labels in the figure are: 1-EEG acquisition equipment, 101-electroencephalogram acquisition patch, 102-electroencephalogram acquisition device, 103-conductive silica gel layer, 104-PET protective film, 105-miniature lithium power supply, 2-central processing unit, 201-signal receiver, 202-preamplifier, 203-secondary amplifier, 204-filter, 205-digital-to-analog converter, 206-optical coupling signal isolator, 207-network transmission module, 3-background analysis terminal, 301-signal receiving module, 302-algorithm analysis module, 303-anesthesia consciousness classification module, 304-display module, 305-data storage module, 306-anesthesia state index module, 307-anesthesia classification module, 308-electroencephalogram module, 309-anesthesia depth trend map module, 310-electromyogram index waveform diagram module, 311-anesthesia index tree diagram module, 312-anesthesia consciousness tree diagram module, 313-first-level index module, 314-second-level index module, 315-third-level index module, 316-fourth-level index module, 317-normal consciousness level, 318-sedation consciousness level, 319-anesthesia consciousness level, 320-excessive anesthesia consciousness level and 4-network display terminal.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments.

As shown in connection with fig. 1-3;

an anesthesia state monitoring system based on electroencephalogram analysis technology comprises an EEG acquisition device 1 for acquiring and preprocessing electroencephalogram signals of a patient in an anesthesia state, a central processing unit 2 for processing and normalizing the acquired electroencephalogram signals of the patient in the anesthesia state, and a background analysis terminal 3 for performing core algorithm analysis processing on the preprocessed electroencephalogram signals of the patient; through setting up EEG collection equipment and backstage analysis terminal, combine the anesthesia depth monitoring of trend analysis and the analysis of brain electricity real-time frequency spectrogram of quantization index, measure and describe the spontaneous or induced rhythmic bioelectricity activity of frontal lobe cortex in order to monitor the unconsciousness state degree of depth under the patient's anesthesia, the high efficiency and the security of this system to anesthesia use monitoring have been improved greatly, specifically set up the carrier that the brain electricity gathered the paster to signal monitoring, and set up the brain electricity collector position of a plurality of special construction of evenly distributed, be used for gathering the brain wave signal, and set up clean self-adhesive medical conductive silica gel layer in the outside that the paster was gathered to the brain electricity, the sanitation of this system has been improved, and then patient's experience and medical staff's work efficiency has been improved greatly.

The EEG collecting equipment 1 comprises an EEG collecting patch 101 attached to the forehead of a patient, an EEG collector 102 for collecting an EEG signal is fixedly mounted on the EEG collecting patch 101, a conductive silica gel layer 103 is further arranged on the outer side of the EEG collecting patch 101, the inner side of the conductive silica gel layer 103 is fixedly connected with the EEG collecting patch, a PET protective film 104 is further bonded on the outer side of the conductive silica gel layer 103, one end of the EEG collector 102 penetrates through the conductive silica gel layer 103 to be contacted with the forehead of the patient, the other end of the EEG collector is connected with the central processing unit 2 through a lead, the EEG collecting patch 101 is of a strip-shaped structure, the part of the EEG collecting patch 101, which is positioned on the EEG collector 102, is any one of a circular structure, a triangular structure, a square structure and a heart-shaped structure, the EEG collectors 10 on the EEG collecting patch 101 are uniformly distributed M, M is more than or equal to 2, and a miniature lithium power supply 105 is further arranged in the EEG collecting patch 101, and the miniature lithium power supply 105 is electrically connected with the electroencephalogram collector 102 and the central processor 2 respectively.

Through arranging the EEG collection patch, the conductive silica gel layer and the PET protection film, the convenient disposable use of EEG collection equipment which is directly contacted with human skin is realized, the arrangement part of the EEG collector adopts a special appearance structure design and has good marking property and identification property, the EEG signal collection and central processing unit preprocess a regular integrated structure integration design, the outermost PET protection film is torn off when in use, the end part of the EEG collector is protruded by 0.5-1mm, and a micro lithium power supply is arranged to supply power to the EEG collection equipment, thereby being reasonable and effective, being convenient for improving the accuracy of signal collection, the whole EEG collection equipment is sleeved for disposable use, being safe and sanitary, preventing cross infection of epidemic diseases, the conductive silica gel is continuous extrusion or compression molding silicon rubber doped with special conductive particles, has moderate hardness, high conductivity and water vapor sealing effect, the electric field distribution of the insulating shielding layer can be improved, the insulation damage is reduced, and the stability of signal acquisition data can be effectively ensured by the conductive silica gel bonded electroencephalogram collector.

The central processing unit 2 comprises a signal receiver 201 for receiving EEG signal data, a preamplifier 202 for performing primary signal processing and amplification on EEG signal raw data, a secondary amplifier 203 for performing secondary amplification on the EEG signal data, a filter 204 for filtering the acquired EEG signal, a digital-to-analog converter 205 for performing digital-to-analog conversion on the EEG signal, an optical coupling signal isolator 206 for performing initial optical coupling signal isolation processing on the EEG signal and a network transmission module 207 for transmitting the EEG signal data after preprocessing and normalization, wherein the network transmission module 207 adopts a 5G network transmission protocol, the central processing unit 2 is integrally installed in the EEG acquisition equipment 1, and can synchronously transmit the EEG signals of up to 20 leads in a time division multiplexing mode through a public telephone network and a frequency modulation channel, the receiver can also know the current consciousness state of the tested person in real time and realize the synchronization of the receiving party and the transmitting party, and the use of the electroencephalogram telemetry system developed on the basis proves that the technology can cover most of the actual needs of clinical diagnosis, and has the advantages of good signal transmission quality, small distortion, no obvious noise pollution and no base drift.

The sampling values of n times (n-bit odd number) are continuously sorted according to the size by a middle guard value filtering method, the intermediate value is taken as the effective value, the signals are subjected to multi-resolution decomposition under different scales by the multi-resolution analysis characteristic of wavelet decomposition and reconstruction denoising wavelets, and mixed signals formed by various different frequencies which are interwoven together are decomposed into sub-signals of different frequency bands, so that the signals have the capacity of processing according to frequency bands. Denoising by using a wavelet decomposition and reconstruction method, which comprises the following specific steps: decomposing a signal containing noise into different frequency bands at a certain scale according to requirements, then zeroing the frequency band where the noise is located (or directly extracting the frequency band where a useful signal is located), and performing wavelet reconstruction so as to achieve the purpose of denoising; EEG signal feature extraction is to use brain electrical signal as source signal, determine various parameters and use them as vectors to form feature vectors characterizing signal features, the number of feature lines mainly includes two categories of time domain signals (such as amplitude) and frequency domain signals (such as frequency), the corresponding feature extraction method also includes time domain method, frequency domain method and time-frequency domain method, the frequency domain analysis method is mainly based on the power of each frequency band of EEG signal, coherence method, etc. the method is based on the assumption that EEG signal has stationary characteristic, and only considers the frequency domain information of signal, neglects the resolution of signal in time. In EEG signal studies, commonly used frequency domain analysis methods include power spectrum estimation (direct and indirect methods) time domain analysis methods, which mainly analyze the geometric properties of the EEG waveform, such as amplitude, mean, variance, skewness, kurtosis, etc.

The brain wave signal preprocessing and regulating device is characterized in that a central processing unit is arranged to realize preprocessing and regulating processing of brain electric signal acquisition under the anesthesia state of a patient, a signal receiver is specifically arranged to receive brain wave signal data acquired by a brain electric acquisition device, a preamplifier is arranged to process and amplify primary signals of brain electric signal raw data, a secondary amplifier is arranged to perform secondary amplification processing on the brain electric signal data, a filter is arranged to perform filtering processing on the acquired brain electric signal, a digital-to-analog converter is arranged to perform digital-to-analog conversion processing on the brain electric signal, an optical coupler signal isolator is arranged to perform initial optical coupler signal isolation processing on the brain electric signal, a network transmission module is arranged to transmit the brain electric signal data after preprocessing and regulating, and the central processing unit is integrated and arranged in an EEG acquisition device to facilitate the realization of the acquisition fixation and data transmission of the brain electric signal of the patient, the loss of cable transmission and the trouble of plugging are reduced.

The background analysis terminal 3 comprises a signal receiving module 301 for receiving the brain wave signal normalized by the preprocessing of the central processing unit 2, an algorithm analysis module 302 for performing core algorithm processing on each parameter of the obtained preprocessed brain wave signal, an anesthesia consciousness classification module 303 for performing index conversion on a reference value based on a processing result of the algorithm analysis module 302, a display module 304 for visually displaying the acquired brain wave signal data and the algorithm analysis data, and a data storage module 305 for storing the acquired brain wave signal data and the algorithm analysis data.

By arranging the background analysis terminal, real-time monitoring, parameter algorithm analysis processing and anesthesia depth index grading judgment of a patient in an anesthesia state are realized, the anesthesia use condition of the patient is reasonably and efficiently ensured, meanwhile, the reasonability of real-time understanding of the anesthesia state of the patient and anesthesia medication of medical staff is also met, resources are saved, and the safety is ensured; the method comprises the steps of receiving brain wave signals which are preprocessed and normalized by a central processing unit to obtain corresponding brain wave data parameters, conducting core algorithm processing on the parameters of the preprocessed brain wave signals to obtain result reference values, conducting index conversion on the result reference values processed by an algorithm analysis module to achieve anesthesia consciousness classification of anesthesia depth of a patient, and meanwhile achieving visual display of the collected brain wave signal data and the algorithm analysis data, such as a brain wave chart, an anesthesia depth trend chart, a myoelectricity index wave chart, an anesthesia index tree chart and an anesthesia consciousness tree chart, and then recording and storing all data to facilitate calling and checking.

The anesthesia consciousness classification module 303 comprises an anesthesia state index module 306 for judging and classifying based on a result value obtained by analyzing and processing an algorithm on electroencephalogram data of a patient and an anesthesia consciousness grading module 307 for judging and grading based on a classification of the anesthesia state index module 306 for analyzing and processing a monitoring algorithm under an anesthesia state of the patient; the anesthesia status index module 306 comprises a first-level index module 313, a second-level index module 314, a third-level index module 315 and a fourth-level index module 316, and the BIS reference value of the first-level index module 313 is 85-100; the BIS reference value of the secondary exponent module 314 is 65-84; the BIS reference value of the tertiary index block 315 is 40-64; the BIS reference value of the fourth-order index block 316 is 0-39, and the anesthesia consciousness classification block 307 coupled to the anesthesia status index block 306 includes a normal consciousness level 317 corresponding to the first-order index block 313, a sedation consciousness level 318 corresponding to the second-order index block 314, an anesthesia consciousness level 319 corresponding to the third-order index block 315, and an over-anesthesia consciousness level 320 corresponding to the fourth-order index block 316.

BIS is a univariate index, and simultaneously comprises three characteristics of frequency, amplitude and phase, can quantitatively analyze indexes of electroencephalogram, can accurately measure brain nerve physiological change, has good correlation with blood concentration of most anesthetic drugs, and is suitable for anesthesia depth monitoring indexes. And judging the anesthetic administration according to the index.

The display module 304 comprises an electroencephalogram waveform diagram module 308 for visually displaying the fluctuation of the electroencephalogram signal of the patient under the anesthesia state, an anesthesia depth trend diagram module 309 for displaying the fluctuation of consciousness after being processed by an analysis algorithm when the patient is under the anesthesia state, an electromyogram index waveform diagram module 310 for displaying the body performance of the patient under the anesthesia state, an anesthesia index tree diagram module 311 for visually displaying the index of the anesthesia state of the patient under the anesthesia state, and an anesthesia consciousness tree diagram module 312 for displaying the index of the anesthesia consciousness of the patient under the anesthesia state, the background analysis terminal 3 is installed on an external network display terminal 4 in an APP manner, and the network display terminal 4 is any one of a computer or a mobile network device.

EEG signal feature extraction is to use EEG signal as source signal, determine various parameters and use them as vector to form feature vector characterizing signal feature, the feature parameter mainly includes two categories of time domain signal (amplitude) and frequency domain signal (frequency), the frequency domain analysis method is mainly based on power and coherence of each frequency band of EEG signal, the common frequency domain analysis method includes power spectrum estimation (direct method and indirect method) time domain analysis method to mainly analyze EEG waveform geometric properties such as amplitude, mean, variance, skewness, kurtosis, etc. in EEG signal research, microcomputer carries out fast Fourier transform on original EEG to generate EEG power, frequency and phase information, synthesizes power and frequency, and eliminates influence of phase information, the generated data includes spectrum analysis and traditional frequency-power analysis, and quantitatively processing the two to obtain a bis anesthesia state index, and determining the secondary nonlinear characteristics of the signals and the degree of deviation from normal distribution by analyzing the phase coupling relation among all components of the electroencephalogram.

The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.

Claims

1. The utility model provides an anesthesia state monitoring system based on brain electrical analysis technique which characterized in that: the device comprises EEG acquisition equipment (1) for acquiring and preprocessing electroencephalogram signals of a patient in an anesthesia state, a central processing unit (2) for processing and normalizing the acquired electroencephalogram signals of the patient in the anesthesia state, and a background analysis terminal (3) for performing core algorithm analysis processing on the preprocessed and normalized electroencephalogram signals of the patient;

the EEG acquisition equipment (1) comprises an EEG acquisition patch (101) attached to the forehead of a patient, an EEG collector (102) used for collecting brain wave signals is fixedly mounted on the EEG acquisition patch (101), a conductive silica gel layer (103) is arranged on the outer side of the EEG acquisition patch (101), a PET protective film rubber sheet (104) is bonded to the outer side of the conductive silica gel layer (103), one end of the EEG collector (102) penetrates through the conductive silica gel layer (103) to be in contact with the forehead of the patient, and the other end of the EEG collector is connected with the central processing unit (2) through a lead;

the central processing unit (2) comprises a signal receiver (201) for receiving electroencephalogram signal data, a preamplifier (202) for performing primary signal processing and amplification on electroencephalogram signal original data, a secondary amplifier (203) for performing secondary amplification on electroencephalogram signal data, a filter (204) for performing filtering processing on acquired electroencephalogram signals, a digital-to-analog converter (205) for performing digital-to-analog conversion processing on electroencephalogram signals, an optical coupler signal isolator (206) for performing initial optical coupler signal isolation processing on electroencephalogram signals and a network transmission module (207) for transmitting the electroencephalogram signal data which are subjected to preprocessing and normalization, and the central processing unit (2) is integrally installed in the EEG acquisition equipment (1);

the background analysis terminal (3) comprises a signal receiving module (301) for receiving the brain wave signal normalized by preprocessing of the central processing unit (2), an algorithm analysis module (302) for performing core algorithm processing on each parameter of the obtained preprocessed electroencephalogram signal, an anesthesia consciousness classification module (303) for performing index conversion on a reference value based on a processing result of the algorithm analysis module (302), a display module (304) for performing visual display on the acquired electroencephalogram signal data and the algorithm analysis data, and a data storage module (305) for storing the acquired electroencephalogram signal data and the algorithm analysis data;

the anesthesia consciousness classification module (303) comprises an anesthesia state index module (306) for judging and classifying based on a result value obtained by analyzing and processing electroencephalogram data of a patient through an algorithm, and an anesthesia consciousness classification module (307) for judging and grading in a classification manner based on the anesthesia state index module (306) for analyzing and processing a monitoring algorithm under the anesthesia state of the patient;

the display module (304) comprises an electroencephalogram (308) waveform module for visually displaying the fluctuation of the electroencephalogram signal under the anesthesia state of the patient, an anesthesia depth trend graph (309) module for displaying the fluctuation of consciousness after being processed by an analysis algorithm when the patient is in the anesthesia state, an electromyogram (310) index waveform graph module for displaying the body expression of the patient under the anesthesia state, an anesthesia index tree graph module (311) for visually displaying the index of the anesthesia state of the patient under the anesthesia state, and an anesthesia consciousness tree graph module (312) for displaying the index of the anesthesia consciousness of the patient under the anesthesia state.

2. The anesthesia state monitoring system based on electroencephalogram analysis technology, which is characterized in that: the electroencephalogram acquisition patch (101) is of a strip-shaped long-strip-shaped structure, and the part, located on the electroencephalogram acquisition patch (101), of the electroencephalogram acquisition device (102) is of any one of a circular structure, a triangular structure, a square structure and a heart-shaped structure.

3. The anesthesia state monitoring system based on electroencephalogram analysis technology, which is characterized in that: the number of the electroencephalogram collectors (102) on the electroencephalogram collecting patch (101) is M, and M is more than or equal to 2.

4. The anesthesia state monitoring system based on electroencephalogram analysis technology, which is characterized in that: the electroencephalogram acquisition patch (101) is also internally provided with a miniature lithium power supply (105), and the miniature lithium power supply (105) is respectively and electrically connected with the electroencephalogram acquisition device (102) and the central processing unit (2).

5. The anesthesia state monitoring system based on electroencephalogram analysis technology, which is characterized in that: the network transmission module (207) adopts a 5G network transmission protocol.

6. The anesthesia state monitoring system based on electroencephalogram analysis technology, which is characterized in that: the index of the anesthesia status module (306) comprises a first-level index module (313), a second-level index module (314), a third-level index module (315) and a fourth-level index module (316), and the BIS reference value of the first-level index module (313) is 85-100; the BIS reference value of the secondary index module (314) is 65-84; the BIS reference value of the tertiary index module (315) is 40-64; the BIS reference value of the four-level exponent block (316) is 0-39.

7. The anesthesia state monitoring system based on electroencephalogram analysis technology, which is characterized in that: the anesthesia awareness stratification module (307) coupled to the anesthesia status index module (306) comprises a normal awareness level (317) corresponding to the primary index module (313), a sedation awareness level (318) corresponding to the secondary index module (314), an anesthesia awareness level (319) corresponding to the tertiary index module (315), and an over anesthesia awareness level (320) corresponding to the quaternary index module (316).

8. The anesthesia state monitoring system based on electroencephalogram analysis technology, which is characterized in that: and the background analysis terminal (3) is installed on an external network display terminal (4) in an APP manner.

9. The anesthesia state monitoring system based on electroencephalogram analysis technology, which is characterized in that: the network display terminal (4) is any one of a computer or a mobile network device.