CN114391810A - Intelligent child anesthesia target control method and system based on electroencephalogram signal monitoring - Google Patents
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Abstract
The invention provides an electroencephalogram signal monitoring-based intelligent method and system for controlling anesthesia targets of children, wherein the method comprises the following steps: collecting brain wave signals of children before and after an anesthesia operation, and recording and storing the collected brain wave signals in real time; determining multi-domain characteristic signals of brain wave signals of the children before and after the anesthesia operation based on the recorded and stored result; and determining the suppression degree of the children to the narcotic drugs and the current anesthesia depth condition of the children based on the multi-domain characteristic signals. By analyzing the brain waves of the children before and after the operation, the inhibition degree of different children on the anesthetic and the anesthesia depth condition are accurately and objectively analyzed, the monitoring of the clinical objective anesthesia condition of the children is facilitated, the anesthesia injury is reduced, and the anesthesia quality is improved.
Description
Technical Field
The invention relates to the technical field of medicine, in particular to an electroencephalogram signal monitoring-based intelligent method and system for controlling child anesthesia targets.
Background
At present, anesthetic is widely applied in medical industry, is used for relieving pain of patients in the treatment process of operations and the like, is convenient for smooth operation of the treatment process of operations and the like, the paralysis degree of the patients is determined by the infusion condition of the anesthetic, and serious influence can be caused to the patients when the paralysis degree is insufficient or excessive;
the traditional anesthetic administration operation is influenced by subjective factors of doctors according to clinical knowledge and experience of each doctor, the administration accuracy is low, and the inhibition degree and the anesthesia depth of each patient on the anesthetic cannot be determined according to the physical condition of each patient;
therefore, the invention provides an intelligent method and system for monitoring the anesthesia target control of children based on electroencephalogram signals, which are used for accurately and objectively analyzing the inhibition degree of different children on anesthetics and the anesthesia depth condition by analyzing the brain waves of the children before and after the operation, are beneficial to monitoring the anesthesia objective condition of clinical children, and improve the anesthesia quality while reducing the anesthesia injury.
Disclosure of Invention
The invention provides an intelligent method and system for monitoring child anesthesia target control based on electroencephalogram signals, which are used for accurately and objectively analyzing the inhibition degree of different children on anesthetics and the anesthesia depth condition by analyzing brain waves of the children before and after an operation, are beneficial to monitoring the clinical objective condition of the children anesthesia, and improve the anesthesia quality while reducing anesthesia injuries.
The invention provides an electroencephalogram signal monitoring-based intelligent method for controlling anesthesia targets of children, which comprises the following steps:
step 1: collecting brain wave signals of children before and after an anesthesia operation, and recording and storing the collected brain wave signals in real time;
step 2: determining multi-domain characteristic signals of brain wave signals of the children before and after the anesthesia operation based on the recorded and stored result;
and step 3: and determining the suppression degree of the children to the narcotic drugs and the current anesthesia depth condition of the children based on the multi-domain characteristic signals.
Preferably, the intelligent method for monitoring the anesthesia target control of the children based on the electroencephalogram signals comprises the following steps of in step 1, collecting the electroencephalogram signals of the children before and after the anesthesia operation, wherein the method comprises the following steps:
acquiring target brain wave acquisition areas of children before and after an anesthesia operation, and simultaneously determining the current use state of a preset brain wave sensor, wherein the target brain wave acquisition areas of the children before and after the anesthesia operation are at the same position;
determining target working parameters corresponding to the acquisition of the brain wave signals of the children based on the target brain wave acquisition area and the current use state of the preset brain wave sensor;
and controlling the preset brain wave sensor to acquire the brain wave signals of the children before and after the anesthesia operation based on the target working parameters.
Preferably, the intelligent method for monitoring the anesthesia target control of the children based on the electroencephalogram signals, which controls the preset electroencephalogram sensor to acquire the electroencephalogram signals of the children before and after the anesthesia operation based on the target working parameters, comprises the following steps:
acquiring collected brain wave signals of children before and after an anesthesia operation, and verifying the integrity of the brain wave signals based on a preset verification scheme;
if the brain wave signals are complete, judging that the brain wave signals collected by the preset brain wave sensor are qualified;
otherwise, judging that the brain wave signals acquired by the preset brain wave sensor are unqualified, and acquiring the brain wave signals of the children before and after the anesthesia operation based on the preset brain wave sensor again until the brain wave signals are judged to be complete.
Preferably, an intelligent method for monitoring child anesthesia target control based on electroencephalogram signals, in step 1, the collected electroencephalogram signals are recorded and stored in real time, and the method comprises the following steps:
acquiring identity information of a child, and acquiring collected brain wave signals of the child before and after an anesthesia operation;
determining a corresponding relation between the brain wave signals and identity information of the children, wherein the corresponding relation is that one child corresponds to one preoperative brain wave signal and one postoperative brain wave signal;
matching a target report template from a preset report template library based on the corresponding relation, and filling the child identity information and the corresponding brain wave signals into a target position in the target report template;
and obtaining a target record report based on the filling result.
Preferably, the intelligent method for monitoring the anesthesia target control of the children based on the electroencephalogram signals, which obtains a target record report based on filling results, comprises the following steps:
acquiring an obtained target record report, and determining attribute information of the target record report;
searching a target storage folder from a preset storage area based on the attribute information, and searching whether the target storage folder has a target storage space based on a preset searching method, wherein the preset storage area comprises a plurality of storage folders, and each folder corresponds to one file type;
when a target storage space exists, storing the target record report to the target storage folder, and determining a storage path of the target storage folder;
generating a target retrieval keyword based on the storage path, and sending the target retrieval keyword to the medical care terminal;
and if not, clearing the record report exceeding the preset storage time length in the target storage folder, and finishing the real-time record storage of the target record report.
Preferably, the intelligent method for monitoring the anesthesia target control of the child based on the electroencephalogram signals, in the step 2, based on the recorded and stored results, determining the multi-domain characteristic signals of the electroencephalogram signals of the child before and after the anesthesia operation, includes:
acquiring collected brain wave signals of the children before and after the anesthesia operation based on the recorded and stored result, preprocessing the brain wave information, and determining the noise waveform characteristics in the brain wave signals;
denoising the brain wave signals based on the noise waveform characteristics to obtain standard brain wave signals;
determining the frequency range of the standard brain wave, and performing wavelet transformation on the standard brain wave based on the frequency range to obtain a wavelet domain brain wave signal corresponding to the standard brain wave signal, wherein the wavelet domain brain wave signal is at least two segments;
determining the average frequency value of each section of wavelet domain brain wave signals, and sequencing the wavelet domain brain wave signals based on the descending order of the average frequency values;
determining a quality coefficient of each section of wavelet domain brain wave signal based on the sequencing result, and determining the weight of each section of wavelet domain brain wave signal based on the quality coefficient;
screening the wavelet domain brain wave signals based on the weight to obtain target wavelet domain brain wave signals, and meanwhile, performing gain processing on the target wavelet domain brain wave signals to obtain reference wavelet domain brain wave signals;
comparing the target wavelet domain brain wave signal with the reference wavelet domain brain wave signal to determine a target time domain segment with large change of the wavelet domain brain wave signal in each segment of the target wavelet domain brain wave signal;
determining a target characteristic signal extraction section in each section of target wavelet domain brain wave signals based on the target time domain section, wherein the number of the target characteristic signal extraction sections is at least one;
and extracting the characteristic signals of the target characteristic signal extraction section based on a preset method to obtain multi-domain characteristic signals of brain wave signals of the children before and after the anesthesia operation.
Preferably, the intelligent method for monitoring the anesthesia target control of the children based on the electroencephalogram signals, which is used for obtaining multi-domain characteristic signals of the electroencephalogram signals of the children before and after the anesthesia operation, comprises the following steps:
acquiring multi-domain characteristic signals of the obtained brain wave signals of the children before and after the anesthesia operation, and determining a first multi-domain characteristic signal corresponding to the brain wave signal of the children before the anesthesia operation and a second multi-domain characteristic signal corresponding to the brain wave signal of the children after the anesthesia operation;
respectively carrying out data packing on the first multi-domain characteristic signal and the second multi-domain characteristic signal to obtain a target data packet to be transmitted;
and the target data packet to be transmitted is placed in a data transmission queue, and the target data packet to be transmitted is sent to a data analysis terminal based on a preset data transmission method.
Preferably, an electroencephalogram signal-based intelligent method for monitoring the anesthesia target control of children, in step 3, the suppression degree of the children on the anesthetic and the current anesthesia depth condition of the children are determined based on the multi-domain characteristic signals, and the method comprises the following steps:
acquiring multi-domain characteristic signals of brain wave signals of children before and after an anesthesia operation, and simultaneously acquiring target anesthesia medicine quantity received by the children;
acquiring multiple groups of historical anesthetic doses and corresponding historical brain wave signals, and dividing the multiple groups of historical anesthetic doses and the corresponding historical brain wave signals into a training set and a testing set, wherein the historical brain wave signals are two groups of historical brain wave signals before and after an anesthesia operation, and each group of historical anesthetic doses and corresponding historical brain wave signals have actual injury indexes and consciousness indexes corresponding to the anesthesia operation;
constructing an initial anesthesia evaluation model, and training the initial anesthesia evaluation model based on a training set to obtain an anesthesia evaluation model;
testing the anesthesia evaluation model based on the test set to obtain the target anesthesia evaluation model, and evaluating the injury index and consciousness index of the child in the child anesthesia operation based on the target anesthesia evaluation model;
determining the consistency between the injury index and consciousness index evaluation result of the target anesthesia evaluation model on the child in the child anesthesia operation and the actual injury index and consciousness index based on the evaluation result;
when the injury index and consciousness index evaluation result is inconsistent with the actual injury index and consciousness index, retraining the initial anesthesia evaluation model;
otherwise, analyzing the target anesthetic dose and multi-domain characteristic signals of brain wave signals of the children before and after the anesthesia operation based on the anesthesia evaluation model, determining the change amplitude of the multi-domain characteristic signals of the brain wave signals of the children before and after the anesthesia operation, and obtaining a target injury index and a target consciousness index corresponding to the children based on the change amplitude;
and determining the suppression degree of the children to the narcotics and the current anesthesia depth condition of the children based on the target injury index and the target consciousness index.
Preferably, the intelligent method for monitoring the anesthesia target control of the children based on the electroencephalogram signals determines the inhibition degree of the children on the anesthetics and the current anesthesia depth condition of the children based on the target injury index and the target consciousness index, and comprises the following steps:
acquiring the inhibition degree of the child on the anesthetic and the current anesthetic depth condition of the child, and transmitting the inhibition degree and the current anesthetic depth condition to a preset data supervision platform;
the preset data supervision platform analyzes the inhibition degree of the child on the narcotic and the current anesthesia depth condition of the child, and judges whether the child can perform the next operation;
if yes, sending operation notice to medical staff;
otherwise, adjusting the target anesthetic dose received by the child.
Preferably, a based on intelligent system of brain electrical signal monitoring children's anesthesia target accuse includes:
the signal acquisition module is used for acquiring brain wave signals of children before and after an anesthesia operation and recording and storing the acquired brain wave signals in real time;
the signal processing module is used for determining multi-domain characteristic signals of the brain wave signals of the children before and after the anesthesia operation based on the recorded and stored result;
and the judgment module is used for determining the suppression degree of the children on the narcotic and the current anesthesia depth condition of the children based on the multi-domain characteristic signals.
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 flowchart of an intelligent method for monitoring child anesthesia target control based on electroencephalogram signals in the embodiment of the invention;
FIG. 2 is a flowchart of step 1 in an intelligent method for monitoring child anesthesia target control based on electroencephalogram signals in the embodiment of the present invention;
fig. 3 is a structural diagram of an intelligent anesthesia target control system for children based on electroencephalogram signal monitoring in the 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.
Example 1:
the embodiment provides an intelligent method for monitoring child anesthesia target control based on electroencephalogram signals, which comprises the following steps of:
step 1: collecting brain wave signals of children before and after an anesthesia operation, and recording and storing the collected brain wave signals in real time;
step 2: determining multi-domain characteristic signals of brain wave signals of the children before and after the anesthesia operation based on the recorded and stored result;
and step 3: and determining the suppression degree of the children to the narcotic drugs and the current anesthesia depth condition of the children based on the multi-domain characteristic signals.
In this embodiment, the collection of the brain wave signals of the child before and after the anesthesia operation means that the brain wave signals of the child are collected before and after the anesthesia, and the two groups of brain wave signals are used.
In this embodiment, the multi-domain feature signal refers to a local brain wave signal region in brain waves before and after an anesthesia operation, which can clearly indicate the consciousness condition of a child.
In this embodiment, the determination of the suppression degree of the anesthetic and the current anesthesia depth of the child based on the multi-domain feature signal refers to determining the consciousness of the child after receiving a certain dose of anesthetic according to the change of the brain wave signals before and after the anesthesia operation, for example, the determination of the anesthesia depth of the child or the suppression of the anesthetic may be performed according to the change amplitude of the brain wave, the change frequency of the brain wave, and the like.
The beneficial effects of the above technical scheme are: by analyzing the brain waves of the children before and after the operation, the inhibition degree of different children on the anesthetic and the anesthesia depth condition are accurately and objectively analyzed, the monitoring of the clinical objective anesthesia condition of the children is facilitated, the anesthesia injury is reduced, and the anesthesia quality is improved.
Example 2:
on the basis of the foregoing embodiment 1, this embodiment provides an intelligent method for monitoring anesthesia target control of children based on electroencephalogram signals, as shown in fig. 2, in step 1, collecting electroencephalogram signals of children before and after an anesthesia operation includes:
step 101: acquiring target brain wave acquisition areas of children before and after an anesthesia operation, and simultaneously determining the current use state of a preset brain wave sensor, wherein the target brain wave acquisition areas of the children before and after the anesthesia operation are at the same position;
step 102: determining target working parameters corresponding to the acquisition of the brain wave signals of the children based on the target brain wave acquisition area and the current use state of the preset brain wave sensor;
step 103: and controlling the preset brain wave sensor to acquire the brain wave signals of the children before and after the anesthesia operation based on the target working parameters.
In this embodiment, the target brain wave collecting region refers to a portion of the scalp where the brain waves of the child are collected, and may be a middle portion of the scalp or the entire head region or the like.
In this embodiment, the preset brain wave sensor is set in advance and is used for collecting brain wave signals of children.
In this embodiment, it is assumed that the current usage state of the brain wave sensor may be an operating condition of the brain wave sensor, and may be, for example, power on or power off.
In this embodiment, the target operating parameter may be the power of the preset brain wave sensor, the frequency of the collected signal, and the like.
The beneficial effects of the above technical scheme are: through confirming the target area of gathering children's brain wave, confirm the working parameter of brain wave sensor simultaneously, than that with accurate to carry out accurate collection to children's brain wave signal to the realization is receiving the narcotic back to children, carries out accurate analysis to the degree of consistency and the anesthesia depth condition of narcotic, helps the monitoring of clinical children's objective condition of anesthesia, has improved the anesthesia quality again when reducing the anesthesia injury.
Example 3:
on the basis of the foregoing embodiment 2, this embodiment provides an intelligent method for monitoring anesthesia target control of a child based on electroencephalogram signals, wherein the controlling the preset brain wave sensor to collect brain wave signals of the child before and after an anesthesia operation based on the target working parameters includes:
acquiring collected brain wave signals of children before and after an anesthesia operation, and verifying the integrity of the brain wave signals based on a preset verification scheme;
if the brain wave signals are complete, judging that the brain wave signals collected by the preset brain wave sensor are qualified;
otherwise, judging that the brain wave signals acquired by the preset brain wave sensor are unqualified, and acquiring the brain wave signals of the children before and after the anesthesia operation based on the preset brain wave sensor again until the brain wave signals are judged to be complete.
In this embodiment, the preset verification scheme is set in advance, and may be, for example, machine verification, manual verification, or the like.
In this embodiment, the integrity checking refers to checking whether the acquired brain wave signals are continuous brain wave signals, and if the brain wave signals are interrupted, determining that the brain wave signals are incomplete.
The beneficial effects of the above technical scheme are: the integrity of the collected brain waves is verified, so that the inhibition condition and the anesthesia depth condition of the child after the anesthetic is injected can be accurately and effectively analyzed according to the collected brain wave signals, and the anesthesia quality can be improved conveniently.
Example 4:
on the basis of the foregoing embodiment 1, this embodiment provides an intelligent method for monitoring anesthesia target control of children based on electroencephalogram signals, and in step 1, the method includes the following steps:
acquiring identity information of a child, and acquiring collected brain wave signals of the child before and after an anesthesia operation;
determining a corresponding relation between the brain wave signals and identity information of the children, wherein the corresponding relation is that one child corresponds to one preoperative brain wave signal and one postoperative brain wave signal;
matching a target report template from a preset report template library based on the corresponding relation, and filling the child identity information and the corresponding brain wave signals into a target position in the target report template;
and obtaining a target record report based on the filling result.
In this embodiment, the identity information of the child refers to the name, age, and the like of the child.
In this embodiment, the preset report template library is set in advance, and a plurality of report templates are stored therein.
In this embodiment, the target report template refers to a report template that is selected from a preset report template library according to the correspondence between the child identity information and the brain waves and is suitable for recording the current child identity reputation brain wave signal, and is one of the preset report template libraries.
In this embodiment, the target position refers to a position where the child identity information and the corresponding brain wave signal are respectively filled in the target report template, for example, the identity information is on the left side of the whole report template, and is listed as a unit, and the identity information of the child is respectively filled in the left data unit.
The beneficial effects of the above technical scheme are: the identity information of the child and the corresponding brain wave signals are generated into the corresponding recording reports, so that the obtained recording reports can be safely and effectively stored, the corresponding brain wave signals can be called in time when the brain wave signals of the child are analyzed, and the brain wave analysis efficiency is improved.
Example 5:
on the basis of the foregoing embodiment 4, this embodiment provides an intelligent method for monitoring anesthesia target control of children based on electroencephalogram signals, and obtaining a target record report based on a filling result, including:
acquiring an obtained target record report, and determining attribute information of the target record report;
searching a target storage folder from a preset storage area based on the attribute information, and searching whether the target storage folder has a target storage space based on a preset searching method, wherein the preset storage area comprises a plurality of storage folders, and each folder corresponds to one file type;
when a target storage space exists, storing the target record report to the target storage folder, and determining a storage path of the target storage folder;
generating a target retrieval keyword based on the storage path, and sending the target retrieval keyword to the medical care terminal;
and if not, clearing the record report exceeding the preset storage time length in the target storage folder, and finishing the real-time record storage of the target record report.
In this embodiment, the attribute information may be a file type of the record report file, an information capacity value of the record report, and the like.
In this embodiment, the preset storage area is set in advance, and may be a hard disk, for example.
In this embodiment, the target storage folder refers to a storage folder in the preset storage area that is consistent with the record report file type, and is one folder in the preset storage area.
In this embodiment, the target storage space refers to whether the remaining storage space in the target folder can successfully store the record report file, i.e., the available storage space in the target storage folder.
In this embodiment, the target search keyword refers to a search instruction corresponding to the storage path, and the location where the record report is stored can be quickly found according to the search instruction.
In this embodiment, the preset storage time length is set in advance, and may be, for example, one month, two months, or the like.
The beneficial effects of the above technical scheme are: by determining the attribute information of the record report, the record report can be accurately stored, and meanwhile, the storage position of the record report is recorded, so that the analysis platform can conveniently and timely acquire the corresponding report file, the consistency degree of the anesthetic and the anesthetic depth condition after the anesthetic is injected by children are timely analyzed, and the analysis efficiency and convenience are improved.
Example 6:
on the basis of the foregoing embodiment 1, this embodiment provides an intelligent method for monitoring anesthesia target control of a child based on electroencephalogram signals, and in step 2, determining multi-domain feature signals of brain wave signals of the child before and after an anesthesia operation based on recorded and stored results, including:
acquiring collected brain wave signals of the children before and after the anesthesia operation based on the recorded and stored result, preprocessing the brain wave information, and determining the noise waveform characteristics in the brain wave signals;
denoising the brain wave signals based on the noise waveform characteristics to obtain standard brain wave signals;
determining the frequency range of the standard brain wave, and performing wavelet transformation on the standard brain wave based on the frequency range to obtain a wavelet domain brain wave signal corresponding to the standard brain wave signal, wherein the wavelet domain brain wave signal is at least two segments;
determining the average frequency value of each section of wavelet domain brain wave signals, and sequencing the wavelet domain brain wave signals based on the descending order of the average frequency values;
determining a quality coefficient of each section of wavelet domain brain wave signal based on the sequencing result, and determining the weight of each section of wavelet domain brain wave signal based on the quality coefficient;
screening the wavelet domain brain wave signals based on the weight to obtain target wavelet domain brain wave signals, and meanwhile, performing gain processing on the target wavelet domain brain wave signals to obtain reference wavelet domain brain wave signals;
comparing the target wavelet domain brain wave signal with the reference wavelet domain brain wave signal to determine a target time domain segment with large change of the wavelet domain brain wave signal in each segment of the target wavelet domain brain wave signal;
determining a target characteristic signal extraction section in each section of target wavelet domain brain wave signals based on the target time domain section, wherein the number of the target characteristic signal extraction sections is at least one;
and extracting the characteristic signals of the target characteristic signal extraction section based on a preset method to obtain multi-domain characteristic signals of brain wave signals of the children before and after the anesthesia operation.
In this embodiment, the preprocessing refers to comparing the brain waves with a standard brain wave form, and determining a noise waveform or the like affecting the brain waves among the collected brain waves.
In this embodiment, the standard brain wave signal refers to a brain wave signal that is obtained by processing the collected brain wave signal and removing noise waveforms or other influence waveforms from the processed brain wave signal, and that can be directly analyzed for the degree of anesthetic inhibition and the depth of anesthesia of the child.
In this embodiment, the noise waveform characteristics refer to waveform characteristics that occur when noise affects brain waves.
In this embodiment, the wavelet transform refers to a cerebellar wave signal that divides a continuous brain wave signal into a plurality of time-domain segments.
In this embodiment, the wavelet domain brain wave signal refers to a wavelet segment obtained by dividing a standard brain wave signal, and is a part of the brain wave signal.
In this embodiment, determining the quality coefficient of each segment of wavelet domain brain wave signal may be determining the quality of the brain waves according to the fluctuation stability, continuity and integrity of the brain waves, and the quality coefficient is higher as the quality is higher.
In this embodiment, the target wavelet brain wave signal refers to a critical wavelet brain wave signal obtained by screening multiple wavelet domains, and the screening criteria is to screen the critical wavelet brain wave signal according to the importance degree of each brain wave in the whole segment.
In this embodiment, the reference wavelet brain wave signal is obtained by performing signal gain processing on the target wavelet brain wave signal, so as to determine a change condition of the target wavelet brain wave signal after being subjected to an external stimulus.
In this embodiment, the target time domain segment refers to a certain segment of brain wave signal that changes greatly after the brain wave signal receives the signal gain.
In this embodiment, the target feature signal extraction segment refers to a brain wave signal segment for which signal features are to be extracted finally, and the brain wave signal segment is important.
In this embodiment, the preset method is set in advance, and may be, for example, performing band extraction on the brain wave signal.
The beneficial effects of the above technical scheme are: through processing the brain wave signal of the collected children before and after the operation, the important brain wave signal section in the brain wave signal is determined, so that the brain wave signal characteristic is accurately extracted, the extracted characteristic signal is ensured to be effective enough, the inhibition degree of the anesthetic for the children and the anesthetic depth condition after receiving the anesthetic are accurately analyzed, the analysis accuracy is improved, the anesthetic quality is convenient to improve, and the injury of the anesthetic to the children is reduced.
Example 7:
on the basis of the foregoing embodiment 6, the present embodiment provides an intelligent method for monitoring anesthesia target control of children based on electroencephalogram signals, and obtains multi-domain feature signals of brain wave signals of children before and after an anesthesia operation, including:
acquiring multi-domain characteristic signals of the obtained brain wave signals of the children before and after the anesthesia operation, and determining a first multi-domain characteristic signal corresponding to the brain wave signal of the children before the anesthesia operation and a second multi-domain characteristic signal corresponding to the brain wave signal of the children after the anesthesia operation;
respectively carrying out data packing on the first multi-domain characteristic signal and the second multi-domain characteristic signal to obtain a target data packet to be transmitted;
and the target data packet to be transmitted is placed in a data transmission queue, and the target data packet to be transmitted is sent to a data analysis terminal based on a preset data transmission method.
In this embodiment, the first multi-domain feature signal refers to a feature signal corresponding to a pre-anesthesia electroencephalogram signal for a child.
In this embodiment, the second multi-domain signature signal refers to a signature signal corresponding to a post-anesthesia electroencephalogram signal for a child.
In this embodiment, the target data packet to be transmitted refers to bag transmission data obtained by packaging the multi-domain characteristic signal.
In this embodiment, the preset data transmission method is set in advance, and may be, for example, wireless data transmission.
The beneficial effects of the above technical scheme are: through the multi-domain characteristic signals obtained before and after the child anesthesia operation, the data analysis terminal can timely analyze the drug resistance degree and the anesthesia depth of the child, the analysis timeliness is improved, and the monitoring of the clinical child anesthesia objective condition is facilitated.
Example 8:
on the basis of the foregoing embodiment 1, this embodiment provides an intelligent method for monitoring anesthesia target control of children based on electroencephalogram signals, and in step 3, determining the degree of inhibition of the children on anesthetics and the current anesthesia depth of the children based on the multi-domain feature signals includes:
acquiring multi-domain characteristic signals of brain wave signals of children before and after an anesthesia operation, and simultaneously acquiring target anesthesia medicine quantity received by the children;
acquiring multiple groups of historical anesthetic doses and corresponding historical brain wave signals, and dividing the multiple groups of historical anesthetic doses and the corresponding historical brain wave signals into a training set and a testing set, wherein the historical brain wave signals are two groups of historical brain wave signals before and after an anesthesia operation, and each group of historical anesthetic doses and corresponding historical brain wave signals have actual injury indexes and consciousness indexes corresponding to the anesthesia operation;
constructing an initial anesthesia evaluation model, and training the initial anesthesia evaluation model based on a training set to obtain an anesthesia evaluation model;
testing the anesthesia evaluation model based on the test set to obtain the target anesthesia evaluation model, and evaluating the injury index and consciousness index of the child in the child anesthesia operation based on the target anesthesia evaluation model;
determining the consistency between the injury index and consciousness index evaluation result of the target anesthesia evaluation model on the child in the child anesthesia operation and the actual injury index and consciousness index based on the evaluation result;
when the injury index and consciousness index evaluation result is inconsistent with the actual injury index and consciousness index, retraining the initial anesthesia evaluation model;
otherwise, analyzing the target anesthetic dose and multi-domain characteristic signals of brain wave signals of the children before and after the anesthesia operation based on the anesthesia evaluation model, determining the change amplitude of the multi-domain characteristic signals of the brain wave signals of the children before and after the anesthesia operation, and obtaining a target injury index and a target consciousness index corresponding to the children based on the change amplitude;
and determining the suppression degree of the children to the narcotics and the current anesthesia depth condition of the children based on the target injury index and the target consciousness index.
In this embodiment, the target amount of anesthetic is the amount of anesthetic injection the child receives.
In this embodiment, the historical anesthetic dose refers to the anesthetic dose that was injected by a medical worker to a child, and has a corresponding brain wave signal, so as to train an anesthesia evaluation model.
In this embodiment, the actual injury index refers to the number of injuries most likely to be caused by the historical amount of anesthetic injected into a child.
In this embodiment, the consciousness level refers to the consciousness level of the child observed by brain waves after the child receives the anesthetic injection.
In this embodiment, the target anesthesia evaluation model refers to a model obtained by training the constructed initial anesthesia evaluation model and capable of directly analyzing the target anesthesia dose currently received by the child.
In this embodiment, the amplitude variation refers to the amplitude variation of the brain wave signals before and after receiving anesthesia.
In this embodiment, the target injury index and the target consciousness index refer to the actual injury level and the actual consciousness level of the child after receiving the target anesthetic dose.
In this embodiment, obtaining the target amount of anesthetic received by the child includes:
the method comprises the following steps of obtaining a pre-estimated time length value required by a child operation, calculating a target anesthetic dose required by the child operation based on the preset time length value, and calculating a time length value for restoring consciousness of the child based on the target anesthetic dose, wherein the specific steps comprise:
calculating the target anesthetic dosage required by the operation of the child according to the following formula:
G＝(1-μ)*[(v1-v2)*(t1-t2)+k]；
wherein G represents the target anesthetic dosage required by the operation of the child; mu represents an error factor, and the value range is (0.05, 0.15); v. of1Representing the speed of the anesthetic injection pump for injecting anesthetic; v. of2Indicates the speed of reduction of blood to narcotics in children, and v2Less than v1；t1The value of the estimated time length required for performing the operation on the child is shown; t is t2Indicating the length of time that the anesthetic acts after the anesthetic injection is stopped; k represents the adsorption amount of the preset anesthetic injection pump to the anesthetic;
the length of time taken for the child to regain consciousness is calculated according to the following formula:
wherein T represents a length of time taken for the child to regain consciousness; ρ represents the density value of the anesthetic; g represents the target anesthetic dosage required by the operation of the child; m represents the amount of blood inside the body of the child; t is t3A value representing the length of time required for a child to degrade an anesthetic in blood; n represents the length value of the buffering time needed by the child after completing the degradation of the anesthetic in the blood; n represents the blood concentration value before the anesthetic is injected into the body of the child;
comparing the calculated time length value with a preset time length value;
if the time length value is larger than or equal to the preset time length value, judging that the physical sign of the child is abnormal, and processing the child based on a preset processing measure to shorten the anesthesia effect of the anesthetic on the child;
otherwise, judging the physical signs of the children to be normal, and finishing the operation on the children.
The preset time length value is set in advance, is used for measuring whether the time for anesthetizing the anesthetized child is in a controllable range or not, and is adjusted according to the dosage of the injected anesthetics.
The adsorption capacity of the preset anesthetic injection pump to the anesthetic refers to the amount of anesthetic additionally attached to the inner wall of the injection tube when the preset anesthetic injection pump injects the anesthetic to children.
The estimated time length value is determined according to the complexity of the operation performed by the child, and is a theoretical time length obtained by analysis of medical care personnel and an intelligent machine.
The beneficial effects of the above technical scheme are: the anesthesia evaluation model is established, accurate analysis of the acquired multi-domain characteristic signals is achieved, injury indexes and consciousness indexes of the children after anesthetic injection are obtained through analysis, accurate analysis of drug resistance degree and anesthesia depth conditions of the children is achieved, the inhibition degree and the anesthesia depth conditions of different children on the anesthetic are analyzed accurately and objectively, monitoring of clinical objective anesthesia conditions of the children is facilitated, and anesthesia quality is improved while anesthesia injury is reduced.
Example 9:
on the basis of the above embodiment 8, this embodiment provides an intelligent method for monitoring anesthesia target control of children based on electroencephalogram signals, and the method for determining the degree of inhibition of the children on the anesthetic and the current anesthesia depth of the children based on the target injury index and the target consciousness index includes:
acquiring the inhibition degree of the child on the anesthetic and the current anesthetic depth condition of the child, and transmitting the inhibition degree and the current anesthetic depth condition to a preset data supervision platform;
the preset data supervision platform analyzes the inhibition degree of the child on the narcotic and the current anesthesia depth condition of the child, and judges whether the child can perform the next operation;
if yes, sending operation notice to medical staff;
otherwise, adjusting the target anesthetic dose received by the child.
In this embodiment, the preset data supervision platform is set in advance and is used for judging the anesthesia degree of the children after receiving the anesthetic.
The beneficial effects of the above technical scheme are: whether the next-stage operation of the children is pre-judged or not is achieved by judging the anesthesia degree of the children, the accuracy of the anesthesia analysis of the children is improved, and the anesthesia quality is improved.
Example 10:
this embodiment provides a children anesthesia target control intelligent system based on brain electrical signal monitoring, as shown in fig. 3, include:
the signal acquisition module is used for acquiring brain wave signals of children before and after an anesthesia operation and recording and storing the acquired brain wave signals in real time;
the signal processing module is used for determining multi-domain characteristic signals of the brain wave signals of the children before and after the anesthesia operation based on the recorded and stored result;
and the judgment module is used for determining the suppression degree of the children on the narcotic and the current anesthesia depth condition of the children based on the multi-domain characteristic signals.
The beneficial effects of the above technical scheme are: by analyzing the brain waves of the children before and after the operation, the inhibition degree of different children on the anesthetic and the anesthesia depth condition are accurately and objectively analyzed, the monitoring of the clinical objective anesthesia condition of the children is facilitated, the anesthesia injury is reduced, and the anesthesia quality is improved.
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. An intelligent method for monitoring child anesthesia target control based on electroencephalogram signals is characterized by comprising the following steps:
step 1: collecting brain wave signals of children before and after an anesthesia operation, and recording and storing the collected brain wave signals in real time;
step 2: determining multi-domain characteristic signals of brain wave signals of the children before and after the anesthesia operation based on the recorded and stored result;
and step 3: and determining the suppression degree of the children to the narcotic drugs and the current anesthesia depth condition of the children based on the multi-domain characteristic signals.
2. The intelligent method for monitoring the anesthesia target control of children based on electroencephalogram signals according to claim 1, wherein in the step 1, the collecting of the electroencephalogram signals of the children before and after the anesthesia operation comprises:
acquiring target brain wave acquisition areas of children before and after an anesthesia operation, and simultaneously determining the current use state of a preset brain wave sensor, wherein the target brain wave acquisition areas of the children before and after the anesthesia operation are at the same position;
determining target working parameters corresponding to the acquisition of the brain wave signals of the children based on the target brain wave acquisition area and the current use state of the preset brain wave sensor;
and controlling the preset brain wave sensor to acquire the brain wave signals of the children before and after the anesthesia operation based on the target working parameters.
3. The intelligent electroencephalogram signal monitoring child anesthesia target control method based on claim 2, wherein controlling the preset electroencephalogram sensor to collect electroencephalogram signals of a child before and after an anesthesia operation based on the target working parameters comprises:
acquiring collected brain wave signals of children before and after an anesthesia operation, and verifying the integrity of the brain wave signals based on a preset verification scheme;
if the brain wave signals are complete, judging that the brain wave signals collected by the preset brain wave sensor are qualified;
otherwise, judging that the brain wave signals acquired by the preset brain wave sensor are unqualified, and acquiring the brain wave signals of the children before and after the anesthesia operation based on the preset brain wave sensor again until the brain wave signals are judged to be complete.
4. The intelligent method for monitoring the anesthesia target control of children based on electroencephalogram signals as claimed in claim 1, wherein in step 1, the real-time recording and storage of the collected electroencephalogram signals comprises:
acquiring identity information of a child, and acquiring collected brain wave signals of the child before and after an anesthesia operation;
determining a corresponding relation between the brain wave signals and identity information of the children, wherein the corresponding relation is that one child corresponds to one preoperative brain wave signal and one postoperative brain wave signal;
matching a target report template from a preset report template library based on the corresponding relation, and filling the child identity information and the corresponding brain wave signals into a target position in the target report template;
and obtaining a target record report based on the filling result.
5. The intelligent method for monitoring child anesthesia target control based on electroencephalogram signals, as claimed in claim 4, wherein obtaining a target record report based on filling results comprises:
acquiring an obtained target record report, and determining attribute information of the target record report;
searching a target storage folder from a preset storage area based on the attribute information, and searching whether the target storage folder has a target storage space based on a preset searching method, wherein the preset storage area comprises a plurality of storage folders, and each folder corresponds to one file type;
when a target storage space exists, storing the target record report to the target storage folder, and determining a storage path of the target storage folder;
generating a target retrieval keyword based on the storage path, and sending the target retrieval keyword to the medical care terminal;
and if not, clearing the record report exceeding the preset storage time length in the target storage folder, and finishing the real-time record storage of the target record report.
6. The intelligent method for monitoring the anesthesia target control of children based on electroencephalogram signals as claimed in claim 1, wherein in the step 2, based on the recorded and stored results, the determining of the multi-domain characteristic signals of the electroencephalogram signals of the children before and after the anesthesia operation comprises:
acquiring collected brain wave signals of the children before and after the anesthesia operation based on the recorded and stored result, preprocessing the brain wave information, and determining the noise waveform characteristics in the brain wave signals;
denoising the brain wave signals based on the noise waveform characteristics to obtain standard brain wave signals;
determining the frequency range of the standard brain wave, and performing wavelet transformation on the standard brain wave based on the frequency range to obtain a wavelet domain brain wave signal corresponding to the standard brain wave signal, wherein the wavelet domain brain wave signal is at least two segments;
determining the average frequency value of each section of wavelet domain brain wave signals, and sequencing the wavelet domain brain wave signals based on the descending order of the average frequency values;
determining a quality coefficient of each section of wavelet domain brain wave signal based on the sequencing result, and determining the weight of each section of wavelet domain brain wave signal based on the quality coefficient;
screening the wavelet domain brain wave signals based on the weight to obtain target wavelet domain brain wave signals, and meanwhile, performing gain processing on the target wavelet domain brain wave signals to obtain reference wavelet domain brain wave signals;
comparing the target wavelet domain brain wave signal with the reference wavelet domain brain wave signal to determine a target time domain segment with large change of the wavelet domain brain wave signal in each segment of the target wavelet domain brain wave signal;
determining a target characteristic signal extraction section in each section of target wavelet domain brain wave signals based on the target time domain section, wherein the number of the target characteristic signal extraction sections is at least one;
and extracting the characteristic signals of the target characteristic signal extraction section based on a preset method to obtain multi-domain characteristic signals of brain wave signals of the children before and after the anesthesia operation.
7. The intelligent method for monitoring the anesthesia target control of children based on electroencephalogram signals according to claim 6, wherein obtaining multi-domain characteristic signals of brain wave signals of children before and after an anesthesia operation comprises:
acquiring multi-domain characteristic signals of the obtained brain wave signals of the children before and after the anesthesia operation, and determining a first multi-domain characteristic signal corresponding to the brain wave signal of the children before the anesthesia operation and a second multi-domain characteristic signal corresponding to the brain wave signal of the children after the anesthesia operation;
respectively carrying out data packing on the first multi-domain characteristic signal and the second multi-domain characteristic signal to obtain a target data packet to be transmitted;
and the target data packet to be transmitted is placed in a data transmission queue, and the target data packet to be transmitted is sent to a data analysis terminal based on a preset data transmission method.
8. The electroencephalogram signal-based intelligent anesthesia target control method for monitoring children according to claim 1, wherein in the step 3, the inhibition degree of the children on the anesthetics and the current anesthesia depth condition of the children are determined based on the multi-domain characteristic signals, and the method comprises the following steps:
acquiring multi-domain characteristic signals of brain wave signals of children before and after an anesthesia operation, and simultaneously acquiring target anesthesia medicine quantity received by the children;
acquiring multiple groups of historical anesthetic doses and corresponding historical brain wave signals, and dividing the multiple groups of historical anesthetic doses and the corresponding historical brain wave signals into a training set and a testing set, wherein the historical brain wave signals are two groups of historical brain wave signals before and after an anesthesia operation, and each group of historical anesthetic doses and corresponding historical brain wave signals have actual injury indexes and consciousness indexes corresponding to the anesthesia operation;
constructing an initial anesthesia evaluation model, and training the initial anesthesia evaluation model based on a training set to obtain an anesthesia evaluation model;
testing the anesthesia evaluation model based on the test set to obtain the target anesthesia evaluation model, and evaluating the injury index and consciousness index of the child in the child anesthesia operation based on the target anesthesia evaluation model;
determining the consistency between the injury index and consciousness index evaluation result of the target anesthesia evaluation model on the child in the child anesthesia operation and the actual injury index and consciousness index based on the evaluation result;
when the injury index and consciousness index evaluation result is inconsistent with the actual injury index and consciousness index, retraining the initial anesthesia evaluation model;
otherwise, analyzing the target anesthetic dose and multi-domain characteristic signals of brain wave signals of the children before and after the anesthesia operation based on the anesthesia evaluation model, determining the change amplitude of the multi-domain characteristic signals of the brain wave signals of the children before and after the anesthesia operation, and obtaining a target injury index and a target consciousness index corresponding to the children based on the change amplitude;
and determining the suppression degree of the children to the narcotics and the current anesthesia depth condition of the children based on the target injury index and the target consciousness index.
9. The intelligent method for monitoring the anesthesia target control of children based on the electroencephalogram signal as claimed in claim 8, wherein the determining the degree of inhibition of the children on the anesthetic and the current anesthesia depth condition of the children based on the target injury index and the target consciousness index comprises:
acquiring the inhibition degree of the child on the anesthetic and the current anesthetic depth condition of the child, and transmitting the inhibition degree and the current anesthetic depth condition to a preset data supervision platform;
the preset data supervision platform analyzes the inhibition degree of the child on the narcotic and the current anesthesia depth condition of the child, and judges whether the child can perform the next operation;
if yes, sending operation notice to medical staff;
otherwise, adjusting the target anesthetic dose received by the child.
10. The utility model provides a based on intelligent system of brain electrical signal monitoring children's anesthesia target accuse which characterized in that includes:
the signal acquisition module is used for acquiring brain wave signals of children before and after an anesthesia operation and recording and storing the acquired brain wave signals in real time;
the signal processing module is used for determining multi-domain characteristic signals of the brain wave signals of the children before and after the anesthesia operation based on the recorded and stored result;
and the judgment module is used for determining the suppression degree of the children on the narcotic and the current anesthesia depth condition of the children based on the multi-domain characteristic signals.
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