CN110811556A - Anesthesia depth monitoring system and method based on electroencephalogram micro-state analysis - Google Patents
Anesthesia depth monitoring system and method based on electroencephalogram micro-state analysis Download PDFInfo
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Abstract
An anesthesia depth monitoring system and method based on electroencephalogram micro-state analysis, the system comprises an electroencephalogram signal acquisition module A, a micro-state time sequence construction module B, a micro-state parameter calculation module C and a classification identification module D, firstly, high-density electroencephalogram is used for acquiring tested whole-brain electroencephalogram signals, and then, a corresponding micro-state time sequence is constructed according to a micro-state algorithm; then calculating the micro-state parameters such as time ratio, energy and the like of the time sequence; then, simplifying the micro-state parameters through principal component analysis to obtain characteristic values, and identifying and classifying by using a support vector machine; the invention combines the time information and the space information of the brain electricity on the basis of taking the brain as an organic whole, reflects the characteristics of the brain electricity topology on the time domain, and can effectively and accurately monitor the anesthesia depth of the patient.
Description
Technical Field
The invention relates to the technical field of biomedical signal processing, in particular to an anesthesia depth monitoring system and method based on electroencephalogram micro-state analysis.
Background
Anesthesia, especially general anesthesia, is a common means of clinical treatment. Generally speaking, in the operation process, the central nerve of a patient is inhibited through inhalation of anesthetic or intravenous injection, so that the patient shows states of unconsciousness, motor function reduction, pain stimulation reaction disappearance and the like, the patient loses the memory of pain sense in the operation, the safety of the operation is improved, and the operation is convenient. The monitoring of the depth of anesthesia is an important method for guaranteeing the quality of anesthesia in clinical operations. If the depth of anesthesia is too heavy, the medication cost is increased, the recovery time of the patient is prolonged, and even anesthesia sequelae are caused to the nervous system. On the other hand, if the degree of anesthesia is shallow, the patient may be "known during the operation", which not only affects the normal operation, but also causes great physical and mental trauma to the patient.
In clinical practice, there is no universal "gold standard" for monitoring the depth of anesthesia, and in actual clinical practice, the application is relatively wide, and there are mainly monitoring methods based on clinical signs of patients and monitoring methods based on electroencephalogram signals. The former is widely used, mainly for Minimum Alveolar Concentration (MAC) monitoring, and is defined as the concentration of inhalation anesthetic in alveolar gas when 50% of subjects do not respond to traumatic stimulation, but has a disadvantage that it can only be used for evaluating the efficacy of inhalation anesthetic, and cannot be used for evaluating the depth of intravenous anesthesia and mixed anesthesia. The latter is mainly used for monitoring the electroencephalogram Bispectrum Index (BIS) of spontaneous brain electricity and monitoring the Auditory Evoked Potential (AEP) of evoked brain electricity. BIS is a dimensionless parameter, defined in the range 0-100, and is evaluated as 100 when the subject is absolutely awake; and evaluated as 0 at the most deeply anesthetized. However, BIS is strongly drug dependent, e.g. with isoflurane and N2O has no correlation. Secondly, BIS also has great variability for different ethnic groups. Furthermore, BIS sometimes fails to predict the patient's time to wake and recovery procedures. AEP utilizes the patient's auditory sensation generated by a repetitive sound stimulusEvoked potentials to monitor depth of anesthesia may reflect neuronal activity in the thalamus and primary auditory cortex, and are not affected by opioid and inductive drugs. However, AEP monitors are susceptible to the surrounding environment and AEP is dependent on human hearing, making this approach difficult for patients with hearing problems.
Disclosure of Invention
In order to overcome the problems of the methods, the invention provides an anesthesia monitoring system and method based on electroencephalogram micro-state, wherein the electroencephalogram micro-state is considered to represent the global scalp electric field activity, and the characteristics of electroencephalogram topology on the time domain can be well reflected by combining the time information and the spatial information of electroencephalogram; the method comprises the steps of constructing the electroencephalogram micro-state time sequences of a tested object in different states and micro-state parameters such as time ratio, appearance frequency, duration, energy and the like corresponding to each micro-state. And then, carrying out feature reduction on the micro-state parameters through Principal Component Analysis (PCA), and inputting the micro-state parameters into a Support Vector Machine (SVM) for anesthesia state monitoring and distinguishing the waking state and the anesthesia state of the tested object. The method takes the brain as an organic whole, monitors the anesthesia depth by utilizing the electroencephalogram micro state, and has higher accuracy and sensitivity by combining with an SVM classifier.
In order to achieve the purpose, the specific technical scheme of the invention is as follows:
an anesthesia depth monitoring system based on electroencephalogram micro-state analysis comprises an electroencephalogram signal acquisition module (A), a micro-state time sequence construction module (B), a micro-state parameter calculation module (C) and a classification identification module (D);
the electroencephalogram signal acquisition module (A): the system is used for collecting electroencephalogram signals of a sample subjected to general anesthesia in different anesthesia states;
the micro-state time sequence construction module (B): firstly, analyzing electroencephalogram signals in different anesthesia states through an electroencephalogram micro-state algorithm on signals acquired by an electroencephalogram signal acquisition module (A) to construct corresponding micro-state time sequences;
the micro-state parameter calculation module (C): according to the obtained micro-state time sequence, calculating corresponding micro-state parameters such as time ratio, appearance frequency, duration, energy and the like;
the classification recognition module (D): and simplifying the obtained micro-state parameters through principal component analysis, inputting the micro-state parameters serving as characteristic values into the SVM for pattern recognition and classification, wherein the two classification results of the SVM are the detection results of the anesthesia depth.
A monitoring method of an anesthesia depth monitoring system based on electroencephalogram micro-state analysis comprises the following steps:
(1) the brain blood oxygen signal acquisition module (A) is used for acquiring electroencephalogram signals of samples subjected to general anesthesia in different anesthesia states;
(2) analyzing the acquired signals by an electroencephalogram micro-state algorithm through a micro-state time sequence construction module (B) to construct corresponding micro-state time sequences;
(3) calculating corresponding micro-state parameters such as time ratio, appearance frequency, duration, energy and the like by using a micro-state parameter calculation module (C) according to the obtained micro-state time sequence;
(4) and (3) carrying out principal component analysis on the obtained micro-state parameters by using a classification and identification module (D), inputting the obtained characteristic values into an SVM (support vector machine) for pattern recognition and classification, wherein the two classification results of the SVM are the detection results of the anesthesia depth.
The step (2) specifically comprises:
(2.1) determining the ideal number of micro-states for the measured brain electrical signals through cross-validation (CV) and Krzanowski-LaiCriterion (KL) criteria;
(2.2) extracting peak points of a global energy spectrum GFP (global field power) of the electroencephalogram signal, wherein the specific formula of the GFP is as follows:
in the formula, K represents the total number of conductive connections, ViA potential representing the ith lead;
(2.3) clustering the EEG signals at the corresponding moment of GFP by using a Modified k-means algorithm to obtain a micro-state topology, calculating a Global interpretable Variance (GEV) while clustering, and calculating 100 GEV to enable the GEV to be maximum so as to ensure that the finally obtained micro-state topology can overcome the randomness of the Modified k-means algorithm to the maximum extent;
(2.4) carrying out spatial correlation pairing on the clustered micro-states and the electroencephalogram of the GFP function peak point of the original electroencephalogram signal, taking the micro-state topology with the highest spatial correlation as the current micro-state, namely marking the electroencephalogram at the moment as the serial number of the micro-state (such as A, B, C);
the formula for spatial correlation is as follows:
wherein C is a spatial correlation, NeThe number of leads is U, the brain electrical mapping of Map u is u, v is the brain electrical mapping of Map v, and i is the ith lead;
and (2.5) marking the rest of the electroencephalogram signals according to the micro-state corresponding to the nearest GFP peak value to obtain the micro-state sequence of the corresponding anesthesia state.
The step (3) specifically comprises:
calculating the micro-state parameters of the obtained micro-state sequence, which are respectively as follows:
(3.1) time-to-time ratio (Coverage): the proportion of each micro-state in the total time;
(3.2) frequency of Occurrence (Occurrence): i.e. the number of occurrences of a certain micro-state within a specified time, which is set to 1 second;
(3.3) Duration (Duration): the average length of each occurrence of a certain micro state is equal to the total time length of the occurrence of the micro state divided by the total number of occurrences;
(3.4) energy (Power): namely the energy sum P of each electroencephalogram channel under a certain micro-state k topologykThe calculation formula is as follows:
in the formula, PkEnergy, s, representing a micro-state kiThe electroencephalogram signal of the ith lead, gammak,tEqual to 1 when time t is marked as micro-state k, otherwise 0, Ls is the length of the micro-state time-series under this anesthesia.
The invention has the advantages that: the invention provides a method for continuously monitoring the anesthesia depth of a patient by combining an electroencephalogram micro state with an SVM. On the basis of taking the brain as an organic whole, the time information and the space information of the electroencephalogram can be combined, the characteristics of the electroencephalogram topology on the time domain can be better reflected, and meanwhile, the anesthesia depth of a patient can be effectively and accurately monitored. But also can provide a certain solution to the problem of the specificity of the anesthesia of different groups to patients.
Drawings
FIG. 1 is a flow chart of the present invention.
Fig. 2 is a diagram showing the selection result of the ideal number of micro-states.
FIG. 3 is a diagram of the results of the micro-topological clustering of 20 cases of anesthesia sessions tested.
FIG. 4 is a global interpretable variance boxplot of 20 tested micro-states at different stages of anesthesia.
Fig. 5 is a graph of the spatial correlation of the BS micro-states of 20 cases tested with the micro-states of all anesthesia phases.
FIG. 6 is a boxplot of the microstate time ratios for 20 cases of anesthesia sessions tested.
FIG. 7 is a box-type chart of the frequency of appearance of each micro-state in 20 cases of anesthesia stages.
FIG. 8 is a box plot of the duration of each of the microstate for 20 different phases of anesthesia tested.
FIG. 9 is a plot of the energy profiles of each of the microstate for 20 different stages of anesthesia tested.
FIG. 10 is a ROC plot of SVM classification results for BS-ML and BS-MD anesthesia phases.
Detailed Description
The present invention will be described in detail with reference to the accompanying drawings. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
Referring to fig. 1, the anesthesia depth monitoring system based on the brain electrical microstate comprises four modules of a brain electrical signal acquisition module (A), a microstate time sequence construction module (B), a microstate parameter calculation module (C) and a classification identification module (D),
the electroencephalogram signal acquisition module (A): the system is used for collecting electroencephalogram signals of a sample subjected to general anesthesia in different anesthesia states;
the micro-state time sequence construction module (B): firstly, analyzing electroencephalogram signals in different anesthesia states through an electroencephalogram micro-state algorithm on signals acquired by an electroencephalogram signal acquisition module (A) to construct corresponding micro-state time sequences;
the micro-state parameter calculation module (C): according to the obtained micro-state time sequence, calculating corresponding micro-state parameters such as time ratio, appearance frequency, duration, energy and the like;
the classification recognition module (D): and simplifying the obtained micro-state parameters through principal component analysis, inputting the micro-state parameters serving as characteristic values into the SVM for pattern recognition and classification, wherein the two classification results of the SVM are the detection results of the anesthesia depth.
The embodiment is a detection method based on the monitoring system, and the detection method comprises the following steps:
(1) an electroencephalogram signal acquisition module (A) is utilized to acquire the whole brain electroencephalogram signals of 20 cases of subjects who are subjected to general anesthesia.
The step (1) specifically comprises:
(1.1) 20 collected subjects were tested for cardiovascular and cerebrovascular disease and were subjected to non-head related surgery;
(1.2) the experiment is divided into four stages, namely, resting state of eyes closed, light anesthesia, Moderate anesthesia and recovery state, which are respectively marked as Baseline (BS), Mild session (ML), Moderate session (MD) and Recovery (RC), and propofol is injected into the plasma to be tested for 10 minutes in each stage under the control of a digital injection pump (the concentration is respectively ML:0.6 mug/ML and MD:1.2 mug/ML);
(1.3) sample length of about 7 minutes, acquired using 128-lead high density EEG (in μ V; sampling frequency of 250 Hz);
(1.4) the EEG signal is not continuous for about 7 minutes per person, but is divided by EEGLAB into data segments (epochs) of 10 seconds in length. The mean (standard deviation) of the number of effectively analyzable epochs corresponding to the four anesthesia phases are: 38(5), 39(4), 38(4) and 40 (2).
(1.5) preprocessing the acquired signals, including correcting a base line, removing artifacts and noises, and filtering in a frequency range of 0.5-45 Hz;
(1.6) the finally obtained data for subsequent research is 91-lead electroencephalogram data.
(2) Analyzing the electroencephalogram signals in different anesthesia states through an electroencephalogram micro-state algorithm on the signals acquired by the electroencephalogram signal acquisition module (A), and constructing corresponding micro-state time sequences;
the step (2) specifically comprises:
(2.1) determining the ideal number of micro-states (the number corresponding to the first maximum value after 3) by two criteria of cross-evaluation (CV) and Krzanowski-LaiCriterion (KL) for the measured brain electrical signals, wherein in the present example, as shown in FIG. 2, although the ideal number of states in the BS and RC periods is 4, the ideal number of states in the ML and MD periods is 5, so that the optimal ideal number of states in the micro-states is 5 for the sake of the uniformity and rationality of the analysis;
(2.2) extracting peak points of a Global energy spectrum (GFP) of the electroencephalogram signal, wherein the specific formula of the GFP is as follows:
in the formula, K represents the total number of conductive connections, ViRepresenting the potential of the ith lead.
Then 91 lead brain electrical data of the moment corresponding to the GFP local maximum value is extracted;
and (2.3) clustering the EEG signals at the corresponding moment of the GFP by using a Modified k-means algorithm to obtain a micro-state topology. In this example, clustering is performed in two passes. Clustering for the first time, and clustering 10 topologies for each signal segment of each person; and for the second clustering, clustering is performed on the first clustering topology result under each anesthesia state, and 5 final topologies are clustered in each state, as shown in fig. 3. Because Global Extended Variance (GEV) can represent the interpretation degree of a given micro-state on total Variance, the GEV is calculated for 100 times to ensure that the finally obtained micro-state topology can overcome the randomness of the Modified k-means algorithm to the maximum extent during clustering;
and (2.4) carrying out spatial correlation pairing on the clustered micro-states and the electroencephalogram of the GFP function peak point of the original electroencephalogram signal, taking the micro-state topology with the highest spatial correlation as the current micro-state, namely marking the electroencephalogram at the moment as the serial number (namely A, B, C, D and F) of the micro-state. The microstate A, B, C, D is a well-known classical brain electrical microstate, and the microstate F mainly presents a topology with a peak value located at the center of the back of the scalp, which is close to the microstate F defined by Custo et al in the 7 brain electrical microstate defined in 2017 by combining with functional nuclear magnetic resonance, so the fifth microstate in this example is defined as microstate F.
The formula for spatial correlation is as follows:
wherein C is a spatial correlation, NeThe number of leads is U, the brain electrical mapping of Map u is u, v is the brain electrical mapping of Map v, and i is the ith lead;
(2.5) marking the rest brain electrical signals according to the micro state corresponding to the nearest GFP peak value, wherein the marking principle is as follows: the brain electrical micro-state at a certain moment is consistent with the brain electrical micro-state corresponding to the marked GFP peak value nearest to the brain electrical micro-state. Then the micro-state sequence of the corresponding anesthesia state can be obtained.
The global interpretable variance boxplot for each microstate at each anesthesia stage is shown in fig. 4, and these five microstates together account for over 60% of the peak variance of GFP in the electroencephalographic data for different tested, different anesthesia states, where the interpretability (i.e., GEV) for microstate C is always highest for all four states. Meanwhile, the spatial correlation between the micro-state topology of the BS and the micro-state topologies of the other three states is calculated to obtain the graph 5, and the difference between the states of the similar topologies is very small, and the topology of the BS and the corresponding topologies of the other three states have very high spatial correlation (mean: 97.1%, standard deviation: 3.6%), and the five micro-states exist stably in the consciousness transition process, and none of the micro-states appears and disappears suddenly.
(3) And calculating the corresponding micro-state parameters such as time ratio, appearance frequency, duration, energy and the like by using a micro-state parameter calculation module (C) according to the obtained micro-state time sequence.
The step (3) specifically comprises:
calculating the micro-state parameters of the obtained micro-state sequence, which are respectively as follows:
(3.1) time-to-time ratio (Coverage): i.e., the fraction of each micro-state in total time, the results are shown in fig. 6;
(3.2) frequency of Occurrence (Occurrence): i.e., the number of occurrences of a certain microstate within a specified time, which is typically set to 1 second, the results are shown in fig. 7;
(3.3) Duration (Duration): i.e. the average length of each occurrence of a certain micro-state, is equal to the total time duration of the occurrence of the micro-state divided by the total number of occurrences, the result is shown in fig. 8;
(3.4) energy (Power): namely the energy sum P of each electroencephalogram channel under a certain micro-state k topologykThe calculation formula is as follows:
in the formula, PkEnergy, s, representing a micro-state kiThe electroencephalogram signal of the ith lead, gammak,tEqual to 1 when time t is marked as micro-state k, otherwise 0, Ls is the length of the micro-state time-series under this anesthesia, and the result is shown in fig. 9.
According to the results of the parameters, the parameters of the micro-states A and B are relatively stable in the anesthesia process, and only some significant differences appear in the posterior paired t test among the states of the individual parameters. The average duration of the micro-state C decreases sharply between BS and MD (p 0.005, t 3.17), and the coverage also changes between ML and MD (ML-MD: t 3.10). Although several parameters of microstate C have decreased more or less with increasing anesthesia, C is still the most important one of the five microstations, which is reflected in the absolute values of the parameters. For the micro-state D, a significant drop occurs only over one parameter, the micro-state duration. It is very noteworthy that the newly appeared microstate F shows very obvious changes on each index in the process of anesthesia. Firstly, it appears more frequently with the depth of anaesthesia (BS-MD: p <0.001, t ═ 6.21), and secondly, it also has a marked increase in coverage (BS-MD: p <0.001, t ═ 5.04). These changes in the micro-regime F disappear again upon recovery from anesthesia and return to a level close to BS.
(4) And (3) carrying out principal component analysis on the obtained micro-state parameters by using a classification and identification module (D), inputting the obtained characteristic values into an SVM (support vector machine) for pattern recognition and classification, wherein the two classification results of the SVM are the detection results of the anesthesia depth.
The step (4) specifically comprises:
(4.1) selecting basic microscopic state parameters (namely time ratio, appearance frequency, duration and energy) of each subject in BS, ML and MD states as features, and then adopting principal component analysis (extracting maximum 99% variance) to remove redundant features;
(4.2) inputting the features after the reduction into an SVM (linear kernel, c is 1) for training and testing;
(4.3) the results were verified by Leave-One-Out Cross Validation (LOOCV). Since each test was run once during LOOCV, the results are extracted to generate a Receiver Operating Characteristic (ROC) curve. In the process of drawing the AUC, the data is divided into a positive class and a negative class according to a two-classification mode, and a calculation formula reflecting the accuracy (accuracyy) of the accuracy standard in all data and the sensitivity (sensitivity) of the accuracy standard in the positive class data is as follows:
in the formula: the TP judges the data number of the positive type actually; the FN judges the number of data of the negative class, actually the positive class. The TN judges as the data number of the negative type, actually the negative type; FP judges the data number of the positive class and actually the negative class.
In the present invention, data in the anesthesia phase is defined as a positive class, data in the waking phase is defined as a negative class, and the drawing result is shown in fig. 10.
Area Under the Curve (AUC) real SVM classifies BS and MD as 0.948, BS and ML as 0.675, accuracy as 0.89(BS-MD) and 0.68(BS-ML), and recall as 0.88(BS-MD) and 0.65 (BS-ML). The method shows good distinguishing capability for distinguishing the waking state from the anesthesia state, and has high operability and application value.
Claims (4)
1. An anesthesia depth monitoring system based on an electroencephalogram micro-state comprises an electroencephalogram signal acquisition module (A), a micro-state time sequence construction module (B), a micro-state parameter calculation module (C) and a classification identification module (D);
the electroencephalogram signal acquisition module (A): the system is used for collecting electroencephalogram signals of a sample subjected to general anesthesia in different anesthesia states;
the micro-state time sequence construction module (B): firstly, analyzing electroencephalogram signals in different anesthesia states through an electroencephalogram micro-state algorithm on signals acquired by an electroencephalogram signal acquisition module (A) to construct corresponding micro-state time sequences;
the micro-state parameter calculation module (C): according to the obtained micro-state time sequence, calculating corresponding micro-state parameters such as time ratio, appearance frequency, duration, energy and the like;
the classification recognition module (D): and simplifying the obtained micro-state parameters through principal component analysis, inputting the micro-state parameters serving as characteristic values into the SVM for pattern recognition and classification, wherein the two classification results of the SVM are the detection results of the anesthesia depth.
2. The monitoring method of the anesthesia depth monitoring system based on the electroencephalogram micro-state is characterized by comprising the following steps of:
(1) the brain blood oxygen signal acquisition module (A) is used for acquiring electroencephalogram signals of samples subjected to general anesthesia in different anesthesia states;
(2) analyzing the acquired signals by an electroencephalogram micro-state algorithm through a micro-state time sequence construction module (B) to construct corresponding micro-state time sequences;
(3) calculating corresponding micro-state parameters such as time ratio, appearance frequency, duration, energy and the like by using a micro-state parameter calculation module (C) according to the obtained micro-state time sequence;
(4) and (3) carrying out principal component analysis on the obtained micro-state parameters by using a classification and identification module (D), inputting the obtained characteristic values into an SVM (support vector machine) for pattern recognition and classification, wherein the two classification results of the SVM are the detection results of the anesthesia depth.
3. The monitoring method according to claim 2, wherein the step (2) specifically comprises:
(2.1) determining the ideal number of the micro-states by two criteria of cross-validation and Krzanowski-Lai Criterion for the measured brain electrical signals;
(2.2) extracting peak points of a Global energy spectrum (GFP) of the electroencephalogram signal, wherein the specific formula of the GFP is as follows:
in the formula, K represents the total number of conductive connections, ViA potential representing the ith lead;
(2.3) clustering the electroencephalogram signals at the corresponding moment of GFP by using a Modified k-means algorithm to obtain a micro-state topology, calculating a global interpretable variance (GEV) while clustering, and calculating 100 GEVs to maximize the GEV so as to ensure that the finally obtained micro-state topology can overcome the randomness of the Modified k-means algorithm to the maximum extent;
(2.4) carrying out spatial correlation pairing on the clustered micro-states and the electroencephalogram of the GFP function peak point of the original electroencephalogram signal, taking the micro-state topology with the highest spatial correlation as the current micro-state, namely marking the electroencephalogram at the moment as the serial number of the micro-state;
the formula for spatial correlation is as follows:
wherein C is a spatial correlation, NeThe number of leads is U, the brain electrical mapping of Map u is u, v is the brain electrical mapping of Map v, and i is the ith lead;
and (2.5) marking the rest of the electroencephalogram signals according to the micro-state corresponding to the nearest GFP peak value to obtain the micro-state sequence of the corresponding anesthesia state.
4. The monitoring method according to claim 2, wherein the step (3) specifically comprises:
calculating the micro-state parameters of the obtained micro-state sequence, which are respectively as follows:
(3.1) time to Coverage: the proportion of each micro-state in the total time;
(3.2) frequency of Occurrence Occurrence: i.e. the number of occurrences of a certain micro-state within a specified time, which is typically set to 1 second;
(3.3) Duration: the average length of each occurrence of a certain micro state is equal to the total time length of the occurrence of the micro state divided by the total number of occurrences;
(3.4) energy Power: namely the energy sum P of each electroencephalogram channel under a certain micro-state k topologykThe calculation formula is as follows:
in the formula, PkEnergy, s, representing a micro-state kiThe electroencephalogram signal of the ith lead, gammak,tEqual to 1 when time t is marked as micro-state k, otherwise 0, Ls is the length of the micro-state time-series under this anesthesia.
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| CN116098637A (en) * | 2023-02-21 | 2023-05-12 | 天津大学 | Brain function evaluation device based on ICA (independent component analysis) optimization correction brain electric micro-state |
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| CN113827256A (en) * | 2021-09-23 | 2021-12-24 | 河北省科学院应用数学研究所 | Electroencephalogram micro-state-based fatigue detection method and device, terminal and storage medium |
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| CN117423428A (en) * | 2023-12-18 | 2024-01-19 | 西南医科大学附属医院 | Anesthetic agent conveying intelligent management system and method based on data analysis |
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