Auditory evoked electroencephalogram-based disturbance of consciousness assessment method and related device

Abstract

The invention provides a disturbance of consciousness assessment method based on auditory evoked electroencephalogram and a related device, and the method comprises the steps: collecting electroencephalogram data of a patient with disturbance of consciousness through an electroencephalogram collection system, and carrying out the data preprocessing of the electroencephalogram data, and obtaining an event related potential; micro-state analysis is carried out according to the time-dependent potential, and micro-state-dependent characteristic parameters are obtained through calculation; quantitatively analyzing space-time dynamic characteristics of the brain function network of the patient based on a pre-constructed dynamic brain function network to obtain an electroencephalogram characteristic difference result of the patient with the disturbance of consciousness; and performing correlation analysis based on the difference result and a behavioral scale to obtain a disturbance of consciousness evaluation result of the disturbance of consciousness patient. According to the method provided by the invention, the brain activity change of the patient in the task state can be carefully captured, a more comprehensive basis is provided for the assessment of the consciousness level, and the accurate assessment of the consciousness level of the patient with disturbance of consciousness is realized.

Description

Technical Field

[0001] The present application belongs to the field of biomedical information application technology, and in particular, relates to a method for assessing consciousness disorders based on auditory evoked electroencephalogram (AEEG) and related devices. Background Art

[0002] Consciousness disorder is a common and serious neurological disease, involving abnormalities in the patient's wakefulness, perception and cognitive function. Its diagnosis, evaluation and awakening treatment are still one of the world's medical problems today. Consciousness disorder assessment technology is of great significance in medical diagnosis and treatment, but the existing technology has the following shortcomings:

[0003] 1. Existing scales and scoring systems have limitations in dealing with subtle changes in consciousness. For example, the Glasgow Coma Scale (GCS) is not sensitive to subtle changes and can easily lead to diagnostic errors; the Coma Recovery Scale-revised (CRS-R) is time-consuming and requires experienced personnel to evaluate.

[0004] 2. Although the resting state analysis method provides information about the brain's spontaneous neural activity, it is insufficient in capturing the patient's EEG response to external stimuli and ignores the dynamic changes in EEG activity. This method assumes that the functional connectivity of the brain is stable in the resting state, but in fact the functional connectivity of the brain changes dynamically.

[0005] 3. Traditional brain network construction methods assume that the statistical dependencies between regions remain constant when the brain processes information, which cannot accurately reflect the functional connectivity state of the brain at different time points. This static functional connectivity analysis cannot capture the real-time and dynamic nature of brain information transmission. Summary of the invention

[0006] In view of this, the present application aims to propose a method and related device for assessing consciousness disorders based on auditory evoked EEG, which analyzes auditory evoked EEG by combining microstate analysis and brain functional network to achieve the assessment of the consciousness level of patients with consciousness disorders.

[0007] To achieve the above purpose, the technical solution of this application is implemented as follows:

[0008] In a first aspect, the present application provides a method for assessing disturbance of consciousness based on auditory evoked electroencephalogram, comprising:

[0009] Collecting EEG data of patients with impaired consciousness through an EEG acquisition system, and performing data preprocessing on the EEG data to obtain event-related potentials;

[0010] Performing microstate analysis according to the time-dependent potential to calculate characteristic parameters related to the microstate, wherein the characteristic parameters at least include an average duration of the microstate;

[0011] Based on the pre-constructed dynamic brain function network, the spatiotemporal dynamic characteristics of the patient's brain function network are quantitatively analyzed to obtain the difference results of EEG characteristics of patients with consciousness disorders, wherein the dynamic brain function network is constructed with the duration of the microstate as the sliding time window and the EEG acquisition channel as the network node;

[0012] A correlation analysis was performed based on the difference results and the behavioral scale to obtain the consciousness disorder assessment results of the consciousness disorder patients.

[0013] In the second aspect, based on the same inventive concept, the present application also provides a consciousness disorder assessment device based on auditory evoked electroencephalogram, comprising:

[0014] A data acquisition module is configured to collect EEG data of patients with impaired consciousness through an EEG acquisition system, and perform data preprocessing on the EEG data to obtain event-related potentials;

[0015] A state analysis module is configured to perform microstate analysis according to the time-dependent potential and calculate characteristic parameters related to the microstate, wherein the characteristic parameters at least include an average duration of the microstate;

[0016] A network analysis module is configured to quantitatively analyze the spatiotemporal dynamic characteristics of the patient's brain function network based on a pre-constructed dynamic brain function network to obtain the difference results of EEG characteristics of patients with consciousness disorders, wherein the dynamic brain function network is constructed with the duration of the microstate as a sliding time window and the EEG acquisition channel as a network node;

[0017] The result output module is configured to perform a correlation analysis based on the difference result and the behavioral scale to obtain a consciousness disorder assessment result of the consciousness disorder patient.

[0018] In a third aspect, based on the same inventive concept, the present application also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method described in the first aspect is implemented.

[0019] In a fourth aspect, based on the same inventive concept, the present application also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the method described in the first aspect.

[0020] Compared with the prior art, the method for assessing disturbance of consciousness based on auditory evoked EEG and the related device described in this application have the following beneficial effects:

[0021] The method and related devices for assessing consciousness disorders based on auditory evoked EEG described in the present application obtain event-related potentials by processing the collected EEG data, perform microstate analysis based on the event-related potentials, and analyze auditory evoked EEG in combination with a constructed dynamic brain function network, thereby achieving an assessment of the consciousness level of patients with consciousness disorders, which can effectively improve the accuracy and reliability of patient assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0023] Figure 1 This is a flow chart of the method for assessing disturbance of consciousness based on auditory evoked EEG described in an embodiment of the present application;

[0024] Figure 2 This is a technical roadmap for the method for assessing disturbance of consciousness based on auditory evoked electroencephalogram as described in Example 1 of the present application;

[0025] Figure 3 This is a relationship diagram between the deviation W and the number of clusters described in Example 1 of the present application;

[0026] Figure 4 This is a graph showing the relationship between the KL value and the number of clusters described in Example 1 of the present application;

[0027] Figure 5 The micro-state topographic map described in Example 1 of the present application;

[0028] Figure 6 This is a micro-state difference diagram of two groups under Sin800Hz pure tone stimulation as described in Example 1 of the present application;

[0029] Figure 7 This is a micro-state difference diagram of two groups under Sin1200Hz pure tone stimulation as described in Example 1 of the present application;

[0030] Figure 8 It is a difference diagram of two groups of microstates under the name-calling stimulus described in Example 1 of the present application;

[0031] Fig. 9 It is the micro-state time series under Sin800Hz pure tone stimulation described in Example 1 of the present application;

[0032] Fig.10 This is a micro-state time series diagram under Sin1200Hz pure tone stimulation as described in Example 1 of the present application;

[0033] Fig.11 This is a time series diagram of microstates under the name-calling stimulus described in Example 1 of the present application;

[0034] Fig.12 This is the difference map of brain networks between two groups under Sin800Hz pure tone stimulation as described in Example 1 of the present application;

[0035] Fig.13 This is the difference map of brain networks between two groups under Sin1200Hz pure tone stimulation as described in Example 1 of the present application;

[0036] Fig.14 This is a difference map of brain networks between two groups under name-calling stimulation as described in Example 1 of the present application;

[0037] Fig.15 This is a schematic diagram of the structure of a consciousness disorder assessment device based on auditory evoked EEG according to an embodiment of the present application;

[0038] Fig.16 This is a schematic diagram of the hardware structure of the electronic device described in the embodiment of the present application. DETAILED DESCRIPTION

[0039] In order to make the objectives, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0040] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present application should be the usual meanings understood by people with ordinary skills in the field to which the present application belongs. The "first", "second" and similar words used in the embodiments of the present application do not represent any order, quantity or importance, but are only used to distinguish different components. "Including" or "comprising" and similar words mean that the elements or objects appearing in front of the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0041] The embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0042] See also Figure 1 As shown, this embodiment provides a method for assessing disturbance of consciousness based on auditory evoked electroencephalogram, which specifically includes the following steps:

[0043] Step S101: Collect EEG data of a patient with impaired consciousness through an EEG acquisition system, and preprocess the EEG data to obtain event-related potentials.

[0044] Specifically, in this embodiment, patients with impaired consciousness were selected as subjects, and the selection rules were as follows: the subjects were right-handed; had no history of mental illness or genetic mental illness; had not used drugs such as anesthetics and analgesics that affect normal thinking and judgment before the test; the onset period was within 28 days and vital signs were stable, and there was no statistical difference in age between the two groups.

[0045] During the acquisition process, a standard 20-channel (10-20 system) EEG acquisition system was used to collect EEG data with a sampling frequency of 1000Hz. The EEG acquisition of patients with impaired consciousness was carried out in the ward, and the environment was kept quiet. Before the experiment, it was ensured that the subjects were not in a sleeping state. Task-state EEG signals were collected from all subjects under the stimulation of the oddball paradigm. The paradigm included three stimuli: Sin800Hz (standard stimulation), Sin1200Hz (deviant stimulation), and patient name (novel stimulation). There were 1000 stimulations in total, and the three stimuli accounted for 70%, 20%, and 10% respectively. During the acquisition process, the impedance of all channels was kept below 5kΩ.

[0046] Traditional methods for assessing patients with impaired consciousness rely on resting-state EEG analysis, which tends to ignore the patient's EEG response to external stimuli. This application uses auditory task-state EEG signals for the assessment of patients with impaired consciousness. Based on the oddball paradigm, an auditory evoked experiment containing three stimuli is designed to extract the patient's ERP under different stimuli and perform EEG microstate analysis, thereby more carefully capturing the changes in the patient's brain activity under the task state, providing a more comprehensive basis for the assessment of the level of consciousness.

[0047] In this embodiment, the MATLAB software plug-in EEGLAB is used to pre-process the collected EEG data. During the pre-processing, the data is first averaged and referenced, and the filter range is controlled at 0.5-30Hz, and the artifacts and noise are manually removed. In order to eliminate the drift of the EEG signal relative to the baseline, the EEG data is baseline corrected. And in order to facilitate the subsequent analysis and processing, the EEG data is cut into 1 second time periods. Then the channels that still have large artifacts after the above-mentioned processing are subjected to bad track difference processing, and finally the electrooculogram and electromyography components in the EEG data are removed by the method of independent component analysis to obtain the original EEG data. The original EEG data is superimposed and averaged, and the obtained event-related potential (ERP) data is used for the following analysis.

[0048] In step S103 of the present application, electroencephalogram (EEG) is used as a detection method to avoid the additional burden and risk of invasive surgery on patients. EEG detection equipment is portable and easy to implement at the patient's bedside. It is particularly suitable for patients with impaired consciousness who are unable to move, and has high clinical practical value.

[0049] Step S102: Perform microstate analysis based on the time-dependent potential to calculate and obtain microstate-related characteristic parameters, wherein the characteristic parameters at least include an average duration of the microstate.

[0050] Specifically, in this embodiment, step S102 includes the following steps:

[0051] Step S201, calculate the global field power (GFP) of the event-related potential time series, and extract the topographic map at the GFP peak. The calculation formula of GFP is as follows:

[0052]

[0053] Where i represents each electrode, n represents the number of electrodes, and u represents the measured voltage of each channel.

[0054] Step S202: ERP data needs to consider polarity, select the k-means clustering algorithm to cluster the extracted topographic maps, and use the global explained variance (GEV) for quantitative evaluation until the template topography has the maximum spatial correlation with the single topography and the GEV reaches stability. GEV is defined as:

[0055]

[0056] Where, GFP x Represents the total electric field power of a single subject, C x,n represents the correlation between the individual topography and the template topography, and X represents the number of electrodes.

[0057] The Krzanowski-Lai (KL) criterion is used to determine the final number of clusters. The deviation W evaluates the clustering effect by reflecting the degree of dispersion between the elements within each cluster group. The calculation formula is as follows:

[0058]

[0059] In the formula, q represents the final selected number of clusters; W q Indicates the deviation value when clustering q classes; m k is the number of ERP topographic map vectors contained in the kth group. When the GEV value is closer to 1 and W is closer to 0, the clustering effect is better.

[0060] The KL value reflects the relative curvature of the qW curve, and the calculation formula is as follows:

[0061]

[0062] d q =W q q 2 / n -W q+1 (q+1) 2 / n (5)

[0063] Where q represents the number of clusters; n represents the number of ERP channels that need to be clustered.

[0064] Step S203: Fit the microstate template topography to the imported ERP data to obtain a microstate time series. Each data sample is assigned to the category of its most similar microstate prototype. Since the data contains noise, it often happens that consecutive time frames are accidentally marked as different.

[0065] Furthermore, even within the same microstate, brief EEG instabilities can occur during polarity changes. To reduce these spurious effects, the microstate labels can be temporally smoothed after backfitting by rejecting segments shorter than 30 ms.

[0066] Step S204, calculate the characteristic parameters of the microstate time series, mainly including the mean microstate duration (MMD), the average number of occurrences per second (OPS), the coverage (COV), and the transition probabilities (TP), etc. Among them, the average number of occurrences per second refers to the average number of occurrences per second during which a microstate appears and remains stable; the average duration is the average length of time that a microstate appears and remains stable; the coverage refers to the ratio of the duration of a given microstate to the total duration of microstates; and the transition probability refers to the transition probability between various microstates.

[0067] Step S103, quantitatively analyzing the spatiotemporal dynamic characteristics of the patient's brain function network based on a pre-constructed dynamic brain function network to obtain the difference results of EEG characteristics of patients with consciousness disorders, wherein the dynamic brain function network is constructed with the duration of the microstate as the sliding time window and the EEG acquisition channel as the network node.

[0068] Traditional static brain networks assume that the statistical dependence between brain regions remains constant when the brain processes information, ignoring the time-varying characteristics of brain activity. This application uses the duration of microstates as a dynamic time window, and by constructing brain network models corresponding to different categories of microstates, explores the dynamic changes in functional connectivity between different brain regions in patients with different levels of consciousness under auditory stimulation tasks, quantitatively analyzes the spatiotemporal dynamic characteristics of brain functional networks in patients with different levels of consciousness, and improves the accuracy and reliability of patient assessments.

[0069] Specifically, in this embodiment, the selection of the window and sliding step size in the dynamic functional connection method will directly affect the results, and a fixed-size window cannot accurately reflect the cognitive processing stage. This embodiment selects the duration of the microstate as the sliding time window and the EEG acquisition channel as the network node to construct a dynamic functional brain network.

[0070] In terms of brain network construction, a method based on phase locking value (PLV) is selected to obtain the correlation between signals between any two channels. PLV measures the degree of synchronization between two or more signals in phase, which is different from coherence. Coherence focuses on the linear correlation between signals, while PLV focuses more on the synchronization of phases. In some cases, even if the amplitudes (i.e., intensities) of two signals are unrelated, their phases can be synchronized, and PLV is still a meaningful metric.

[0071] Compared with traditional synchronization analysis methods, the PLV method can more accurately remove the influence of signal amplitude on experimental results, thereby more purely reflecting the phase synchronization between signals.

[0072]

[0073] In the formula, t represents the time point (or trial point), N represents the total time (total number of trials), i represents the phase angle, Respectively represent the phase angles of signals x and y at time point t.

[0074] Through the above steps, an N*N PLV matrix can be obtained. The value range of PLV is [0, 1]. The size of the PLV value reflects the degree of signal correlation between different channels. By selecting an appropriate threshold, the PLV matrix is ​​converted into a binary matrix. When the mutual correlation coefficient between channels is greater than the threshold, the element at the corresponding position of the PLV binary matrix is ​​1; conversely, when the mutual correlation coefficient between channels is less than the threshold, the element at the corresponding position of the PLV binary matrix is ​​0.

[0075] The PLV binary matrix of each EEG channel under different conditions was calculated respectively, and the binary matrix of the subjects was normalized to obtain the PLV binary matrix diagram under different states. Based on the PLV binary matrix, the brain network connection diagram was constructed.

[0076] First, the brain network should be a functionally connected network, that is, there are no isolated brain areas that are not connected to other brain areas. Secondly, the brain network has the characteristics of sparsity, and the network density should be less than 50%. Therefore, the present invention uses 0.01-0.99 as the threshold range, 0.01 as the step size, traverses the threshold range, and selects a suitable threshold to construct a brain function network.

[0077] On the basis of constructing the brain functional network, the parameters of the brain functional network such as node degree, clustering coefficient and average path length were obtained. By comparing and analyzing the differences in network parameters, the characteristics of the brain functional network of patients with consciousness disorders at different levels were quantitatively analyzed.

[0078] ① Node degree: In the brain function network, the definition of a node is the number of nodes connected to the node in the network, that is, the number of neighboring nodes of the node in the network. The larger the degree of a node, the more nodes are connected to this node, that is, the greater the influence of this node in the network.

[0079] ② Clustering coefficient: The clustering coefficient can assess the degree of network integration and indicates the probability that the neighbors of a certain node i are neighbors of each other. It represents the overlap of nodes with a common connection relationship between two adjacent nodes. The larger the clustering coefficient, the tighter the connection between neurons in the local area of ​​the brain network.

[0080] ③Average path length: In a network, the shortest path length between two nodes i and j refers to the minimum number of edges connecting node i and node j. The average path length is the average of the path lengths of all nodes in the network. The shorter the average path, the faster the information transmission and the better the connectivity of the network.

[0081] In step S103 of the present application, by quantitatively analyzing parameters such as node degree, clustering coefficient and average path length of the brain functional network of patients with different consciousness levels, the characteristics of the brain functional network of patients with different consciousness levels can be quantitatively evaluated, and objective evaluation indicators are provided, which helps to improve the reliability and objectivity of the evaluation of patients with consciousness disorders.

[0082] This embodiment uses advanced algorithms and technologies to capture and analyze the time-varying characteristics of EEG signals of patients with impaired consciousness. Compared with static models, dynamic brain network models can more accurately reflect the temporal changes in brain activity, providing more detailed and comprehensive information for understanding and evaluating the patient's brain function status.

[0083] Step S104: performing correlation analysis based on the difference results and the behavioral scale to obtain a consciousness disorder assessment result for the consciousness disorder patient.

[0084] Specifically, in this embodiment, the difference results are compared with a standard behavioral scale, and image relationship analysis is performed based on the comparison results to obtain the consciousness disorder assessment result of the consciousness disorder patient.

[0085] The evaluation method described in the present application designs an auditory evoked experiment involving three stimuli, extracts the patient's event-related potentials under different stimuli, and performs EEG microstate analysis, thereby more carefully capturing the changes in the patient's brain activity under task conditions, providing a more comprehensive basis for the evaluation of the level of consciousness, achieving accurate evaluation of the level of consciousness of patients with consciousness disorders, and providing a reference for subsequent clinical treatment.

[0086] like Figure 2 As shown, the present application also provides the following specific embodiment 1:

[0087] 1.1 Subject information

[0088] Finally, 33 patients with acute consciousness disorders from a hospital in Tianjin were recruited as experimental subjects, including 16 VS patients and 17 MCS patients. All of them were right-handed; had no history of mental illness or genetic mental illness; had not used drugs such as anesthetics and analgesics that affect normal thinking and judgment before the experiment; had an onset period of less than 28 days and stable vital signs, and there was no statistical difference in age between the two groups.

[0089] The CRS-R scale was used to assess the patients' level of consciousness before the experiment.

[0090] 1.2 Microstate Template

[0091] This experiment uses ERP data clustering microstate templates, so the polarity-considering k-means algorithm is selected for clustering, and 2 to 8 microstate topographic maps of task-state ERP data in the 0.5-30Hz frequency band are obtained. Deviation W can be regarded as an error metric, the lower the better. The KL standard is a method for selecting the number of clusters to use based on deviation W. A high value of KL usually represents the optimal number of clusters. The relationship between deviation W, KL value and number of clusters is constructed respectively, as shown in Figure 3 and Figure 4 shown.

[0092] Depend on Figure 3 , Figure 4 It can be seen that when the number of clusters is 6, the KL value is the largest, and when it increases from 6 to 8, the W value changes very little, following the principle of the number of clusters being as small as possible. Therefore, the final cluster number is determined to be 6 as the optimal cluster number. The final cluster microstate template is as follows: Figure 5 shown.

[0093] 1.3 Microstate parameters

[0094] In order to further explore the influence of different consciousness levels on microstate parameters, this example calculated the average number of microstates per second (OPS), mean duration (MMD), coverage (COV), and transition rate (TP) between microstates in the MCS group and VS group under three auditory stimuli in the 1-30 Hz frequency range. The data followed a normal distribution, so the independent sample t test was used to compare the differences between the groups.

[0095] The results of microstate difference analysis between MCS group and VS group under Sin800Hz pure tone stimulation are as follows Figure 6 As shown. Under Sin800Hz pure tone stimulation, the coverage rate of microstate A in the MCS group was the highest, the coverage rate of microstate C was the lowest, the average number of occurrences per second of microstate A was the largest, the average number of occurrences per second of microstate C was the smallest, the average duration of microstate A was the longest, and the average duration of microstate F was the shortest. The coverage rate of microstate E in the VS group was the highest, the coverage rate of microstate C was the lowest, the average number of occurrences per second of microstate E was the largest, the average number of occurrences per second of microstate A was the smallest, the average duration of microstate A was the longest, and the minimum duration of microstate C was the shortest.

[0096] The coverage rate of microstate A in the MCS group was significantly higher than that in the VS group (P<0.001), and there was no significant difference in the coverage rate of other microstates between the two groups. The number of occurrences of microstate A per second in the MCS group was significantly higher than that in the VS group, and there was no significant difference in the average number of occurrences per second of other microstates between the two groups (P<0.05). No difference in the average duration of any microstate was observed between the MCS group and the VS group. The statistical results are as follows Figure 6 Figure (d) depicts the difference in transition probability between the MCS group and the VS group, where the solid line represents that the transition probability of the MCS group is higher than that of the VS group, and the dashed line represents that the transition probability of the MCS group is lower than that of the VS group.

[0097] The results of microstate difference analysis between MCS group and VS group under Sin1200Hz pure tone stimulation are as follows Figure 7 As shown. Under Sin1200Hz pure tone stimulation, the coverage rate of microstate A in the MCS group was the highest, and the coverage rate of microstate F was the lowest. The average number of occurrences per second of microstate D was the largest, and the average number of occurrences per second of microstate F was the smallest. The average duration of microstate A was the longest, and the average duration of microstate B was the shortest. The coverage rate of microstate D in the VS group was the highest, and the coverage rate of microstate B was the lowest. The average number of occurrences per second of microstate D was the largest, and the average number of occurrences per second of microstate B was the smallest. The average duration of microstate A was the longest, and the minimum duration of microstate C was the shortest. There were no significant differences in all microstate parameters between the two groups. The statistical results are shown in Figure 7As shown in Figure (d), the difference in transition probability between the two groups.

[0098] The results of microstate difference analysis between the MCS group and the VS group under name-calling stimulation are as follows Figure 8 As shown. Under the name-calling stimulus, the coverage rate of microstate A in the MCS group was the highest, and the coverage rate of microstate B was the lowest. The average number of occurrences per second of microstate A was the highest, and the average number of occurrences per second of microstate B was the lowest. The average duration of microstate A was the longest, and the average duration of microstate C was the shortest. The coverage rate of microstate D in the VS group was the highest, and the coverage rate of microstate B was the lowest. The average number of occurrences per second of microstate C was the highest, and the average number of occurrences per second of microstate A was the lowest. The average duration of microstate D was the longest, and the minimum duration of microstate B was the shortest.

[0099] The coverage rate of microstate A in the MCS group was significantly higher than that in the VS group (P<0.05), and there was no significant difference in the coverage rate of other microstates between the two groups. The number of occurrences of microstate A per second in the MCS group was significantly higher than that in the VS group, and there was no significant difference in the average number of occurrences per second of other microstates between the two groups (P<0.001). There was no significant difference in the average duration and transition probability between the MCS group and the VS group under name-calling stimulation.

[0100] 1.4 Microstate Time Series

[0101] Fig. 9 , Fig.10 , Fig.11 The microstate time series of the group and the VS group under Sin800Hz pure tone stimulation, Sin1200Hz pure tone stimulation, and name-calling stimulation are shown in the results. It can be seen from the results that under the same stimulation, the microstate time series between the two groups remain generally similar, with some differences, which may be caused by the different levels of consciousness of the two groups of patients.

[0102] 1.5 Brain network parameters

[0103] The differences in brain network parameters between the two groups under Sin800Hz pure tone stimulation are as follows Fig.12 The results showed that under Sin800Hz pure tone stimulation, the average node degree of the MCS group in each microstate was higher than that of the VS group, indicating that the node density of the brain functional network was better than that of the VS group, and the characteristic path length in each microstate was lower than that of the VS group. The lower characteristic path length indicated that the inter-regional integration ability of the brain functional network was better than that of the VS group.

[0104] The differences in brain network parameters between the two groups under Sin1200Hz pure tone stimulation are as follows Fig.13The results showed that under Sin1200Hz pure tone stimulation, the average node degree of the MCS group and the VS group was significantly different in each microstate. The node degree of the MCS group was higher than that of the VS group, indicating that the node density of the brain functional network was better than that of the VS group. The clustering coefficient of the MCS group in each microstate was lower than that of the VS group, indicating that the node aggregation degree of the VS group was higher. The characteristic path length of the MCS group in microstate E was significantly lower than that of the VS group, indicating that the inter-regional integration ability of the brain functional network of the MCS group was stronger in microstate E.

[0105] The differences in brain network parameters between the two groups under name-calling stimulation are as follows Fig.14 The results showed that the average node degree of the MCS group in microstates B and F was significantly higher than that of the VS group, indicating that the node density of the brain function network in microstates B and D of the MCS group was better than that of the VS group, and the clustering coefficient in microstate E was significantly lower than that of the VS group, indicating that the VS group had a higher degree of node aggregation in microstate E, and the characteristic path length in microstate F was significantly lower than that of the VS group. The lower characteristic path length indicates that the inter-regional integration ability of the brain function network is better than that of the VS group.

[0106] 1.6 Conclusion

[0107] This example analyzes and compares microstates between 17 MCS patients and 16 VS patients, and identifies six microstate categories: A, B, C, D, E, and F. Current neuroimaging studies have linked EEG microstates to specific cognitive functions. This is because different microstate topography maps are produced by the activation of different brain regions responsible for specific cognitive functions. Microstate A is believed to be related to the brain's speech circuit, which is responsible for storing and controlling verbal information in the working memory model proposed by Baddeley. The phenomenon that the parameters of microstate A decrease significantly with the decrease in the level of consciousness under name-calling stimulation and Sin800Hz pure tone stimulation reflects that the language circuit of the brain is dominated by the level of consciousness.

[0108] Node degree reflects the density of connections between a node and other nodes in the brain network, and further reveals the potential role of the node in information transmission and integration. The clustering coefficient reflects the tightness of connections between local regions in the brain network, revealing the trade-off between functional differentiation and functional integration between brain regions. A higher clustering coefficient indicates that there are stronger connections between local brain regions, which facilitates local processing and rapid transmission of information. Characteristic path length is an important indicator of the global connectivity characteristics of the brain network, which measures the efficiency of information transmission in the brain network. A shorter characteristic path length means that information can be transmitted quickly and efficiently in the brain network, which helps to achieve complex cognitive functions.

[0109] Overall, under different stimulations, the average node degree of the MCS group was higher than that of the VS group, indicating that the node density of the brain functional network of the MCS group was better than that of the VS group, and the clustering coefficient was lower than that of the VS group, indicating that the node clustering degree of the VS group was higher, the characteristic path length was lower than that of the VS group, and the inter-regional integration ability of the brain functional network was better than that of the VS group.

[0110] This embodiment attempts to use portable, economical, and non-invasive EEG technology combined with microstate brain function network analysis to identify and evaluate patients with impaired consciousness, and explore key reliable objective biomarkers. Changes in the coverage rate and average number of occurrences per second of microstate A under Sin800Hz and name-calling stimulation; changes in the characteristic path length of microstate E under Sin1200Hz stimulation; changes in the average node degree and characteristic path length of microstate F under name-calling stimulation; changes in the average node degree of microstate B play a key role in distinguishing and evaluating patients with impaired consciousness, and provide auxiliary support for the current diagnosis and treatment model that mainly relies on clinical manifestations and physicians to conduct CRS-R scale evaluation.

[0111] It should be noted that the above describes some embodiments of the present application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0112] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, an embodiment of the present application also provides a consciousness disorder assessment device based on auditory evoked EEG.

[0113] like Fig.15 As shown, the consciousness disorder assessment device based on auditory induced EEG comprises:

[0114] The data acquisition module 11 is configured to collect EEG data of patients with impaired consciousness through an EEG acquisition system, and perform data preprocessing on the EEG data to obtain event-related potentials;

[0115] The state analysis module 12 is configured to perform microstate analysis according to the time-dependent potential and calculate characteristic parameters related to the microstate, wherein the characteristic parameters at least include an average duration of the microstate;

[0116] The network analysis module 13 is configured to quantitatively analyze the spatiotemporal dynamic characteristics of the patient's brain function network based on a pre-constructed dynamic brain function network to obtain the difference results of EEG characteristics of patients with consciousness disorders, wherein the dynamic brain function network is constructed with the duration of the microstate as the sliding time window and the EEG acquisition channel as the network node;

[0117] The result output module 14 is configured to perform a correlation analysis based on the difference result and the behavioral scale to obtain a consciousness disorder assessment result of the consciousness disorder patient.

[0118] For the convenience of description, the above devices are described in terms of functions and are divided into various modules. Of course, when implementing the embodiments of the present application, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0119] The device of the above embodiment is used to implement the corresponding method in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be described in detail here.

[0120] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, an embodiment of the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method described in any of the above embodiments is implemented.

[0121] Fig.16 A more specific schematic diagram of the hardware structure of an electronic device provided in this embodiment is shown, and the device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are connected to each other through the bus 1050 in the device.

[0122] The processor 1010 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0123] The memory 1020 may be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 may store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program codes are stored in the memory 1020 and are called and executed by the processor 1010.

[0124] The input / output interface 1030 is used to connect the input / output module to realize information input and output. The input / output module can be configured in the device as a component (not shown in the figure), or it can be externally connected to the device to provide corresponding functions. The input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device may include a display, a speaker, a vibrator, an indicator light, etc.

[0125] The communication interface 1040 is used to connect a communication module (not shown) to realize communication interaction between the device and other devices. The communication module can realize communication through a wired mode (such as USB, network cable, etc.) or a wireless mode (such as mobile network, WIFI, Bluetooth, etc.).

[0126] The bus 1050 includes a path that transmits information between the various components of the device (eg, the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040).

[0127] It should be noted that, although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040 and the bus 1050, in the specific implementation process, the device may also include other components necessary for normal operation. In addition, it can be understood by those skilled in the art that the above device may also only include the components necessary for implementing the embodiments of the present specification, and does not necessarily include all the components shown in the figure.

[0128] The electronic device of the above embodiment is used to implement the corresponding method in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be described in detail here.

[0129] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments, the present application also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the method described in any of the above embodiments.

[0130] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0131] The computer instructions stored in the storage medium of the above embodiments are used to enable the computer to execute the method described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0132] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present application (including the claims) is limited to these examples. In line with the concept of the present application, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present application as described above, which are not provided in detail for the sake of simplicity.

[0133] In addition, to simplify the description and discussion, and in order not to make the embodiments of the present application difficult to understand, the known power supply / ground connection with the integrated circuit (IC) chip and other components may or may not be shown in the provided drawings. In addition, the device can be shown in the form of a block diagram to avoid making the embodiments of the present application difficult to understand, and this also takes into account the fact that the details of the implementation of these block diagram devices are highly dependent on the platform to be implemented in the embodiments of the present application (that is, these details should be fully within the scope of understanding of those skilled in the art). In the case of elaborating specific details (e.g., circuits) to describe exemplary embodiments of the present application, it is obvious to those skilled in the art that the embodiments of the present application can be implemented without these specific details or when these specific details are changed. Therefore, these descriptions should be considered to be illustrative rather than restrictive.

[0134] Although the present application has been described in conjunction with specific embodiments of the present application, many replacements, modifications and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed.

[0135] The embodiments of the present application are intended to cover all such substitutions, modifications and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present application should be included in the scope of protection of the present application.

Claims

1. A method for assessing disturbance of consciousness based on auditory evoked electroencephalogram, characterized in that: include: Collecting EEG data of patients with impaired consciousness through an EEG acquisition system, and performing data preprocessing on the EEG data to obtain event-related potentials; Performing microstate analysis according to the time-dependent potential to calculate characteristic parameters related to the microstate, wherein the characteristic parameters at least include an average duration of the microstate; Based on the pre-constructed dynamic brain function network, the spatiotemporal dynamic characteristics of the patient's brain function network are quantitatively analyzed to obtain the difference results of EEG characteristics of patients with consciousness disorders, wherein the dynamic brain function network is constructed with the duration of the microstate as the sliding time window and the EEG acquisition channel as the network node; A correlation analysis was performed based on the difference results and the behavioral scale to obtain the consciousness disorder assessment results of the consciousness disorder patients.

2. The method according to claim 1, characterized in that The data preprocessing includes: The collected EEG data are averaged for reference, and the filtering range is controlled within a preset range, then the EEG data are baseline corrected, and the EEG data are cut into fixed time periods, and the cut EEG data are processed for bad track difference; Removing the electrooculogram and electromyography components from the EEG data by an independent component analysis method to obtain original EEG data; The raw EEG data are subjected to superposition and averaging processing to obtain event-related potentials.

3. The method according to claim 1, characterized in that The microstate analysis is performed according to the time-dependent potential to calculate the microstate-related characteristic parameters, wherein the characteristic parameters at least include the average duration of the microstate, the average number of occurrences per second, the coverage rate and the transition probability, including: The global field power of the event-related potential time series was calculated, and the topography at the peak of the global field power was extracted; The extracted topographic maps were clustered by the k-means clustering algorithm and quantitatively evaluated using the global explained variance until the template topography had the maximum spatial correlation with the individual topography, and the final number of clusters was determined by the Krzanowski-Lai criterion; Fit the microstate template topography to the imported event-related potential data to obtain the microstate time series; The characteristic parameters of the microstate time series are calculated, including the average duration of the microstate, the average number of occurrences per second, the coverage rate, and the transition probability.

4. The method according to claim 3, characterized in that: The global field power calculation formula is as follows: In the formula, i represents each electrode, n represents the number of electrodes, and u represents the measured voltage of each channel; The global explained variance calculation formula is as follows: In the formula, GFP x Represents the total electric field power of a single subject, C x,n represents the correlation between the individual topography and the template topography, and X represents the number of electrodes.

5. The method according to claim 1, characterized in that The method of quantitatively analyzing the spatiotemporal dynamic characteristics of the patient's brain function network based on the pre-constructed dynamic brain function network to obtain the difference results of EEG characteristics of patients with consciousness disorders includes: A method based on phase-locked value is selected to obtain the correlation between the signals of any two channels, and an N*N PLV matrix is ​​obtained; By selecting and setting a threshold, the PLV matrix is ​​converted into a binary matrix. When the mutual correlation coefficient between channels is greater than the threshold, the element at the corresponding position of the PLV binary matrix is ​​1; When the mutual correlation coefficient between channels is less than the threshold, the element of the corresponding position of the PLV binary matrix is ​​0; The PLV binary matrix of each EEG channel under different conditions was calculated respectively, and the patient's binary matrix was normalized to obtain the PLV binary matrix diagram under different states. The brain network connection diagram was constructed based on the PLV binary matrix.

6. The method according to claim 5, characterized in that The phase lock value formula is: In the formula, t represents the time point, N represents the total time, i represents the phase angle, Respectively represent the phase angles of signals x and y at time point t.

7. The method according to claim 5, characterized in that Also includes: On the basis of constructing the brain functional network, the parameters of the brain functional network are obtained, and by comparing and analyzing the differences in network parameters, the brain functional network characteristics of patients with consciousness disorders are quantitatively analyzed to obtain the results of the differences in EEG characteristics of patients with consciousness disorders; wherein the brain functional network parameters include at least node degree, clustering coefficient and average path length.

8. A device for assessing disturbance of consciousness based on auditory evoked electroencephalogram, characterized in that: include: A data acquisition module is configured to collect EEG data of patients with impaired consciousness through an EEG acquisition system, and perform data preprocessing on the EEG data to obtain event-related potentials; A state analysis module is configured to perform microstate analysis according to the time-dependent potential and calculate characteristic parameters related to the microstate, wherein the characteristic parameters at least include an average duration of the microstate; A network analysis module is configured to quantitatively analyze the spatiotemporal dynamic characteristics of the patient's brain function network based on a pre-constructed dynamic brain function network to obtain the difference results of EEG characteristics of patients with consciousness disorders, wherein the dynamic brain function network is constructed with the duration of the microstate as a sliding time window and the EEG acquisition channel as a network node; The result output module is configured to perform a correlation analysis based on the difference result and the behavioral scale to obtain a consciousness disorder assessment result of the consciousness disorder patient.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method according to any one of claims 1 to 7 is implemented.

10. A non-transitory computer-readable storage medium, characterized in that: in, The non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to cause a computer to execute the method according to any one of claims 1 to 7.

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