CN114305451A - Method for constructing children electroencephalogram function connection map based on entropy stability criterion - Google Patents
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Abstract
The invention discloses a child electroencephalogram function connection map construction method based on an entropy stability criterion. Firstly, data preprocessing is carried out, then, a function connection network is constructed, and finally, network feature extraction and analysis are carried out. The optimal threshold value is searched by adopting a combination method of a random network and Shannon entropy measurement, and the binary network constructed based on the method has stronger intra-group network stability compared with the binary network constructed by other methods, namely, the personalized connection part of the network is less, and the age characteristic is more prominent. The graph theory characteristics of individual level and age group level functional connection are respectively extracted, and the graph theory characteristics comprise function separation, function integration, network centrality measurement and analysis of the change rule of the functional connection along with the age increase and the characteristics of the functional connection mode of a specific age stage. Wherein the change trend of the functional connection is consistent with the common medical knowledge, which verifies the rationality of the functional connection construction method.
Description
Technical Field
The invention belongs to the field of intelligent biomedical signal processing, and relates to a child electroencephalogram function connection map construction method based on an entropy stability criterion.
Background
During childhood, the brain undergoes considerable changes in structure and function. The subtle changes in this stage may be greatly amplified as different evolutionary processes are developed, resulting in profound effects. Deviations from normal progression may lead to diseases such as hyperactivity, autism and schizophrenia. The macroscopic activity of the brain is mainly reflected in the interaction between different brain regions. The traditional univariate characteristics cannot capture the information, and the functional connection is used as the relevance characteristics between bivariables, so that the information interaction degree between brain areas can be described. Therefore, the reliable normal child brain function connection map is constructed, the change track of child brain function connection can be analyzed, and the method has important significance for detecting brain diseases.
Functional connections are typically constructed based on functional magnetic resonance imaging (fMRI) data or electroencephalography (EEG) data. Compared with fMRI, EEG data has higher resolution in a time domain, can meet the requirement of cortical functional connection analysis in a space domain, and has relatively low EEG signal acquisition cost. Thus, more and more researchers are using EEG data for brain functional connectivity related studies. At present, the study based on EEG functional connection mainly focuses on two directions of encephalopathy detection and age analysis:
and (3) detecting encephalopathy: the encephalopathy includes epilepsy, Alzheimer's disease, depression, and the like. The difference of brain function connections of a patient with a brain disease and a normal person is analyzed by using an intelligent decision model, so that the detection of the brain disease is realized.
Age analysis: the effects of aging on functional connections in the brain are generally studied. For example, individuals are simply divided into young and old age groups for comparative analysis, or the trend of increasing age based on individual study functional connectivity characteristics at a greater age span.
The studies in the above-mentioned fields have the following problems: 1. intelligent detection of encephalopathy to distinguish patients from normal controls of the corresponding age, functional connections are typically constructed by maximizing inter-group variability, resulting in functional connections of normal controls that are contrary to reality. 2. The field of age analysis lacks methods for constructing and analyzing functional links of the brain for each important age stage of a child. The method is also significant for assisting the diagnosis of the encephalopathy of the children.
In addition to this, many functional connection-related studies do not binarize functional connections, which would result in noise-related connections not being removed.
Disclosure of Invention
Aiming at the problems and the defects of the brain function connection analysis method, the invention provides a child electroencephalogram function connection map construction method based on an entropy stability criterion. According to the method, children of 0-17 years old are divided into 7 age groups, individual partial functional connection is weakened, individual horizontal functional connection and age group horizontal functional connection with high stability are extracted, and brain functional connection differences and frequency band importance of different age groups are analyzed by using a graph theory and a statistical method.
Since age affects the pattern of functional connectivity, brain disease detection based on functional connectivity is usually compared with normal functional connectivity of the corresponding age and analyzed for the presence of abnormal connectivity, but its functional connectivity construction method aims to maximize the inter-group variability, and the functional connectivity of the normal control group under this method is not close to the true situation. Under the stability criterion, the invention can construct the function connection related to the outstanding age, and can overcome the defects of the function connection construction method of the normal control group in the current brain disease detection. In addition, analysis is carried out based on age groups, so that the change rule of the functional connection of normal children along with the increase of the ages can be obtained, and the functional connection characteristics of a specific age group are reflected. The method is suitable for processing electroencephalogram data of children of all ages, and ideal analysis results can be obtained.
In the invention, the original signal data is preprocessed, including channel selection, digital filtering, signal slicing, sample filtering based on threshold value and standard frequency band decomposition. Threshold optimization is carried out on the preprocessed EEG data through combination of stability measurement based on entropy and a random network agent method, and accordingly binarization functional connection of individual levels is constructed. Individual level functional links in turn construct age group level functional links by majority voting. In order to evaluate the function separation and function integration degree of brain function connection in different age groups, the global clustering coefficients and characteristic path length characteristics of individual level function connection are respectively extracted to analyze the change trend of the individual level function connection along with the age increase, and a statistical test method is combined to carry out frequency band importance analysis; in order to evaluate the brain network centrality, the degree characteristics of functional connection at the age group level are extracted, and the area where the network centre is located and the balance degree of degree distribution are analyzed.
The technical scheme of the invention mainly comprises the following steps:
step 1: and (4) preprocessing data.
Channel selection is performed on the original multi-channel quiet sleep period EEG signal data. And (3) performing 1-30HZ band-pass filtering and 50HZ power frequency filtering to remove signal noise based on the frequency band of main information of the EEG signal. Cutting the signal into 1s segments, setting a 150uV threshold value according to the amplitude range of the physiological signal to remove the segments containing the non-physiological artifact signals to obtain a pure EEG signal, and finally performing signal decomposition on the EEG according to a standard frequency band.
Step 2: and constructing a functional connection network.
An ICoh correlation feature matrix is calculated for the EEG segments for each frequency band. And finding an optimal threshold value through a random network agent based on a stability criterion to construct the binaryzation function connection of the individual level. And finally, establishing age group level functional connection by using a voting method.
And step 3: and extracting and analyzing network features. Extracting global clustering coefficients and characteristic path lengths of individual horizontal functional connections, evaluating key frequency bands based on a statistical test method, and analyzing change rules of characteristics under the key frequency bands along with age increase. And in addition, the degrees of the horizontal functional connection of the age group are extracted, and the network centrality and the degree distribution balance degree are analyzed.
The specific process of the step 1 is as follows:
1-1, age group division. The electroencephalogram data sets of children of 0-17 years old are divided into 7 age groups, wherein the age groups are less than 1 month, 1-3 months, 3 months-1 year old, 1-3 years old, 3-7 years old, 7-14 years old and 14-17 years old. Each age group contained 6 children's individual data.
And 1-2, selecting a channel. The a1, a2, Fz, Cz, Pz channels in the original multi-channel EEG signal are removed, and 16 channels are selected, including Fp1, Fp2, F3, F4, C3, C4, P3, P4, O1, O2, F7, F8, T3, T4, T5, and T6.
1-3, unifying sampling frequency. The channel selected EEG signals are down-sampled to the lowest sampling frequency of the data set for uniformity.
1-4, removing high and low frequency and alternating current noise. The EEG signal is subjected to a base pre-processing by a 1-30HZ band pass filter, a 50HZ notch filter.
1-5, data segmentation and non-physiological signal filtration. Dividing the preprocessed data into a plurality of periods with the duration of 1 second, setting 150uV as a threshold value, deleting signal segments and removing irrelevant non-physiological signals. Each individual retained 100 1 second segments of the 16 channel EEG signal.
1-6, signal decomposition. The signal is decomposed into 4 standard frequency bands of delta (1-4hz), theta (4-8hz), alpha (8-14hz) and beta (14-30hz) by using a band-pass filter.
The specific process of the step 2 is as follows:
and 2-1, calculating an ICoh correlation characteristic matrix of all EEG segments under each standard frequency band.
And (3) carrying out coherence imaginary part ICoh correlation characteristic extraction on the preprocessed 16-channel 1-second EEG samples, wherein the index can overcome false connection caused by volume conduction effect. The ICoh feature calculation formula is:
Pi,j(f) for channel i and channel j (channel i, j is set to node v during the construction of the functional connectioni,vj) Cross power spectral density, P, betweeni,iAnd Pj,jThe self-power spectral densities of the channel i and the channel j are respectively, and N is the number of signal sampling points. The set of ICoh indices between all channel pairs constitutes the correlation feature matrix, with the matrix diagonals all set to 0.
And 2, averaging ICoh correlation characteristic matrixes of all fragments in the individual to obtain an individual-level weighted connection matrix.
2-3, optimizing a threshold value:
1) firstly, selecting an initial proportion threshold value t in the range of (0, 0.5), and carrying out binarization on the weighted connection matrix to obtain a binary connection matrix. Individual binary connectivity matrices of the same age group are then averaged and the shannon entropy of the matrices is calculated to measure the binary network stability at this threshold. The calculation formula of the shannon entropy is as follows:
where x represents a channel pair connection state variable. P (x) is the connection probability between each channel pair, i.e. the average of the elements of the corresponding positions of all binary networks of the same age group.
2) A random network is constructed to assist threshold value optimization, and the method comprises the following steps:
the degrees of all nodes of the actual individual binary connection matrix are kept unchanged, and the connection edges are disturbed. In this way 100 random networks were constructed for each individual. Similarly, shannon entropy is used for measuring stability of the random network, then the measurement result is averaged to represent the entropy value of the network in the unstable state under the threshold value, and the entropy value is compared with the actual network and is subjected to difference.
3) And traversing all proportional thresholds by taking 0.02 as a step length in the range of the (0, 0.5), finding out the proportional threshold corresponding to the position with the maximum difference between the stability of the random network and the stability of the actual network, and determining the proportional threshold as the optimal threshold. By the method, the optimal threshold corresponding to the functional connection of each standard frequency band of each age group is obtained.
And 2-4, unifying the threshold values.
After the optimal threshold is obtained, it is necessary to avoid the influence of different numbers of the connecting edges on the graph theory index. Averaging and unifying proportional thresholds of different age groups, and finally constructing a stable individual binary network under each standard frequency band by using the averaged thresholds.
And 2-5, constructing functional connection of the age group level.
The method is realized by adopting a majority voting method, namely when more than half of connecting edges between certain two vertexes are 1 in a group, the connecting edges are reserved, and otherwise, the connecting edges are set to be 0.
The specific process of the step 3 is as follows:
and 3-1, extracting a global clustering coefficient from the functional connection at the individual level to measure the functional separation of the brain.
Functional isolation of the brain refers to the ability to perform specific processing in a closely connected group of brain regions. Global clustering coefficients are used here to measure the functional separation of the brain. Global clustering coefficient
Average of local clustering coefficients for all vertices:
where n is the number of vertices and the local clustering coefficient CiThe calculation formula of (a) is as follows:
Aijconnecting the elements of the matrix A, i.e. the node v, for individual binariesiAnd node vjThe connection weight between (0 or 1). And k is calculated as follows:
3-2, extracting characteristic path length from the individual level functional connection to measure the functional integration of brain.
Functional integration of the brain refers to the ability to rapidly integrate specialized information from different brain regions. Here, the functional integration of the brain is measured using the characteristic path length, lGThe calculation formula of (a) is as follows:
wherein d (v)i,vj) Is a node vi，vjThe shortest distance therebetween.
And 3, respectively analyzing the global clustering coefficient and the characteristic path length distribution difference among different age groups by using a Wilcoxon rank sum test method. The average of the p values obtained by the rank sum test is used as the band importance index.
And 3-4, extracting centroidal features from the functional connection at the age group level.
The centrality is the most direct measure for characterizing the centrality of nodes in network analysis. The node degree of a node is larger, which means that the node degree is more central, and the node is more important in the network. The calculation formula of the centrality of the node degree is as follows:
wherein k isiRepresentation and node viThe number of connected edges. N-1 represents a node viThe number of edges connected to all other nodes.
And 3-5, superposing the centrality of the node degrees under each standard frequency band, calculating the variance of the node degrees, and finally normalizing. The calculation formula is as follows:
wherein v isa,bA degree distribution vector at the b-th band representing the a-th age group. By va,bThe degree of imbalance in the central distribution of the brain functional network at the cross-frequency level was measured. This index is low at an age of less than one month, because the neonatal neurons have not developed to maturity for less than one month, resulting in a more random firing without the formation of a distinct network center. This index rises significantly during the 1-3 month age period, which may be a result of the local brain regions developing rapidly during this period and exhibiting greater activity than other brain regions during sleep. The index gradually decreases with increasing age in the age range of 1 month to 14 years, i.e. the degree distribution of functional connections gradually approaches equilibrium. The change of the index is closely related to the age increase, and can reflect the brain maturity to a certain extent.
The invention has the following beneficial effects:
1. the optimal threshold value is searched by using a combination method of a random network and Shannon entropy measurement, and the binary network constructed based on the method has stronger intra-group network stability compared with the binary network constructed by other methods, namely, the personalized connection part of the network is less, and the age characteristic of the network is more prominent.
2. The graph theory characteristics of individual level and age group level functional connection are respectively extracted, and the graph theory characteristics comprise function separation, function integration, network centrality measurement and analysis of the change rule of the functional connection along with the age increase and the characteristics of the functional connection mode of a specific age stage. Wherein the change trend of the functional connection is consistent with the common medical knowledge, which verifies the rationality of the functional connection construction method.
Drawings
FIG. 1 is a flow chart of a functional connection establishment according to an embodiment of the present invention;
FIG. 2 is an ICoh correlation feature matrix and an ICoh distribution diagram according to an embodiment of the present invention;
FIG. 3 is a diagram of an iteration of threshold optimization according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of an age group level functional link according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of global clustering coefficients and characteristic path lengths in a beta band according to an embodiment of the present invention;
fig. 6 is a schematic diagram of the variance of the cross-frequency node degree centrality distribution according to the embodiment of the present invention.
Detailed Description
The invention is further described with reference to the following figures and examples.
The first main step of the invention is signal acquisition and preprocessing, and the specific implementation steps are as follows:
1-1, collecting electroencephalogram data of a normal child aged 0-17 years including a sleep period from a hospital, firstly, manually screening original data, discarding data with poor signal quality (such as including electrocardio-artifact, ref-artifact, bad track and the like), and intercepting EEG signals in a quiet sleep period. The screened data was then divided by age group. The electroencephalogram data sets of children of 0-17 years old are divided into 7 age groups, wherein the age groups are less than 1 month, 1-3 months, 3 months-1 year old, 1-3 years old, 3-7 years old, 7-14 years old and 14-17 years old. Each age group contained 6 pediatric individuals.
1-2, remove the A1, A2, Fz, Cz, Pz channels from the original multi-channel EEG signal.
1-3, down-sampling the channel-selected EEG signal to the lowest sampling frequency of the data set for uniformity.
And 1-4, carrying out signal denoising through a 1-30HZ band-pass filter and a 50HZ notch filter.
1-5, dividing the denoised data into a plurality of periods with the duration of 1 second, and screening signal segments by taking 150uV as a threshold value.
1-6, decomposing the signal into delta (1-4hz), theta (4-8hz), alpha (8-14hz) and beta (14-30hz)4 standard frequency bands by using a band-pass filter.
The second main step of the present invention is to construct functional connection, and fig. 1 is a flow chart of the step, which specifically comprises the following implementation steps:
and 2-1, carrying out ICoh channel correlation characteristic extraction on the preprocessed EEG sample. The ICoh correlation feature matrix and ICoh distribution are shown in fig. 2.
And 2, averaging the ICoh correlation characteristic matrixes of the fragments to obtain an individual-level weighted connection matrix.
2-3, optimizing a threshold value: 1) firstly, selecting an initial proportion threshold value t in the range of (0, 0.5), and binarizing the weighted connection matrix. Then averaging the individual binary connected matrixes of the same age group, and calculating the Shannon entropy of the matrixes. 2) Keeping the degree of each node of the actual individual binary network unchanged, disturbing the connecting edges of the nodes, and constructing 100 random networks for each individual. Similarly, the shannon entropy is used for measuring the stability of the random network, then the measurement result is averaged and compared with the actual network, and the difference value is calculated. 3) And traversing all the proportional thresholds by taking 0.02 as a step length in the range of the (0, 0.5) interval, finding the proportional threshold corresponding to the position with the maximum stability difference between the random network and the actual network, and determining the proportional threshold as the optimal threshold. Fig. 3 illustrates an iterative process of shannon entropy based threshold optimization. The triangle label corresponds to the scale threshold that maximizes functional connection stability.
And 2-4, averaging the proportional thresholds of different age groups to unify the thresholds, and constructing a stable binary network of individuals under a standard frequency band by using the unified thresholds.
And 2-5, adopting a majority voting method to construct age group level functional connection. Fig. 4 is an age group level functional connection.
The third main step of the invention is to extract the graph theory characteristic of the functional connection, and the concrete implementation steps are as follows:
and 3-1, extracting global clustering coefficients from the functional connection at the individual level to measure the functional separation of the brain, and extracting characteristic path lengths to measure the functional integration of the brain. Fig. 5 reflects the trend of the global clustering coefficients and the characteristic path lengths with increasing age.
And 3-2, respectively carrying out distribution difference analysis on the global clustering coefficients and the characteristic path lengths of different age groups by using a Wilcoxon rank sum test method. And calculates an average p-value as a band importance measure.
And 3-3, extracting centroidal features from the functional connection at the age group level.
And 3-4, overlapping the degree vectors of the functional connection under each standard frequency band, calculating the variance of the degree vectors, and finally normalizing to measure the centrality distribution balance of the brain functional network on a cross-frequency level, wherein the measurement result of each age group is shown in fig. 6.
3-5, analyzing the characteristics of the functional link by combining the current medical common sense so as to check the reasonability of the construction method of the functional link at the level of individuals and age groups.
The method provided by the invention is applied to the brain function connection difference analysis of children in different age groups and auxiliary brain disease diagnosis. After an ICoh connection matrix of standard sub-band sleep period EEG is extracted, individual horizontal function connection constructed based on stability measurement of Shannon entropy and age group horizontal function connection constructed based on majority voting present a change rule in accordance with medical general knowledge under the measurement of graph theory. Therefore, the functional connection constructed based on the method can be used as a brain network map of children of 0-17 years old, and provides a reliable reference for EEG-based brain disease detection.
Claims (4)
1. The method for constructing the child electroencephalogram function connection map based on the entropy stability criterion is characterized by comprising the following steps of:
step 1: preprocessing data;
selecting channels for original multi-channel EEG signal data in a quiet sleep period; based on the frequency band of main information of the EEG signal, performing 1-30HZ band-pass filtering and 50HZ power frequency filtering to remove signal noise; cutting the signal into 1s segments, setting a 150uV threshold value according to the amplitude range of the physiological signal to remove the segments containing the non-physiological artifact signals to obtain a pure EEG signal, and finally performing signal decomposition on the EEG according to a standard frequency band;
step 2: constructing a function connection network;
calculating an ICoh correlation characteristic matrix for the EEG segments of each frequency band; based on a stability criterion, searching an optimal threshold value through a random network agent to construct an individual level binaryzation function connection; finally, establishing age group level functional connection by using a voting method;
and step 3: extracting and analyzing network characteristics; extracting global clustering coefficients and characteristic path lengths of individual horizontal functional connections, evaluating key frequency bands based on a statistical test method, and analyzing change rules of characteristics under the key frequency bands along with age increase; and in addition, the degrees of the horizontal functional connection of the age group are extracted, and the network centrality and the degree distribution balance degree are analyzed.
2. The method for constructing the children electroencephalogram function connection map with the entropy stability criterion according to claim 1, wherein the specific process of the step 1 is as follows:
1-1, dividing age groups; dividing the electroencephalogram data sets of children of 0-17 years into 7 age groups, wherein the age groups are less than 1 month, 1-3 months, 3 months-1 year, 1-3 years, 3-7 years, 7-14 years and 14-17 years; each age group contained 6 children's individual data;
1-2, selecting a channel; removing 16A 1, A2, Fz, Cz and Pz channels in the original multichannel EEG signal, wherein the selected channels comprise Fp1, Fp2, F3, F4, C3, C4, P3, P4, O1, O2, F7, F8, T3, T4, T5 and T6;
1-3, unifying sampling frequency; down-sampling the channel-selected EEG signal to the lowest sampling frequency of the data set for uniformity;
1-4, removing high, low frequency and alternating current noise; carrying out basic pretreatment on the EEG signal through a 1-30HZ band-pass filter and a 50HZ notch filter;
1-5, data segmentation and non-physiological signal filtration; dividing the preprocessed data into a plurality of periods with the duration of 1 second, setting 150uV as a threshold value, deleting signal segments and removing irrelevant non-physiological signals; each individual retained 100 1 second segments of the 16 channel EEG signal;
1-6, signal decomposition; the signal is decomposed into 4 standard frequency bands of delta (1-4hz), theta (4-8hz), alpha (8-14hz) and beta (14-30hz) by using a band-pass filter.
3. The method for constructing the children electroencephalogram function connection map with the entropy stability criterion according to claim 2, wherein the specific process of the step 2 is as follows:
2-1, calculating an ICoh correlation characteristic matrix of all EEG segments under each standard frequency band;
performing coherence imaginary part ICoh correlation feature extraction on the preprocessed 1-second EEG samples of 16 channels, wherein the index can overcome false connection caused by volume conduction effect; the ICoh feature calculation formula is:
Pi,j(f) for channel i and channel j (channel i, j is set to node v during the construction of the functional connectioni,vj) Cross power spectral density, P, betweeni,iAnd Pj,jRespectively the self-power spectral density of the channel i and the channel j, and N is the number of signal sampling points; the ICoh index sets among all the channel pairs form a correlation characteristic matrix, and the diagonal lines of the matrix are all set to be 0;
2-2, averaging ICoh correlation characteristic matrixes of all fragments in an individual to obtain an individual-level weighted connection matrix;
2-3, optimizing a threshold value:
1) firstly, selecting an initial proportion threshold value t in the range of (0, 0.5), and binarizing a weighted connection matrix to obtain a binary connection matrix; then averaging the individual binary connection matrixes of the same age group, and calculating the Shannon entropy of the matrixes to measure the binary network stability under the threshold value; the calculation formula of the shannon entropy is as follows:
wherein x represents a channel pair connection state variable; p (x) is the connection probability between each channel pair, i.e. the average value of the elements of the corresponding positions of all binary networks in the same age group;
2) a random network is constructed to assist threshold value optimization, and the method comprises the following steps:
keeping the degree of each node of the actual individual binary connection matrix unchanged, and disturbing the connection edge; in this way 100 random networks were constructed for each individual; similarly, shannon entropy is used for carrying out stability measurement on the random network, then the measurement result is averaged to represent the entropy value of the network in the unstable state under the threshold value, and the entropy value is compared with the actual network and is subjected to difference;
3) traversing all proportional thresholds by taking 0.02 as a step length in the range of (0, 0.5), finding out the proportional threshold corresponding to the position with the maximum difference between the stability of the random network and the stability of the actual network, and determining the proportional threshold as an optimal threshold; obtaining an optimal threshold value corresponding to the functional connection of each standard frequency band of each age group by the method;
2-4, unifying the threshold values;
after the optimal threshold value is obtained, the influence of different numbers of connecting edges on the graph theory index needs to be avoided; averaging and unifying proportional thresholds of different age groups, and finally constructing a stable individual binary network under each standard frequency band by using the averaged thresholds;
2-5, constructing age group level functional connection;
the method is realized by adopting a majority voting method, namely when more than half of connecting edges between certain two vertexes are 1 in a group, the connecting edges are reserved, and otherwise, the connecting edges are set to be 0.
4. The method for constructing the children electroencephalogram function connection map with the entropy stability criterion according to claim 3, wherein the specific process of the step 3 is as follows:
3-1, extracting global clustering coefficients from the functional connection of the individual level to measure the functional separation of the brain;
functional isolation of the brain refers to the ability to perform specific processing in a group of closely connected brain regions; here global clustering coefficients are used to measure the functional separation of the brain; global clustering coefficient
Average of local clustering coefficients for all vertices:
where n is the number of vertices and the local clustering coefficient CiThe calculation formula of (a) is as follows:
Aijconnecting the elements of the matrix A, i.e. the node v, for individual binariesiAnd node vjThe connection weight value between the two; and k is calculated as follows:
3-2, extracting characteristic path length from the functional connection of individual level to measure the functional integration of brain;
functional integration of the brain refers to the ability to rapidly integrate specialized information from different brain regions; here, the functional integration of the brain is measured using the characteristic path length, lGThe calculation formula of (a) is as follows:
wherein d (v)i,vj) Is a node vi，vjThe shortest distance therebetween;
3-3, analyzing the global clustering coefficient and the characteristic path length distribution difference among different age groups by using a Wilcoxon rank sum test method; averaging the p values obtained by rank sum test to be used as the frequency band importance degree index;
3-4, extracting centrometric features from the functional connections at the age group level;
the centrality is the most direct measurement index for describing the centrality of the nodes in the network analysis; the node degree of a node is larger, which means that the centrality of the node is higher, and the node is more important in the network; the calculation formula of the centrality of the node degree is as follows:
wherein k isiRepresentation and node viThe number of connected edges; n-1 represents a node viThe number of edges connected to all other nodes;
3-5, overlapping the centrality of the node degrees under each standard frequency band, calculating the variance of the node degrees, and finally normalizing the node degrees; the calculation formula is as follows:
wherein v isa,bA degree distribution vector representing a b-th band of the a-th age group; by va,bThe degree of imbalance in the central distribution of the brain functional network at the cross-frequency level was measured.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7433732B1 (en) * | 2004-02-25 | 2008-10-07 | University Of Florida Research Foundation, Inc. | Real-time brain monitoring system |
| CN109524112A (en) * | 2018-12-26 | 2019-03-26 | 杭州电子科技大学 | A kind of brain function network establishing method orienting coherent method based on part |
| CN110859614A (en) * | 2019-11-22 | 2020-03-06 | 东南大学 | Mathematical astronomical analysis method for teenager brain network based on weighted phase lag index |
| CN112914587A (en) * | 2021-02-18 | 2021-06-08 | 郑州大学 | Apoplexy rehabilitation assessment model construction method and assessment method based on resting state electroencephalogram signal coherence brain function network |
| CN113576491A (en) * | 2021-07-26 | 2021-11-02 | 深圳市人民医院 | Method and system for automatically analyzing frequency domain characteristics and brain network based on resting EEG |
| KR102322647B1 (en) * | 2021-05-25 | 2021-11-09 | 주식회사 아이메디신 | Method, server and computer program for diagnosing alzheimer's disease dementia using correlation between eeg signals |
-
2022
- 2022-01-26 CN CN202210094726.0A patent/CN114305451B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7433732B1 (en) * | 2004-02-25 | 2008-10-07 | University Of Florida Research Foundation, Inc. | Real-time brain monitoring system |
| CN109524112A (en) * | 2018-12-26 | 2019-03-26 | 杭州电子科技大学 | A kind of brain function network establishing method orienting coherent method based on part |
| CN110859614A (en) * | 2019-11-22 | 2020-03-06 | 东南大学 | Mathematical astronomical analysis method for teenager brain network based on weighted phase lag index |
| CN112914587A (en) * | 2021-02-18 | 2021-06-08 | 郑州大学 | Apoplexy rehabilitation assessment model construction method and assessment method based on resting state electroencephalogram signal coherence brain function network |
| KR102322647B1 (en) * | 2021-05-25 | 2021-11-09 | 주식회사 아이메디신 | Method, server and computer program for diagnosing alzheimer's disease dementia using correlation between eeg signals |
| CN113576491A (en) * | 2021-07-26 | 2021-11-02 | 深圳市人民医院 | Method and system for automatically analyzing frequency domain characteristics and brain network based on resting EEG |
Non-Patent Citations (1)
| Title |
|---|
| 马涛: "基于电生理的颞叶癫痫脑网络机制研究", 《中国优秀硕士论文全文数据库》, 15 February 2018 (2018-02-15) * |
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