CN116616722B

CN116616722B - Brain function abnormality detection method and device based on dynamic cerebral cortex function connection

Info

Publication number: CN116616722B
Application number: CN202310907261.0A
Authority: CN
Inventors: 刘燕; 戴亚康; 彭博; 李晓琳; 周王成
Original assignee: Suzhou Guoke Kangcheng Medical Technology Co ltd
Current assignee: Suzhou Guoke Kangcheng Medical Technology Co ltd
Priority date: 2023-07-24
Filing date: 2023-07-24
Publication date: 2023-10-03
Anticipated expiration: 2043-07-24
Also published as: CN116616722A

Abstract

The application relates to the technical field of brain function detection, and discloses a brain function abnormality detection method and device based on dynamic cerebral cortex function connection, wherein the method comprises the following steps: performing functional clustering operation on each first-type scalp electroencephalogram signal to obtain a second preset number of brain functional states corresponding to each section of scalp electroencephalogram signal; fitting a second preset number of brain function states corresponding to the first scalp electroencephalogram signals with the first scalp electroencephalogram signals to obtain a second type of scalp electroencephalogram signals including a first number of time periods; counting the occurrence frequency of each brain function state from each scalp electroencephalogram signal of the second class; and determining the starting moment when the target object is in an abnormal state according to the occurrence frequency of each brain function state in all the second scalp electroencephalogram signals. The starting time of the epileptic state, for example, when the target object is in an abnormal state, can be accurately determined, so that the accurate positioning of the epileptic focus is improved.

Description

Brain function abnormality detection method and device based on dynamic cerebral cortex function connection

Technical Field

The application relates to the technical field of brain function detection, in particular to a brain function abnormality detection method and device based on dynamic cortex function connection.

Background

Epilepsy is a common neurological disorder affecting the normal life of a patient. In particular, seizures which cannot be effectively controlled by medication can later develop into refractory seizures, which require surgical treatment of the epileptic focus to control seizures. Therefore, accurate positioning of epileptic focus is an important link in the treatment of refractory epileptic patients.

Currently, a positioning method for an epileptic focus is generally to position the epileptic focus based on cortex electric activity after scalp brain electric signal source imaging. The method mainly constructs a static brain function network or a dynamic brain function network based on the cortex electric activity of the brain, and digs epileptic electric activities at different times by analyzing the topological characteristics and the change of the functional states in the brain function network so as to position epileptic foci. The static brain function network is mainly an average network constructed based on brain electrical signals in a period of time, but cannot deeply mine epileptic status in the brain, so that the starting moment of the epileptic status cannot be accurately determined. The dynamic brain function network is characterized in that scalp brain electric signals within a period of time are divided into a plurality of equal-length signal segments by utilizing a time window so as to determine the starting moment of an epileptic state, then brain function networks corresponding to the signal segments are calculated, the time-varying characteristics of the topological characteristics of each network are analyzed, and then an epileptic focus corresponding to the abnormal variation of the topological characteristics is found. However, the method can destroy potential functional states of the brain, influence accuracy of topology characteristic calculation in a brain functional network, and further influence accuracy of epileptic focus positioning.

Therefore, how to achieve accurate positioning of the starting time of the epileptic status, so as to improve accurate positioning of the epileptic focus is a problem to be solved urgently.

Disclosure of Invention

In view of the above, the application provides a brain function abnormality detection method and device based on dynamic cerebral cortex function connection, so as to solve the problem of how to realize accurate positioning of epileptic status at the starting moment, thereby improving accurate positioning of epileptic foci.

In a first aspect, the present application provides a brain dysfunction detection method based on dynamic cortical functional connection, the method comprising:

acquiring a first preset number of first scalp electroencephalogram signals generated when a target object is in a preset state;

performing functional clustering operation on each first-type scalp electroencephalogram signal to obtain a second preset number of brain functional states corresponding to each section of scalp electroencephalogram signal;

fitting a second preset number of brain function states corresponding to the first scalp electroencephalogram signals with the first scalp electroencephalogram signals to obtain second scalp electroencephalogram signals including a first number of time periods, wherein the first scalp electroencephalogram signals are any one of the first scalp electroencephalogram signals with the first preset number;

Counting the occurrence frequency of each brain function state from each scalp electroencephalogram signal of the second class;

and determining the starting moment when the target object is in an abnormal state according to the occurrence frequency of each brain function state in all the second scalp electroencephalogram signals.

According to the technical scheme, when a target object is in a preset state, for example, one minute before and after epileptic seizure, a first preset number of first scalp electroencephalogram signals are generated, and then each first scalp electroencephalogram signal is subjected to functional clustering operation to obtain a second preset number of brain functional states corresponding to each section of scalp electroencephalogram signal, and the transition of each functional state of the brain in the preset state is considered. And fitting a second preset number of brain function states corresponding to the first scalp electroencephalogram signals with the first scalp electroencephalogram signals to obtain second scalp electroencephalogram signals including a first number of time periods, wherein the first scalp electroencephalogram signals are any one of the first scalp electroencephalogram signals with the first preset number, and the abnormal states such as epileptic states can be deeply excavated by considering the dynamic transition of the brain function states in each first scalp electroencephalogram signal. The scalp electroencephalogram signals are divided in a fixed time scale, but the problem of damage to potential functional states of the brain caused by dynamic changes of the functional states of the brain is avoided. The occurrence frequency of each brain function state in the second type scalp electroencephalogram signal can be further clarified, so that the occurrence frequency of each brain function state in each second type scalp electroencephalogram signal can be counted, the purpose of deep mining of each brain function state in the second type scalp electroencephalogram signal is achieved, and further the starting moment of an epileptic state when a target object is in an abnormal state can be accurately determined according to the occurrence frequency of each brain function state in all the second type scalp electroencephalogram signals. The accurate positioning of the starting moment of the epileptic state is realized, so that the accurate positioning of an epileptic focus is improved.

In some alternative embodiments, determining the starting time when the target object is in the abnormal state according to the occurrence frequency of each brain function state in all the scalp electroencephalogram signals of the second type includes:

counting the occurrence frequency of each brain function state in each second type scalp electroencephalogram signal by taking each brain function state as a reference, and generating a second preset number of frequency curve segments;

respectively determining a correlation coefficient between each frequency curve segment and a preset function signal;

according to the correlation coefficients between all the frequency curve segments and the preset function signals, respectively grouping all the correlation coefficients by taking each brain function state as a reference;

respectively counting the average value of the correlation coefficients of each group;

selecting the brain functional state corresponding to the group to which the maximum correlation coefficient average value belongs as an abnormal state;

and determining the starting moment of the abnormal state in each second type scalp electroencephalogram signal according to all the frequency curve segments in the group corresponding to the abnormal state.

In the above technical solution, the correlation coefficient between the determined frequency curve segment and the preset function signal is counted by the second scalp electroencephalogram signal including a plurality of time periods after being fitted with the brain function state, so as to calculate the average value of the correlation coefficient of each group taking each brain function state as a reference, and then the brain function state corresponding to the group to which the maximum correlation coefficient average value belongs is determined as the abnormal state. The correlation between the dynamic change of the occurrence frequency of each brain function state in the scalp electroencephalogram signals of the second type and the preset function signals is considered, the purpose of deep mining of each brain function state is achieved, abnormal states in the brain function states can be accurately judged, and then the starting moment of the abnormal states in each scalp electroencephalogram signal of the second type can be accurately determined according to all frequency curve segments in the group corresponding to the abnormal states. The problem of damaging potential brain functional states of the brain based on dividing scalp electroencephalogram signals in a fixed time scale is avoided, and the accuracy of positioning of the initial time of an epileptic state can be improved.

In some optional embodiments, determining the starting time of the abnormal state in each scalp electroencephalogram signal of the second type according to all the frequency curve segments in the group corresponding to the abnormal state includes:

connecting the head and tail straight lines of each frequency curve segment in the group corresponding to the abnormal state respectively to obtain a first preset number of datum lines, wherein the datum lines correspond to each frequency curve segment in the group corresponding to the abnormal state one by one;

determining a first preset number of target curve segments below the reference lines according to each reference line in the first preset number of reference lines and the corresponding frequency curve segments, wherein the target curve segments are partial line segments in the frequency curve segments;

and respectively selecting a target point with the largest distance from each target curve segment and the reference line, and determining the moment of the target point as the starting moment of the abnormal state in the second scalp electroencephalogram signal corresponding to the frequency curve segment of the target curve segment.

In the above technical solution, since the first preset number of reference lines are the first-to-last straight lines of each frequency curve segment in the group corresponding to the abnormal state, the slope of the reference line obtained after the first-to-last straight lines are connected may represent the average increasing speed of the occurrence frequency of the abnormal state in each second-class scalp electroencephalogram signal. The target curve segment of each frequency curve segment in the grouping corresponding to the abnormal state, which is located below the datum line corresponding to the frequency curve segment, means that the increasing speed of the occurrence frequency of the abnormal state at a certain moment in the scalp electroencephalogram signals of the second type to which the abnormal state belongs deviates from the average increasing speed. And then, respectively selecting the moment corresponding to the target point with the largest distance between the target curve segments and the reference line from each item of target curve segment, and indicating the moment when the increasing speed of the occurrence frequency of the abnormal state in the scalp electroencephalogram signals of the second type corresponding to the target curve segment reaches the peak value, namely, the starting moment of the abnormal functional state in the scalp electroencephalogram signals of the second type corresponding to the target curve segment. The method considers the trend that the frequency of the abnormal state in the early stage of the scalp electroencephalogram signal is firstly stable and then steeply rises, can accurately represent the abnormal state, for example, the starting time of the epileptic state, improves the accurate positioning of the starting time of the epileptic state, and can further improve the accurate positioning of an epileptic stove.

In some alternative embodiments, the period of time in which the abnormal state is in includes a plurality of periods, the method further comprising:

acquiring nuclear magnetic resonance brain images of a target object;

constructing a real head model of the target object according to the nuclear magnetic resonance brain image;

dividing the real head model into a plurality of brain regions based on a preset brain region template;

based on the real head model, a plurality of brain regions, the first type scalp electroencephalogram signals and the starting moment of the abnormal state in each second type scalp electroencephalogram signal, constructing a function connection matrix set corresponding to a first time period in a plurality of time periods, wherein the first time period is a time period comprising the starting moment; determining the average node core degree of each node in the functional connection matrix set, wherein the nodes are used for indicating brain areas in the real head model, and the average node core degree is used for indicating the abnormal degree of the brain cortex electric activity in the brain areas;

and locating a target area of the abnormal state in the brain region according to the average node core degree of each node.

In the technical scheme, after the initial moment of the abnormal state is accurately positioned, a real head model of a target object is constructed based on a target-to-nuclear magnetic resonance brain image, the real head model is further divided into a plurality of brain regions based on a preset brain region template, and then a functional connection matrix set corresponding to the initial moment of the real head model, the plurality of brain regions, the second scalp electroencephalogram signals and the abnormal state in each second scalp electroencephalogram signal in a plurality of time periods is constructed based on the initial moment of the real head model, the second scalp electroencephalogram signals, so that dynamic changes of brain function states in the second scalp electroencephalogram signals are considered in the functional connection matrix set, and on the premise that potential brain function states are not damaged, the change of brain function network topology characteristics of the target object in the abnormal state including the initial moment is reflected according to the average node core degree of each node in the functional connection matrix set, and a target area of the abnormal state is positioned. And then can realize finding the epileptic state on the premise of not damaging the potential brain functional state of the brain, and analyzing the change of the brain functional network topology characteristic under the epileptic state, thereby finding the more obvious change of the brain functional network topology characteristic, and finally realizing the accurate positioning of the epileptic focus.

In some optional embodiments, the set of functional connection matrices includes a first preset number of functional connection matrices, and based on a real head model, a plurality of brain regions, a first type of scalp electroencephalogram signal, and a starting time of an abnormal state in each segment of the second type of scalp electroencephalogram signal, the set of functional connection matrices corresponding to a first time segment of the plurality of time segments is constructed, including:

for each second type of scalp electroencephalogram signal, mapping scalp electroencephalogram signals corresponding to the first time period in the second type of scalp electroencephalogram signals onto cortex of brain areas in the real head model respectively so as to determine a plurality of brain area cortex computer signals corresponding to the second type of scalp electroencephalogram signals;

respectively aiming at each second type of scalp electroencephalogram signal, determining a Pearson correlation coefficient between the second type of scalp electroencephalogram signal corresponding to each brain region cortex computer signal, and obtaining a functional connection matrix of a target object under each second type of scalp electroencephalogram signal;

and determining the functional connection matrix of the target object under all the scalp electroencephalogram signals of the second type as a functional connection matrix set corresponding to the first time period in the plurality of time periods.

In the above technical solution, for each second type of scalp electroencephalogram signal, the scalp electroencephalogram signal corresponding to the first time period in the second type of scalp electroencephalogram signal is mapped onto the cortex of the brain region in the real head model to obtain a plurality of brain region cortex computer signals corresponding to each second type of scalp electroencephalogram signal in the first time period, so that the pearson correlation coefficient between the brain region cortex computer signals corresponding to the second type of scalp electroencephalogram signal is conveniently and subsequently determined, the functional connection matrix of the target object in the first time period in each second type of scalp electroencephalogram signal is obtained, the functional connection matrix of the target object in all the first time periods is obtained, and the functional connection matrix set corresponding to the first time period in the plurality of time periods is obtained. The determination of the functional connection matrix under the non-abnormal state and the abnormal state except the first time period is avoided, so that the abnormal state can be better prevented from being positioned on other brain tissues of the brain region, which are affected in the process of transmitting the abnormal state, in the real region of the brain region, the abnormal state can be conveniently and subsequently determined to be a target region of the brain region, and the positioning accuracy of the target region of the abnormal state is improved.

In some alternative embodiments, locating a target region to which an abnormal state belongs in a brain region according to an average node core degree of each node includes:

determining a node corresponding to a brain region with the maximum average node core degree as an occurrence region of an abnormal state;

acquiring the electrical activity intensity of the cerebral cortex in the occurrence area;

among the occurrence areas, an area where the intensity of the electrical activity exceeds a preset intensity threshold is determined as a target area to which the abnormal state belongs in the brain region.

In the above technical solution, the brain region corresponding to the node with the largest average node core degree is determined as the occurrence region of the abnormal state, that is, the brain region with the largest abnormal degree of the brain cortex electric activity in the brain region in the first time period is determined as the occurrence region of the abnormal state. Thus, the electric activity intensity of the cerebral cortex in the occurrence area is acquired, and in the occurrence area, the area where the electric activity intensity exceeds the preset intensity threshold is determined as the target area to which the abnormal state belongs in the cerebral area.

In some optional embodiments, before performing a functional state clustering operation on the first type of scalp electroencephalogram signals of each segment to obtain M brain functional states corresponding to each segment of scalp electroencephalogram signals, the method further includes:

And carrying out notch, filtering, artifact removal through independent component analysis and downsampling on each first type of scalp electroencephalogram signal in sequence to obtain a third type of scalp electroencephalogram signal so as to carry out functional clustering operation on the third type of scalp electroencephalogram signal.

In the technical scheme, notch, filtering, artifact removal through independent component analysis and downsampling operations are sequentially carried out on each first type of scalp electroencephalogram signal to obtain a third type of scalp electroencephalogram signal, denoising of the first type of scalp electroencephalogram signal is achieved, accurate clustering results are obtained when functional state clustering operation is carried out on the third type of scalp electroencephalogram signal subsequently, and therefore accuracy of starting time of determining abnormal states subsequently is improved.

In a second aspect, the present application provides an abnormality detection apparatus for brain function signals, the apparatus comprising:

the acquisition module is used for acquiring a first preset number of first scalp electroencephalogram signals generated when the target object is in a preset state;

the clustering module is used for carrying out functional clustering operation on each first-class scalp electroencephalogram signal to obtain a second preset number of brain functional states corresponding to each section of scalp electroencephalogram signal;

The fitting module is used for fitting a second preset number of brain function states corresponding to the first scalp electroencephalogram signals with the first scalp electroencephalogram signals to obtain second scalp electroencephalogram signals including a first number of time periods, wherein the first scalp electroencephalogram signals are any one of the first scalp electroencephalogram signals with the first preset number;

the statistics module is used for respectively counting the occurrence frequency of each brain function state from each scalp electroencephalogram signal of the second class;

the determining module is used for determining the starting moment when the target object is in an abnormal state according to the occurrence frequency of each brain function state in all the second scalp electroencephalogram signals.

In a third aspect, the present application provides a computer device comprising: the brain function abnormality detection method based on the dynamic cortex function connection comprises a memory and a processor, wherein the memory and the processor are in communication connection, computer instructions are stored in the memory, and the processor executes the computer instructions, so that the brain function abnormality detection method based on the dynamic cortex function connection is executed.

In a fourth aspect, the present application provides a computer-readable storage medium having stored thereon computer instructions for causing a computer to execute the brain function abnormality detection method based on dynamic cortical functional connection of the first aspect or any one of its corresponding embodiments.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present application, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.

Fig. 1 is a schematic structural diagram of an abnormality detection system for brain function signals according to an embodiment of the present application;

FIG. 2 is a flow chart of a brain dysfunction detection method based on dynamic cortical functional connections according to the present application;

FIG. 3 is a schematic illustration of a second type of scalp electroencephalogram signal including a first number of time periods, as obtained after fitting in an application scenario;

FIG. 4 is a flow chart of a method for detecting brain dysfunction based on dynamic cortical functional connections according to the present application;

FIG. 5 is a schematic diagram of a generated frequency curve segment after counting the occurrence frequency of each brain function state in a scalp electroencephalogram of a certain second class in an application scene;

FIG. 6 is a specific workflow diagram of a brain dysfunction detection method based on dynamic cortical functional connectivity in an application scenario;

FIG. 7 is a flow chart of a method for detecting brain dysfunction based on dynamic cortical functional connections according to the present application;

FIG. 8a is a graph comparing the onset of status epilepticus determined by the brain dysfunction detection method based on dynamic cortical functional connectivity of the present application with the onset of status epilepticus determined by the method of locating epileptic foci based on epileptic seizure onset time source imaging results;

FIG. 8b is a source imaging result graph of a method of locating epileptic foci based on epileptic seizure onset moment source imaging results;

FIG. 8c is a graph of source imaging results based on the brain dysfunction detection method of the present application based on dynamic cortical functional connectivity;

FIG. 9 is a graph of the results of calculation of an epileptic initiation time window in a method for locating epileptic foci based on a fixed time scale dynamic cortical functional network constructed based on a fixed time window;

FIG. 10 is a graph of average source imaging results over an epileptic initiation time window in a method of locating epileptic foci based on a fixed time scale dynamic cortical functional network constructed over a fixed time window;

FIG. 11 is an average node core level determined based on the brain dysfunction detection method of the present application based on dynamic cortical functional connections;

FIG. 12 is an average node core degree determined by a method of locating epileptic foci based on a fixed time scale dynamic cortical functional network constructed over a fixed time window;

Fig. 13 is a schematic structural diagram of an abnormality detection device for brain function signals of the application;

fig. 14 is a schematic diagram of a hardware structure of a computer device according to an embodiment of the present application.

Detailed Description

For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.

It should be understood that the "indication" mentioned in the embodiments of the present application may be a direct indication, an indirect indication, or an indication having an association relationship. For example, a indicates B, which may mean that a indicates B directly, e.g., B may be obtained by a; it may also indicate that a indicates B indirectly, e.g. a indicates C, B may be obtained by C; it may also be indicated that there is an association between a and B.

In the description of the embodiments of the present application, the term "corresponding" may indicate that there is a direct correspondence or an indirect correspondence between the two, or may indicate that there is an association between the two, or may indicate a relationship between the two and the indicated, configured, etc.

In the embodiment of the present application, the "predefining" may be implemented by pre-storing corresponding codes, tables or other manners that may be used to indicate relevant information in devices (including, for example, terminal devices and network devices), and the present application is not limited to the specific implementation manner thereof.

Fig. 1 is a schematic structural diagram of an abnormality detection system for brain function signals according to an embodiment of the present application. The abnormality detection system includes an electroencephalogram monitoring apparatus 110 and an upper computer 120.

The electroencephalogram monitoring device 110 is used for monitoring scalp electroencephalogram signals generated when a target object is in a preset state, after the electroencephalogram monitoring device 110 monitors the scalp electroencephalogram signals of the target object, the scalp electroencephalogram signals can be sent to the upper computer 120, and the upper computer can process and analyze the scalp electroencephalogram signals to obtain the starting moment when the target object is in an abnormal state. The electroencephalogram monitoring apparatus 110 can monitor and store scalp electroencephalogram signals of a target subject through scalp electrodes mounted on the brain.

Optionally, the upper computer 120 may also acquire structural MRI brain images of the target object through a magnetic resonance imaging (Magnetic resonance imaging, MRI) apparatus to construct a real-head model of the target object. And then positioning the area causing the abnormal state of the target object according to the real head model of the target object and the starting time when the abnormal state is caused.

The upper computer 120 may be a processor, a calculator, a server, or the like, or may be a device having the data calculation, analysis, and processing capabilities, for example, a computer having a processor, a smart phone, a wearable device, or the like, a data system having a server, an arithmetic system, or the like.

Optionally, the upper computer 120 may also be in communication connection with the electroencephalogram monitoring device 110 through a transmission network (e.g., a wireless network), and may also be integrated with the electroencephalogram monitoring device 110.

Optionally, the abnormality detection system may further include a management device for managing the abnormality detection system (e.g., managing a connection state between each of the upper computers and the MRI apparatus, etc.), and the management device is connected to the upper computers through a communication network. Optionally, the communication network is a wired network or a wireless network.

Alternatively, the wireless network or wired network described above uses standard communication techniques and/or protocols. The network is typically the internet, but may be any other network including, but not limited to, a local area network, a metropolitan area network, a wide area network, a mobile, a limited or wireless network, a private network, or any combination of virtual private networks. In some embodiments, techniques and/or formats including hypertext markup language, extensible markup language, and the like are used to represent data exchanged over a network. All or some of the links may also be encrypted using conventional encryption techniques such as secure socket layer, transport layer security, virtual private network, internet protocol security, etc. In other embodiments, custom and/or dedicated data communication techniques may also be used in place of or in addition to the data communication techniques described above.

Fig. 2 is a flow chart of a brain function abnormality detection method based on dynamic cortex function connection according to the present application, which may be performed by a computing device, which may be the upper computer 120 of fig. 1. As shown in fig. 2, the brain function abnormality detection method based on dynamic cortex function connection may specifically include the following steps:

step 201, obtaining a first preset number of first scalp electroencephalogram signals generated when a target object is in a preset state.

The target object may be a person to be detected, or the brain of the person. The preset state is a state of the target object from one minute before the onset of the brain disease to one minute after the onset of the brain disease, in the embodiment of the present application, the brain disease is taken as an example of epilepsy, and it can be understood that the time period before and after the onset of the brain disease can be set by itself or can be a time period of 30 seconds or two minutes, etc. The specific number of the first preset number may be set by itself, and in general, the first preset number is greater than or equal to 2, and in order to facilitate understanding of the embodiment of the present application, the first predicted number is hereinafter referred to as N. The computer equipment can monitor each electroencephalogram signal generated in each channel of a specified preset threshold value through the electroencephalogram monitoring equipment when each target object is in a preset state, so that first scalp electroencephalogram signals are obtained, the first scalp electroencephalogram signals are stored, and N first scalp electroencephalograms signals generated when the target object is in the preset state are obtained.

Step 202, performing functional clustering operation on each first-class scalp electroencephalogram signal to obtain a second preset number of brain functional states corresponding to each section of scalp electroencephalogram signal.

In order to facilitate understanding of the embodiments of the present application, the second preset number is designated as M below, and according to the theory of micro-states, it is known that there are a limited number of functional states of the brain, and information processing of the brain is completed through short-term maintenance and rapid switching of each functional state. Therefore, the computer equipment can divide each first-type scalp electroencephalogram signal in the N first-type scalp electroencephalogram signals into M categories by using a K-means clustering algorithm, and each category is a brain function state, so that M brain function states corresponding to each section of scalp electroencephalogram signal are obtained.

Step 203, fitting a second preset number of brain function states corresponding to the first scalp electroencephalogram signals with the first scalp electroencephalogram signals to obtain a second type of scalp electroencephalogram signals including a first number of time periods.

The first scalp electroencephalogram signals are any one of first scalp electroencephalogram signals with a first preset number. In order to facilitate understanding of the embodiment of the application, the first number is counted as T, and after the base K-means clustering algorithm divides each first scalp electroencephalogram signal into M brain function states, the computer equipment divides the first scalp electroencephalogram signal by taking each brain function state as a reference, so that each divided signal segment has a brain function state corresponding to the divided signal segment, and fitting operation of the first scalp electroencephalogram signal is completed, and a second scalp electroencephalogram signal comprising a first number of time periods is obtained. It will be appreciated that T represents the total number of occurrences of M brain function states corresponding to the first scalp electroencephalogram signal in the first scalp electroencephalogram signal, and generally the first number will be greater than or equal to the second preset number. In this way, the first scalp electroencephalogram signal divided by the duration of each brain function state can be obtained, and the first scalp electroencephalogram signal is not divided by a fixed time scale.

For example, a certain second type of scalp electroencephalogram signal including a first number of time periods, which is obtained after fitting in an application scenario, shown in fig. 3, fp1 and Fp2 … O2 from top to bottom in fig. 3 represent acquisition channels (channels) of the second type of scalp electroencephalogram signal corresponding to each electroencephalogram signal in the first type of scalp electroencephalogram signal, and Sampling Points of the second type of scalp electroencephalogram signal corresponding to each electroencephalogram signal in the first type of scalp electroencephalogram signal. One of the second type of scalp electroencephalogram signals including the first number of time periods obtained after fitting in the figure includes 3 brain function states, the 3 brain function states appear 6 times in total in the second type of scalp electroencephalogram signals, and M1 to M3 in the figure represent different types of brain function states.

Step 204, counting the occurrence frequency of each brain function state from each scalp electroencephalogram signal of the second type.

After each of the second type scalp electroencephalogram signals obtained in step 203 includes the first number of time periods, the number of occurrences of each brain function state in the second type scalp electroencephalogram signals can be clarified, so that the occurrence frequency of each brain function state in each second type scalp electroencephalogram signal can be counted.

Step 205, determining the starting time when the target object is in an abnormal state according to the occurrence frequency of each brain function state in all the scalp electroencephalogram signals of the second class.

The abnormal state in the embodiment of the application corresponds to the brain disease of the target object, and can be an epileptic state, the computer equipment analyzes the change trend of the occurrence frequency of each brain function state in each scalp electroencephalogram signal of the second type, determines the brain function state which is stable after the occurrence frequency is stable, and finally tends to be stable after the steep rise in each scalp electroencephalogram signal of the second type as the abnormal state, and determines the moment indicated by the corresponding sampling point at the beginning of the abnormal state as the starting moment of the target object in the abnormal state under each scalp electroencephalogram signal of the second type when the abnormal state only occurs once in each scalp electroencephalogram signal of the second type. When the occurrence frequency of the abnormal state in each second type scalp electroencephalogram signal is greater than or equal to two, determining the moment corresponding to the sampling point with the maximum occurrence frequency increasing speed of the abnormal state in the second type scalp electroencephalogram signal as the starting moment when the target object is in the abnormal state under each second type scalp electroencephalogram signal.

In the embodiment of the application, when a target object is in a preset state, for example, one minute before and after epileptic seizure, a first preset number of first scalp electroencephalogram signals are generated, and then each first scalp electroencephalogram signal is subjected to functional clustering operation to obtain a second preset number of brain functional states corresponding to each section of scalp electroencephalogram signal, and the transition of each functional state of the brain in the preset state is considered. And fitting a second preset number of brain function states corresponding to the first scalp electroencephalogram signals with the first scalp electroencephalogram signals to obtain second scalp electroencephalogram signals including a first number of time periods, wherein the first scalp electroencephalogram signals are any one of the first scalp electroencephalogram signals with the first preset number, and the abnormal states such as epileptic states can be deeply excavated by considering the dynamic transition of the brain function states in each first scalp electroencephalogram signal. The scalp electroencephalogram signals are divided in a fixed time scale, but the problem of damage to potential functional states of the brain caused by dynamic changes of the functional states of the brain is avoided. The occurrence frequency of each brain function state in the second type scalp electroencephalogram signal can be further clarified, so that the occurrence frequency of each brain function state in each second type scalp electroencephalogram signal can be counted, the purpose of deep mining of each brain function state in the second type scalp electroencephalogram signal is achieved, and further the starting moment of an epileptic state when a target object is in an abnormal state can be accurately determined according to the occurrence frequency of each brain function state in all the second type scalp electroencephalogram signals. The accurate positioning of the starting moment of the epileptic state is realized, so that the accurate positioning of an epileptic focus is improved.

In order to more precisely determine the starting time of the abnormal state, a brain function abnormality detection method based on dynamic cortex function connection according to the present application as shown in fig. 4 is proposed, and the brain function abnormality detection method based on dynamic cortex function connection may be executed by a computing device, which may be the upper computer 120 of fig. 1. As shown in fig. 4, the brain function abnormality detection method based on dynamic cortex function connection may specifically include the following steps:

step 401, performing notch, filtering, artifact removal through independent component analysis and downsampling on each first type of scalp electroencephalogram signal in sequence to obtain a third type of scalp electroencephalogram signal, so as to perform functional clustering operation on the third type of scalp electroencephalogram signal.

In order to improve the accuracy of subsequent processing of the first type of scalp electroencephalogram signals, the computer equipment sequentially performs notch, filtering, independent component analysis (Independent Component Analysis, ICA) artifact removal and downsampling operations on each first type of scalp electroencephalogram signal to obtain a denoised first type of scalp electroencephalogram signal, namely a third type of scalp electroencephalogram signal so as to perform functional clustering operation on the third type of scalp electroencephalogram signal.

Step 402, obtaining a first preset number of first scalp electroencephalogram signals generated when a target object is in a preset state.

Referring to step 201 in the embodiment shown in fig. 2, details are not repeated here.

Step 403, performing functional clustering operation on each first scalp electroencephalogram signal to obtain a second preset number of brain functional states corresponding to each section of scalp electroencephalogram signal.

Referring to step 202 in the embodiment shown in fig. 2, details are not repeated here.

Step 404, fitting a second preset number of brain function states corresponding to the first scalp electroencephalogram signal with the first scalp electroencephalogram signal to obtain a second type of scalp electroencephalogram signal including a first number of time periods.

Referring to step 203 in the embodiment shown in fig. 2 in detail, a detailed description is omitted here.

Step 405, counting the occurrence frequency of each brain function state from each scalp electroencephalogram signal of the second type.

Referring to step 204 in the embodiment shown in fig. 2, details are not repeated here.

Step 406, determining the starting time when the target object is in the abnormal state according to the occurrence frequency of each brain function state in all the scalp electroencephalogram signals of the second class.

Referring to step 205 in the embodiment shown in fig. 2 in detail, a detailed description is omitted here.

Optionally, step 406, determining the starting time when the target object is in the abnormal state according to the occurrence frequency of each brain function state in all the scalp electroencephalogram signals of the second type may include the following steps 4061 to 4066:

step 4061, counting the occurrence frequency of each brain function state in each scalp electroencephalogram signal of the second class by taking each brain function state as a reference, and generating a second preset number of frequency curve segments.

The computer equipment counts the occurrence frequency of each brain function state in each second type scalp electroencephalogram signal along with the change of the sampling point in the second type scalp electroencephalogram signal, and generates M frequency curve segments. It can be understood that the sampling points of the second type of scalp electroencephalogram signals are the sampling points corresponding to the first type of scalp electroencephalogram signals.

In an application scenario, as shown in fig. 5, the computer device counts the occurrence frequency of each brain function state in a certain second type scalp electroencephalogram signal along with the change of the sampling point in the second type scalp electroencephalogram signal, so as to generate 5 frequency curve segments, and curves 1 to 5 respectively represent one brain function state. The occurrence frequency of the micro state in the graph is the occurrence frequency of the brain functional state.

Step 4062, determining a correlation coefficient between each frequency curve segment and the preset function signal.

The preset function signal is an activation (sigmoid) function signal, and since the occurrence frequency of the abnormal state in the scalp electroencephalogram signal has a tendency of going to be stable after a smooth and steep rise and finally going to be stable along with the beginning to the end of the brain disease attack of the target object in the technical scheme of the application, the function signal is similar to the waveform of the sigmoid function signal, so that the correlation coefficient between each frequency curve segment and the sigmoid function signal is calculated, and the correlation coefficient is used for indicating the degree of correlation between each frequency curve segment and the waveform of the sigmoid function signal (namely, the correlation coefficient between two curves) and can be a pearson correlation coefficient. N can be obtained after the correlation coefficient between all the frequency curve segments and the sigmoid function signal is calculatedM correlation coefficients.

Step 4063, grouping all correlation coefficients based on each brain function state according to the correlation coefficients between all the frequency curve segments and the preset function signal.

The computer device can respectively take each brain function state as a reference, for N M correlation coefficients are grouped to obtain M groups, and each group contains N correlation coefficients belonging to the same brain function state.

Step 4064, the average value of the correlation coefficients of each group is counted.

And calculating the weighted average value of N correlation coefficients in each group aiming at the correlation coefficient of each group in the M groups to obtain the correlation coefficient average value of each group.

Step 4065, selecting the brain function state corresponding to the group to which the maximum correlation coefficient average value belongs as the abnormal state.

The most relevant between each frequency curve segment in the group to which the maximum correlation coefficient average value belongs and the waveform of the sigmoid function signal, so that the computer equipment can select the brain function state corresponding to the group to which the maximum correlation coefficient average value belongs as an abnormal state.

Step 4066, determining the starting time of the abnormal state in each second type scalp electroencephalogram signal according to all the frequency curve segments in the group corresponding to the abnormal state.

The computer device analyzes the increasing speed of the occurrence frequency of the abnormal state in the scalp electroencephalogram signal of the second type corresponding to each frequency curve segment according to each frequency curve segment in the group corresponding to the abnormal state, further finds a sampling point in each scalp electroencephalogram signal of the second type, which means that the increasing speed of the occurrence frequency of the abnormal state is the largest, and determines the corresponding moment of the sampling point as the starting moment of the abnormal state under the scalp electroencephalogram signal of the second type.

And counting the average value of the correlation coefficients of each group taking each brain function state as a reference by the correlation coefficients between the determined frequency curve segment and the preset function signal through the second scalp electroencephalogram signals which comprise a plurality of time periods after being fitted with the brain function states, and further determining the brain function state corresponding to the group to which the maximum average value of the correlation coefficients belongs as an abnormal state. The correlation between the dynamic change of the occurrence frequency of each brain function state in the scalp electroencephalogram signals of the second type and the preset function signals is considered, the purpose of deep mining of each brain function state is achieved, abnormal states in the brain function states can be accurately judged, and then the starting moment of the abnormal states in each scalp electroencephalogram signal of the second type can be accurately determined according to all frequency curve segments in the group corresponding to the abnormal states. The problem of damaging potential brain functional states of the brain based on dividing scalp electroencephalogram signals in a fixed time scale is avoided, and the accuracy of positioning of the initial time of an epileptic state can be improved.

Optionally, step 4066, determining the starting time of the abnormal state in each second type scalp electroencephalogram signal according to all the frequency curve segments in the group corresponding to the abnormal state may include the following steps:

the head and tail straight lines of the frequency curve sections are connected, the slope of the obtained straight line can indicate the corresponding brain function state of the frequency curve section, the average increasing speed of the occurrence frequency in the second type scalp electroencephalogram signals to which the frequency curve section belongs can be used for searching the sampling point section with the highest increasing speed of the occurrence frequency of the abnormal state in each second type scalp electroencephalogram signal. Therefore, the computer equipment can respectively connect the head line and the tail line of each frequency curve segment in the grouping corresponding to the abnormal state to obtain a first preset number of datum lines.

The computer equipment determines a part of each of the N frequency curve segments in the group corresponding to the abnormal state, which is positioned below the corresponding datum line, as a target curve segment, and finally obtains N target curve segments. It can be understood that, since the slope of each reference line may represent the average increasing speed of the frequency of the brain function state corresponding to the frequency curve segment corresponding to the reference line in the second scalp electroencephalogram signal to which the brain function state belongs, the target curve segment below the reference line means that the increasing speed of the frequency of the abnormal state in the second scalp electroencephalogram signal corresponding to the frequency curve segment to which the target curve segment belongs deviates from the average increasing speed.

Then, the target point with the largest distance between each item of standard curve section and the corresponding reference line means that the degree that the increasing speed of the occurrence frequency of the abnormal brain function state in the second scalp electroencephalogram signal corresponding to the frequency curve section of the target curve section deviates from the average increasing speed is the deepest, and the increasing speed of the occurrence frequency of the abnormal brain function state at the next moment is the largest, that means that the abnormal state reaches the state that the occurrence frequency is steep rising in the second scalp electroencephalogram signal corresponding to the frequency curve section of the target curve section, so the moment of the target point can be the starting moment of the abnormal state in the second scalp electroencephalogram signal corresponding to the frequency curve section of the target curve section.

In an application scenario, as shown in fig. 6, taking an epileptic as an example, the computer device performs the following steps when positioning the epileptic in an epileptic state:

1) Firstly, acquiring N scalp brain electrical signals of a epileptic patient from 1min before to 1min after the epileptic seizure, and structurally MRI brain images of the epileptic patient;

2) Sequentially carrying out 50Hz notch, 1-40H filtering, ICA artifact removal and 250Hz downsampling operation on each of the N first scalp electroencephalogram signals to obtain N third scalp electroencephalogram signals; fig. 6 shows scalp electroencephalogram signals of each of the first, second and third types by signal segments 1 to N;

3) Based on the micro-state theory, clustering each of the N third scalp electroencephalograms to obtain M brain function states (the micro-states in fig. 6 are brain function states, and states 1 to M in fig. 6 represent different brain function states), and fitting the M brain function states with each of the third scalp electroencephalograms to obtain N second scalp electroencephalograms segmented according to the adaptive time scale of the M brain function states (i.e. to obtain the second scalp electroencephalograms including the first number of time periods);

4) Calculating the frequency of occurrence of M brain function states of each second type scalp electroencephalogram signal in the N second type scalp electroencephalograms respectively, obtaining M frequency curve segments in each second type scalp electroencephalogram signal, and then calculating the correlation (namely correlation coefficient) between the M frequency curve segments in each second type scalp electroencephalogram signal and a sigmoid function to obtain NM correlations. And then N->The M correlations are grouped based on each of the M brain functional states, an average value of the correlations is obtained, M correlation average values (that is, correlation coefficient average values) are obtained, a state corresponding to a maximum correlation average value (shown by "most correlated (average) with a sigmoid function" in fig. 6) is selected as an epileptic state, finally, a starting time of the epileptic state is determined according to an increasing rate of occurrence frequency of the epileptic state, and the epileptic state including the starting time is determined as a starting epileptic state (a time period corresponding to the starting epileptic state is the first time period).

Since the first preset number of reference lines are the average increasing speed of the occurrence frequency of the abnormal state in each second type scalp electroencephalogram signal, the slope of the reference lines obtained after the head and tail lines of each frequency curve segment in the group corresponding to the abnormal state are connected can be represented. The target curve segment of each frequency curve segment in the grouping corresponding to the abnormal state, which is located below the datum line corresponding to the frequency curve segment, means that the increasing speed of the occurrence frequency of the abnormal state at a certain moment in the scalp electroencephalogram signals of the second type to which the abnormal state belongs deviates from the average increasing speed. And then, respectively selecting the moment corresponding to the target point with the largest distance between the target curve segments and the reference line from each item of target curve segment, and indicating the moment when the increasing speed of the occurrence frequency of the abnormal state in the scalp electroencephalogram signals of the second type corresponding to the target curve segment reaches the peak value, namely, the starting moment of the abnormal functional state in the scalp electroencephalogram signals of the second type corresponding to the target curve segment. The method considers the trend that the frequency of the abnormal state in the early stage of the scalp electroencephalogram signal is firstly stable and then steeply rises, can accurately represent the abnormal state, for example, the starting time of the epileptic state, improves the accurate positioning of the starting time of the epileptic state, and can further improve the accurate positioning of an epileptic stove.

In the embodiment of the application, after notch, filtering, artifact removal by independent component analysis and downsampling operations are sequentially carried out on the first type scalp electroencephalogram signals, each first type scalp electroencephalogram signal is subjected to functional state clustering and fitting to obtain the denoised second type scalp electroencephalogram signals including the first number of time periods. And further determining the correlation coefficient between each frequency curve segment and the preset function signal in a second preset number corresponding to each scalp electroencephalogram signal of the second type, counting the average value of the correlation coefficient of each group taking each brain function state as a reference, and further determining the brain function state corresponding to the group to which the maximum average value of the correlation coefficient belongs as an abnormal state. And connecting the head and tail straight lines of each frequency curve section in the corresponding group of abnormal states respectively to obtain a first preset number of datum lines, and representing the average increasing speed of the occurrence frequency of the abnormal states in each second type scalp electroencephalogram signal by the slope of the datum lines. And then, the moment corresponding to the target point with the largest distance between the reference lines, which is selected from each target curve section positioned below the reference lines, can indicate the moment when the increasing speed of the occurrence frequency of the abnormal state in the scalp electroencephalogram signal of the second type corresponding to the target curve section reaches the peak value, namely, the starting moment of the abnormal function state in the scalp electroencephalogram signal of the second type corresponding to the target curve section. The method considers the trend that the frequency of the abnormal state in the early stage of the scalp electroencephalogram signal is firstly stable and then steeply rises, can accurately represent the abnormal state, for example, the starting time of the epileptic state, improves the accurate positioning of the starting time of the epileptic state, and can further improve the accurate positioning of an epileptic stove.

Fig. 7 is a flowchart of still another brain function abnormality detection method based on dynamic cortex function connection according to the present application, which may be executed by a computing device, which may be the upper computer 120 of fig. 1. As shown in fig. 7, the brain function abnormality detection method based on dynamic cortex function connection may specifically include the following steps:

step 701, obtaining a first preset number of first scalp electroencephalogram signals generated when a target object is in a preset state.

Please refer to step 402 in the embodiment shown in fig. 4 in detail, which is not described herein.

Step 702, performing functional clustering operation on each first-class scalp electroencephalogram signal to obtain a second preset number of brain functional states corresponding to each section of scalp electroencephalogram signal.

Please refer to step 403 in the embodiment shown in fig. 4 in detail, which will not be described herein.

Step 703, fitting a second preset number of brain function states corresponding to the first scalp electroencephalogram signal with the first scalp electroencephalogram signal to obtain a second type of scalp electroencephalogram signal including a first number of time periods.

Please refer to step 404 in the embodiment shown in fig. 4 in detail, which is not described herein.

Step 704, counting occurrence frequency of each brain function state from each scalp electroencephalogram signal of the second type.

Please refer to step 405 in the embodiment shown in fig. 4 in detail, which is not described herein.

Step 705, determining the starting time when the target object is in the abnormal state according to the occurrence frequency of each brain function state in all the scalp electroencephalogram signals of the second class.

Please refer to step 406 in the embodiment shown in fig. 4 in detail, which is not described herein.

Step 706, acquiring a nuclear magnetic resonance brain image of the target object.

The computer device acquires a structural MRI brain image of the target object via the MRI imaging instrument.

Step 707, constructing a real head model of the target object according to the nuclear magnetic resonance brain image.

The computer device builds a head model and a source model conduction model of the target object based on the structural MRI brain image of the target object based on the current head model building technology, so as to obtain a real head model of the target object.

At step 708, the real head model is divided into a plurality of brain regions based on a preset brain region template.

The preset brain region template is a Destrieux brain region template, and the computer equipment divides the real head model into a plurality of brain regions through each brain region in the Destrieux brain region template.

Step 709, constructing a functional connection matrix set corresponding to the first time period in the plurality of time periods based on the real head model, the plurality of brain regions, the second scalp electroencephalogram signals and the starting time of the abnormal state in each of the second scalp electroencephalogram signals.

The time period in which the abnormal state is located comprises a plurality of time periods, and the first time period is a time period comprising the starting time. The computer device estimates brain region cortex computer signals of each brain region in the real head model based on the second type scalp electroencephalogram signals by using a weighted minimum norm estimation (weighted minimum normes timates, wMNE) brain source imaging technology, and further constructs a functional connection matrix set of the target object in an abnormal state including the starting moment based on the brain region cortex computer signals.

Optionally, step 709, based on the real head model, the plurality of brain regions, the second scalp electroencephalogram signals, and the starting time of the abnormal state in each of the second scalp electroencephalogram signals, constructs a set of functional connection matrices corresponding to the first time period of the plurality of time periods, may include the following steps:

for each second type of scalp electroencephalogram signal, mapping the scalp electroencephalogram signal corresponding to the first time period in the second type of scalp electroencephalogram signal onto the cortex of the brain region in the real head model so as to determine a plurality of brain region cortex computer signals corresponding to the second type of scalp electroencephalogram signal;

For each second type of scalp electroencephalogram signal, the computer equipment maps the scalp electroencephalogram signal corresponding to the first time period in the second type of scalp electroencephalogram signal onto the cortex of each brain region in the real head model by utilizing a wMNE brain source imaging technology, so as to estimate the brain region cortex computer signal of each brain region in the real head model, and further obtain a plurality of brain region cortex computer signals corresponding to the second type of scalp electroencephalogram signal. And calculating the pearson correlation coefficient between the corresponding plurality of brain region cortex computer signals according to each second type of scalp electroencephalogram signal, so that a correlation matrix of the whole brain in the corresponding time period of each second type of scalp electroencephalogram signal, namely a functional connection matrix, can be obtained, and further, the functional connection matrices of all second types of scalp electroencephalogram signals in the first time period are collected into a functional connection matrix set to obtain the functional connection matrix set corresponding to the first time period in a plurality of time periods.

Step 710, determining an average node core degree of each node in the function connection matrix set.

Wherein, the node is used for indicating brain area in the real head model, and the average node core degree is used for indicating the abnormal degree of cerebral cortex electric activity in brain area. The computer device calculates a node core degree of the node according to the following formula 1 for each node in each of the set of function connection matrices. The average node core degree of each node in the functional connection matrix set is further calculated as the following formula 2. Equation 1 and equation 2 are as follows:

wherein, the liquid crystal display device comprises a liquid crystal display device,is the firstiThe second scalp electroencephalogram signal is in the node core degree of a certain node in the brain function network (namely the function connection matrix) in the first time period, < ->Is the firstiPartial efficiency of brain function network of scalp brain electrical signal of second class in first time period,/-head>Is the firstiThe node dielectric centrality of brain function network of scalp electroencephalogram signals of the second type in the first time period,/head>Is the average node core degree.

Step 711, locating a target area of the abnormal state in the brain area according to the average node core degree of each node.

The computer equipment can analyze the abnormal degree of the cerebral cortex electric activity in the cerebral area indicated by the average node core degree, determine the cerebral area with the most abnormal cerebral cortex electric activity as an abnormal cerebral area, and further locate the target area of the abnormal state in the cerebral area according to the electric activity intensity in the abnormal cerebral area. Specifically, the computer equipment determines a brain region corresponding to a node with the largest average node core degree as an occurrence region of an abnormal state; acquiring the electrical activity intensity of the cerebral cortex in the occurrence area; among the occurrence areas, an area where the electrical activity intensity exceeds a preset intensity threshold (for example, thirty percent) is determined as a target area to which the abnormal state belongs in the brain region.

In one application scenario, with continued reference to fig. 6, the computer device may then perform the following steps:

5) Constructing a head model and a source model conduction model of an epileptic patient according to an MRI brain image of the personalized structure of the epileptic patient so as to obtain a real head model of the epileptic patient, and estimating cortex computer signals of each brain region by utilizing a wMNE source imaging method based on a Destrieux brain region template (the cortex brain electricity of each brain region in FIG. 6 is the cortex computer signals of each brain region);

6) The step of calculating the functional connection matrix (pearson correlation coefficient) of the cerebral cortex of the initial epileptic state in fig. 6 is completed by calculating the functional connection matrix of the cerebral cortex of the initial epileptic state with each cerebral area as a node and the pearson correlation coefficient between the cortical computer signals of each cerebral area as a side. Calculating the average node core degree (shown as the node core degree in fig. 6) of each node (shown as the core node in fig. 6) in the functional connection matrix corresponding to the N-segment signals;

7) And (3) finding a functional area corresponding to the node with the largest average node core degree as an area where the epileptic focus is located, and taking the area with the electric activity intensity exceeding the maximum intensity by more than 30% as the epileptic focus (the epileptic focus is a target area where the abnormal state belongs in the brain area when the abnormal state is the epileptic state).

In the embodiment of the application, after the initial moment of the abnormal state is accurately positioned, a real head model of a target object is constructed based on a target-to-nuclear magnetic resonance brain image, the real head model is further divided into a plurality of brain regions based on a preset brain region template, and then a functional connection matrix set corresponding to the first time period in a plurality of time periods is constructed based on the initial moment of the real head model, the plurality of brain regions, the second scalp electroencephalogram signals and the abnormal state in each second scalp electroencephalogram signal, so that the dynamic change of the brain function state in the second scalp electroencephalogram signals is considered in the functional connection matrix set, and the change of the brain function network topology characteristics of the target object in the abnormal state including the initial moment is reflected according to the average node core degree of each node in the functional connection matrix set on the premise of not damaging the potential brain state, thereby positioning the target region of the abnormal state in the brain region. And then can realize finding the epileptic state on the premise of not damaging the potential brain functional state of the brain, and analyzing the change of the brain functional network topology characteristic under the epileptic state, thereby finding the more obvious change of the brain functional network topology characteristic, and finally realizing the accurate positioning of the epileptic focus.

In order to verify the effectiveness and superiority of the brain function abnormality detection method based on dynamic cerebral cortex function connection, experiments are carried out on refractory epileptics without abnormality based on an MRI brain image of one example of structure. The experiment respectively compares the brain function abnormality detection method based on dynamic cerebral cortex function connection with the method for positioning the epileptic focus based on the epileptic seizure starting moment source imaging result and the method for positioning the epileptic focus based on the fixed time scale dynamic cerebral cortex function network constructed based on the fixed time window. Fig. 8a to 8c are results of comparing brain dysfunction detection methods based on dynamic cortical functional connection with imaging results based on epileptic seizure onset time source according to the present application.

As can be seen from fig. 8a, the starting time of the epileptic status determined by the brain dysfunction detection method based on dynamic cortex function connection (indicated by the "proposed method defining epileptic starting time" in fig. 8 a) is to be earlier than the starting time of the manual labeling (indicated by the "manual labeling epileptic starting time" in fig. 8 a). As can be seen from fig. 8b and fig. 8c, compared with the result of imaging the starting time source of the epileptic status defined by the proposed brain function abnormality detection method based on dynamic cortex function connection, the intensity of the epileptic electric activity at the starting time of manual labeling is more divergent, a reasonable explanation is that the epileptic electric activity has already been propagated at the starting time of manual labeling, and the proposed brain function abnormality detection method based on dynamic cortex function connection can capture the epileptic electric activity earlier, so that the epileptic focus can be better prevented from being positioned to the brain tissue involved in the propagation process of the epileptic electric activity.

For the method for positioning epileptic focus by using the fixed time scale dynamic cerebral cortex function network constructed based on the fixed time window, firstly, the length of the time window needs to be selected, then the initial time window of epileptic is defined according to the topological characteristics of the functional network in each time window, and finally, the epileptic initial time window is matched according to the epileptic initial time windowThe functional network finds out the core node as the area where the epileptic focus is located. Wherein, the index defining the epileptic starting time window isGFI=(1-A)×BWherein, the method comprises the steps of, wherein,Ain order to average the local efficiency of the device,Bis the average mesenchyma centrality difference value of the left brain and the right brain,GFIthe time window corresponding to the maximum value is an epileptic starting time window. In this experiment, a time window of 1s length was selected and the first 320 time windows are shown in FIG. 9GFIValues and a determined epileptic starting time window. The average source imaging results over the epileptic initiation time window are shown in fig. 10. As can be seen from fig. 10, the source imaging result is similar to the source imaging result of the epileptic status defined by the brain function abnormality detection method based on dynamic cortex function connection according to the present application, which proves that both the brain function abnormality detection method based on dynamic cortex function connection and the time window method of the present application can mine epileptic electric activity at an earlier time. Fig. 11 and fig. 12 are diagrams comparing brain function abnormality detection methods based on dynamic cortex function connection with methods for locating epileptic foci based on a fixed time scale dynamic cortex function network constructed based on a fixed time window, and the core degree of each average node of the brain function network and the area related to the epileptic foci within a defined epileptic state or time window (the core nodes in fig. 11 and fig. 12 are each node in a function connection matrix, and the ordinate represents the average node core degree). From fig. 12, it can be seen that the brain function abnormality detection method based on dynamic cortex function connection of the present application can adaptively define the initial epileptic status, and compared with the comparison method, the method can mine more remarkable core nodes, and is easier to determine the real epileptic focus.

The embodiment also provides an abnormality detection device for brain function signals, which is used for implementing the foregoing embodiments and preferred embodiments, and is not described in detail. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. While the means described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated. It should be noted that 0 to 1 on the color scale in fig. 8b to 8c and fig. 10 are used to indicate the possibility that a certain area in the brain region is an epileptic focus.

The present embodiment provides an abnormality detection apparatus for brain function signals, as shown in fig. 13, including:

an obtaining module 1310, configured to obtain a first preset number of first scalp electroencephalogram signals generated when the target object is in a preset state;

the clustering module 1320 is configured to perform a functional clustering operation for each first type of scalp electroencephalogram signal to obtain a second preset number of brain functional states corresponding to each section of scalp electroencephalogram signal;

the fitting module 1330 is configured to fit a second preset number of brain functional states corresponding to the first scalp electroencephalogram signal with the first scalp electroencephalogram signal to obtain a second type of scalp electroencephalogram signal including a first number of time periods, where the first scalp electroencephalogram signal is any one of the first type of scalp electroencephalogram signal of the first preset number;

A statistics module 1340, configured to respectively count occurrence frequencies of each brain function state from each second type scalp electroencephalogram signal;

the determining module 1350 is configured to determine a starting time when the target object is in the abnormal state according to the occurrence frequency of each brain function state in all the second scalp electroencephalogram signals.

In some alternative embodiments, the determining module includes:

the first statistics unit is used for counting the frequency of each brain function state in each scalp electroencephalogram signal of the second class by taking each brain function state as a reference respectively, and generating a second preset number of frequency curve segments;

the first determining unit is used for determining a correlation coefficient between each frequency curve segment and a preset function signal respectively;

the grouping unit is used for grouping all the correlation coefficients according to the correlation coefficients between all the frequency curve segments and the preset function signals by taking each brain function state as a reference;

the second statistical unit is used for respectively counting the average value of the correlation coefficient of each group;

the selecting unit is used for selecting the brain functional state corresponding to the group to which the maximum correlation coefficient average value belongs as an abnormal state;

the second determining unit is used for determining the starting moment of the abnormal state in each second type scalp electroencephalogram signal according to all the frequency curve segments in the group corresponding to the abnormal state.

In some alternative embodiments, the second determining unit is further configured to:

In some alternative embodiments, the abnormality detection device for brain function signals further includes:

the acquisition model is also used for acquiring nuclear magnetic resonance brain images of the target object;

the construction module is used for constructing a real head model of the target object according to the nuclear magnetic resonance brain image;

the dividing module is used for dividing the real head model into a plurality of brain areas based on a preset brain area template;

The construction module is further used for constructing a functional connection matrix set corresponding to a first time period in a plurality of time periods based on the real head model, the plurality of brain regions, the second scalp electroencephalogram signals and the starting time of the abnormal state in each second scalp electroencephalogram signal, wherein the first time period is a time period comprising the starting time.

The determining module is also used for determining the average node core degree of each node in the functional connection matrix set, the nodes are used for indicating brain areas in the real head model, and the average node core degree is used for indicating the abnormal degree of the brain cortex electric activity in the brain areas;

and the positioning module is used for positioning a target area of the abnormal state in the brain area according to the average node core degree of each node.

In some alternative embodiments, the build module includes:

a third determining unit, configured to map, for each of the second type of scalp electroencephalogram signals, a scalp electroencephalogram signal corresponding to a first time period in the second type of scalp electroencephalogram signal onto a cortex of a brain region in the real head model, so as to determine a plurality of brain region cortex computer signals corresponding to the second type of scalp electroencephalogram signals;

the fourth determining unit is used for determining the pearson correlation coefficient between the brain region cortex computer signals corresponding to the second type scalp electroencephalogram signals according to each second type scalp electroencephalogram signal respectively to obtain a functional connection matrix of the target object under each second type scalp electroencephalogram signal;

And a fifth determining unit, configured to determine a functional connection matrix of the target object under all scalp electroencephalogram signals of the second type as a set of functional connection matrices corresponding to a first time period among the multiple time periods.

In some alternative embodiments, the positioning module comprises:

a sixth determining unit, configured to determine, as an occurrence area of an abnormal state, a node corresponding to a brain area with the largest average node core degree;

the acquisition unit is used for acquiring the electric activity intensity of the cerebral cortex in the occurrence area;

a seventh determination unit configured to determine, in the occurrence region, a region in which the electrical activity intensity exceeds a preset intensity threshold as a target region to which the abnormal state belongs in the brain region.

the acquisition module is further used for carrying out notch, filtering, artifact removal through independent component analysis and downsampling on each first type of scalp electroencephalogram signal in sequence to obtain a third type of scalp electroencephalogram signal so as to carry out functional clustering operation on the third type of scalp electroencephalogram signal.

Further functional descriptions of the above respective modules and units are the same as those of the above corresponding embodiments, and are not repeated here.

The abnormality detection device of the brain function signal in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit ) circuit, a processor and a memory that execute one or more software or a fixed program, and/or other devices that can provide the above functions.

The embodiment of the application also provides computer equipment, which is provided with the abnormality detection device of the brain function signal shown in the figure 13.

Referring to fig. 14, fig. 14 is a schematic structural diagram of a computer device according to an alternative embodiment of the present application, as shown in fig. 14, the computer device includes: one or more processors 10, memory 20, and interfaces for connecting the various components, including high-speed interfaces and low-speed interfaces. The various components are communicatively coupled to each other using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions executing within the computer device, including instructions stored in or on memory to display graphical information of the GUI on an external input/output device, such as a display device coupled to the interface. In some alternative embodiments, multiple processors and/or multiple buses may be used, if desired, along with multiple memories and multiple memories. Also, multiple computer devices may be connected, each providing a portion of the necessary operations (e.g., as a server array, a set of blade servers, or a multiprocessor system). One processor 10 is illustrated in fig. 14.

The processor 10 may be a central processor, a network processor, or a combination thereof. The processor 10 may further include a hardware chip, among others. The hardware chip may be an application specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general-purpose array logic, or any combination thereof.

Wherein the memory 20 stores instructions executable by the at least one processor 10 to cause the at least one processor 10 to perform a method for implementing the embodiments described above.

The memory 20 may include a storage program area that may store an operating system, at least one application program required for functions, and a storage data area; the storage data area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, memory 20 may optionally include memory located remotely from processor 10, which may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

Memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk, or solid state disk; the memory 20 may also comprise a combination of the above types of memories.

The computer device also includes a communication interface 30 for the computer device to communicate with other devices or communication networks.

The embodiments of the present application also provide a computer readable storage medium, and the method according to the embodiments of the present application described above may be implemented in hardware, firmware, or as a computer code which may be recorded on a storage medium, or as original stored in a remote storage medium or a non-transitory machine readable storage medium downloaded through a network and to be stored in a local storage medium, so that the method described herein may be stored on such software process on a storage medium using a general purpose computer, a special purpose processor, or programmable or special purpose hardware. The storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, a solid state disk or the like; further, the storage medium may also comprise a combination of memories of the kind described above. It will be appreciated that a computer, processor, microprocessor controller or programmable hardware includes a storage element that can store or receive software or computer code that, when accessed and executed by the computer, processor or hardware, implements the methods illustrated by the above embodiments.

Although embodiments of the present application have been described in connection with the accompanying drawings, various modifications and variations may be made by those skilled in the art without departing from the spirit and scope of the application, and such modifications and variations fall within the scope of the application as defined by the appended claims.

Claims

1. A brain dysfunction detection method based on dynamic cortical functional connection, the method comprising:

acquiring a first preset number of first scalp electroencephalogram signals generated when a target object is in a preset state, wherein the preset state is a state from one minute before the onset of brain diseases to one minute after the onset of brain diseases of the target object;

performing functional clustering operation on each first scalp electroencephalogram signal to obtain a second preset number of brain functional states corresponding to each section of scalp electroencephalogram signal, wherein the brain functional states are functional states divided based on a micro-state theory, and the functional states are used for brain information processing;

fitting a second preset number of brain function states corresponding to a first scalp electroencephalogram signal with the first scalp electroencephalogram signal to obtain a second type of scalp electroencephalogram signal comprising a first number of time periods, wherein the first scalp electroencephalogram signal is any one of the first type of scalp electroencephalogram signal of the first preset number;

Counting the occurrence frequency of each brain function state from each scalp electroencephalogram signal of the second type;

determining the starting moment when the target object is in an abnormal state according to the occurrence frequency of each brain function state of all the scalp electroencephalogram signals of the second type in each scalp electroencephalogram signal;

the determining the starting time when the target object is in the abnormal state according to the occurrence frequency of each brain function state in each of all the second scalp electroencephalogram signals comprises the following steps:

counting the occurrence frequency of each brain function state in each scalp electroencephalogram signal of the second class by taking each brain function state as a reference, and generating a second preset number of frequency curve segments;

2. The method according to claim 1, wherein determining a starting time of the abnormal state in each scalp electroencephalogram signal of the second type according to all the frequency curve segments in the group corresponding to the abnormal state includes:

connecting the head and tail lines of each frequency curve segment in the group corresponding to the abnormal state respectively to obtain a first preset number of datum lines, wherein the datum lines correspond to each frequency curve segment in the group corresponding to the abnormal state one by one;

determining a first preset number of target curve segments below the datum lines according to each datum line in the first preset number of datum lines and the corresponding frequency curve segments, wherein the target curve segments are partial line segments in the frequency curve segments;

and respectively selecting a target point with the largest distance from each target curve segment and the reference line, and determining the moment of the target point as the starting moment of the abnormal state in the second scalp electroencephalogram signals corresponding to the frequency curve segment of the target curve segment.

3. The method according to any one of claims 1 to 2, wherein the period of time in which the abnormal state is present includes a plurality of, the method further comprising:

acquiring a nuclear magnetic resonance brain image of the target object;

constructing a functional connection matrix set corresponding to a first time period in a plurality of time periods based on the real head model, the brain regions, the second scalp electroencephalogram signals and the starting time of the abnormal state in each of the second scalp electroencephalogram signals, wherein the first time period is a time period comprising the starting time;

determining the average node core degree of each node in the functional connection matrix set, wherein the nodes are used for indicating brain areas in the real head model, and the average node core degree is used for indicating the abnormal degree of the brain cortex electric activity in the brain areas;

4. A method according to claim 3, wherein said constructing a set of functional connection matrices corresponding to a first time period of a plurality of time periods based on the actual head model, a plurality of brain regions, the second type of scalp electroencephalogram signals, and a starting time of the abnormal state in each of the second type of scalp electroencephalogram signals comprises:

For each second type of scalp electroencephalogram signal, mapping scalp electroencephalogram signals corresponding to the first time period in the second type of scalp electroencephalogram signals onto cortex layers of brain areas in the real head model so as to determine a plurality of brain area cortex computer signals corresponding to the second type of scalp electroencephalogram signals;

respectively aiming at each second type of scalp electroencephalogram signal, determining a pearson correlation coefficient between the second type of scalp electroencephalogram signals corresponding to each brain region cortex computer signal, and obtaining a functional connection matrix of the target object under each second type of scalp electroencephalogram signal;

and determining the functional connection matrix of the target object under all the second scalp electroencephalogram signals as a functional connection matrix set corresponding to the first time period in a plurality of time periods.

5. A method according to claim 3, wherein said locating a target area in the brain region to which the abnormal state belongs according to the average node core degree of the respective nodes comprises:

determining a brain region corresponding to a node with the largest average node core degree as an occurrence region of the abnormal state;

And in the occurrence area, determining an area of which the electric activity intensity exceeds a preset intensity threshold as a target area of which the abnormal state belongs in the brain area.

6. The method according to claim 1, wherein before performing a functional clustering operation on the first type of scalp electroencephalogram signals of each segment to obtain M brain functional states corresponding to each segment of scalp electroencephalogram signals, the method further comprises:

7. An abnormality detection device for brain function signals, the device comprising:

the acquisition module is used for acquiring a first preset number of first scalp electroencephalogram signals generated when a target object is in a preset state, wherein the preset state is a state from one minute before the onset of a brain disease to one minute after the onset of the brain disease of the target object;

the clustering module is used for carrying out functional clustering operation on each first-class scalp electroencephalogram signal to obtain a second preset number of brain functional states corresponding to each section of scalp electroencephalogram signal, wherein the brain functional states are functional states based on micro-state theory division, and the functional states are used for brain information processing;

The fitting module is used for fitting a second preset number of brain function states corresponding to the first scalp electroencephalogram signals with the first scalp electroencephalogram signals to obtain second-class scalp electroencephalogram signals including a first number of time periods, wherein the first scalp electroencephalogram signals are any one of the first-class scalp electroencephalogram signals of the first preset number;

the statistics module is used for respectively counting the occurrence frequency of each brain function state from each scalp electroencephalogram signal of the second type;

the determining module is used for determining the starting moment when the target object is in an abnormal state according to the occurrence frequency of each brain function state in each of the second scalp electroencephalogram signals;

the determining module includes:

and the second determining unit is used for determining the starting moment of the abnormal state in each second type scalp electroencephalogram signal according to all frequency curve segments in the group corresponding to the abnormal state.

8. A computer device, comprising:

a memory and a processor, the memory and the processor being communicatively connected to each other, the memory having stored therein computer instructions, the processor executing the computer instructions to perform the brain function abnormality detection method based on dynamic cortical functional connection of any one of claims 1 to 6.

9. A computer-readable storage medium, wherein computer instructions for causing a computer to execute the brain function abnormality detection method based on dynamic cortex function connection according to any one of claims 1 to 6 are stored thereon.