Spinal cord injury detection method, system, medium and device based on brain network

Abstract

The present disclosure provides a method, system, medium and equipment for detecting spinal cord injury based on brain network, and relates to the technical field of electroencephalogram signal, including collecting electroencephalogram signals of patients with spinal cord injury without CNP, patients with spinal cord injury with CNP and healthy people, and preprocessing the electroencephalogram signals; extracting each brain function network node in the preprocessed electroencephalogram signal, and taking each brain function network node as a channel, obtaining the coupling relationship between every two channels during the motor imagery time, and forming a weighted connection matrix of the channel during the motor imagery time of the subject; according to the weighted connection matrix, respectively obtaining brain network comparison connection diagrams of patients with spinal cord injury with CNP and patients without CNP, and patients with spinal cord injury with CNP and healthy people, to realize the detection of the degree of spinal cord injury of patients. The detection of the degree of spinal cord injury of patients can also detect the pain caused by spinal cord injury with central nervous system.

Description

Technical Field

[0001] The present disclosure relates to the field of electroencephalogram (EEG) signal technology, and in particular to a method, system, medium and device for detecting spinal cord injury based on a brain network. Background Art

[0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art.

[0003] Spinal cord injury is one of the most harmful diseases, often accompanied by impaired limb function, with high morbidity and difficult treatment, thus affecting the daily life of 40%-50% of the injured population, bringing a heavy burden to society and families. With the development of social science and technology, the study of the mechanism of EEG changes and neural plasticity after spinal cord injury has become a hot topic in brain science.

[0004] At present, the diagnosis of spinal cord injury is usually based on injury history, cerebrospinal fluid examination, somatosensory evoked potential examination, electromyography examination and CT scan examination. In recent years, researchers have found that the mechanism of brain function changes in patients can also be used as a diagnostic method. However, most of the researchers' research on brain function in spinal cord injury is based on functional magnetic resonance imaging as research data. Functional magnetic resonance imaging is sensitive to slow oscillations and has low temporal resolution, so it is mostly based on resting state data. EEG is a manifestation of spontaneous and rhythmic electrical activity of brain nerve cell groups. This electrical activity causes changes in the potential on the scalp surface. This potential changes over time and reflects the functional state of the brain. It has the characteristics of no damage and high temporal resolution. The existing information under dynamic or task state is insufficiently utilized. Basically, it only considers the impact of injury on the patient's brain function, and often ignores the impact of pain in CNP patients with spinal cord injury on the patient's brain function. Summary of the invention

[0005] In order to solve the above problems, the present invention proposes a spinal cord injury detection method, system, medium and equipment based on brain network, which utilizes the patient's task state EEG data and adopts a spinal cord injury detection method based on brain network technology to detect the patient's spinal cord injury degree and central nervous system pain caused by spinal cord injury.

[0006] According to some embodiments, the present disclosure adopts the following technical solutions:

[0007] Brain network-based spinal cord injury detection method, including:

[0008] Collect EEG signals from patients with spinal cord injury without CNP, patients with spinal cord injury with CNP, and healthy people, and pre-process the EEG signals;

[0009] Extract each brain function network node in the preprocessed EEG signal, and take each brain function network node as a channel, obtain the coupling relationship between every two channels during the motor imagery time, and form a weighted connection matrix of the channel during the subject's motor imagery time;

[0010] According to the weighted connection matrix, the brain network comparison connection diagrams of patients with spinal cord injury accompanied by CNP and patients without CNP, and patients with spinal cord injury accompanied by CNP and healthy people are obtained respectively, so as to detect the degree of spinal cord injury in patients.

[0011] According to some embodiments, the present disclosure adopts the following technical solutions:

[0012] The spinal cord injury detection system based on brain network includes:

[0013] The EEG signal acquisition module is used to collect EEG signals from patients with spinal cord injury without CNP, patients with spinal cord injury with CNP, and healthy people, and to pre-process the EEG signals;

[0014] The signal processing module extracts each brain function network node from the preprocessed EEG signal, and uses each brain function network node as a channel to obtain the coupling relationship between every two channels during the motor imagery time, and forms a weighted connection matrix of the channel during the subject's motor imagery time; based on the weighted connection matrix, the brain network comparison connection diagrams of patients with spinal cord injury and CNP and patients without CNP, and patients with spinal cord injury and CNP and healthy people are obtained respectively;

[0015] The signal detection and display module can detect the degree of spinal cord injury of patients.

[0016] According to some embodiments, the present disclosure adopts the following technical solutions:

[0017] A non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, the brain network-based spinal cord injury detection method is implemented.

[0018] According to some embodiments, the present disclosure adopts the following technical solutions:

[0019] An electronic device comprises: a processor, a memory and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory so that the electronic device implements the spinal cord injury detection method based on brain network.

[0020] Compared with the prior art, the present invention has the following beneficial effects:

[0021] The present invention collects the EEG signals of motor imagery of patients with spinal cord injury CNP, patients without CNP and healthy people, and performs filtering in different frequency bands, segmentation and artifact removal, and then calculates the phase locking value of each two channels respectively, and forms the functional connection matrix of each type of subjects according to PLV, sets the connection strength threshold, and obtains the brain network comparison connection diagram of patients with spinal cord injury accompanied by CNP and patients without CNP, and patients with spinal cord injury accompanied by CNP and healthy people respectively. Based on graph theory, the network coefficients of the three types of subjects are calculated. The differences in brain networks and network coefficients between patients and healthy people are compared and analyzed, which not only realizes the detection of the degree of spinal cord injury of patients, but also can detect the pain caused by spinal cord injury with central nervous system. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0023] Figure 1 This is a flow chart of functional module connections in an embodiment of the present disclosure;

[0024] Figure 2 The data processing method of the embodiment of the present disclosure is a flow chart. DETAILED DESCRIPTION

[0025] The present disclosure is further described below in conjunction with the accompanying drawings and embodiments.

[0026] It should be noted that the following detailed descriptions are all illustrative and are intended to provide further explanation of the present disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present disclosure belongs.

[0027] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present disclosure. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0028] Example 1

[0029] In one embodiment of the present disclosure, a method for detecting spinal cord injury based on a brain network is provided, comprising:

[0030] Step 1: Collect EEG signals from patients with spinal cord injury without CNP, patients with spinal cord injury with CNP, and healthy people, and pre-process the EEG signals;

[0031] Step 2: Extract each brain function network node in the preprocessed EEG signal, and take each brain function network node as a channel to obtain the coupling relationship between every two channels during the motor imagery time, and form a weighted connection matrix of the channel during the subject's motor imagery time;

[0032] Step 3: According to the weighted connection matrix, the brain network comparison connection diagrams of patients with spinal cord injury and CNP and patients without CNP, and patients with spinal cord injury and CNP and healthy people are obtained respectively to detect the degree of spinal cord injury of patients.

[0033] Specifically, as an embodiment, Figure 2 As shown, the specific implementation details of the method described in steps 1-3 above are as follows:

[0034] In step 1, the EEG signals of patients with spinal cord injury without CNP, patients with spinal cord injury with CNP, and healthy people are collected and preprocessed;

[0035] Specifically, the electroencephalogram (EEG) signals of patients with spinal cord injury without CNP, patients with spinal cord injury with CNP, and healthy people are collected; wherein the position of each electrode in the electroencephalogram (EEG) acquisition device represents a node in the brain network, and the number of electrodes is the number of nodes N;

[0036] Preferably, the EEG signal acquisition device in step 1 is a NeuroScan 64-256-conductor EEG system, including a 64-electrode EEG cap for acquiring EEG signals at the electrodes on the head of the patient under stimulation; a stimulus presentation computer for playing the acquisition paradigm; a recording computer for recording the acquired EEG information; a signal amplifier for amplifying and processing the signal; a multi-parameter synchronizer for synchronous marking and event marking of the received stimulation; an intelligent synchronization center for wirelessly connecting the wireless EEG / ERP amplifier to the computer via Wi-Fi; and a transmission line for transmitting signals.

[0037] The stimulation generation mode is played on a computer display screen. The patient is seated in front of the display screen. Before performing motor imagery (MI) each time, prompts and arrow patterns of motor imagery will appear on the screen. The patient is prompted to imagine left-hand movement or right-hand movement, and each motor imagery duration is 3 seconds, with 4 seconds of rest time during each motor imagery. Each random motor imagery of the left and right hands is performed 10 times. The patient needs to perform 4-6 groups of motor imagery tests, with a 90-second rest period during each group of motor imagery tests.

[0038] The preprocessing process is as follows: first, the EEG signal is bandpass filtered and divided into δ wave (0.5-4) Hz, θ wave (4-8) Hz, α wave (8-13) Hz, β wave (13-30) Hz, and γ (30-45) Hz for study. Since the subjects, i.e., the patients, perform the MI task within the corresponding 7s task cycle, the EEG data is segmented into 7s data for further analysis. Finally, artifact removal is performed (±70μv is used as the threshold for eye artifacts). In order to reduce the influence of volume conduction between electrodes, we selected 21 electrodes ('Fp1', 'Fpz', 'Fp2', 'F7', 'F3', 'Fz', 'F4', 'F8', 'T7', 'C3', 'Cz', 'C4', 'T8', 'P7', 'P3', 'Pz', 'P4', 'P8', 'O1', 'O2', 'Oz') to construct the brain functional network for research.

[0039] In step 2, the process of obtaining the coupling relationship between every two channels during the motor imagery time and forming a weighted connection matrix of the channels during the motor imagery time of the subject is as follows: extract each brain function network node in the preprocessed EEG signal, and take each brain function network node as a channel, calculate the phase locking value of every two channels during motor imagery to obtain the coupling relationship between the two channels during the motor imagery time, and use the phase locking values ​​of the three types of subjects to form a weighted connection matrix of the channels during the motor imagery time of the subjects.

[0040] Specifically, each brain function network node in the preprocessed EEG signal is used as a channel, and the phase locking value PLV of each two channels during motor imagery is calculated to obtain the coupling relationship between the two channels during the motor imagery time; the phase locking value PLV represents the connection strength between the two channels, and the phase locking values ​​PLV of the three types of subjects are used to form a weighted connection matrix of the channel during the motor imagery time of the subjects. The number of brain function network nodes is 21, and the size of the designed weighted connection matrix is: 21×21. Any element of the matrix represents the coupling relationship between the two nodes, and the element is the corresponding phase locking value PLV obtained by calculation;

[0041] That is, if the instantaneous phase φ of the signal x(t) x (t), the instantaneous phase of the signal y(t) is φy(t), then the PLV calculation formula is defined as,

[0042]

[0043] Where i is a node in the brain function network, t is a time point, and N is the sampling number of each signal. The symbol * represents the complex conjugate of the variable. Δφ(t) represents the phase difference between signals x(t) and y(t), Δt is the sampling period, and j is the jth sampling point. φx(t) is the instantaneous phase of signal x(t), and φy(t) is the instantaneous phase of signal y(t).

[0044] In order to further analyze the impact of injury and pain on the functional connectivity between patients' brain regions, the network coefficients of the patients were calculated based on the weighted connection matrix calculated by S3.

[0045] The differences in the topological structures of brain functional networks of the three groups of subjects were quantitatively analyzed; the four groups of network coefficients were clustering coefficient (C), characteristic path length (L), global efficiency (Ge) and local efficiency (Le), and the network coefficients were defined as,

[0046] The clustering coefficient C describes the degree of clustering of nodes in a network and reflects the possibility of establishing connections between any two nodes in the constructed network.

[0047]

[0048] The characteristic path length L describes the ability of information transmission in the network. The shorter the characteristic path length, the higher the efficiency of information transmission between two nodes and the better the connectivity of the network.

[0049]

[0050] The global efficiency Ge reflects the average effect of the relevant brain network and represents the overall information transmission capacity of the brain network.

[0051]

[0052] Local efficiency Le describes the ability of the information contained in a node to be transmitted in a local area of ​​the network.

[0053]

[0054] The two groups of network coefficients, clustering coefficient and local efficiency, express the functional connection effect between brain regions and reflect the ability of the brain network to process local information; the two groups of network coefficients, global efficiency and characteristic path length, reflect the ability of the brain network to process global information. From the above description, it can be seen that these indicators of network coefficients can comprehensively measure the relevant characteristics of the brain network and evaluate the information transmission efficiency of the brain network from all aspects.

[0055] In the formula, C ij is the connection strength between nodes i and j, N is the number of nodes, θ is the set of all nodes in the network, d ijRepresents the shortest path length between i and j.

[0056] In step 3, the process of obtaining the brain network comparative connection map of patients with spinal cord injury and CNP and patients without CNP, and patients with spinal cord injury and CNP and healthy people according to the weighted connection matrix is ​​as follows: subtracting the weighted connection matrices obtained from patients with spinal cord injury and CNP and patients without CNP, and patients with spinal cord injury and CNP and healthy people in pairs, setting the connection strength threshold, and performing t-test and FDR check, and then comparing the connection strength between channels in pairs to obtain their functional brain network comparative connection map.

[0057] Specifically, the adjacency matrices obtained from patients with neuropathic pain in spinal cord injury and healthy people, as well as patients with non-pain in spinal cord injury and patients with central neuropathic pain were subtracted pairwise, and a connection strength threshold was set, where the connection strength threshold was set to 0.05. After performing t-test and FDR check, the inter-channel connection strengths were compared pairwise to obtain their functional brain network comparative connection map.

[0058] The calculated network coefficients are averaged and visualized.

[0059] Example 2

[0060] In one embodiment of the present disclosure, a spinal cord injury detection system based on brain network is provided. Figure 1 Shown include:

[0061] The EEG signal acquisition module is used to collect EEG signals from patients with spinal cord injury without CNP, patients with spinal cord injury with CNP, and healthy people, and to pre-process the EEG signals;

[0062] The signal processing module extracts each brain function network node from the preprocessed EEG signal, and uses each brain function network node as a channel to obtain the coupling relationship between every two channels during the motor imagery time, and forms a weighted connection matrix of the channel during the subject's motor imagery time; based on the weighted connection matrix, the brain network comparison connection diagrams of patients with spinal cord injury and CNP and patients without CNP, and patients with spinal cord injury and CNP and healthy people are obtained respectively;

[0063] The signal processing module can be divided into a preprocessing module, a brain network construction module, and a network coefficient calculation module.

[0064] The signal detection and display module can detect the degree of spinal cord injury of patients.

[0065] Specifically, the EEG signal acquisition module includes using a 64-channel EEG signal acquisition device (NeuroScan 64-256-channel EEG system, Australia) to collect EEG signals of patients with spinal cord injury without CNP, patients with spinal cord injury with CNP, and healthy people; wherein the position of each electrode in the EEG signal (Electroencephalogram, EEG) acquisition device represents a node in the brain network, and the number of electrodes is the number of nodes N;

[0066] The EEG signal acquisition module also includes a stimulus presentation computer, a recording computer, a signal amplifier, a multi-parameter synchronizer, an intelligent synchronization center and a transmission line.

[0067] The signal detection and display module includes an image display module and a data visualization module.

[0068] Example 3

[0069] In one embodiment of the present disclosure, a non-transitory computer-readable storage medium is provided, wherein the non-transitory computer-readable storage medium is used to store computer instructions, and when the computer instructions are executed by a processor, the steps of the brain network-based spinal cord injury detection method are implemented.

[0070] Example 4

[0071] In one embodiment of the present disclosure, an electronic device is provided, including: a processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory, so that the electronic device executes the steps of implementing the brain network-based spinal cord injury detection method.

[0072] The present disclosure is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present disclosure. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0073] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0074] Although the above describes the specific implementation methods of the present disclosure in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present disclosure. Technical personnel in the relevant field should understand that on the basis of the technical solution of the present disclosure, various modifications or variations that can be made by those skilled in the art without creative work are still within the scope of protection of the present disclosure.

Claims

1. A method for detecting spinal cord injury based on brain network, characterized in that: include: Collect EEG signals from patients with spinal cord injury without CNP, patients with spinal cord injury with CNP, and healthy people, and pre-process the EEG signals; Each brain function network node in the preprocessed EEG signal was extracted, and each brain function network node was used as a channel to obtain the coupling relationship between every two channels during the motor imagery time, forming a weighted connection matrix of the channel during the subject's motor imagery time. The network coefficient of the patient was calculated based on the calculated weighted connection matrix; the differences in the topological structure of the brain function network of the subjects were quantitatively analyzed, and the four groups of network coefficients were clustering coefficient, characteristic path length, global efficiency and local efficiency, and the calculated network coefficients were averaged and visualized; According to the weighted connection matrix, the brain network comparison connection diagrams of patients with spinal cord injury and CNP and patients without CNP, and patients with spinal cord injury and CNP and healthy people are obtained to detect the degree of spinal cord injury in patients. The process of obtaining the coupling relationship between every two channels during the motor imagery time and forming the weighted connection matrix of the channels during the motor imagery time of the subject is as follows: extracting each brain function network node in the preprocessed EEG signal, and taking each brain function network node as a channel, calculating the phase locking value of every two channels during the motor imagery time to obtain the coupling relationship between the two channels during the motor imagery time, and using the phase locking values ​​of the three types of subjects to form the weighted connection matrix of the channels during the motor imagery time of the subject; The process of obtaining the brain network comparative connection diagrams of patients with spinal cord injury accompanied by CNP and patients without CNP, and patients with spinal cord injury accompanied by CNP and healthy people according to the weighted connection matrix is ​​as follows: subtracting the weighted connection matrices obtained from patients with spinal cord injury accompanied by CNP and patients without CNP, and patients with spinal cord injury accompanied by CNP and healthy people in pairs, setting the connection strength threshold, and performing t-test and FDR check, and then comparing the connection strengths between channels in pairs to obtain their functional brain network comparative connection diagrams.

2. The brain network-based spinal cord injury detection method according to claim 1, characterized in that: The method of collecting EEG signals of patients with spinal cord injury without CNP, patients with spinal cord injury with CNP and healthy people is to collect EEG signals at electrodes on the patient's head under stimulation.

3. The brain network-based spinal cord injury detection method according to claim 2, characterized in that: The stimulation is produced by playing on a computer screen, and the patient performs motor imagination according to the corresponding stimulation. Before each motor imagination, left and right prompts and arrow patterns of the motor imagination will appear on the screen, and the patient is prompted to imagine left-hand movement or right-hand movement.

4. The brain network-based spinal cord injury detection method according to claim 3, characterized in that: Each motor imagery lasted for 3 seconds, with a 4-second rest period between each motor imagery. Each random motor imagery was performed 10 times for each hand.

5. The brain network-based spinal cord injury detection method according to claim 1, characterized in that: The preprocessing process is to first perform bandpass filtering on the EEG signal, dividing it into δ wave of 0.5-4Hz, θ wave of 4-8Hz, α wave of 8-13Hz, β wave of 13-30Hz, and γ of 30-45Hz; then segment the EEG data; and finally remove artifacts.

6. A spinal cord injury detection system based on brain network, characterized in that: include: The EEG signal acquisition module is used to collect EEG signals from patients with spinal cord injury without CNP, patients with spinal cord injury with CNP, and healthy people, and to pre-process the EEG signals; The signal processing module extracts each brain function network node from the preprocessed EEG signal, and takes each brain function network node as a channel to obtain the coupling relationship between every two channels during the motor imagery time, forming a weighted connection matrix of the channel during the subject's motor imagery time; the network coefficient of the patient is calculated based on the calculated weighted connection matrix; the differences in the topological structure of the brain function network of the subjects are quantitatively analyzed, and the four groups of network coefficients are clustering coefficient, characteristic path length, global efficiency and local efficiency, and the calculated network coefficients are averaged and visualized; According to the weighted connection matrix, the brain network comparison connection diagrams of patients with spinal cord injury and CNP and patients with injury but without CNP, and patients with spinal cord injury and CNP and healthy people were obtained respectively; The process of obtaining the coupling relationship between every two channels during the motor imagery time and forming the weighted connection matrix of the channels during the motor imagery time of the subject is as follows: extracting each brain function network node in the preprocessed EEG signal, and taking each brain function network node as a channel, calculating the phase locking value of every two channels during the motor imagery time to obtain the coupling relationship between the two channels during the motor imagery time, and using the phase locking values ​​of the three types of subjects to form the weighted connection matrix of the channels during the motor imagery time of the subject; The process of obtaining the brain network comparative connection diagrams of the patients with spinal cord injury accompanied by CNP and the patients without CNP, and the patients with spinal cord injury accompanied by CNP and the healthy people respectively according to the weighted connection matrix is ​​as follows: subtracting the weighted connection matrices obtained from the patients with spinal cord injury accompanied by CNP and the patients without CNP, and the patients with spinal cord injury accompanied by CNP and the healthy people in pairs, setting the connection strength threshold, and performing t-test and FDR check, and then comparing the connection strengths between channels in pairs to obtain the functional brain network comparative connection diagram; The signal detection and display module can detect the degree of spinal cord injury of the patient.

7. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium is used to store computer instructions, and when the computer instructions are executed by the processor, the brain network-based spinal cord injury detection method as described in any one of claims 1-5 is implemented.

8. An electronic device, characterized in that: include: A processor, a memory and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory to enable the electronic device to implement the brain network-based spinal cord injury detection method as described in any one of claims 1-5.

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