CN113397559B - Stereotactic electroencephalogram analysis method, stereotactic electroencephalogram analysis device, computer equipment and storage medium - Google Patents
Stereotactic electroencephalogram analysis method, stereotactic electroencephalogram analysis device, computer equipment and storage medium Download PDFInfo
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Abstract
The application discloses a stereotactic electroencephalogram analysis method, a stereotactic electroencephalogram analysis device, computer equipment and a storage medium, wherein the stereotactic electroencephalogram analysis method comprises the following steps: acquiring original data of a stereotactic electroencephalogram, preprocessing the original data, and extracting to obtain an Epoch signal; performing time-frequency analysis on the Epoch signals by using a multi-window method or a wavelet algorithm, and performing connectivity analysis on electrodes corresponding to the Epoch signals by using a connectivity analysis method; and combining a time-frequency analysis result and a connectivity analysis result, and visualizing the position information of the stereotactic electroencephalogram through a pre-constructed brain three-dimensional model to acquire the information of the brain region where the electrode is positioned. According to the application, through preprocessing the original data of the stereotactic electroencephalogram, performing time-frequency analysis and connectivity analysis on the extracted Epoch signals, and performing visual display on the analysis result, the analysis efficiency of the stereotactic electroencephalogram can be effectively improved, and the exploration of brain cognitive functions by using the stereotactic electroencephalogram can be facilitated.
Description
Technical Field
The application relates to the technical field of electroencephalogram detection, in particular to a stereotactic electroencephalogram analysis method, a stereotactic electroencephalogram analysis device, computer equipment and a storage medium.
Background
The refractory epileptic is characterized in that an electrode-stereotactic electroencephalogram (SEEG) is usually needed to be placed in the deep part of the brain for accurately positioning an epileptic focus, so that the spatial information in the brain is obtained, and great convenience is brought to the exploration of the cognitive function of the brain in the field of cognitive neuroscience. However, the current processing and analysis of cognitive seg data generally requires writing a large amount of codes or using a plurality of different methods to analyze the seg, which results in low analysis efficiency and causes more inconvenience for subsequent research work due to the adoption of the plurality of different methods.
Disclosure of Invention
The embodiment of the application provides a stereotactic electroencephalogram analysis method, a stereotactic electroencephalogram analysis device, computer equipment and a storage medium, aiming at improving the analysis efficiency of the stereotactic electroencephalogram.
In a first aspect, an embodiment of the present application provides a stereotactic electroencephalogram analysis method, including:
acquiring original data of a stereotactic electroencephalogram, preprocessing the original data, and extracting to obtain an Epoch signal;
performing time-frequency analysis on the Epoch signals by using a multi-window method or a wavelet algorithm, and performing connectivity analysis on electrodes corresponding to the Epoch signals by using a connectivity analysis method;
and combining a time-frequency analysis result and a connectivity analysis result, and visualizing the position information of the stereotactic electroencephalogram through a pre-constructed brain three-dimensional model to acquire the information of the brain region where the electrode is positioned.
In a second aspect, an embodiment of the present application provides a stereotactic electroencephalogram analysis apparatus, including:
the data preprocessing unit is used for acquiring original data of the stereotactic electroencephalogram, preprocessing the original data and extracting to obtain an Epoch signal;
the signal analysis unit is used for carrying out time-frequency analysis on the Epoch signals by utilizing a multi-window method or a wavelet algorithm and carrying out connectivity analysis on electrodes corresponding to the Epoch signals by utilizing a connectivity analysis method;
the first visualization processing unit is used for combining the time-frequency analysis result and the connectivity analysis result, and visualizing the position information of the stereotactic electroencephalogram through a pre-constructed brain three-dimensional model so as to acquire the information of the brain region where the electrode is located.
In a third aspect, an embodiment of the present application provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the stereotactic electroencephalogram analysis method according to the first aspect when the computer program is executed.
In a fourth aspect, embodiments of the present application provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the stereotactic electroencephalogram analysis method according to the first aspect.
The embodiment of the application provides a stereotactic electroencephalogram analysis method, a stereotactic electroencephalogram analysis device, computer equipment and a storage medium, wherein the stereotactic electroencephalogram analysis method comprises the following steps: acquiring original data of a stereotactic electroencephalogram, preprocessing the original data, and extracting to obtain an Epoch signal; performing time-frequency analysis on the Epoch signals by using a multi-window method or a wavelet algorithm, and performing connectivity analysis on electrodes corresponding to the Epoch signals by using a connectivity analysis method; and combining a time-frequency analysis result and a connectivity analysis result, and visualizing the position information of the stereotactic electroencephalogram through a pre-constructed brain three-dimensional model to acquire the information of the brain region where the electrode is positioned. According to the embodiment of the application, the original data of the stereotactic electroencephalogram is preprocessed, the data analysis, namely the frequency analysis and the connectivity analysis, are carried out on the Epoch signals extracted after the preprocessing, and meanwhile, the visual display is carried out on the analysis result, so that the analysis efficiency of the stereotactic electroencephalogram can be effectively improved, and the exploration of the brain cognitive function by using the stereotactic electroencephalogram can be facilitated.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a stereotactic electroencephalogram analysis method according to an embodiment of the present application;
FIG. 2 is a schematic sub-flowchart of a stereotactic electroencephalogram analysis method according to an embodiment of the present application;
FIG. 3 is a schematic view of another sub-flowchart of a stereotactic electroencephalogram analysis method according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a brain template in a stereotactic electroencephalogram analysis method according to an embodiment of the present application;
FIG. 5 is a schematic block diagram of a stereotactic electroencephalogram analysis apparatus according to an embodiment of the present application;
FIG. 6 is a sub-schematic block diagram of a stereotactic electroencephalogram analysis method according to an embodiment of the present application;
fig. 7 is another sub-schematic block diagram of a stereotactic electroencephalogram analysis method according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the 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 terms "comprises" and "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
Referring to fig. 1, fig. 1 is a schematic flow chart of a stereotactic electroencephalogram analysis method according to an embodiment of the present application, which specifically includes: steps S101 to S103.
S101, acquiring original data of a stereotactic electroencephalogram, preprocessing the original data, and extracting to obtain an Epoch signal;
s102, performing time-frequency analysis on the Epoch signals by using a multi-window method or a wavelet algorithm, and performing connectivity analysis on electrodes corresponding to the Epoch signals by using a connectivity analysis method;
s103, combining a time-frequency analysis result and a connectivity analysis result, and visualizing the position information of the stereotactic electroencephalogram through a pre-constructed brain three-dimensional model to obtain the information of the brain region where the electrode is located.
In this embodiment, first, preprocessing is performed on the acquired original data of a stereotactic electroencephalogram (SEEG) to extract a corresponding Epoch signal (i.e., a time window signal), and then time-frequency analysis and connectivity analysis are performed on the Epoch signal to explore a time-dependent change process of power of each frequency component in the Epoch signal, determine a frequency band with stronger energy, and obtain correlation/coherence between electrodes, so as to obtain correlation/coherence between brain regions. Meanwhile, the position information of SEEG is visualized by using a brain three-dimensional model so as to acquire the information of the brain region where the electrode is positioned.
According to the embodiment, the SEEG original data is preprocessed, the data analysis, namely the time-frequency analysis and the connectivity analysis, are performed on the Epoch signals extracted after the preprocessing, and meanwhile, the visual display is performed on the analysis result, so that the analysis efficiency of the stereotactic electroencephalogram can be effectively improved, and the exploration of the human brain cognitive function by using the stereotactic electroencephalogram can be facilitated.
In one embodiment, as shown in fig. 2, the step S101 includes: steps S201 to S206.
S201, performing visualization processing on the original data by adopting a visualization function;
in the step, the SEEG data display function of the MNE library is called, SEEG original data selected by a user at present is acquired and is input into the MNE library as parameters, and the visual display effect can be obtained by utilizing the visual function in the SEEG original data.
S202, acquiring a target frequency band of a user, and resampling the visualized original data according to N times of the target frequency band;
in the step, as the sampling rate of the SEEG signal channel obtained clinically is higher, the common sampling rate can reach 2000Hz, so that the data volume is large, the large storage space is occupied, the subsequent data analysis calculation amount is large, and the analysis time is long. According to the Nyquist sampling theorem, the data sampling rate is ensured to be greater than N times of the target frequency band, for example, greater than twice of the maximum value of the target frequency band, so that the data volume can be greatly reduced after resampling processing, and the calculation cost is reduced. For example, a new sampling rate set by the user, such as 1000Hz, is obtained, and if the sampling rate of the original signal is 2000Hz, the original signal is sampled at intervals of 2.
S203, filtering the resampled original data by adopting an FIR filter or an IIR filter;
because the signal acquisition process is affected by noise, such as power frequency noise, the data needs to be filtered. For SEEG signals, due to the higher signal-to-noise ratio, only power frequency interference is usually filtered out, and then signals of a frequency band of interest (namely the target frequency band) are extracted. This step provides two filters, FIR and IIR, respectively, for signal filtering. The FIR filter directly calls a firwin function of a signal module in the scipy library, SEEG original data and cut-off frequency are used as parameters, the function is input to carry out FIR filtering, a default Hamming window is used for a window, the length of the filter is 6.6 times of the shortest transition frequency band, and the forward and reverse bidirectional filtering design compensates the delay of the filter to realize zero-phase filtering of signals; the IIR filter defaults to a fourth order butterworth filter. The specific workflow of the FIR filter is as follows: acquiring a cut-off frequency; obtaining a transition band bandwidth f through the cut-off frequency; setting the Fourier transform length to be 6.6f; and filtering the original data.
S204, obtaining a target channel name marked in the original data by a user, and clearing a channel signal corresponding to the target channel name in the filtered original data;
this step aims at removing bad conductors, i.e. channels with poor signal quality. Specifically, the target channel names marked by the user are firstly obtained, meanwhile, the target channel names can be stored in a list, when the bad guide removal processing is needed, the corresponding target channel names are obtained from the list, and then signals related to the target channel names are cleared. At the same time, a new Raw type of Raw data is reconstructed.
S205, performing re-reference processing on the original data of the channel signal by using a re-reference algorithm to obtain target data; wherein the re-reference algorithm comprises a CAR algorithm, a GWR algorithm, an ESR algorithm, a Bipolar algorithm, a Monopolar algorithm and a Laplacian algorithm;
the signal is re-referenced, so that the correlation with a reference electrode can be reduced, the difference is highlighted, and the signal effective components related to tasks are prevented from being annihilated due to the strong correlation between channels. Six re-reference algorithms are provided in this section: CAR, GWR, ESR, bipolar, monopolar and Laplacian. Specifically:
the CAR algorithm refers to averaging all channels;
the GWR algorithm needs to lead in electrode MNI coordinate information in advance (taking a. Txt file as an example), and distinguishes gray matter information of the electrodes by calling a method module for partitioning the electrodes by Visbrain, respectively taking all gray matter signal average values and white matter average values as references for the gray matter and white matter electrodes, and storing the calculated heavy reference signals;
the ESR algorithm takes the mean value of all signals on the same electrode needle as a reference. In SEEG data, electrodes on the same electrode needle usually take the same letter as an electrode name prefix, so that signals can be divided according to the electrode needle, and re-referencing is realized;
the Bipolar algorithm takes the electrode signal of the previous potential of the same electrode needle as a reference to make difference;
the Monopolar algorithm takes the average value of two white matter electrode signals as a reference, and the reference electrode can be selected by itself;
the Laplacian algorithm uses the mean value of the front electrode and the rear electrode on the same electrode needle as a reference to make difference.
S206, extracting an Epoch signal from the target data through a pre-generated event code.
Task-state SEEGs in cognitive experiments typically involve event triggering. In the process of performing a cognitive experiment, equipment (such as a notebook) can automatically perform coding and labeling according to an experiment program when stimulus is generated, namely, the event code is generated, and corresponding data segments are required to be extracted according to the event code to form an Epoch signal when data are analyzed in the later period, so that the Epoch signal in the time period of the stimulus event is analyzed.
In the embodiment, the purpose of cleaning and extracting the original data is achieved by sequentially carrying out visualization processing, resampling processing, filtering processing and re-reference path outputting on the original data. Meanwhile, the data formats supported by the embodiment include EDF, SET, FIF and the like.
In one embodiment, the step S206 includes:
setting corresponding time information for a pre-generated event code;
intercepting a corresponding target data segment for the target data by combining the time information;
setting a baseline time period based on the tested performance in the process of acquiring the stereotactic electroencephalogram signals;
and taking the mean value of the signal amplitude values in the baseline time period as a reference, and taking the signal obtained by subtracting the reference from the target data period as the Epoch signal.
Task-state SEEGs in cognitive experiments typically involve event triggering. In the process of performing a cognitive experiment, equipment (such as a notebook) can automatically perform coding and labeling according to an experiment program when stimulus is generated, namely, the event code is generated, and corresponding data segments are required to be extracted according to the event code to form an Epoch signal when data are analyzed in the later period, so that the Epoch signal in the time period of the stimulus event is analyzed.
In this embodiment, when extracting the Epoch signal, a time window corresponding to the event code, for example (-0.5, 2), is set first, and the time information is acquired, and then the corresponding target data segment is intercepted according to the time information, for example, the target data segment from 0.5 seconds before to two seconds after the event code is intercepted. Then, because certain baseline drift can be generated due to the tested breath, the movement of limbs and the hardware cause in the SEEG signal acquisition process, a baseline time period is set, the average value of the signal amplitude in the baseline time period is taken as a reference, the reference is subtracted from the signal in the intercepted target data period, the signal interference is reduced, and the Epoch signal is obtained.
In one embodiment, as shown in fig. 3, the step S102 includes: steps S301 to S303.
S301, removing noise, prominence or stimulation signals in the Epoch signals by using event-related potentials;
s302, calculating the power spectral density of each channel in the Epoch signal by a Welch wavelet method or a multi-window method;
s303, carrying out addition average processing on the power spectrum densities of all channels to obtain the total power spectrum density of the Epoch signal.
The time-frequency analysis is a common analysis method of SEEG signals, and the embodiment provides functions of Event-related potential (ERP-Related Potential), ERP Image, time-frequency response, power spectral density and the like.
Event-related potential is the most common method in electroencephalogram analysis, and the method carries out superposition averaging on all data segments of the same event code to remove noise and highlight signals related to experimental stimulation, so as to explore what effect the stimulation has on the brain. When calculating the power spectral density of the Epoch signals, a Welch wavelet and Multitaper method is adopted for calculation, and the power spectral densities of all the channels of all the epochs are added and averaged to obtain the total power spectral density of all the signals.
The time-frequency response can explore the time-dependent power change process of each frequency component, and can help to see which frequency band or frequency bands have stronger energy. The time-frequency response can be calculated, and the inter-test coherence can be calculated at the same time, so that the method is a measurement standard of the phase consistency among all data segments of the Epoch signals. In this embodiment, the time-frequency response is calculated by using Multitaper, morlet wavelet or a Stockwell method of the mno library, and specifically, the method can be determined according to the actual situation.
In an embodiment, the step S102 further includes:
calculating the frequency domain information of the Epoch signals by a connectivity analysis method;
based on the frequency domain information, averaging all connectivity of the frequency domain information corresponding to the target frequency band selected by the user to obtain average connection strength of the target frequency band;
and carrying out sliding window processing by utilizing short-time Fourier transform based on the frequency domain information to obtain connectivity results of all time points of the Epoch signals; or performing Fourier transform on the Epoch signals to obtain an average connectivity analysis result in the whole time range corresponding to the Epoch signals.
In this embodiment, the processing for the frequency band may be classified as frequency band averaging or non-averaging. The frequency band averaging means that all connectivity of the frequency band range selected by the user (i.e. the target frequency band) is averaged to obtain a result, which represents the average connection strength of the channel x and the channel y in the frequency band range. The processing in time can be divided into a sliding window and a non-sliding window. If the change trend of the connectivity with time needs to be observed, carrying out sliding window processing on the Epoch signals by using short-time Fourier transform, and calculating the connectivity result of each time point. And otherwise, carrying out Fourier transformation on the whole Epoch signal to obtain an average connectivity analysis result in the whole time range.
The SEEG signals go deep into the brain region, information of the deep brain region can be obtained, and the correlation/coherence between the electrodes can be obtained by carrying out connectivity analysis on the electrodes of the SEEG, so that the correlation/coherence between the brain regions can be obtained. The present embodiment calculates connectivity between the individual electrodes using the MNE library and the spectral_connectivity library. And provides seven connectivity analysis methods: coherence, imaginary Coherence, phase-Lag Index, weighted PLI, unbiased Squared PLI, unbiased Squared WPLI, and Phase Log Value, each of which may employ Multitaper or Morlet for frequency domain information computation. The calculation formula of each method is as follows:
Coherence：
Imaginary Coherence:
Phase-Lag Index:
C＝|E(sign(Im(S xy )))|
Weighted Phase-Lag Index:
Unbiased Squared PLI：
Unbiased Squared WPLI：
Phase Log Value：
C＝|E(S xy /|S xy |)|
where C represents frequency domain information, S represents the cross spectral density of channel x and channel y, and E represents the expectation. And calculating connectivity of the selected channel of each data segment in the Epoch signal by the connectivity analysis method, and finally averaging the results of all the data segments to obtain a final result.
In one embodiment, the step S103 includes:
creating a canvas for displaying the three-dimensional model of the brain;
when a data file of a brain template is obtained, constructing a brain template object according to the data file, and displaying a construction result on the canvas;
acquiring electrode coordinate files to be tested, grouping electrodes according to the electrode coordinate files to acquire electrode axis information of each electrode, and performing color rendering on each electrode according to motor axis information;
acquiring the brain region of interest selected by the user and coordinate information corresponding to the brain region of interest, and performing color rendering on the brain region of interest according to the coordinate information.
In this embodiment, in conjunction with fig. 4, five common brain templates of B1, B2, B3, insulated and white are provided, considering that different brain templates may be required for display for different study contents. Firstly, automatically creating a VisBraincanvas canvas to enable the canvas to display various three-dimensional models, then acquiring the name of a currently selected brain template, reading a corresponding data file, constructing BrainObj objects, and adding the Braincanvas to the VisBraincanvas for display. After the tested electrode coordinate file (for example, the. Txt format) is obtained, the electrodes are grouped according to the electrode names, the electrode axis information of each electrode is obtained, and then the color provided by Matplotlib is used for rendering based on the motor axis information, so that the effect of distinguishing the electrodes is achieved. Meanwhile, after the ROI brain region selected by the user (namely the brain region of interest) is acquired, the coordinate information corresponding to the corresponding brain region is read from the nii.gz file, and the data points corresponding to the acquired coordinate information are rendered by adopting the same color so as to display the brain region. Of course, if there are multiple brain regions of interest, a random sampling method may be used to randomly extract different colors from the color schemes provided in the Matplotlib library for rendering, thereby achieving the effect of distinguishing brain regions.
The SEEG electrode goes deep into the brain of the tested, and the space position information is the biggest advantage of the SEEG technology for the research of cognitive neuroscience. To facilitate the user's view of the see location information. The embodiment of the application designs a brain three-dimensional model display function, and invokes a VisBrainCanvas, brainObj, sourceObj and RoiObj module of Visbrain to realize brain template, SEEG electrode visualization and ROI visualization.
In an embodiment, the step S103 further includes:
performing brain region division on the brain template by using a preset segmentation template;
and when the electrode axis information of each electrode and/or the coordinate information corresponding to the brain region of interest are acquired, performing color rendering based on the divided brain regions.
According to the embodiment, the brain regions are divided into the brain templates through the preset dividing templates, when each electrode is subjected to color rendering or the brain regions of interest are subjected to color rendering, corresponding rendering can be performed according to the divided brain regions, so that the rendering efficiency and the rendering accuracy can be improved, and the influence on incoherent brain regions in the rendering process is avoided.
This example provides Talairach, AAL and Brodmann three segmentation templates, each of which segments the brain differently. The Talairach segmentation template is developed by Jean Talairach and is the earliest 3D brain template, and the template is a first space coordinate system which defines that the boundary point of an AC-PC line and a central sagittal plane is used as a space coordinate origin, the left-right direction is an X axis, the front-back direction is a Y axis and the up-down direction is a Z axis. AAL, collectively Anatomical Automatic Labeling, is provided by the Montreal Neurological Institute (MNI) institution, and has a total of 116 regions of AAL templates, 90 belonging to the brain and the remaining 26 belonging to the cerebellar structure. The Brodmann segmentation template is a system that divides the cerebral cortex into a series of anatomical regions according to cellular structures, which in the neuroanatomy refers to the organization of neurons observed in stained brain tissue. The Brodmann partition includes 52 regions per hemisphere, some of which are subdivided today, e.g., region 23 is divided into regions 23a and 23b, etc. After the MNI coordinates of the electrodes are imported, the positions of the brain areas where the electrodes are located can be obtained by comparing the coordinate information of each brain area of the segmentation template with the coordinate information of the electrodes.
Fig. 5 is a schematic block diagram of a stereotactic electroencephalogram analysis apparatus 500 according to an embodiment of the present application, the apparatus 500 comprising:
a data preprocessing unit 501, configured to obtain original data of a stereotactic electroencephalogram, perform preprocessing on the original data, and extract an Epoch signal;
the signal analysis unit 502 is configured to perform time-frequency analysis on the Epoch signal by using a multi-window method or a wavelet algorithm, and perform connectivity analysis on an electrode corresponding to the Epoch signal by using a connectivity analysis method;
the first visualization processing unit 503 is configured to combine the time-frequency analysis result and the connectivity analysis result, and visualize the position information of the stereotactic electroencephalogram through a pre-constructed brain three-dimensional model, so as to obtain information of a brain region where the electrode is located.
In one embodiment, as shown in fig. 6, the data preprocessing unit 501 includes:
a second visualization processing unit 601, configured to perform visualization processing on the raw data by using a visualization function;
the resampling processing unit 602 is configured to obtain a target frequency band of a user, and resample the visualized raw data according to N times of the target frequency band;
a filtering unit 603, configured to perform filtering processing on the resampled original data by using an FIR filter or an IIR filter;
the bad guide removing unit 604 is configured to obtain a target channel name marked in the original data by the user, and clear a channel signal corresponding to the target channel name in the filtered original data;
a re-reference processing unit 605, configured to perform re-reference processing on the original data of the clear channel signal by using a re-reference algorithm, so as to obtain target data; wherein the re-reference algorithm comprises a CAR algorithm, a GWR algorithm, an ESR algorithm, a Bipolar algorithm, a Monopolar algorithm and a Laplacian algorithm;
and a signal extraction unit 606, configured to extract an Epoch signal from the target data through a pre-generated event code.
In an embodiment, the signal extraction unit 606 includes:
a time information setting unit for setting corresponding time information for a pre-generated event code;
the data segment intercepting unit is used for intercepting a corresponding target data segment for the target data in combination with the time information;
the time period setting unit is used for setting a baseline time period based on the tested performance in the process of acquiring the stereotactic electroencephalogram signals;
and the data segment processing unit is used for taking the average value of the signal amplitude values in the baseline time period as a reference, and taking the signal obtained by subtracting the reference from the target data segment as the Epoch signal.
In one embodiment, as shown in fig. 7, the signal analysis unit 502 includes:
a signal processing unit 701 for removing noise, protrusion or stimulus signals in the Epoch signals using event-related potentials;
a power spectral density calculating unit 702, configured to calculate a power spectral density of each channel in the Epoch signal by a Welch wavelet method or a multi-window method;
and the adding and averaging unit 703 is configured to perform adding and averaging processing on the power spectrum densities of all channels, so as to obtain a total power spectrum density of the Epoch signal.
In an embodiment, the signal analysis unit 502 further includes:
the frequency domain information calculation unit is used for calculating the frequency domain information of the Epoch signals through a connectivity analysis method;
the connectivity averaging unit is used for averaging all connectivity of the frequency domain information corresponding to the target frequency band selected by the user based on the frequency domain information to obtain average connection strength of the target frequency band;
the Fourier transform unit is used for carrying out sliding window processing by utilizing short-time Fourier transform based on the frequency domain information to obtain a connectivity result of each time point of the Epoch signal; or performing Fourier transform on the Epoch signals to obtain an average connectivity analysis result in the whole time range corresponding to the Epoch signals.
In an embodiment, the first visualization processing unit 503 includes:
the canvas creation unit is used for creating a canvas for displaying the brain three-dimensional model;
the object construction unit is used for constructing a brain template object according to the data file when the data file of the brain template is acquired, and displaying a construction result on the canvas;
the first rendering unit is used for acquiring electrode coordinate files to be tested, grouping electrodes according to the electrode coordinate files to acquire electrode axis information of each electrode, and performing color rendering on each electrode according to the motor axis information;
the second rendering unit is used for acquiring the brain region of interest selected by the user and coordinate information corresponding to the brain region of interest, and performing color rendering on the brain region of interest according to the coordinate information.
In an embodiment, the first visualization processing unit 503 further includes:
the brain region dividing unit is used for dividing brain regions of the brain template by utilizing a preset dividing template;
and the third rendering unit is used for performing color rendering based on the divided brain regions when acquiring the electrode axis information of each electrode and/or the coordinate information corresponding to the brain region of interest.
Since the embodiments of the apparatus portion and the embodiments of the method portion correspond to each other, the embodiments of the apparatus portion are referred to the description of the embodiments of the method portion, and are not repeated herein.
The embodiment of the present application also provides a computer readable storage medium having a computer program stored thereon, which when executed can implement the steps provided in the above embodiment. The storage medium may include: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The embodiment of the application also provides a computer device, which can comprise a memory and a processor, wherein the memory stores a computer program, and the processor can realize the steps provided by the embodiment when calling the computer program in the memory. Of course, the computer device may also include various network interfaces, power supplies, and the like.
In the description, each embodiment is described in a progressive manner, and each embodiment is mainly described by the differences from other embodiments, so that the same similar parts among the embodiments are mutually referred. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section. It should be noted that it will be apparent to those skilled in the art that various modifications and adaptations of the application can be made without departing from the principles of the application and these modifications and adaptations are intended to be within the scope of the application as defined in the following claims.
It should also be noted that in this specification, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Claims (6)
1. A method of stereotactic electroencephalogram analysis, comprising:
acquiring original data of a stereotactic electroencephalogram, preprocessing the original data, and extracting to obtain an Epoch signal;
the method for obtaining the original data of the stereotactic electroencephalogram, preprocessing the original data, extracting to obtain an Epoch signal comprises the following steps:
performing visualization processing on the original data by adopting a visualization function;
acquiring a target frequency band of a user, and resampling the visualized original data according to N times of the target frequency band;
filtering the resampled original data by adopting an FIR filter or an IIR filter;
acquiring a target channel name marked in original data by a user, and clearing a channel signal corresponding to the target channel name in the original data after filtering;
re-referencing the original data of the channel signal by using a re-referencing algorithm to obtain target data; wherein the re-reference algorithm comprises a CAR algorithm, a GWR algorithm, an ESR algorithm, a Bipolar algorithm, a Monopolar algorithm and a Laplacian algorithm;
extracting an Epoch signal from the target data through a pre-generated event code;
performing time-frequency analysis on the Epoch signals by using a multi-window method or a wavelet algorithm, and performing connectivity analysis on electrodes corresponding to the Epoch signals by using a connectivity analysis method;
the time-frequency analysis of the Epoch signals by using a multi-window method or a wavelet algorithm comprises the following steps:
removing noise, highlighting or stimulating signals in the Epoch signals using event-related potentials;
calculating the power spectral density of each channel in the Epoch signal by a Welch wavelet method or a multi-window method;
adding and averaging the power spectrum densities of all channels to obtain the total power spectrum density of the Epoch signal;
the method for carrying out connectivity analysis on the electrode corresponding to the Epoch signal by the connectivity analysis method comprises the following steps:
calculating the frequency domain information of the Epoch signals by a connectivity analysis method;
based on the frequency domain information, averaging all connectivity of the frequency domain information corresponding to the target frequency band selected by the user to obtain average connection strength of the target frequency band;
and carrying out sliding window processing by utilizing short-time Fourier transform based on the frequency domain information to obtain connectivity results of all time points of the Epoch signals; or carrying out Fourier transform on the Epoch signals to obtain an average connectivity analysis result in the whole time range corresponding to the Epoch signals;
combining a time-frequency analysis result and a connectivity analysis result, and visualizing the position information of the stereotactic electroencephalogram through a pre-constructed brain three-dimensional model to obtain the information of a brain region where an electrode is positioned;
the combination of the time-frequency analysis result and the connectivity analysis result visualizes the position information of the stereotactic electroencephalogram through a pre-constructed brain three-dimensional model to obtain the information of the brain region where the electrode is located, and the method comprises the following steps:
creating a canvas for displaying the three-dimensional model of the brain;
when a data file of a brain template is obtained, constructing a brain template object according to the data file, and displaying a construction result on the canvas;
acquiring electrode coordinate files to be tested, grouping electrodes according to the electrode coordinate files to acquire electrode axis information of each electrode, and performing color rendering on each electrode according to the electrode axis information;
acquiring the brain region of interest selected by the user and coordinate information corresponding to the brain region of interest, and performing color rendering on the brain region of interest according to the coordinate information.
2. The stereotactic electroencephalogram analysis method according to claim 1, wherein the extracting of an Epoch signal to the target data by a pre-generated event code comprises:
setting corresponding time information for a pre-generated event code;
intercepting a corresponding target data segment for the target data by combining the time information;
setting a baseline time period based on the tested performance in the process of acquiring the stereotactic electroencephalogram signals;
and taking the mean value of the signal amplitude values in the baseline time period as a reference, and taking the signal obtained by subtracting the reference from the target data period as the Epoch signal.
3. The method according to claim 1, wherein the combining of the time-frequency analysis result and the connectivity analysis result performs a visualization process on the position information of the stereotactic electroencephalogram through a pre-constructed brain three-dimensional model, further comprising:
performing brain region division on the brain template by using a preset segmentation template;
and when the electrode axis information of each electrode and/or the coordinate information corresponding to the brain region of interest are acquired, performing color rendering based on the divided brain regions.
4. A stereotactic electroencephalogram analysis apparatus comprising:
the data preprocessing unit is used for acquiring original data of the stereotactic electroencephalogram, preprocessing the original data and extracting to obtain an Epoch signal;
the data preprocessing unit includes:
the second visual processing unit is used for performing visual processing on the original data by adopting a visual function;
the resampling processing unit is used for acquiring a target frequency band of a user and resampling the visualized original data according to N times of the target frequency band;
the filtering processing unit is used for filtering the resampled original data by adopting an FIR filter or an IIR filter;
the bad guide removing unit is used for obtaining a target channel name marked in the original data by a user and removing a channel signal corresponding to the target channel name in the filtered original data;
the re-reference processing unit is used for re-referencing the original data of the channel signal by utilizing a re-reference algorithm to obtain target data; wherein the re-reference algorithm comprises a CAR algorithm, a GWR algorithm, an ESR algorithm, a Bipolar algorithm, a Monopolar algorithm and a Laplacian algorithm;
a signal extraction unit for extracting an Epoch signal from the target data by a pre-generated event code;
the signal analysis unit is used for carrying out time-frequency analysis on the Epoch signals by utilizing a multi-window method or a wavelet algorithm and carrying out connectivity analysis on electrodes corresponding to the Epoch signals by utilizing a connectivity analysis method;
the signal analysis unit includes:
a signal processing unit for removing noise, prominence or stimulus signals in the Epoch signals using event-related potentials;
the power spectral density calculation unit is used for calculating the power spectral density of each channel in the Epoch signal through a Welch wavelet method or a multi-window method;
the adding average unit is used for carrying out adding average processing on the power spectrum densities of all channels to obtain the total power spectrum density of the Epoch signal;
the signal analysis unit further includes:
the frequency domain information calculation unit is used for calculating the frequency domain information of the Epoch signals through a connectivity analysis method;
the connectivity averaging unit is used for averaging all connectivity of the frequency domain information corresponding to the target frequency band selected by the user based on the frequency domain information to obtain average connection strength of the target frequency band;
the Fourier transform unit is used for carrying out sliding window processing by utilizing short-time Fourier transform based on the frequency domain information to obtain a connectivity result of each time point of the Epoch signal; or carrying out Fourier transform on the Epoch signals to obtain an average connectivity analysis result in the whole time range corresponding to the Epoch signals;
the first visualization processing unit is used for combining the time-frequency analysis result and the connectivity analysis result, and visualizing the position information of the stereotactic electroencephalogram through a pre-constructed brain three-dimensional model so as to acquire the information of a brain region where the electrode is positioned;
the first visualization processing unit includes:
the canvas creation unit is used for creating a canvas for displaying the brain three-dimensional model;
the object construction unit is used for constructing a brain template object according to the data file when the data file of the brain template is acquired, and displaying a construction result on the canvas;
the first rendering unit is used for acquiring electrode coordinate files to be tested, grouping electrodes according to the electrode coordinate files to acquire electrode axis information of each electrode, and performing color rendering on each electrode according to the electrode axis information;
the second rendering unit is used for acquiring the brain region of interest selected by the user and coordinate information corresponding to the brain region of interest, and performing color rendering on the brain region of interest according to the coordinate information.
5. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the stereotactic electroencephalogram analysis method according to any one of claims 1 to 3 when the computer program is executed by the processor.
6. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a computer program which, when executed by a processor, implements the stereotactic electroencephalogram analysis method as claimed in any one of claims 1 to 3.
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