CN110833407B - MATLAB-based cortical-intercortical evoked potential data processing method - Google Patents
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Abstract
The invention relates to a cortical-intercortical evoked potential data processing method based on MATLAB, which comprises S1, acquiring CCEP data; s2, previewing data, and checking whether CCEP data is correct; s3, dividing CCEP data, and calculating the average superposition result of the whole CCEP data; s4, calculating average leads; s5, converting into a bipolar lead; s6, correcting the base line; s7, generating waveforms for the selected stimulation number and/or electrode number. The invention can accurately and automatically identify the stimulation starting time, can realize batch average superposition, has the functions of separating and sequencing, lead conversion, baseline correction, batch or one-by-one waveform display and waveform report generation, and has high data processing efficiency.
Description
Technical Field
The invention relates to a MATLAB-based cortical-intercortical evoked potential data processing method, a system, a computer program and a computer readable storage medium, relating to the technical field of epileptic surgery data processing.
Background
In recent years, with the development of functional neuroscience, evoked potential technology has been widely developed in the field of epilepsy surgery. Cortical-Cortical Evoked Potential (Cortical-Cortical Evoked Potential CCEP) is an electrophysiological technique based on intracranial electrodes, which refers to Evoked potentials recorded in other brain areas by stimulating local brain areas. The method is provided by Riki Maustmoto of Japan scholars in 2004 for the earliest time, can assist in treating brain diseases such as epilepsy, and has good clinical availability and high scientific research value.
The CCEP electrical stimulation consists of more than 50 times of single-pulse square wave electrical stimulation, data needs to be averagely superposed after the stimulation is completed, a special method is not available at present, the data can be realized by applying the self-carried function of electroencephalogram acquisition equipment, but the method has the obvious defects that the stimulation starting moment needs to be manually marked, the operation steps are complicated, the efficiency is low, the workload is large, the waveform result is limited by the internal function of software, and more complex analysis work cannot be carried out.
Disclosure of Invention
In view of the above problems, the present invention aims to provide a method for processing cortical-cortical evoked potential data based on MATLAB, which automatically marks the stimulation start time, and has simple operation steps and high efficiency.
In order to achieve the purpose, the invention adopts the following technical scheme:
in a first aspect of the embodiments of the present invention, a method for processing cortical-intercortical evoked potential data based on MATLAB is provided, which includes:
s1, acquiring CCEP data;
s2, previewing data, and checking whether CCEP data is correct;
s3, dividing CCEP data, and calculating the average superposition result of the whole CCEP data;
s4, calculating average leads;
s5, converting into a bipolar lead;
s6, correcting the base line;
and S7, generating waveforms of the selected stimulation numbers and/or electrode numbers.
Furthermore, the CCEP data is a corresponding file derived from a CCEP data file collected by an electroencephalogram collecting device, and the format of the derived file comprises a TRC file and a corresponding edf file.
Further, the specific process of S2 is as follows:
s21, inputting corresponding stimulation parameters, wherein the stimulation parameters comprise the number of electrodes, the interval time before positive stimulation, the interval time before negative stimulation, the stimulation frequency and the number of stimulation pulses;
s22, previewing the data, specifically: the data should be a CCEP stimulation paradigm sequence, the ordered electrical stimulation will see corresponding ordered occurrence artifacts in the preview waveform, determine whether the importing process is correct, and if no due stimulation artifacts appear in the preview waveform, the data should be removed.
Further, the specific process of S3 is as follows:
s31, selecting a first pseudo error of the first stimulation based on a data cursor tool of figure in Matlab to obtain a time coordinate value of a first stimulation point, assigning the time coordinate value to be k, simultaneously setting the first half second of k as a first check point g, and calculating the stimulation time after the first check point by taking the check point as a reference;
s32, generating a stimulation check point and intercepting data
Calculating the time used by each section of stimulation by calculating the set stimulation parameters, wherein the later check point is the previous check point plus the section of time; all the check points are required to be positioned before the first stimulation artifact of each section of stimulation, the set time is not exceeded, the check points are divided into small sections at set time intervals from the check points, and each section is a one-dimensional time sequence;
s33, automatically identifying the stimulation starting point, and calculating the difference between the stimulation starting point and the checking point
Deriving the time series obtained in step S32, and checking a first time point at which the derivative value exceeds the upper limit of the stimulation artifact within a set time period from the checkpoint, the first time point being the difference between the stimulation start point and the checkpoint;
s34, reading edf data, and carrying out average superposition
Segmenting each stimulation according to the obtained stimulation initial point information, wherein one section of stimulation comprises n times of single pulse stimulation, segmenting each section of stimulation into segments of n times of single stimulation, and adding amplitude values of n times of stimulation at the same time and dividing the sum by n to obtain average superposed amplitude;
s35, automatically recognizing stimulating electrode
Carrying out square summation on the time sequences after average superposition, wherein the two channels with the largest result are the stimulation electrodes;
s36, repeating the steps to enable all data to be evenly superposed to obtain CDM.ERP data;
further, the specific process of S4 is as follows:
s41, importing CDM.ERP data;
s42, inputting the beginning and the end of each segment of data of CCEP, and performing segmentation sequencing and recombination through matrix separation and combination functions of MATLAB self-carrying, wherein the sequenced data are saved as CDM.DIERPst;
s43, loading CDM.DIERPst data, calculating average leads, and storing the data as CDM.ERPrwavgst;
further, the specific process of S6 is as follows:
loading CDM.ERPrwavgst data and calculating a baseline, correcting the baseline, intercepting the time sequence by adopting a matrix separation function of matlab, subtracting the average value of the baseline of each time sequence in CDM.ERPrwavgst to obtain the corrected time sequence, and storing the time sequence as a CDM.ERPblrwavgst data matrix.
Further, the specific process of S7 is as follows:
s71, loading CDM.ERPblrwavgst data, inputting a stimulation number to be called, and outputting the waveform of each electrode during the stimulation by applying a plot function in MATLAB;
s72, loading cdm.
In a second aspect of the embodiments of the present invention, there is provided a MATLAB-based system for processing cortical-to-cortical evoked potential data, the system including:
the data acquisition module is used for acquiring CCEP data;
the data preview module is used for previewing data and checking whether CCEP data are correct or not;
the average superposition calculation module is used for dividing the CCEP data and calculating the average superposition result of the whole CCEP data;
a lead calculation module for calculating an average lead and a bipolar lead;
the baseline correction module is used for performing baseline correction on the data after the lead calculation;
and the waveform map generation module is used for generating waveforms of the selected stimulation numbers and the electrode numbers.
In a third aspect of the embodiments of the present invention, a computer program is provided, which includes computer program instructions, where the program instructions, when executed by a processor, are configured to implement the steps corresponding to the MATLAB-based method for processing cortical-inter-cortical evoked potential data.
In a fourth aspect of the embodiments of the present invention, a computer-readable storage medium is provided, where computer program instructions are stored on the computer-readable storage medium, where the program instructions, when executed by a processor, are configured to implement the steps corresponding to the MATLAB-based method for processing cortical-intercortical evoked potential data.
Due to the adoption of the technical scheme, the invention has the following advantages: the invention can accurately and automatically identify the stimulation starting time, can realize batch average superposition, has the functions of separating and sequencing, lead conversion, baseline correction, batch or one-by-one waveform display and waveform report generation, and has high data processing efficiency.
Drawings
FIG. 1 is a schematic diagram illustrating the principle of the method for processing the data of the cortical-intercortical evoked potential in this embodiment 1;
FIG. 2 is a schematic diagram of the average overlapping process of CCEP data in this embodiment 1;
FIG. 3 is a schematic diagram illustrating a flow of sorting data according to the embodiment 1;
FIG. 4 is a schematic diagram of the lead calculation flow of this embodiment 1;
fig. 5 is a schematic diagram of a baseline correction process in the embodiment 1;
fig. 6 is a schematic diagram of a waveform generation flow of this embodiment 1.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
Example 1:
as shown in fig. 1 to 6, the method for processing cortical-cortical evoked potential data based on MATLAB provided in this embodiment includes the following steps:
s1, acquiring CCEP data, and inputting relevant clinical information of patients
Specifically, the CCEP Data of this embodiment may be derived from a CCEP Data file collected by an electroencephalogram collection device of Micromed italy, and the Format of the derived file includes one TRC file and one corresponding edf file, where the TRC file is channel signal Data generated by an electroencephalogram collection system, the edf file is in different formats (European Data Format) of the same TRC file, and the two formats are used as raw Data in this embodiment, and are processed to place the derived CCEP Data file in an appropriate path.
Specifically, the patient-related clinical information includes: name, Age, Sex of Sex, Casenb case number, Imagenb image number, EEGnb electroencephalogram number, Histroy medical history time, Type electrode Type, Side Type, Nbchan electrode number and Srate sampling rate.
S2, previewing data, and checking whether CCEP data is correct, wherein the specific process is as follows:
and S21, inputting stimulation parameters implanted by the patient, wherein the stimulation parameters comprise the number of electrodes, the interval time before positive stimulation, the interval time before negative stimulation, the stimulation frequency, the number of stimulation pulses and the like, and the stimulation parameters are determined by the parameters used in recording and are parameters describing a stimulation implementation paradigm.
S22, previewing data
Specifically, the data should be a sequence of CCEP stimulation patterns, for example, single-pulse electrical stimulation with a stimulation pattern of 1HZ, so that the ordered electrical stimulation will see corresponding ordered artifacts in the preview waveform, and help the operator know whether the importing process is correct, for example, if no due stimulation artifacts appear in the preview waveform, the data should be rejected.
S3, intercepting the data, and calculating the average superposition result of the whole CCEP data, wherein the specific calculation process is as follows:
s31, selecting the first artifact of the first stimulation by applying a data cursor tool of figure in Matlab, obtaining the time coordinate value of the first stimulation point, assigning the time coordinate value to k, setting the first half second of k as the first check point g, and calculating the later stimulation time by taking the check point as the reference.
S32, generating a stimulation check point and intercepting data
By calculating the stimulation parameters in S21, the time taken for each stimulation session can be calculated, the latter checkpoint being the previous checkpoint + the session. All checkpoints are required to be no more than 1 second before the first stimulation artifact per segment of stimulation. Starting at the checkpoint, every 1 second thereafter, a small segment is divided, and if the number of stimulation pulses is 26, 26 segments are intercepted. Each segment is a one-dimensional time sequence, and the 26 segments of the time sequence are listed by time, and averaged to obtain an averaged time sequence. For example: for a total of 64 stimulations, 64 different time sequences were obtained.
And S33, automatically identifying the stimulation starting point and calculating the difference between the stimulation starting point and the check point.
The derivative of each sequence obtained in S32 is checked for the first time when the derivative value exceeds the upper limit of the stimulation artifact within 1 second from the check point, which is the difference between the stimulation start point and the check point. For example, 64 segments of stimulation will get 64 differences, with the stimulation sequence number as the x-axis and the differences as the y-axis, and draw a line graph to return to the user interface to help determine whether the automatically identified starting point is accurate. When the line graph is around 512, a steady or slow change indicates that the start point identification is accurate (512 is half a second when the sampling rate is 1024, i.e., the position of g in S31). If 100 appears, the prompting threshold is too large (100 is a set error reporting value which is naturally rare and is set as 100), and if 1 or one digit appears, the prompting threshold is too small, and the threshold needs to be properly adjusted to obtain a satisfactory result.
S34, reading edf data and performing average superposition
Edf data are in the same format as the TRC file, contain filtering and lead information and are generated by a MicroMed electroencephalogram acquisition system. And segmenting each stimulation by using the stimulation starting point information obtained in the previous step, wherein one segment of stimulation comprises 52 times of single-pulse stimulation, and each segment of stimulation is segmented into segments of 52 times of single stimulation, and the length of each segment of stimulation is 1024(1 second at a 1024Hz sampling rate). The amplitude values at the same time point for 52 times of stimulation were added and divided by 52 (the number of stimulation) to obtain the average amplitude after superposition.
S35, automatically identifying the stimulation electrodes in order to know which electrode is stimulated for subsequent analysis.
The stimulation electrode has obvious difference with other electrodes in energy because of huge pseudo-difference generated by stimulation, and two channels with the largest result are taken as the stimulation electrodes by carrying out square summation on the averaged time sequence. The stimulation electrodes are also returned in a line graph form, when the stimulation electrodes are in an ordered parallel double line form, the stimulation electrodes are normal, and when abnormal fluctuation occurs, manual modification can be carried out through a workspace module in MATLAB according to the actual situation of the stimulation records.
And S36, repeating the steps until the patient data are all evenly superposed, and generating a CDM.
S4, calculating average leads, and the specific process is as follows:
and S41, importing CDM.
S42, inputting the start and end of each piece of data, sorting by segments and recombining
Each piece of data refers to each TRC and edf data, because the stimulation sequence is not fixed and possibly out of order when electroencephalogram is actually acquired, and the data usually consists of a plurality of TRCs and edf data, the data needs to be sorted according to the actual acquisition condition and finally combined into a whole data file, the data is separated and combined through a matrix of MATLAB, the data needs to be input from the first electrode to the second electrode when the acquisition is needed, if the data is sequentially acquired, the data does not need to be sorted, and the sorted data is saved as CDM. The stimulation list is also modified and saved as cdm.
S43, loading cdm. dierpst data, calculating average leads, wherein the calculation principle of the average leads is as follows:
for example, if the patient had 127 electrode recordings, there were 127 time series, the sequence of two stimulation electrodes was first rejected, and the abnormal timing was further rejected by 5% (rounded, (127-2) × 5% ≈ 6, and the first 6 with the highest energy were further rejected for 6 channels). And averaging the rest time sequences to obtain a time sequence. This time series is the average electrode, which is subtracted from all 127 time series to obtain the average lead data. The timing at this point begins with the stimulation time and the subsequent processing will involve the baseline, since this average superimposed segment is a cyclic segment, so the 0.2s portion after the timing is truncated and shifted to the front of the sequence. The matrix separation combinatorial function of MATLAB is also used. Erprwavgst finally saved as cdm. erprwavgst means overwritten, averaged leads and sorted.
S5 conversion to bipolar leads
Bipolar leads display the potential difference between two adjacent electrodes and are more specific than unipolar leads. Based on bipolar lead parameters and according to the electrode serial number, subtracting the amplitude of the n channel from the signal of the n +1 channel to obtain a bipolar lead waveform between the two leads.
S6: baseline correction, specifically:
loading CDM.ERPrwavgst data, calculating a base line, and correcting the base line, wherein the base line is a signal 200ms-20ms before the stimulation time, and intercepting the time sequence by adopting a matrix separation function of matlab, and the time sequence is a time sequence of 184 sampling points under a 1024 sampling rate. The array is averaged and the variance, standard deviation, calculated. All CCEP time sequences have their own baselines, so the base line standard deviation matrix in the example is; a two-dimensional matrix of 127 electrodes in 126 stimulations. And subtracting the average value of the self base line from each time sequence in the CDM.ERPrwavgst to obtain a corrected time sequence, and storing the corrected time sequence as a CDM.ERPblrwavgst data matrix.
S7, storing CDM data structure matrix _ CDM
S8: generating a waveform report, wherein the specific process comprises the following steps:
s71, loading cdm. erpblrwavgst data, inputting the stimulation number to be called (looking at the result generated by which stimulation), and applying plot function in MATLAB to output the waveform of each electrode at this stimulation, the result will be used for clinical meaning analysis by the clinician.
S72, load cdm. erpblrwavgst data, input the number of stimulation that is desired to be called, and the number of recording electrode that is desired to be called (see which stimulation produces the result at which electrode), apply plot function in MATLAB to output a single waveform, and the result will be used for clinician analysis of clinical significance.
S73, calculation, analysis and drawing
Specifically, a section with one point is preset before analysis, an MATLAB function is used for automatically searching a wave crest or a wave trough in the section, the information is recorded and stored, a data table is generated, and a plot function is used for drawing a waveform, so that a clinician can conveniently interpret the waveform.
Example 2:
the present embodiment also provides a MATLAB-based cortical-intercortical evoked potential data processing system, including:
the data acquisition module is used for acquiring CCEP data;
the data preview module is used for previewing data and checking whether CCEP data are correct or not;
the average superposition calculation module is used for dividing the CCEP data and calculating the average superposition result of the whole CCEP data;
a lead calculation module for calculating an average lead and a bipolar lead;
the baseline correction module is used for performing baseline correction on the data after the lead calculation;
and the waveform diagram generation module is used for batch screenshot and generating waveforms of the selected stimulation numbers and the electrode numbers.
Example 3:
the present embodiments also provide a computer program comprising computer program instructions, wherein the program instructions, when executed by a processor, are adapted to implement the corresponding steps of the MATLAB-based cortical-to-cortical evoked potential data processing method.
Example 4:
the present embodiments also provide a computer readable storage medium having stored thereon computer program instructions, wherein the program instructions, when executed by a processor, are adapted to implement the corresponding steps of the MATLAB-based cortical-intercortical evoked potential data processing method.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the scope of protection thereof, and although the present application is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: numerous variations, modifications, and equivalents will occur to those skilled in the art upon reading the present application and are within the scope of the claims appended hereto.
Claims (9)
1. A method for processing cortical-intercortical evoked potential data based on MATLAB, comprising:
s1, acquiring CCEP data;
s2, previewing data, and checking whether CCEP data is correct;
s3, dividing CCEP data, and calculating the average superposition result of the whole CCEP data, wherein the specific process is as follows:
s31, selecting a first pseudo error of the first stimulation based on a data cursor tool of figure in Matlab to obtain a time coordinate value of a first stimulation point, assigning the time coordinate value to be k, simultaneously setting the first half second of k as a first check point g, and calculating the stimulation time after the first check point by taking the check point as a reference;
s32, generating a stimulation check point, and intercepting data:
calculating the time used by each section of stimulation by calculating the set stimulation parameters, wherein the later check point is the previous check point plus the section of time; all the check points are required to be positioned before the first stimulation artifact of each section of stimulation, the set time is not exceeded, the check points are divided into small sections at set time intervals from the check points, and each section is a one-dimensional time sequence;
s33, automatically identifying a stimulation starting point, and calculating the difference between the stimulation starting point and the check point:
deriving the time series obtained in step S32, and checking a first time point at which the derivative value exceeds the upper limit of the stimulation artifact within a set time period from the checkpoint, the first time point being the difference between the stimulation start point and the checkpoint;
s34, reading edf data, and performing average superposition:
segmenting each stimulation according to the obtained stimulation initial point information, wherein one section of stimulation comprises n times of single pulse stimulation, segmenting each section of stimulation into segments of n times of single stimulation, and adding amplitude values of n times of stimulation at the same time and dividing the sum by n to obtain average superposed amplitude;
s35, automatically identifying the stimulation electrode:
carrying out square summation on the time sequences after average superposition, wherein the two channels with the largest result are the stimulation electrodes;
s36, repeating the steps to enable all data to be evenly superposed to obtain CDM.ERP data;
s4, calculating average leads;
s5, converting into a bipolar lead;
s6, correcting the base line;
and S7, generating waveforms of the selected stimulation numbers and/or electrode numbers.
2. The method of claim 1, wherein the CCEP data is derived from a CCEP data file collected by an electroencephalography collection device, and the derived file format comprises a TRC file and a corresponding edf file.
3. The method according to claim 1, wherein the specific process of S2 is as follows:
s21, inputting corresponding stimulation parameters, wherein the stimulation parameters comprise the number of electrodes, the interval time before positive stimulation, the interval time before negative stimulation, the stimulation frequency and the number of stimulation pulses;
s22, previewing the data, specifically: the data should be a CCEP stimulation paradigm sequence, the ordered electrical stimulation will see corresponding ordered occurrence of artifacts in the preview waveform, determine whether the importing process is correct, and if no due stimulation artifacts appear in the preview waveform, the segment of data should be removed.
4. The method according to claim 1, wherein the specific process of S4 is as follows:
s41, importing CDM.ERP data;
s42, inputting the beginning and the end of each segment of data of CCEP, and performing segmentation sequencing and recombination through matrix separation and combination functions of MATLAB self-carrying, wherein the sequenced data are saved as CDM.DIERPst;
s43, load cdm.dierpst data, calculate average leads, and save the data as cdm.erprwavgst.
5. The method according to claim 1, wherein the specific process of S6 is as follows:
loading CDM.ERPrwavgst data and calculating a baseline, correcting the baseline, intercepting the time sequence by adopting a matrix separation function of matlab, subtracting the average value of the baseline of each time sequence in CDM.ERPrwavgst to obtain the corrected time sequence, and storing the time sequence as a CDM.ERPblrwavgst data matrix.
6. The method according to claim 1, wherein the specific process of S7 is:
s71, loading CDM.ERPblrwavgst data, inputting a stimulation number to be called, and outputting the waveform of each electrode during the stimulation by applying a plot function in MATLAB;
s72, loading cdm.
7. A MATLAB-based cortical-intercortical evoked potential data processing system, comprising:
the data acquisition module is used for acquiring CCEP data;
the data preview module is used for previewing data and checking whether CCEP data are correct or not;
the average superposition calculation module is used for dividing the CCEP data and calculating the average superposition result of the whole CCEP data, and the specific process is as follows:
1) selecting a first pseudo difference of first stimulation based on a data cursor tool of figure in Matlab to obtain a time coordinate value of a first stimulation point, assigning the time coordinate value to be k, simultaneously setting the first half second of k as a first check point g, and calculating the stimulation time after the first check point by taking the check point as a reference;
2) generating a stimulation check point, and carrying out data interception:
calculating the time used by each section of stimulation by calculating the set stimulation parameters, wherein the later check point is the previous check point plus the section of time; all check points are required to be positioned before the first stimulation artifact of each section of stimulation, the set time is not exceeded, the check points are divided into small sections at set time intervals from the check points, and each section is a one-dimensional time sequence;
3) automatically identifying a stimulation starting point, and calculating the difference between the stimulation starting point and the check point:
deriving each time sequence obtained in the step 2), and checking the first moment when a derivative function value exceeds the upper limit of the stimulation artifact within a set time from the check point to the back, wherein the moment is the difference between the stimulation starting point and the check point;
4) and reading edf data, and performing average superposition:
segmenting each stimulation according to the obtained stimulation initial point information, wherein one section of stimulation comprises n times of single pulse stimulation, segmenting each section of stimulation into segments of n times of single stimulation, and adding amplitude values of n times of stimulation at the same time and dividing the sum by n to obtain average superposed amplitude;
5) automatically identifying the stimulation electrode:
carrying out square summation on the time sequences after average superposition, wherein the two channels with the largest result are the stimulation electrodes;
6) repeating the steps to enable all the data to be evenly superposed to obtain CDM.
A lead calculation module for calculating an average lead and a bipolar lead;
the baseline correction module is used for performing baseline correction on the data after the lead calculation;
and the waveform diagram generation module is used for batch screenshot and generating waveforms of the selected stimulation numbers and the electrode numbers.
8. A computer device comprising computer program instructions, wherein said program instructions, when executed by a processor, are adapted to carry out the steps corresponding to the MATLAB-based cortical-intercortical evoked potential data processing method according to any one of claims 1-6.
9. A computer readable storage medium having stored thereon computer program instructions, wherein the program instructions, when executed by a processor, are adapted to carry out the steps corresponding to the MATLAB-based cortical-intercortical evoked potential data processing method according to any one of claims 1-6.
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