CN111700610B - Method, device and system for analyzing electroencephalogram outbreak suppression mode and storage medium thereof - Google Patents
Method, device and system for analyzing electroencephalogram outbreak suppression mode and storage medium thereof Download PDFInfo
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Abstract
The invention discloses an analysis method, a device and a system of an electroencephalogram outbreak suppression mode and a storage medium thereof, wherein the method comprises the following steps: acquiring an electroencephalogram signal; carrying out segmentation processing on the electroencephalogram signals through a statistical variable point algorithm to obtain segmented electroencephalogram signals; calculating the absolute amplitude mean value of the segmented computer signals according to the segmented electroencephalogram signals; and analyzing the segmented electroencephalogram signals according to the absolute amplitude mean value to obtain an electroencephalogram inhibition interval and/or a corresponding electroencephalogram outbreak interval. The statistical variable point algorithm is used for carrying out segmentation processing, and the electroencephalogram suppression interval section and/or the corresponding electroencephalogram outbreak interval section are/is obtained through absolute amplitude mean value analysis of segmented computer signal calculation, so that the technical problem that a unified analysis method is lacked in electroencephalogram signal outbreak suppression mode analysis in the prior art is solved.
Description
Technical Field
The invention relates to the field of electroencephalogram signal processing, in particular to an electroencephalogram outbreak suppression mode analysis method, device and system and a storage medium thereof.
Background
At present, in the field of electroencephalogram signal processing, analysis and processing aiming at a burst suppression mode are always a big problem in the field, the burst suppression mode is a burst electroencephalogram brain electrical activity characteristic which appears after electroencephalograms with extremely low amplitude or equipotential appear, and the burst suppression mode aims to divide electroencephalogram signals under continuous time into an electroencephalogram suppression interval section and an electroencephalogram burst interval section. The outbreak inhibition mode is originally discovered on organisms anesthetized by the isopentbarbital in 1949, and symptoms such as anesthesia, traumatic brain injury, hypothermia, hypoxemia and the like all show the outbreak inhibition phenomenon of electroencephalogram from then on, for example, the electroencephalogram signal frequency of a brain electrical explosion zone section and the time of the brain electrical inhibition zone section in anesthesia depth monitoring, and the anesthesia concentration have an important relationship, wherein existing researches show that the longer the time of a single electroencephalogram inhibition zone section of the electroencephalogram is, the deeper the anesthesia depth is shown, and the evolution of the outbreak inhibition mode also provides important prognosis information. In addition, in addition to the pathological conditions mentioned above, it will also have a certain reference effect on other brain activities.
However, in the prior art, the burst suppression mode is analyzed by electroencephalograph experts through visual analysis and judgment of electroencephalograms, or a fixed threshold is set to determine the burst suppression mode of electroencephalograms. However, the method is time-consuming and labor-consuming through intuitive analysis and judgment of electroencephalograph experts, and given results may be different due to inconsistent judgment standards of each expert. Therefore, a set of standard, reliable and quantifiable analysis method is lacked in the analysis of the explosion suppression mode of the electroencephalogram signal.
Therefore, the inventor finds that in the prior art, due to the lack of a unified standard and a quantifiable analysis method, the technical problem that explosion suppression mode analysis of the electroencephalogram signals is inaccurate exists.
Disclosure of Invention
The application provides an analysis method of an electroencephalogram outbreak suppression mode, which comprises the following steps:
acquiring an electroencephalogram signal;
carrying out segmentation processing on the electroencephalogram signals through a statistical variable point algorithm to obtain segmented electroencephalogram signals;
calculating the absolute amplitude mean value of the segmented computer signals according to the segmented electroencephalogram signals;
and analyzing the segmented electroencephalogram signals according to the absolute amplitude mean value to obtain an electroencephalogram inhibition interval section and/or a corresponding electroencephalogram outbreak interval section.
In an embodiment, the obtaining a segmented electroencephalogram signal by performing segmentation processing according to the electroencephalogram signal through a statistical variable point algorithm includes:
filtering the EEG signal at 3.5-30Hz to obtain the EEG signal without artifact interference;
and carrying out segmentation processing on the electroencephalogram signal without artifact interference through a statistical variable point algorithm to obtain the segmented electroencephalogram signal.
In an embodiment, the obtaining of the segmented electroencephalogram signal by performing the segmentation processing according to the electroencephalogram signal through a statistical variable point algorithm includes:
segmenting the electroencephalogram signal through a statistical variable point algorithm to obtain a segmented electroencephalogram signal to be detected;
calculating an absolute amplitude mean value of the segmented electroencephalogram signals to be detected according to the segmented electroencephalogram signals to be detected;
judging whether the absolute amplitude mean value of the adjacent segmented electroencephalograms to be detected meets a preset combination condition or not,
and if the absolute amplitude mean value of the adjacent segmented electroencephalograms to be detected accords with the preset combination condition, respectively taking the adjacent segmented electroencephalograms to be detected as segmented electroencephalograms.
In an embodiment, the determining whether the absolute amplitude mean value of the adjacent segmented electroencephalograms to be detected meets a predetermined merging condition further includes:
and if the absolute amplitude mean value of the adjacent segmented electroencephalograms to be detected does not accord with the preset combination condition, combining the adjacent segmented electroencephalograms to be detected to be used as segmented electroencephalograms.
In an embodiment, after the step of obtaining the electroencephalogram suppression section and/or the corresponding electroencephalogram explosion section through the segmented electroencephalogram signal according to the absolute amplitude mean value, the method further includes:
and when the electroencephalogram inhibition interval and the corresponding electroencephalogram outbreak interval are obtained, calculating to obtain a single outbreak time ratio according to the time of the electroencephalogram inhibition interval and the corresponding electroencephalogram outbreak interval.
The present application also provides an analysis apparatus of an electroencephalogram explosion suppression pattern, the apparatus including:
the acquisition module is used for acquiring the electroencephalogram signals;
the segmentation module is used for carrying out segmentation processing on the electroencephalogram signal through a statistical variable point algorithm to obtain a segmented electroencephalogram signal; the device is also used for calculating the absolute amplitude mean value of the segmented computer signals according to the segmented electroencephalogram signals;
and the analysis module is used for analyzing the segmented electroencephalogram signals according to the absolute amplitude mean value to obtain an electroencephalogram inhibition interval and/or a corresponding electroencephalogram outbreak interval.
In one embodiment, the apparatus further comprises:
the filtering module is used for filtering the electroencephalogram signals at 3.5-30Hz to obtain the electroencephalogram signals without artifact interference;
the segmentation module is also used for carrying out segmentation processing on the electroencephalogram signal without artifact interference through a statistical variable point algorithm to obtain the segmented electroencephalogram signal. In an embodiment, the calculating module is further configured to calculate a single-burst time ratio according to the time of the electroencephalogram suppression interval section and the corresponding electroencephalogram burst interval section when the electroencephalogram suppression interval section and the corresponding electroencephalogram burst interval section are obtained.
In one embodiment, the present application further provides a system for analyzing a brain burst suppression pattern, the system comprising:
the acquisition device is used for acquiring an electroencephalogram signal;
the processor unit is used for carrying out segmentation processing on the electroencephalogram signals through a statistical variable point algorithm to obtain segmented electroencephalogram signals; calculating the absolute amplitude mean value of the segmented computer signals according to the segmented electroencephalogram signals; obtaining a electroencephalogram inhibition interval section and/or a corresponding electroencephalogram outbreak interval section through the analysis of the segmented electroencephalogram signals according to the absolute amplitude mean value;
and the display is used for displaying the electroencephalogram signal, and the electroencephalogram inhibition interval section and/or the corresponding electroencephalogram outbreak interval section on the image corresponding to the electroencephalogram signal.
In an embodiment, the present application further provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor, implements the steps of the method for analyzing a burst suppression pattern of brain electricity as described.
Based on the above embodiment, the statistical variable point algorithm is used for carrying out segmentation processing, segmenting the electroencephalogram signal to obtain the segmented electroencephalogram signal, and analyzing the segmented electroencephalogram signal according to the absolute amplitude mean value to obtain an electroencephalogram inhibition interval and/or a corresponding electroencephalogram outbreak interval after the absolute amplitude mean value is calculated through the segmented computer signal. The method for uniformly analyzing the electroencephalogram signals is provided, and the technical problem that a uniform analysis method is lacked in the existing electroencephalogram signal explosion suppression mode analysis is solved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive labor.
FIG. 1 is a schematic flow chart of the method for analyzing the burst suppression pattern of electroencephalogram according to the present invention;
FIG. 2 is a schematic diagram of an image of an electroencephalogram signal x (t) function according to the present invention;
FIG. 3 is a schematic diagram of a segmented electroencephalogram image of an electroencephalogram signal segmented by a statistical variable point algorithm according to the present invention;
FIG. 4 is a schematic diagram of a segmented electroencephalogram signal segmented interval after merging according to the present invention;
FIG. 5 is a single burst time ratio image diagram of the electroencephalogram suppression segment and the corresponding electroencephalogram burst segment according to the present invention;
FIG. 6 is a schematic diagram of an analysis apparatus for electroencephalogram burst suppression mode according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The applicant finds that the quantitative analysis of the electroencephalogram explosion suppression mode in the prior art has a huge problem. Firstly, the electroencephalogram outbreak suppression mode is two segments of computer signals in a time range in the electroencephalogram signals, namely an electroencephalogram suppression zone time segment and an electroencephalogram outbreak time segment. The analysis of the brain wave explosion suppression mode is to find the time range and identify the brain wave suppression interval and the brain wave explosion interval by analyzing the time range. It should be noted that the electroencephalogram signals described above can be understood as a function of the corresponding voltage of the electroencephalogram and time.
In 2012, the american clinical neuroscience gave a quantitative description of the burst suppression of the brain electrical activity in the EEG description standard:
firstly, in an explosion suppression event, the duration of a brain wave explosion time period is at least more than 0.5s and can last to 30s at most;
second, for a brain electrical suppression interval, the voltage amplitude must be less than 50% of the background voltage amplitude, and the duration of the brain electrical suppression interval in a burst suppression event should exceed 50% of the entire event.
Although a quantitative criterion for the duration and amplitude variation of the burst suppression pattern is given above, the electroencephalogram amplitude varies widely, different subjects are in the same amplitude of the electroencephalogram, but their electroencephalogram states are different, and different recordings may differ depending on the physical conditions of the subjects and the measurement factors, such as electrode type, electrode position, impedance and amplifier.
When the electroencephalogram amplitude range changes, the threshold is difficult to determine. Although the signal complexity characteristic of entropy is robust to the dynamic range, it is still difficult to describe the characteristics when the dynamic range changes.
FIG. 1 is a flow chart of the method for analyzing the EEG explosion suppression mode. As shown in fig. 1, in one embodiment, the present application provides a method for analyzing an electroencephalogram burst suppression pattern, the method comprising:
s101, acquiring an electroencephalogram signal.
In this step, a specific step of acquiring the electroencephalogram signal is provided, in which stepThe electroencephalogram signal of the test target needs to be acquired, and it should be noted that the electroencephalogram signal is a signal under uniform sampling and can be represented by x (t), that is, a function of time and voltage, and t > 0. In addition, the time domain function of x (t) can be converted into a frequency domain function by Fourier transform, and is marked as S xx (w) =0, | w | > B, where B is a constant. When the frequency domain function is qualified, the following processing can be better performed.
FIG. 2 is a schematic diagram of an image of an electroencephalogram signal x (t) function according to the present invention, and as shown in FIG. 2, an electroencephalogram signal under uniform sampling expressed by the x (t) function is obtained, and x (t) satisfies a frequency domain function S after Fourier transform xx (w) =0, | w | > B, wherein B is a constant;
FIG. 3 is a schematic diagram of a segmented electroencephalogram image of an electroencephalogram signal segmented by a statistical variable point algorithm. As shown in figure 3 of the drawings,
and S102, carrying out segmentation processing on the electroencephalogram signal through a statistical variable point algorithm to obtain a segmented electroencephalogram signal.
The step provides a specific step of carrying out segmentation processing on the electroencephalogram signal through a statistical variable point algorithm, a sliding window W is arranged on the uniformly sampled electroencephalogram signal sequence, wherein the window length of the sliding window is
The step of the sliding is s and, the uniformly sampled brain electrical signal within one window can be written as X = { X = { [ X ]) 1 ,x 2 ,…,x n I =1,2, \8230n, where i is the sampling instant, the electroencephalogram signal within the window is assumed at an unknown instant τ 1 ,τ 2 ,…,τ K-1 Mutations occurred, where K is the number of unknown data segments and K-1 is the number of transition points.
And taking K =2 according to the above formula to determine the point changing time of the brain electrical signal in the window. As the duration of the explosion section or the suppression section of the explosion suppression mode of the electroencephalogram signal is at least 0.5s, the window sliding step length s =0.5s, so that all possible positions of the explosion suppression variable points of the electroencephalogram can be found, and the brain of the scoring section is scoredThe electrical sequence being Y 1 ,Y 2 ,…,Y L Where L is the number of segments.
K is an integer, let τ = (τ) 1 ,τ 2 ,…,τ K-1 ) Is a sequence of integers. Where 0 < tau 1 ＜τ 2 ＜…＜τ K-1 < n. For any K equal to or greater than 1 and equal to or less than K, a penalty function for the statistic theta is constructed
It is assumed that for each k there is an estimate value ∑ related to θ>
So that the penalty function J takes the minimum value, then
The statistic theta is taken as the variance, and then the penalty function is
S103, calculating the absolute amplitude mean value of the segmented computer signals according to the segmented electroencephalogram signals.
The step provides a specific step of calculating the absolute amplitude mean value of the segmented electroencephalogram signal, and a segmented electroencephalogram sequence Y is obtained on the specific step 1 ,Y 2 ,…,Y L Where L is the number of segments, then for each segmented brain electrical sequence Y j ＝(y j,1 ,y j,2 ,…,y j,N ) The mean absolute magnitude of which can be expressed as
And S104, analyzing the segmented electroencephalogram signals according to the absolute amplitude mean value to obtain an electroencephalogram inhibition interval and/or a corresponding electroencephalogram outbreak interval.
In this step, a specific implementation manner of obtaining the electroencephalogram suppression interval section and/or the corresponding electroencephalogram burst interval section according to the absolute amplitude mean value analysis is provided, and a mean value sequence T = (T) is obtained according to the absolute amplitude mean value formula in the previous step 1 ,T 2 ,…,T H ) And searching for a minimum value of T in the sequence, wherein the electroencephalogram section corresponding to each minimum value is the suppression section of the electroencephalogram in the burst suppression mode, and the next adjacent section is the burst section.
In the embodiment, the electroencephalogram signal is analyzed through a variable point algorithm, and finally, a specific implementation mode of the electroencephalogram inhibition interval section and/or the corresponding electroencephalogram outbreak interval section is obtained. Firstly, acquiring an electroencephalogram signal, then carrying out segmentation processing on the electroencephalogram signal through a statistical variable point algorithm to obtain a segmented electroencephalogram signal, then calculating an absolute amplitude mean value of the segmented computer signal according to the segmented electroencephalogram signal, and finally analyzing the segmented electroencephalogram signal according to the absolute amplitude mean value to obtain an electroencephalogram inhibition interval section and/or a corresponding electroencephalogram outbreak interval section. In this embodiment, a method for performing an explosion suppression pattern analysis on the electroencephalogram signal through a statistical transformation algorithm is provided, so as to solve the problem that a set of standard, reliable and quantifiable explosion suppression pattern analysis method is lacking in the prior art.
In one embodiment, the acquiring the electroencephalogram signal according to the above includes:
s201, performing band-pass filtering with the passband frequency of 3.5 Hz-30 Hz on the electroencephalogram signal to obtain the electroencephalogram signal without artifact interference;
in the step, a specific step of removing artifact interference in the electroencephalogram signal is provided, namely the influence of artifact interference on the electroencephalogram signal is eliminated.
In addition, in x (t) corresponding to the electroencephalogram signals, the burst suppression mode characteristics of electroencephalogram in the frequency range of 3.5 Hz-30 Hz are more obvious, because the suppression section electroencephalogram in the frequency range has almost no energy, and the energy of the burst section electroencephalogram is mainly concentrated in the frequency range.
S202, carrying out segmentation processing on the electroencephalogram signal without artifact interference through a statistical variable point algorithm to obtain the segmented electroencephalogram signal.
In the step, a specific step of carrying out a statistical variable point algorithm on the electroencephalogram signals with the artifact interference removed for segmentation is provided.
In this embodiment, a specific implementation manner is provided for obtaining the segmented electroencephalogram signal by segmenting the electroencephalogram signal after removing the interference frequency band.
In an embodiment, the obtaining of the segmented electroencephalogram signal by performing the segmentation processing according to the electroencephalogram signal through a statistical variable point algorithm includes:
s301, segmenting the electroencephalogram signal through a statistical variable point algorithm to obtain a segmented electroencephalogram signal to be detected.
And in the step, the electroencephalogram signal to be detected is obtained by segmenting through the electroencephalogram signal statistical variable point algorithm.
S302, calculating an absolute amplitude mean value of the segmented electroencephalogram signals to be detected according to the segmented electroencephalogram signals to be detected.
In the step, a specific implementation mode for calculating the segmented electroencephalogram signal to be detected is provided, and then whether the segmented electroencephalogram signal to be detected meets a judgment condition that the amplitude of the electroencephalogram of the suppression segment in the explosion suppression mode of the electroencephalogram signal is smaller than 50% of the background voltage or not is detected.
S303, judging whether a preset combination condition is met or not according to the absolute amplitude mean value of the adjacent segmented electroencephalograms to be detected, and if the absolute amplitude mean value of the adjacent segmented electroencephalograms to be detected meets the preset combination condition, respectively taking the adjacent segmented electroencephalograms to be detected as segmented electroencephalograms.
In this step, a specific implementation manner for judging whether the adjacent segmented electroencephalograms to be detected need to be combined is provided.
In the explosion suppression mode of the electroencephalogram signal, the amplitude of the electroencephalogram of a suppression section must be smaller than that of the background voltage by 50%, and in order to describe the characteristic, the amplitude comparison is carried out on the segmented electroencephalogram by utilizing the absolute amplitude mean value of adjacent segmented electroencephalograms, namely the amplitude comparison is carried out on the segmented electroencephalograms, namely
Where α is an empirical coefficient.
In this embodiment, a specific implementation manner is provided in which the electroencephalogram signal is segmented to obtain the segmented electroencephalogram signal to be detected, and whether the segmented electroencephalogram signal to be detected meets the predetermined merging condition is determined, first, the segmented electroencephalogram signal to be detected is segmented by a statistical variable point algorithm according to the electroencephalogram signal. And then, calculating the absolute amplitude mean value of the segmented electroencephalogram signal to be detected according to the segmented electroencephalogram signal to be detected. And finally, judging whether a preset combination condition is met or not according to the absolute amplitude mean value of the adjacent segmented electroencephalograms to be detected, and if the absolute amplitude mean value of the adjacent segmented electroencephalograms to be detected meets the preset combination condition, respectively taking the adjacent segmented electroencephalograms to be detected as segmented electroencephalograms.
In an embodiment, the determining whether a predetermined combination condition is met according to the absolute amplitude mean value of the adjacent segmented electroencephalograms to be detected further includes:
and if the absolute amplitude mean value of the adjacent segmented electroencephalograms to be detected does not accord with the preset combination condition, combining the adjacent segmented electroencephalograms to be detected to be used as the segmented electroencephalograms.
In this embodiment, a specific implementation is provided in which the absolute amplitude mean value does not meet the predetermined combination condition, and the adjacent segmented electroencephalograms to be detected need to be combined, and after combination, a formula may be further required
And judging, namely, not combining again until the combination is not needed any more, and finally taking the combined segmented electroencephalogram signal to be detected which accords with the formula as the segmented electroencephalogram signal.
In an embodiment, after the step of segmenting the electroencephalogram signal according to the statistical variable point segmentation method to obtain an electroencephalogram suppression interval section and/or an electroencephalogram explosion interval section, the method further includes:
and when the electroencephalogram inhibition interval and the corresponding electroencephalogram outbreak interval are obtained, calculating to obtain a single outbreak time ratio according to the time of the electroencephalogram inhibition interval and the corresponding electroencephalogram outbreak interval.
A specific implementation of calculating the single burst time ratio is provided in this embodiment, as it is also very important measurement data about the burst suppression period.
FIG. 5 is a single burst time ratio image diagram of the electroencephalogram suppression interval segment and the corresponding electroencephalogram burst interval segment of the present invention, as shown in FIG. 5, the single burst time ratio formula is:
FIG. 6 is a schematic diagram of an analysis apparatus for electroencephalogram burst suppression mode according to the present invention. As shown in fig. 6, the present application also provides an apparatus for analyzing a brain electrical burst suppression pattern, the apparatus including:
an acquisition module 101, configured to acquire an acquired electroencephalogram signal;
the segmentation module 102 is configured to perform segmentation processing on the electroencephalogram signal through a statistical variable point algorithm to obtain a segmented electroencephalogram signal;
the computing module 103 is further configured to compute an absolute amplitude mean of the segmented computer signals according to the segmented electroencephalogram signals;
and the analysis module 104 is used for analyzing the segmented electroencephalogram signals according to the absolute amplitude mean value to obtain an electroencephalogram inhibition interval and/or a corresponding electroencephalogram outbreak interval.
In one embodiment, the apparatus further comprises:
the filtering module 105 is used for filtering the electroencephalogram signals at 3.5-30Hz to obtain the electroencephalogram signals without artifact interference;
the segmentation module 102 performs segmentation processing on the electroencephalogram signal without artifact interference through a statistical variable point algorithm to obtain the segmented electroencephalogram signal. In an embodiment, the calculating module 102 is further configured to calculate, when the electroencephalogram suppression interval section and the corresponding electroencephalogram burst interval section are obtained, a single burst time ratio according to the time of the electroencephalogram suppression interval section and the corresponding electroencephalogram burst interval section.
In one embodiment, the present application further provides a system for analyzing a brain burst suppression pattern, the system comprising:
the acquisition device is used for acquiring electroencephalogram signals;
the processor unit is used for carrying out segmentation processing on the electroencephalogram signals through a statistical variable point algorithm to obtain segmented electroencephalogram signals; calculating the absolute amplitude mean value of the segmented computer signals according to the segmented electroencephalogram signals; obtaining an electroencephalogram inhibition interval and/or a corresponding electroencephalogram outbreak interval through the analysis of the segmented electroencephalogram signals according to the absolute amplitude mean value;
and the display is used for displaying the electroencephalogram signal, and the electroencephalogram inhibition interval section and/or the corresponding electroencephalogram outbreak interval section on the image corresponding to the electroencephalogram signal.
In an embodiment, the present application further provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor, implements the steps of the method for analyzing a burst suppression pattern of brain electricity as described.
In practical applications, the computer readable medium may be included in the apparatus/device/system described in the above embodiments, or may exist alone without being assembled into the apparatus/device/system. The above-mentioned computer-readable storage medium carries one or more programs which, when executed, implement the image data processing method of the described data.
According to embodiments disclosed herein, the computer-readable storage medium may be a non-volatile computer-readable storage medium, which may include, for example and without limitation: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing, without limiting the scope of the present disclosure. In the embodiments disclosed herein, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (9)
1. A method for analyzing a pattern of suppression of an electroencephalogram burst, the method comprising:
acquiring an electroencephalogram signal;
carrying out segmentation processing on the electroencephalogram signals through a statistical variable point algorithm to obtain segmented electroencephalogram signals;
calculating the absolute amplitude mean value of the segmented computer signals according to the segmented electroencephalogram signals;
obtaining a electroencephalogram inhibition interval section and/or a corresponding electroencephalogram outbreak interval section by analyzing the segmented electroencephalogram signals according to the absolute amplitude mean value;
the step of obtaining the segmented electroencephalogram signal by performing segmentation processing according to the electroencephalogram signal through a statistical variable point algorithm comprises the following steps:
filtering the EEG signal at 3.5-30Hz to obtain the EEG signal without artifact interference;
segmenting the electroencephalogram signal through a statistical variable point algorithm to obtain a segmented electroencephalogram signal to be detected;
calculating an absolute amplitude mean value of the segmented electroencephalogram signals to be detected according to the segmented electroencephalogram signals to be detected;
judging whether the absolute amplitude mean value of the adjacent segmented electroencephalograms to be detected meets a preset combination condition or not,
and if the absolute amplitude mean value of the adjacent segmented electroencephalograms to be detected accords with the preset combination condition, respectively taking the adjacent segmented electroencephalograms to be detected as segmented electroencephalograms.
2. The method for analyzing brain electrical burst suppression pattern according to claim 1, wherein said obtaining a segmented brain electrical signal by performing a segmentation process according to said brain electrical signal through a statistical variable point algorithm further comprises:
and carrying out segmentation processing on the electroencephalogram signal without artifact interference through a statistical variable point algorithm to obtain the segmented electroencephalogram signal.
3. The method for analyzing EEG burst suppression pattern according to claim 1, wherein said determining whether a predetermined merging condition is met according to said absolute amplitude mean of adjacent segmented EEG signals to be detected further comprises:
and if the absolute amplitude mean value of the adjacent segmented electroencephalograms to be detected does not accord with the preset combination condition, combining the adjacent segmented electroencephalograms to be detected to be used as segmented electroencephalograms.
4. The method for analyzing EEG burst suppression pattern according to claim 1, wherein after said step of obtaining an EEG suppression interval and/or a corresponding EEG burst interval from said segmented EEG signal according to said absolute amplitude mean, said method further comprises:
and when the electroencephalogram inhibition interval and the corresponding electroencephalogram outbreak interval are obtained, calculating to obtain a single outbreak time ratio according to the time of the electroencephalogram inhibition interval and the corresponding electroencephalogram outbreak interval.
5. An apparatus for analyzing a burst suppression pattern of brain electrical activity, the apparatus comprising:
the acquisition module is used for acquiring the electroencephalogram signals;
the segmentation module is used for carrying out segmentation processing on the electroencephalogram signal through a statistical variable point algorithm to obtain a segmented electroencephalogram signal;
the computing module is also used for computing the absolute amplitude mean value of the segmented computer signals according to the segmented electroencephalogram signals;
the analysis module is used for analyzing the segmented electroencephalogram signals according to the absolute amplitude mean value to obtain an electroencephalogram inhibition interval and/or a corresponding electroencephalogram outbreak interval;
the filtering module is used for filtering the electroencephalogram signals at 3.5-30Hz to obtain the electroencephalogram signals without artifact interference;
the step of carrying out segmentation processing on the electroencephalogram signals through a statistical variable point algorithm to obtain segmented electroencephalogram signals comprises the following steps:
segmenting the electroencephalogram signal through a statistical variable point algorithm to obtain a segmented electroencephalogram signal to be detected;
calculating the absolute amplitude mean value of the segmented electroencephalogram signal to be detected according to the segmented electroencephalogram signal to be detected;
judging whether a preset combination condition is met or not according to the absolute amplitude mean value of the adjacent segmented electroencephalogram signals to be detected,
and if the absolute amplitude mean value of the adjacent segmented electroencephalograms to be detected accords with the preset combination condition, respectively taking the adjacent segmented electroencephalograms to be detected as segmented electroencephalograms.
6. The device for analyzing a pattern of suppression of brain electrical bursts according to claim 5, further comprising:
the segmentation module is also used for carrying out segmentation processing on the electroencephalogram signal without artifact interference through a statistical variable point algorithm to obtain the segmented electroencephalogram signal.
7. The electroencephalogram explosion suppression pattern analysis apparatus according to claim 5,
the calculation module is also used for calculating the single-burst time ratio according to the time of the electroencephalogram inhibition interval section and the corresponding electroencephalogram burst interval section when the electroencephalogram inhibition interval section and the corresponding electroencephalogram burst interval section are obtained.
8. An analysis system for a pattern of suppression of brain electrical bursts, the system comprising:
the acquisition device is used for acquiring an electroencephalogram signal;
the processor unit is used for carrying out segmentation processing on the electroencephalogram signals through a statistical variable point algorithm to obtain segmented electroencephalogram signals; calculating the absolute amplitude mean value of the segmented computer signals according to the segmented electroencephalogram signals; obtaining a electroencephalogram inhibition interval section and/or a corresponding electroencephalogram outbreak interval section through the analysis of the segmented electroencephalogram signals according to the absolute amplitude mean value;
the display is used for displaying the electroencephalogram signals and the electroencephalogram suppression section and/or the corresponding electroencephalogram outbreak section on the image corresponding to the electroencephalogram signals;
the processor unit is also used for filtering the electroencephalogram signal at 3.5-30Hz to obtain the electroencephalogram signal without artifact interference;
the step of carrying out segmentation processing on the electroencephalogram signals through a statistical variable point algorithm to obtain segmented electroencephalogram signals comprises the following steps:
segmenting the electroencephalogram signal through a statistical variable point algorithm to obtain a segmented electroencephalogram signal to be detected;
calculating an absolute amplitude mean value of the segmented electroencephalogram signals to be detected according to the segmented electroencephalogram signals to be detected;
judging whether a preset combination condition is met or not according to the absolute amplitude mean value of the adjacent segmented electroencephalogram signals to be detected,
and if the absolute amplitude mean value of the adjacent segmented electroencephalograms to be detected accords with the preset combination condition, respectively taking the adjacent segmented electroencephalograms to be detected as segmented electroencephalograms.
9. A computer-readable storage medium on which a computer program is stored, characterized in that the program, when being executed by a processor, realizes the steps of the method for analyzing a brain electrical burst suppression pattern according to any one of claims 1 to 4.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2013104888A1 (en) * | 2012-01-10 | 2013-07-18 | The Secretary Of State For Defence | Signature identification |
| CN104239739A (en) * | 2014-09-26 | 2014-12-24 | 华南理工大学 | Piecewise fitting method and system of lightning current amplitude probability distribution function |
| CN104436597A (en) * | 2013-09-19 | 2015-03-25 | 卡西欧计算机株式会社 | Exercise support device and exercise support method |
| CN109152555A (en) * | 2016-03-18 | 2019-01-04 | 哈佛大学校长及研究员协会 | Automatic classification animal behavior |
| CN109300544A (en) * | 2018-12-10 | 2019-02-01 | 南京伟思医疗科技股份有限公司 | A kind of seven step analysis method of newborn baby function standardizing |
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| US10383574B2 (en) * | 2013-06-28 | 2019-08-20 | The General Hospital Corporation | Systems and methods to infer brain state during burst suppression |
| CN106943118A (en) * | 2014-09-29 | 2017-07-14 | 浙江普可医疗科技有限公司 | A kind of EEG signals monitoring process method |
| CN104720799A (en) * | 2015-04-03 | 2015-06-24 | 上海帝仪科技有限公司 | Fatigue detection method and system based on low-frequency electroencephalogram signals |
| CN105193409B (en) * | 2015-08-07 | 2018-01-16 | 深圳大学 | A kind of brain electricity suppression level appraisal procedure and system |
| CN109464142A (en) * | 2018-09-03 | 2019-03-15 | 曹君利 | It is a kind of to calculate the method that outburst inhibits preclinical method and prediction outburst inhibiting rate using EEG signals |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| WO2013104888A1 (en) * | 2012-01-10 | 2013-07-18 | The Secretary Of State For Defence | Signature identification |
| CN104436597A (en) * | 2013-09-19 | 2015-03-25 | 卡西欧计算机株式会社 | Exercise support device and exercise support method |
| CN104239739A (en) * | 2014-09-26 | 2014-12-24 | 华南理工大学 | Piecewise fitting method and system of lightning current amplitude probability distribution function |
| CN109152555A (en) * | 2016-03-18 | 2019-01-04 | 哈佛大学校长及研究员协会 | Automatic classification animal behavior |
| CN109300544A (en) * | 2018-12-10 | 2019-02-01 | 南京伟思医疗科技股份有限公司 | A kind of seven step analysis method of newborn baby function standardizing |
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