CN113491525A - Electroencephalogram signal analysis-based method - Google Patents
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Abstract
The invention discloses an electroencephalogram signal analysis-based method, which comprises the steps of carrying out denoising pretreatment on acquired electroencephalogram data through big data; performing 10-second windowing calculation on the preprocessed electroencephalogram data, and respectively calculating five parameters of sample entropy, permutation entropy, wavelet entropy, explosion suppression rate and edge frequency according to the data of each window; all the obtained parameters are input into the established SVC model of the support vector machine for training to obtain the BIS index, and the BIS index and the outbreak suppression rate are output. The invention realizes that the BIS calculation data window is a 10-second window, which is greatly improved compared with the 30-second window used in the prior art; meanwhile, the implementation processing speed is accelerated through the established SVC training model, and the training model for big data training also ensures that noise can be effectively filtered and the numerical value can be accurately calculated by selecting a shorter data window.
Description
Technical Field
The invention relates to the field of physiological electric signal processing, in particular to an electroencephalogram signal analysis-based method which can be applied to accurate evaluation of electroencephalogram signals or electroencephalogram scientific research.
Background
Consciousness monitoring is needed in anesthesia operation to prevent poor postoperative effect and complications caused by over-deep and over-shallow anesthesia. When a patient is administered an anesthetic during surgery, the judgment of consciousness monitoring can be made by the index of depth of anesthesia (BIS): the index ranges from 0 to 100, wherein 100 is a completely awake state and 0 is a completely unconscious state. The BIS index is converted into a clinically approved credible number after a series of calculations are carried out on the electroencephalogram signal. Carrying out effectual anesthesia consciousness monitoring can reduce the intraoperative awareness that leads to when the depth of anesthesia is too shallow to and the bad prognosis that leads to when too deep, can also help to reduce the operation cost, accelerate that the patient is clear-headed after the postoperative. Therefore, the precise BIS index is of great significance for clinical applications.
Chinese patent document CN 104545949 discloses an electroencephalogram-based anesthesia depth monitoring method, which adopts a decision tree classifier, weight parameters are invariable, the universality is poor, the used window is 30 seconds, and the instantaneity is poor.
Chinese patent document CN110840411 discloses a method for measuring anesthesia depth, a storage medium and an electronic device, which collects a multi-lead electroencephalogram generated analog signal to perform anesthesia depth evaluation, and needs to model the cranium and generate analog electroencephalograms corresponding to points, which is poor in reliability.
Disclosure of Invention
The invention aims to solve the problem that the anesthesia depth index obtained after the electroencephalogram signals generated by anesthesia are processed in the prior art is poor in real-time performance and reliability, and therefore, the invention provides a method based on electroencephalogram signal analysis, so that the processed electroencephalogram data have better real-time performance and universality.
The invention adopts the following technical scheme:
a method based on electroencephalogram signal analysis, the method comprising the steps of:
step 1, carrying out denoising pretreatment on acquired electroencephalogram data through big data;
step 2, performing 10-second windowing calculation on the preprocessed electroencephalogram data, and calculating sample entropy, permutation entropy, wavelet entropy, explosion suppression rate and edge frequency parameters according to the data of each window;
and 3, inputting all the parameters obtained in the step 2 into the established SVC model of the support vector machine for training to obtain the BIS index, and outputting the BIS index and the explosion suppression rate.
And (3) after iterative computation of a machine learning algorithm SVC, carrying out weight distribution on the sample entropy, the permutation entropy and the wavelet entropy obtained in the step (2) through sample training, combining and computing a signal complexity index of the data, and outputting the signal complexity index.
In the step 1, the high-pass filtering and low-pass filtering are combined to remove the environmental noise and the biological signal noise contained in the brain waves.
The calculation method of the electroencephalogram sample entropy in the step 2 comprises the following steps:
in the time sequence formed by the acquired N pieces of electroencephalogram data, a group of vector sequences with dimension m is formed according to the sequence numbers, and two definition vectors Xm(i) And Xm(j) If X is givenm(i) Statistics of Xm(i) To Xm(j) The number of j between which the distance is less than r is marked as Bi：
And define
Increase dimension to m +1, pair Bi m+1(r) and Bm+1(r) performing operation again, and calculating the brain wave sample entropy as follows:
the method for calculating the EEG arrangement entropy in the step 2 comprises the following steps:
performing phase space reconstruction (the size of the phase space is recorded as m) on the time sequence X of the acquired electroencephalogram data to obtain a matrix, wherein each row in the matrix is a sequence with the length of the phase space;
rearranging each row in the matrix in ascending order, recording the subscript sequence before the row is sequenced after the sequencing to obtain a group of symbol sequences, and mapping m!on the m-dimensional phase space! Different symbol sequences are distinguished;
the probability of occurrence of each symbol sequence is denoted as P1，P2，...PkThe permutation entropy of the k different symbol sequences of the time sequence x (i) is defined as pe (m) - Σ P, as defined by the information entropyjlnPj
The method for calculating the electroencephalogram wavelet entropy in the step 2 comprises the following steps:
after the dwt constant C and the quantity vector L are calculated through the electroencephalogram data, the total wave energy E of the electroencephalogram signals is calculatedtUsing the formula WE ═ sum (E)t.*log(Et) And) calculating the final total wavelet entropy.
The calculation method of the electroencephalogram explosion suppression rate in the step 2 comprises the following steps:
judging that the suppression is carried out before the outbreak when the difference between adjacent peaks and troughs in the electroencephalogram data is less than 5 mu V and the duration is more than 0.5 s;
recording the quantity of continuous data of which the difference between adjacent peaks and troughs is less than 5 mu V in the electroencephalogram data, and representing the section of data between the peaks and the troughs by 1 as suppression part data;
recording the continuous data quantity of the difference between adjacent wave crests and wave troughs in the electroencephalogram data, wherein the difference is larger than 5 mu V, and representing the section of data between the wave crests and the wave troughs by 0 as other parts;
and performing non-overlapping windowing calculation on the electroencephalogram data, and calculating the percentage of the data quantity of the inhibition part in each window to all the data quantity in the total window to obtain the electroencephalogram explosion inhibition rate.
The calculation method of the electroencephalogram edge frequency in the step 2 comprises the following steps:
drawing an fft spectrogram of the electroencephalogram data, calculating the total area of the frequency spectrum of 90% or 95% below the frequency spectrum data, and obtaining the frequency corresponding to the total area of the frequency spectrum, namely the edge frequency.
The technical scheme of the invention has the following advantages:
A. compared with the prior art, the method has better real-time performance, can realize the coexistence of high real-time performance and high precision of electroencephalogram data, can also realize that a BIS (anesthesia depth index) calculation data window is a 10-second window, and is greatly improved compared with a 30-second window used in the prior art; meanwhile, the implementation processing speed is accelerated through the established SVC training model, and the training model for big data training also ensures that noise can be effectively filtered and the numerical value can be accurately calculated by selecting a shorter data window.
Most of the existing commercial devices are based on the parametric model described in the paper (a Primer for EEG Signal Processing in Anesthesia), including the parameters of the bispectrum domain, the burst suppression ratio, the edge frequency, the beta ratio, etc., wherein the calculated amount of the bispectrum domain parameters is the largest, including fourier transform, and the time complexity is power level. Meanwhile, in order to ensure the accuracy of the parameters of the bispectrum domain, the electroencephalogram signals of longer time segments must be selected, so that the subsequent commercial equipment based on the paper continues to use the setting of a 30-second window. The invention optimizes the parameter group, abandons the double-spectrum domain parameter with high time complexity, and adopts a plurality of parameter groups with simple calculation and wide feature coverage to describe the anesthesia depth, thereby improving the real-time property. The method selects the entropy parameters as the substitution of the bispectrum domain parameters, can effectively detect the characteristics of the electroencephalogram at different anesthesia depths, and ensures the accuracy of the anesthesia depth calculation.
B. The method adopts five parameters of sample entropy, permutation entropy, wavelet entropy, outbreak inhibition rate and edge frequency to be fused, and the BIS index, the outbreak inhibition rate and the signal complexity index are obtained after the calculation of the established and trained SVC training model of the support vector machine, so that a doctor can accurately judge the anesthesia depth according to the obtained parameters; by utilizing the output signal complexity index numerical value and the explosion suppression rate numerical value, the method has practical application effect in the real-time monitoring scene in the operation and has higher practicability.
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In order to more clearly illustrate the embodiments of the present invention, the drawings which are needed to be used in the embodiments will be briefly described below, and it is apparent that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained from the drawings without inventive labor to those skilled in the art.
FIG. 1 is a schematic structural diagram of the electroencephalogram signal analysis method provided by the present invention.
FIG. 2 is a flow chart of an electroencephalogram signal analysis method provided by the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. 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 invention.
As shown in fig. 1 and 2, the present invention provides a method based on electroencephalogram signal analysis, comprising the following steps:
step 1, carrying out denoising pretreatment on acquired electroencephalogram data through big data; removing environmental noise and biological signal noise contained in brain waves by adopting a low-pass filtering and high-pass filtering combined mode; the biological signal noise is easy to simultaneously acquire the eye electricity close to the distance of the brain electricity in the process of acquiring the brain electricity, and therefore the eye electricity removing filtering is needed.
The big data source here is divided into two parts: one part is self-acquiring electroencephalogram data; the other part is from https:// vitaldb. net, vitaldb open source anesthesia database.
Step 2, performing 10-second windowing calculation on the preprocessed electroencephalogram data, and calculating sample entropy, permutation entropy, wavelet entropy, explosion suppression rate and edge frequency parameters according to the data of each window;
carrying out weight distribution on the sample entropy, the permutation entropy and the wavelet entropy, combining and calculating a signal complexity index of the data, and outputting the signal complexity index; calculating an explosion suppression rate to obtain an explosion suppression rate parameter;
and performing windowing calculation on the preprocessed electroencephalogram data, wherein a 10-second window is preferably adopted, and all parameters are calculated and output through the data in each window. As each new brain electrical data entry, the 10 second window is shifted backwards, with each parameter increasing in length, for real-time parameter output.
And 3, inputting all the five parameters obtained in the step 2 into the established SVC model of the support vector machine for training to obtain a BIS index, and outputting the BIS index, the explosion suppression rate and the signal complexity index.
The invention can realize the coexistence of high real-time performance and high precision of the electroencephalogram data, can realize that the window of the BIS calculation data is a 10-second window, and has larger improvement compared with the 30-second window used in the prior art. According to the method, the entropy parameters are selected as substitutes of the parameters of the double-spectrum domain, and a plurality of parameter groups which are simple in calculation and wide in feature coverage are selected to describe the anesthesia depth, so that the real-time performance is improved, the implementation processing speed is accelerated, the features of the electroencephalogram at different anesthesia depths can be effectively detected, and the accuracy of the calculation of the anesthesia depth is ensured.
The calculation methods of the five parameters are respectively as follows:
(1) the method for calculating the entropy of the electroencephalogram sample comprises the following steps:
in the time sequence formed by the acquired N pieces of electroencephalogram data, a group of vector sequences with dimension m is formed according to the sequence numbers, and the vector Xm(i)、Xm(j) Are subsequences of an array formed by N pieces of electroencephalogram data respectively. In two definition vectors Xm(i) And Xm(j) If X is givenm(i) Statistics of Xm(i) To Xm(j) The number of j with the vector distance between the two being less than r is marked as Bi：
Given threshold r (r)>0) Statistics of d [ X ]m(i)，Xm(j)]<r, and the ratio of the number of r to the total number of vectors N-m
And averaging the results obtained by the above formula, i.e. defining
Increase dimension to m +1, pair Bi m+1(r) and Bm+1(r) performing operation again, and calculating the brain wave sample entropy as follows:
(2) the method for calculating the EEG arrangement entropy comprises the following steps:
performing phase space reconstruction (the size of the phase space is recorded as m) on the time sequence X of the acquired electroencephalogram data to obtain a matrix, wherein each row in the matrix is a sequence with the length of the phase space;
rearranging each row in the matrix in ascending order, recording the subscript sequence before the row is sequenced after the sequencing to obtain a group of symbol sequences, and mapping m!on the m-dimensional phase space! Different symbol sequences are distinguished;
the probability of occurrence of each symbol sequence is denoted as P1，P2，...PkThe permutation entropy of the k different symbol sequences of the time sequence x (i) is defined as pe (m) - Σ P, as defined by the information entropyjlnPj
(3) The method for calculating the electroencephalogram wavelet entropy comprises the following steps:
j is the wavelet decomposition level number. In the range of j, cj (k) is the decomposition coefficient and has wavelet energy
LjIs Cj(k) The number of (2).
Thus, the total energy of the signal is:
the relative wavelet energy is:
finally, calculating the wavelet entropy as:
(4) the calculation method of the electroencephalogram explosion suppression rate comprises the following steps:
judging that the suppression is carried out before the outbreak when the difference between adjacent peaks and troughs in the electroencephalogram data is less than 5 mu V and the duration is more than 0.5 s;
recording the quantity of continuous data of which the difference between adjacent peaks and troughs is less than 5 mu V in the electroencephalogram data, and representing the section of data between the peaks and the troughs by 1;
recording the continuous data quantity of the difference between adjacent wave crests and wave troughs in the electroencephalogram data, wherein the difference is larger than 5 mu V, and representing the section of data between the wave crests and the wave troughs by 0;
when all data are represented by 1 and 0, 1 is suppression partial data and 0 is the other part. And performing non-overlapping windowing calculation on the electroencephalogram data, and calculating the percentage of the sum of the data quantity of the inhibition parts in all windows to all the data quantity in the total window to obtain the explosion inhibition rate of the electroencephalogram. Namely:
BSR-amount of suppressed partial data in window/amount of total data in window 100%.
(5) The calculation method of the electroencephalogram edge frequency comprises the following steps:
drawing an fft spectrogram of the electroencephalogram data, calculating the total area of the frequency spectrum of 90% or 95% below the frequency spectrum data, and obtaining the frequency corresponding to the total area of the frequency spectrum, namely the edge frequency.
Firstly, using fft function to calculate the magRD, fRD;
calculating the total spectral area
smMag＝sum(magRD(1:round(length(magRD)/2)).^2)；
Setting curSum as 0;
for i＝1:len
curSum＝curSum+magRD(i)^2；
when currsum > smMag 0.95, the output dataSEF is fRD (i-2).
The invention adopts five parameters of sample entropy, permutation entropy, wavelet entropy, outbreak inhibition rate and edge frequency to be fused, and the BIS index, the outbreak inhibition rate and the signal complexity index are obtained after the calculation of the established SVC training model of the support vector machine, thereby being beneficial to doctors to accurately judge the anesthesia depth according to the obtained parameters. Meanwhile, the analysis method is embedded into intraoperative real-time monitoring scene equipment by utilizing the output signal complexity index numerical value and the explosion suppression rate numerical value, and the method has a good practical application effect.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications therefrom are intended to be within the scope of the invention.
Claims (8)
1. A method based on electroencephalogram signal analysis, characterized by comprising the steps of:
step 1, carrying out denoising pretreatment on acquired electroencephalogram data through big data;
step 2, performing 10-second windowing calculation on the preprocessed electroencephalogram data, and respectively calculating sample entropy, permutation entropy, wavelet entropy, explosion suppression rate and edge frequency parameters according to the data of each window;
and 3, inputting all the parameters obtained in the step 2 into the established SVC model of the support vector machine for training to obtain the BIS index, and outputting the BIS index and the explosion suppression rate.
2. The electroencephalogram signal analysis-based method according to claim 1, wherein after iterative computation of a machine learning algorithm SVC, weight distribution is performed on the sample entropy, the permutation entropy and the wavelet entropy obtained in the step 2 through sample training, and a signal complexity index of data is combined and calculated and output.
3. The method for analyzing electroencephalogram signals according to claim 1, wherein in the step 1, the ambient noise and the biological signal noise contained in the brain waves are removed by adopting a combination of high-pass filtering and low-pass filtering.
4. The method for electroencephalogram signal analysis based on claim 1, wherein the method for calculating the entropy of the electroencephalogram samples in the step 2 is as follows:
in the time sequence formed by the acquired N pieces of electroencephalogram data, a group of vector sequences with dimension m is formed according to the sequence numbers, and two definition vectors Xm(i) And Xm(j) If X is givenm(i) Statistics of Xm(i) To Xm(j) The number of j between which the distance is less than r is marked as Bi：
And define
Increase dimension to m +1, pair Bi m+1(r) and Bm+1(r) performing operation again, and calculating the EEG data sample entropy as follows:
5. the electroencephalogram signal analysis-based method according to claim 1, wherein the method for calculating the entropy of electroencephalogram arrangement in the step 2 is as follows:
performing phase space reconstruction (the size of the phase space is recorded as m) on the time sequence X of the acquired electroencephalogram data to obtain a matrix, wherein each row in the matrix is a sequence with the length of the phase space;
rearranging each row in the matrix in ascending order, recording the subscript sequence before the row is sequenced after the sequencing to obtain a group of symbol sequences, and mapping m!on the m-dimensional phase space! Different symbol sequences are distinguished;
the probability of occurrence of each symbol sequence is denoted as P1，P2，...PkThe permutation entropy of the k different symbol sequences of the time sequence x (i) is defined as pe (m) - Σ P, as defined by the information entropyjlnPj
6. The electroencephalogram signal analysis-based method according to claim 1, wherein the electroencephalogram wavelet entropy calculation method in the step 2 is as follows:
after calculating discrete wavelet transform dwt constant C and quantity vector L by electroencephalogram data, calculating total wave energy E of electroencephalogram signalstUsing the formula
And calculating the final total wavelet entropy.
7. The method for electroencephalogram signal analysis based on claim 1, wherein the method for calculating the suppression rate of electroencephalogram burst in step 2 is:
judging that the suppression is carried out before the outbreak when the difference between adjacent peaks and troughs in the electroencephalogram data is less than 5 mu V and the duration is more than 0.5 s;
recording the quantity of continuous data of which the difference between adjacent peaks and troughs is less than 5 mu V in the electroencephalogram data, and representing the section of data between the peaks and the troughs by 1 as suppression part data;
recording the continuous data quantity of the difference between adjacent wave crests and wave troughs in the electroencephalogram data, wherein the difference is larger than 5 mu V, and representing the section of data between the wave crests and the wave troughs by 0 as other parts;
and performing non-overlapping windowing calculation on the electroencephalogram data, and calculating the percentage of the data quantity of the inhibition part in each window to all the data quantity in the total window to obtain the electroencephalogram explosion inhibition rate.
8. The method for electroencephalogram signal analysis based on claim 1, wherein the method for calculating the electroencephalogram edge frequency in the step 2 is as follows:
drawing an fft spectrogram of the electroencephalogram data, calculating the total area of the frequency spectrum of 90% or 95% below the frequency spectrum data, and obtaining the frequency corresponding to the total area of the frequency spectrum, namely the edge frequency.
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| CN112438701A (en) * | 2019-08-16 | 2021-03-05 | 浙江象立医疗科技有限公司 | Method for detecting blood parameters and electrophysiological parameters based on photoelectric fusion |
| CN112515685A (en) * | 2020-11-10 | 2021-03-19 | 上海大学 | Multi-channel electroencephalogram signal channel selection method based on time-frequency co-fusion |
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| CN112438701A (en) * | 2019-08-16 | 2021-03-05 | 浙江象立医疗科技有限公司 | Method for detecting blood parameters and electrophysiological parameters based on photoelectric fusion |
| CN112515685A (en) * | 2020-11-10 | 2021-03-19 | 上海大学 | Multi-channel electroencephalogram signal channel selection method based on time-frequency co-fusion |
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| 孙媛;刘子毓;侯玉文;王铁英;张励;刘东培;: "麻醉深度监测系统的优化算法研究", 光电技术应用, no. 04, 15 August 2013 (2013-08-15), pages 41 - 44 * |
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