CN111938631A - Convulsion detection method based on neonatal electroencephalogram signal - Google Patents
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Abstract
The invention discloses a convulsion detection method based on neonatal electroencephalogram signals, which comprises a training part and a testing part, wherein the training part is used for filtering data by 0.5-40 Hz, then carrying out down-sampling of 32Hz, then carrying out filtering of 0.5-12.5 Hz, then dividing all data into 24s sections, extracting and standardizing characteristic values, carrying out classification marking on the characteristic values, and training by using an SVM (support vector machine) to obtain a model; and the test part is used for filtering the data at 0.5-40 Hz, then carrying out down sampling at 32Hz, then carrying out filtering at 0.5-12.5 Hz, extracting a characteristic value of the data every 24s, eliminating non-convulsion data according to a threshold condition, standardizing the data which cannot be eliminated, predicting by using a model, and judging whether the data is convulsion data. The method can detect the convulsion of the neonatal electroencephalogram signal, can assist medical personnel in diagnosing the convulsion of the neonatal, and improves the diagnosis efficiency.
Description
Technical Field
The invention relates to a convulsion detection method based on a neonatal electroencephalogram signal, and belongs to an image processing technology.
Background
Convulsion is the abnormal discharge of brain cells caused by the temporary disorder of cerebral cortex function, which is manifested by the sudden occurrence of involuntary myotonia and clonic convulsion in the systemic or local skeletal muscle group and the resulting joint movement, mostly systemic and symmetric, often accompanied by disturbance of consciousness. Convulsions can be caused by abnormal discharges in the central nervous system only, but such abnormal discharges can be caused by many primary intracranial lesions (meningitis, cerebrovascular accidents, encephalitis, intracranial haemorrhages, tumors), or secondary to systemic or metabolic (e.g. ischemia, hypoxia, hypoglycemia, hypocalcemia, hyponatremia) diseases. Complications such as asphyxia and cerebral anoxia easily occur in neonatal convulsion, and care, prevention and health care of neonates should be closely paid attention.
Therefore, the detection significance for the neonatal convulsion is important, the detection equipment for the neonatal convulsion is not common at present, and the neonatal electroencephalogram is monitored to detect whether the neonatal convulsion occurs or not without precedent.
Reference to the literature
【1】Temko,A.,et al.,EEG-based neonatal seizure detection with Support Vector Machines.Clinical Neurophysiology,2011.122(3):p.464-473.
【2】Gotman,J.,et al.,Automatic seizure detection in the newborn:methods and initial evaluation.Electroencephalography and Clinical Neurophysiology,1997.103(3):p.356-362.
【3】Temko,A.,et al.,Clinical implementation of a neonatal seizure detection algorithm.Decision Support Systems,2015.70:p.86-96.
Disclosure of Invention
The purpose of the invention is as follows: in order to overcome the defects of the prior art, the invention provides a convulsion detection method based on a neonatal electroencephalogram signal, which can detect whether a neonate has convulsion and prevent the neonate from suffering from the convulsion.
The technical scheme is as follows: in order to achieve the purpose, the invention adopts the technical scheme that:
a convulsion detection method based on neonatal brain electrical signals comprises a training phase and a detection phase, and specifically comprises the following steps:
a training stage:
s11, collecting electroencephalogram signals of P (P is more than 50) convulsion newborns as samples, and performing band-pass filtering for removing error data; the original electroencephalogram signals contain a large amount of noise, and can not be directly interpreted manually, wherein a band-pass filter for eliminating error data is firstly carried out, and then manual interpretation can be carried out; preferably, 0.5-40 Hz band-pass filtering is adopted, and certainly, the given cut-off frequency is not fixed, 0.5-70 Hz band-pass filtering can also be adopted, or the determination is carried out according to the quality of the electroencephalogram signals;
s12, performing down-sampling on all samples obtained in the step S11, preferably adopting 32Hz down-sampling; performing band-pass filtering for calculating data screening, wherein the band-pass filtering is to remove unnecessary signals, and preferably adopts 0.5-12.5 Hz band-pass filtering (reference 1);
s13, dividing each sample obtained in the step S12 into data segments of ks segment, and discarding the data segments which are less than ks;
s14, identifying all data segments, dividing the data segments into three data sets according to the existence of convulsion, wherein the three data sets are a convulsion data set, a non-convulsion data set and other data sets respectively, all convulsion data form the convulsion data set, all non-convulsion data form the non-convulsion data set, and ks data segments except the convulsion data and the non-convulsion data form other data sets; the convulsion data is a ks data section with a convulsion signal appearing every second in the ks data section, and the non-convulsion data is a ks data section without a convulsion signal appearing every second in the ks data section;
s15, marking the convulsion data as a and the non-convulsion data as b;
s16, extracting characteristic values of all the convulsion data and non-convulsion data;
s17, standardizing all the characteristic values to obtain the average value and the variance of normal distribution;
s18, respectively and randomly selecting Q (Q is more than or equal to 1000 and is not more than 30 percent of all data) data from the convulsive data and the non-convulsive data according to the normalized characteristic values, and selecting all or part of the characteristic values extracted in the step S16 to perform classification training to obtain a classification Model;
and (3) a testing stage:
s21, performing band-pass filtering processing for eliminating error data on the collected neonatal electroencephalogram signals;
s22, down-sampling the data obtained in the step S21, performing band-pass filtering for data screening, and dividing the obtained data into a detected part and an undetected part;
s23, if the length of the undetected part is greater than or equal to ks, extracting a front ks data segment of the undetected part;
s24, extracting characteristic values of the ks data segments extracted in the step S23;
s25, carrying out preliminary judgment on the characteristic values extracted in the step S24, and confirming whether the ks data segments extracted in the step S23 are non-convulsive data or not;
s26, if the feature value extracted in step S24 cannot be determined as non-convulsive data in step S25, normalizing the feature value, and predicting using the classification Model obtained in step S18: if the prediction is a, the ks data segment extracted in the step S23 is regarded as the convulsion data; if b is predicted, the ks data segment extracted in step S23 is regarded as non-convulsive data;
s27, marking the ks data segments extracted in the step S23 as detection parts and removing the ks data segments from the non-detection parts;
s28, looping the steps S23-S27 until the length of all the detected data or the undetected part is less than ks.
Preferably, in step S18, a classification Model is obtained by performing classification training using a support vector machine, a decision tree, a neural network, and the like, wherein the classification accuracy of the support vector machine is higher; if a support vector machine is adopted for classification training, the model used in the training is an SVM classification model (support vector machine classification model), the kernel function is an RBF kernel function (radial basis kernel function), the penalty coefficient of the kernel function is 10, and the gamma function gamma of the kernel function is the reciprocal of the total number of the feature vectors.
Preferably, in step S16, for any ks data segment, the feature value is extracted as follows:
(a) frequency domain main peak average frequency: firstly, dividing a ks data segment into m k/ms data segments; then calculating the power density spectrum of each k/ms data segment, and taking the peak with the maximum amplitude in each k/ms data segment as the main peak of the k/ms data segment to obtain the corresponding main peak frequency; finally, averaging the m main peak frequencies, namely the frequency domain main peak average frequency of the ks data segment; wherein m is a positive integer;
(b) mean frequency standard deviation of main peak in frequency domain: based on the step (a), calculating the standard deviation of the m main peak frequencies, namely the frequency domain main peak average frequency standard deviation of the ks data segment;
(c) average wave width of main peak in frequency domain: based on the step (a), averaging the half wave widths corresponding to the m main peaks, namely the average wave width of the main peak of the frequency domain of the ks data segment;
(d) frequency domain main peak power ratio average: based on the step (a), calculating power of a half-wave-width area corresponding to each main peak, taking the ratio of the power to the total power of the k/ms data segment as the main peak power ratio of the k/ms data segment, and averaging m main peak power ratios to obtain the average value of the frequency-domain main peak power ratio of the ks data segment;
(e) time domain average peak height: dividing a ks data segment into n sections of k/ns data segments; then the highest peak height of each k/ns data segment is taken as a peak extreme value; finally, averaging the n peak extreme values, namely the time domain average peak height of the ks data segment; wherein n is a positive integer greater than m;
(f) height stability of time domain peak: based on step (e), taking the maximum/minimum value of the n peak extrema as the time domain peak height stability of the ks data segment;
(g) time domain average peak width: based on the step (e), averaging the half wave widths corresponding to the n peak extrema, namely the time domain average peak width of the ks data segment;
(h) stability of time domain peak width: and (e) based on the step (e), taking the maximum value/minimum value in the half wave width corresponding to the n peak extremum values as the time domain peak width stability of the ks data segment.
Preferably, in step S18, after removing two feature values of the time domain peak height stability and the time domain peak width stability, performing classification training on the remaining feature values to obtain a classification Model; during testing, the two items are eliminated, and the accuracy of the classification Model cannot be greatly influenced, so that the two items are eliminated during training, and the performance problem is mainly considered.
Preferably, in step S25, the determination is performed according to the following conditions (the threshold value in the following determination conditions is variable, and is determined after the test), and if one or more of the following conditions are satisfied, the ks data segment extracted in step S23 is regarded as non-convulsive data:
frequency domain main peak average frequency fm：fm<0.5Hz or fm>5Hz；
Frequency domain main peak average frequency standard deviation sigmam：σm>7Hz；
③ frequency domain main peak average bandwidth wf：wf>2Hz；
Frequency domain main peak power ratio average value r: r <0.2 or r > 0.8;
time domain average peak height ht：ht<20 μ V or ht>250μV
Time domain peak height stability s1：s1<1 or s1>6；
Seventhly, the time domain average peak width wt：wt<0.18s or wt>1s；
Stability s of width of time domain wave peak2：s2<1 or s2>6。
Preferably, k is 24, 10 or 8, and after multiple tests, the detection result obtained by adopting the value of k-24 can better meet the application requirement; m-3, which is a more preferred value for k-24; n is 12, which is a preferable value for k is 24, because the frequency of the convulsion signal is generally 0.5 to 5Hz, and the time domain period when n is 12 is 2s, if there is convulsion, there is a convulsion signal in the time domain period.
Has the advantages that: the convulsion detection method based on the neonatal electroencephalogram signals can be used for detecting convulsions of the neonatal electroencephalogram signals, can assist medical staff in diagnosing the convulsions of the neonates, and improves diagnosis efficiency.
Drawings
Both (a) and (b) in fig. 1 are brain electrical signal samples with convulsion;
both (a) and (b) in fig. 2 are normal brain electrical signal samples;
FIG. 3 is a schematic diagram of a training process;
FIG. 4 is a schematic diagram of a test flow;
Detailed Description
The invention is described in detail below with reference to the figures and the embodiments.
In the description of the present invention, it is to be understood that the terms "center", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on those shown in the drawings, and are used only for convenience in describing the present invention and for simplicity in description, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and thus, are not to be construed as limiting the present invention. Furthermore, the terms "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
A convulsion detection method based on neonatal brain electrical signals comprises a training phase and a detection phase, and specifically comprises the following steps:
a training stage:
s11, collecting electroencephalogram signals of P convulsion neonates as samples, and performing band-pass filtering at 0.5-40 Hz;
s12, carrying out 32Hz down-sampling on all samples obtained in the step S11, and carrying out 0.5-12.5 Hz band-pass filtering;
s13, dividing each sample obtained in the step S12 into 24S data segments, and discarding the data segments less than 24S;
s14, identifying all data segments, dividing the data segments into three data sets according to the existence of convulsion, wherein the three data sets are a convulsion data set, a non-convulsion data set and other data sets respectively, all convulsion data form the convulsion data set, all non-convulsion data form the non-convulsion data set, and 24S data segments except the convulsion data and the non-convulsion data form other data sets; the convulsion data is a 24s data segment with a convulsion signal appearing every second in the 24s data segment, and the non-convulsion data is a 24s data segment without the convulsion signal appearing every second in the 24s data segment;
s15, labeling the convulsion data as 1 and the non-convulsion data as-1;
s16, extracting characteristic values of all the convulsion data and non-convulsion data; for any 24s data segment, extracting characteristic values according to the following modes:
(a) frequency domain main peak average frequency: firstly, dividing a 24s data section into three 8s data sections; then calculating the power density spectrum of each section of 8s data segment, and taking the peak with the maximum amplitude in each section of 8s data segment as the main peak of the 8s data segment to obtain the corresponding main peak frequency; finally, averaging the three main peak frequencies, namely the frequency domain main peak average frequency of the 24s data segment;
(b) mean frequency standard deviation of main peak in frequency domain: based on the step (a), calculating the standard deviation of the three main peak frequencies, namely the standard deviation of the frequency domain main peak average frequency of the 24s data segment;
(c) average wave width of main peak in frequency domain: based on the step (a), averaging the half wave widths corresponding to the three main peaks, namely the frequency domain main peak average wave width of the 24s data segment;
(d) frequency domain main peak power ratio average: based on the step (a), calculating power of a half-wave-width region corresponding to each main peak, taking the ratio of the power to the total power of the corresponding 8s data segment as the main peak power ratio of the 8s data segment, and averaging the three main peak power ratios to obtain the frequency domain main peak power ratio average value of the 24s data segment;
(e) time domain average peak height: firstly, dividing a 24s data section into twelve sections of 2s data sections; then taking the highest peak height of each 2s data segment as a peak extreme value; finally, averaging twelve peak extreme values, namely the time domain average peak height of the 24s data segment;
(f) height stability of time domain peak: based on step (e), taking the maximum/minimum of the twelve peak extrema as the temporal peak height stability of the 24s data segment;
(g) time domain average peak width: based on the step (e), averaging the half wave widths corresponding to the twelve peak extrema, namely the time domain average peak width of the 24s data segment;
(h) stability of time domain peak width: based on the step (e), taking the maximum value/minimum value in the half wave width corresponding to the twelve peak extrema as the time domain peak width stability of the 24s data segment;
s17, standardizing all the characteristic values to obtain the average value and the variance of normal distribution;
s18, respectively and randomly selecting 1000 pieces of data from the convulsive data and the non-convulsive data as training data for the normalized characteristic values, removing two characteristic values of time domain peak height stability and time domain peak width stability, and then carrying out classification training by using a support vector machine to obtain a classification Model; the model used in training is an SVM classification model (support vector machine classification model), the kernel function is an RBF kernel function (radial basis kernel function), the penalty coefficient of the kernel function is 10, and the gamma function of the kernel function is 1/6 (gamma is 1/6);
and (3) a testing stage:
s21, filtering the collected neonatal electroencephalogram signals by using a 0.5-40 Hz band-pass filter;
s22, carrying out down-sampling at 32Hz on the data obtained in the step S21, carrying out band-pass filtering at 0.5-12.5 Hz, and dividing the obtained data into a detected part and an undetected part;
s23, if the length of the undetected part is more than or equal to 24S, extracting the first 24S data segment of the undetected part;
s24, extracting characteristic values of the 24S data segments extracted in the step S23;
s25, the eigenvalue extracted in step S24 is judged according to the following conditions, and if one or more of the following conditions are satisfied, the 24S data segment extracted in step S23 is considered as non-convulsive data:
frequency domain main peak average frequency fm：fm<0.5Hz or fm>5Hz；
Frequency domain main peak average frequency standard deviation sigmam：σm>7Hz；
③ frequency domain main peak average bandwidth wf：wf>2Hz；
Frequency domain main peak power ratio average value r: r <0.2 or r > 0.8;
time domain average peak height ht：ht<20 μ V or ht>250μV
Time domain peak height stability s1：s1<1 or s1>6；
Seventhly, the time domain average peak width wt：wt<0.18s or wt>1s；
Stability s of width of time domain wave peak2：s2<1 or s2>6；
S26, if the feature value extracted in step S24 does not satisfy any of the conditions given in step S25, normalizing the feature value extracted in step S24, and predicting using the classification Model obtained in step S18: if the prediction is 1, the 24S data segment extracted in the step S23 is regarded as convulsion data; if the prediction is-1, the 24S data segment extracted in the step S23 is regarded as non-convulsive data;
s27, marking the 24S data segment extracted in the step S23 as a detection part and removing the detection part from the non-detection part;
s28, looping the steps S23-S27 until the length of the detected or undetected part of all the data is less than 24S.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing illustrates and describes the principles, general features, and advantages of the present invention. It should be understood by those skilled in the art that the above embodiments do not limit the present invention in any way, and all technical solutions obtained by using equivalent alternatives or equivalent variations fall within the scope of the present invention.
Claims (7)
1. A convulsion detection method based on neonatal electroencephalogram signals is characterized in that: the method comprises a training stage and a detection stage, and specifically comprises the following steps:
a training stage:
s11, collecting electroencephalogram signals of P convulsion neonates as samples, and performing band-pass filtering for removing error data;
s12, down-sampling all samples obtained in the step S11, and calculating band-pass filtering of data screening;
s13, dividing each sample obtained in the step S12 into data segments of ks segment, and discarding the data segments which are less than ks;
s14, identifying all data segments, dividing the data segments into three data sets according to the existence of convulsion, wherein the three data sets are a convulsion data set, a non-convulsion data set and other data sets respectively, all convulsion data form the convulsion data set, all non-convulsion data form the non-convulsion data set, and ks data segments except the convulsion data and the non-convulsion data form other data sets; the convulsion data is a ks data section with a convulsion signal appearing every second in the ks data section, and the non-convulsion data is a ks data section without a convulsion signal appearing every second in the ks data section;
s15, marking the convulsion data as a and the non-convulsion data as b;
s16, extracting characteristic values of all the convulsion data and non-convulsion data;
s17, standardizing all the characteristic values to obtain the average value and the variance of normal distribution;
s18, respectively and randomly selecting Q pieces of data from the convulsive data and the non-convulsive data as training data for the normalized characteristic values, and selecting all or part of the characteristic values extracted in the step S16 to perform classification training to obtain a classification Model;
and (3) a testing stage:
s21, performing band-pass filtering processing for eliminating error data on the collected neonatal electroencephalogram signals;
s22, down-sampling the data obtained in the step S21, performing band-pass filtering for data screening, and dividing the obtained data into a detected part and an undetected part;
s23, if the length of the undetected part is greater than or equal to ks, extracting a front ks data segment of the undetected part;
s24, extracting characteristic values of the ks data segments extracted in the step S23;
s25, carrying out preliminary judgment on the characteristic values extracted in the step S24, and confirming whether the ks data segments extracted in the step S23 are non-convulsive data or not;
s26, if the feature value extracted in step S24 cannot be determined as non-convulsive data in step S25, normalizing the feature value, and predicting using the classification Model obtained in step S18: if the prediction is a, the ks data segment extracted in the step S23 is regarded as the convulsion data; if b is predicted, the ks data segment extracted in step S23 is regarded as non-convulsive data;
s27, marking the ks data segments extracted in the step S23 as detection parts and removing the ks data segments from the non-detection parts;
s28, looping the steps S23-S27 until the length of all the detected data or the undetected part is less than ks.
2. A method of detecting convulsions based on a neonatal brain electrical signal according to claim 1, wherein: in the step S18, a support vector machine is used to perform classification training to obtain a classification Model; the model used in training is an SVM classification model, the kernel function is an RBF kernel function, and the penalty coefficient of the kernel function is 10.
3. A method of detecting convulsions based on a neonatal brain electrical signal according to claim 1, wherein: in step S16, for any ks data segment, the feature value is extracted as follows:
(a) frequency domain main peak average frequency: firstly, dividing a ks data segment into m k/ms data segments; then calculating the power density spectrum of each k/ms data segment, and taking the peak with the maximum amplitude in each k/ms data segment as the main peak of the k/ms data segment to obtain the corresponding main peak frequency; finally, averaging the m main peak frequencies, namely the frequency domain main peak average frequency of the ks data segment; wherein m is a positive integer;
(b) mean frequency standard deviation of main peak in frequency domain: based on the step (a), calculating the standard deviation of the m main peak frequencies, namely the frequency domain main peak average frequency standard deviation of the ks data segment;
(c) average wave width of main peak in frequency domain: based on the step (a), averaging the half wave widths corresponding to the m main peaks, namely the average wave width of the main peak of the frequency domain of the ks data segment;
(d) frequency domain main peak power ratio average: based on the step (a), calculating power of a half-wave-width area corresponding to each main peak, taking the ratio of the power to the total power of the k/ms data segment as the main peak power ratio of the k/ms data segment, and averaging m main peak power ratios to obtain the average value of the frequency-domain main peak power ratio of the ks data segment;
(e) time domain average peak height: dividing a ks data segment into n sections of k/ns data segments; then the highest peak height of each k/ns data segment is taken as a peak extreme value; finally, averaging the n peak extreme values, namely the time domain average peak height of the ks data segment; wherein n is a positive integer greater than m;
(f) height stability of time domain peak: based on step (e), taking the maximum/minimum value of the n peak extrema as the time domain peak height stability of the ks data segment;
(g) time domain average peak width: based on the step (e), averaging the half wave widths corresponding to the n peak extrema, namely the time domain average peak width of the ks data segment;
(h) stability of time domain peak width: and (e) based on the step (e), taking the maximum value/minimum value in the half wave width corresponding to the n peak extremum values as the time domain peak width stability of the ks data segment.
4. A method of detecting convulsions based on neonatal brain electrical signals according to claim 3, wherein: in step S18, after two feature values of the time domain peak height stability and the time domain peak width stability are removed, the remaining feature values are classified and trained to obtain a classification Model.
5. A method of detecting convulsions based on neonatal brain electrical signals according to claim 3, wherein: in step S25, the ks data segment extracted in step S23 is regarded as the non-convulsive data if at least one of the following conditions is satisfied:
frequency domain main peak average frequency fm：fm<0.5Hz or fm>5Hz；
Frequency domain main peak average frequency standard deviation sigmam：σm>7Hz；
③ frequency domain main peak average bandwidth wf：wf>2Hz；
Frequency domain main peak power ratio average value r: r <0.2 or r > 0.8;
time domain average peak height ht：ht<20 μ V or ht>250μV
Time domain peak height stability s1：s1<1 or s1>6；
Seventhly, the time domain average peak width wt：wt<0.18s or wt>1s；
Stability s of width of time domain wave peak2：s2<1 or s2>6。
6. A method of detecting convulsions based on neonatal brain electrical signals according to claim 3, wherein: k is 24, m is 3, and n is 12.
7. A method of detecting convulsions based on neonatal brain electrical signals according to claim 3, wherein: the band-pass filtering for eliminating the error data is carried out, and the filtering range is 0.5-40 Hz; the band-pass filtering of the calculation data screening is carried out, and the filtering range is 0.5-12.5 Hz; the down-sampling has a frequency of 32 Hz.
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