CN107049239A - Epileptic electroencephalogram (eeg) feature extracting method based on wearable device - Google Patents
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Abstract
The present invention discloses a kind of epileptic electroencephalogram (eeg) feature extracting method based on wearable device, comprises the following steps：The unmarked epileptic electroencephalogram (eeg) data of marked epileptic electroencephalogram (eeg) data and the wearable device collection gathered according to hospital, the calculation process calculated by core principle component analysis, multi-kernel support vector machine and migration feature, the grader that output is migrated with feature；The unmarked real-time epileptic electroencephalogram (eeg) data gathered to wearable device carry out feature extraction using core principle component analysis, the unmarked real-time epileptic electroencephalogram (eeg) data after feature extraction are put into grader, output predicts the outcome.The present invention is integrated the image resource of each hospital image section office and image center, is formed an epileptic electroencephalogram (eeg) feature extracting method based on wearable device for serving clinic, is improved clinical position efficiency.The epileptic electroencephalogram (eeg) feature extracting method that the present invention is provided can quickly, accurately be used for feature extraction and the prediction of result of the epileptic electroencephalogram (eeg) of wearable device.
Description
Technical Field
The invention relates to the technical field of electroencephalogram data processing, in particular to an epilepsia electroencephalogram feature extraction method based on wearable equipment.
Background
Epilepsy is a transient cerebral dysfunction caused by paroxysmal abnormality of cerebral neurons, and has the characteristics of complexity and recurrence. EEG (electroencephalogram) is an important means for recognizing epilepsy, and epilepsy electroencephalogram recognition based on a feature extraction technology has recently attracted more and more attention.
The wearable electroencephalogram detection equipment provides great convenience for patients and medical personnel, but due to the particularity and timeliness of the wearable electroencephalogram detection equipment, the feasibility of manual marking of the wearable electroencephalogram detection equipment is low.
Disclosure of Invention
Aiming at the defects in the technology, the invention provides the wearable equipment-based epilepsia electroencephalogram feature extraction method, and the method for extracting the epilepsia electroencephalogram feature for the wearable equipment is rapid and high in accuracy through the methods of kernel principal component analysis, multi-core support vector machine and transfer learning.
To achieve these objects and other advantages in accordance with the present invention, the present invention is implemented by the following solutions:
the invention provides a wearable device-based epilepsia electroencephalogram feature extraction method, which comprises the following steps:
outputting a classifier with feature migration through the operation processing of kernel principal component analysis, a multi-kernel support vector machine and migration feature calculation according to marked epilepsy electroencephalogram data collected by a hospital and unmarked epilepsy electroencephalogram data collected by wearable equipment;
and performing feature extraction on the unmarked real-time epilepsy electroencephalogram data acquired by the wearable equipment by adopting kernel principal component analysis, putting the unmarked real-time epilepsy electroencephalogram data after the feature extraction into the classifier, and outputting a prediction result.
Preferably, the operation processing of the kernel principal component analysis, the multi-kernel support vector machine and the migration feature calculation includes the following steps:
acquiring marked epilepsy electroencephalogram data collected by a hospital and unmarked epilepsy electroencephalogram data collected by wearable equipment in groups, and respectively preprocessing the two groups of data;
performing feature extraction on the preprocessed marked epilepsy electroencephalogram data by adopting kernel principal component analysis, and outputting a first feature parameter matrix;
performing classifier training on the marked epilepsy electroencephalogram data after feature extraction by adopting a multi-core support vector machine, and outputting a first classifier;
performing feature extraction on the preprocessed unmarked epilepsia electroencephalogram data by adopting kernel principal component analysis, and outputting a second feature parameter matrix;
performing space calculation on the first characteristic parameter matrix and the second characteristic parameter matrix, and outputting migration characteristics;
and putting the migration features into the classifier, performing migration learning, and outputting an updated second classifier.
Preferably, the spatial calculation of the first characteristic parameter matrix and the second characteristic parameter matrix and the output of the migration characteristic include the following steps:
defining the Euclidean distance between the n-dimensional principal components of the marked epileptic electroencephalogram data in the first characteristic parameter matrix and the unmarked epileptic electroencephalogram data in the second characteristic parameter matrix as dis (n), and then, if dis (n) ═ PCAlabel-PCAunlabel；
Setting a distance threshold, sorting dis (n), deleting the feature dimension of which the value of dis (n) is smaller than the distance threshold, and finishing feature migration.
Preferably, the method for extracting the characteristics of the unmarked real-time epilepsia electroencephalogram data collected by the wearable device by adopting kernel principal component analysis comprises the following steps:
preprocessing the unmarked real-time epilepsia electroencephalogram data;
and performing feature extraction on the preprocessed unmarked real-time epilepsia electroencephalogram data by adopting kernel principal component analysis, and outputting a third feature parameter matrix.
Preferably, the preprocessing includes sequentially performing a process of removing electro-oculogram, filtering, and baseline calibration on each set of data.
Preferably, the filtering comprises EEMD-ICA based filtering.
Preferably, the method further comprises the steps of:
adding unmarked real-time epilepsia electroencephalogram data acquired by wearable equipment into the unmarked epilepsia electroencephalogram data, and outputting a classifier updated in real time during operation processing together with marked epilepsia electroencephalogram data acquired by a hospital.
Preferably, the feature extraction by using the kernel principal component analysis comprises the following steps:
the mapping of the input space X to the feature space F, i.e. X, is realized by transforming hl＝＞h(xl)；
Principal component analysis is used in the feature space, namely, a feature solution is solved: lambda [ alpha ]iui＝Cui(ii) a 1, 2.. ·, l; wherein,is a sample covariance matrix, λ, in the feature spaceiIs a non-zero eigenvalue of C, uiIs λiThe corresponding feature vector;
and arranging the eigenvectors into a matrix from top to bottom according to the size of the corresponding eigenvalue, and taking the first k rows to form a matrix P, wherein P is the obtained eigenvector matrix.
Preferably, the classifier training is performed by using a multi-core support vector machine, and comprises the following steps:
a kernel matrix is established for each feature using radial basis functions: based on n training samples, the feature vector of the ith sample is defined asThe label corresponding to each feature vector is yi-1,1 }; wherein M represents M types of features;
using a weighting factor βmAnd forming a mixed kernel matrix by the multi-kernel support vector machine, wherein the mixed kernel matrix is defined as:
the decision function is
Wherein,when 0 is less than or equal to αiWhen the content is less than or equal to C,phi (-) represents a kernel-function-guided mapping function; k is a radical of(m)(xi (m),xj (m)) Representing a training sample xi (m)And xj (m)In the mth characteristic, the kernel matrix is a Lagrange multiplier, and represents the inner product operation, βmAnd more than or equal to 0 represents the weight factor of the mth characteristic, and C represents the number of the constraint conditions in the model parameter.
The invention at least comprises the following beneficial effects:
according to the epilepsy electroencephalogram feature extraction method based on the wearable device, a classifier with feature migration is output through operation processing of kernel principal component analysis, a multi-core support vector machine and migration feature calculation according to marked epilepsy electroencephalogram data collected by a hospital and unmarked epilepsy electroencephalogram data collected by the wearable device, unmarked real-time epilepsy electroencephalogram data collected by the wearable device after feature extraction is put into the classifier, a prediction result is output, and the method can be used for feature extraction and result prediction of the epilepsy electroencephalogram of the wearable device quickly and accurately.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention.
Drawings
Fig. 1 is a schematic diagram of an epilepsia electroencephalogram feature extraction method based on wearable equipment;
FIG. 2 is a flow chart of an epilepsia electroencephalogram feature extraction method based on a wearable device according to the present invention;
FIG. 3 is a flowchart of the computational processing of the kernel principal component analysis, the multi-kernel support vector machine, and the migration feature calculation of the present invention;
FIG. 4 is a flow chart of a method for feature extraction using kernel principal component analysis according to the present invention.
Detailed Description
The present invention is further described in detail below with reference to the attached drawings so that those skilled in the art can implement the invention by referring to the description text.
It will be understood that terms such as "having," "including," and "comprising," as used herein, do not preclude the presence or addition of one or more other elements or groups thereof.
As shown in fig. 1 and fig. 2, the invention provides a wearable device-based epilepsia electroencephalogram feature extraction method, which includes the following steps:
s10, outputting a classifier with feature migration through operation processing of kernel principal component analysis, a multi-kernel support vector machine and migration feature calculation according to marked epilepsy electroencephalogram data acquired by a hospital and unmarked epilepsy electroencephalogram data acquired by wearable equipment;
and S20, performing feature extraction on the unmarked real-time epilepsy electroencephalogram data acquired by the wearable device by adopting kernel principal component analysis, putting the unmarked real-time epilepsy electroencephalogram data after feature extraction into a classifier, and outputting a prediction result.
In the above embodiment, as can be seen from fig. 1 and 3, the operation processing of the kernel principal component analysis, the multi-kernel support vector machine, and the migration feature calculation in step S10 includes the following steps:
s11, acquiring marked epilepsy electroencephalogram data acquired by a hospital and unmarked epilepsy electroencephalogram data acquired by wearable equipment in groups, and preprocessing the two groups of data respectively;
s12, performing feature extraction on the preprocessed marked epilepsy electroencephalogram data by adopting kernel principal component analysis, and outputting a first feature parameter matrix;
s13, performing classifier training on the marked epilepsy electroencephalogram data after feature extraction by adopting a multi-core support vector machine, and outputting a first classifier; the method for training the classifier by adopting the multi-core support vector machine specifically comprises the following steps:
s131, establishing a kernel matrix for each feature by using the radial basis function: based on n training samples, the feature vector of the ith sample is defined as xi＝{xi (1),...,xi (M)And y is the label corresponding to each feature vectori-1,1 }; wherein M represents M types of features;
s132, applying a weighting factor βmAnd forming a mixed kernel matrix by the multi-kernel support vector machine, wherein the mixed kernel matrix is defined as:
the decision function is
Wherein,when 0 is less than or equal to αiWhen the content is less than or equal to C,phi (-) represents a kernel-function-guided mapping function; k is a radical of(m)(xi (m),xj (m)) Representing a training sample xi (m)And xj (m)In the mth characteristic, the kernel matrix is a Lagrange multiplier, and represents the inner product operation, βmAnd more than or equal to 0 represents the weight factor of the mth characteristic, and C represents the number of the constraint conditions in the model parameter.
S14, performing feature extraction on the preprocessed unmarked epilepsia electroencephalogram data by adopting kernel principal component analysis, and outputting a second feature parameter matrix;
s15, performing spatial calculation on the first characteristic parameter matrix and the second characteristic parameter matrix, and outputting the migration characteristic, specifically including the following steps:
s151, defining an euclidean distance between the n-th dimensional principal components of the labeled epileptic electroencephalogram data in the first characteristic parameter matrix and the unlabeled epileptic electroencephalogram data in the second characteristic parameter matrix as dis (n), and then, dis (n) ═ PCAlabel-PCAunlabel；
S152, setting a distance threshold, sequencing dis (n), deleting the feature dimension of which the value of dis (n) is smaller than the distance threshold, and finishing feature migration.
And S16, putting the migration features into the classifier, performing migration learning, and outputting an updated second classifier. In the above embodiment, the feature extraction by using kernel principal component analysis includes the following steps:
s111, mapping the input space X to the feature space F is realized through transformation h, namely Xl＝＞h(xl)；
And S112, using principal component analysis in the feature space, namely solving a feature solution: lambda [ alpha ]iui＝Cui(ii) a 1, 2.. ·, l; wherein,is specially designed forSample covariance matrix in eigenspace, λiIs a non-zero eigenvalue of C, uiIs λiThe corresponding feature vector;
s113, arranging the eigenvectors into a matrix from top to bottom according to the size of the corresponding eigenvalue, and taking the first k rows to form a matrix P, wherein P is the obtained eigenvector. In this embodiment, since a large number of complex nonlinear relations exist in the unlabeled epileptic electroencephalogram data acquired by the wearable device, compared with the Principal Component Analysis (PCA) in the prior art, the Kernel Principal Component Analysis (KPCA) adopted in the present application is more suitable for feature extraction of high-dimensional nonlinear data, and has higher processing accuracy.
Transfer learning can identify knowledge learned from a previous domain and apply it to another domain. Therefore, in the step S15 and the step S16, the step of putting the output migration features and the migration features into a classifier for migration learning means that the first feature parameter matrix is used as a source field, the second feature parameter matrix is used as a target field, and the first feature parameter matrix and the second feature parameter matrix are respectively subjected to feature extraction by using kernel principal component analysis, so that data of the first feature parameter matrix and data of the second feature parameter matrix belong to one feature space, and only feature distribution is different, so that the first feature parameter matrix and the second feature parameter matrix are subjected to spatial calculation, and after the migration features are output, the migration features are put into the classifier for migration learning, so that the second classifier suitable for classifying unmarked epileptic electroencephalogram data acquired by wearable equipment corresponding to the second feature parameter matrix can be output. The second classifier is used as an initial value, and can be used for training the unmarked real-time epilepsia electroencephalogram data after the feature extraction in the step S20, so that a prediction result is obtained.
In the foregoing embodiment, as shown in fig. 4, the performing, in step S20, feature extraction on the unmarked real-time epilepsy electroencephalogram data acquired by the wearable device by using kernel principal component analysis includes the following steps:
s21, preprocessing unmarked real-time epilepsia electroencephalogram data;
and S22, performing feature extraction on the preprocessed unmarked real-time epilepsia electroencephalogram data by adopting kernel principal component analysis, and outputting a third feature parameter matrix. In this embodiment, the preprocessing includes performing a process of removing electro-oculogram, filtering, and baseline calibration on each set of data in sequence. Further, the filtering includes filtering based on EEMD (noise aided data analysis method) -ICA (Independent Component analysis).
According to the wearable device-based epilepsy electroencephalogram feature extraction method provided by the embodiment, firstly, according to marked epilepsy electroencephalogram data collected by a hospital and unmarked epilepsy electroencephalogram data collected by a wearable device, through operation processing of kernel principal component analysis, a multi-core support vector machine and migration feature calculation, after a classifier with feature migration is output, namely, the second classifier in the embodiment is adopted, a large amount of unmarked real-time epilepsy electroencephalogram data collected subsequently by the wearable device can be classified and predicted through the second classifier, and a wearable device holder can conveniently monitor the unmarked real-time epilepsy electroencephalogram data in real time and obtain a high-precision classification result.
As another embodiment of the present invention, the wearable device-based epileptic electroencephalogram feature extraction method further includes the steps of:
and S30, adding the unmarked real-time epilepsy electroencephalogram data acquired by the wearable device into the unmarked epilepsy electroencephalogram data, and outputting a classifier updated in real time during the operation processing together with the marked epilepsy electroencephalogram data acquired by the hospital.
In the embodiment, the unmarked real-time epilepsy electroencephalogram data collected by the wearable device is added into the unmarked epilepsy electroencephalogram data, and the unmarked real-time epilepsy electroencephalogram data and the marked epilepsy electroencephalogram data collected by the hospital are subjected to operation processing together to update the classifier in real time, namely update the second classifier in real time. Along with the continuous change and the update of unmarked real-time epilepsia electroencephalogram data collected by the wearable device, the model of the second classifier can be gradually optimized, and the classification accuracy is improved.
While embodiments of the invention have been disclosed above, it is not intended to be limited to the uses set forth in the specification and examples. It can be applied to all kinds of fields suitable for the present invention. Additional modifications will readily occur to those skilled in the art. It is therefore intended that the invention not be limited to the exact details and illustrations described and illustrated herein, but fall within the scope of the appended claims and equivalents thereof.
Claims (9)
1. A epilepsia electroencephalogram feature extraction method based on wearable equipment is characterized by comprising the following steps:
outputting a classifier with feature migration through the operation processing of kernel principal component analysis, a multi-kernel support vector machine and migration feature calculation according to marked epilepsy electroencephalogram data collected by a hospital and unmarked epilepsy electroencephalogram data collected by wearable equipment;
and performing feature extraction on the unmarked real-time epilepsy electroencephalogram data acquired by the wearable equipment by adopting kernel principal component analysis, putting the unmarked real-time epilepsy electroencephalogram data after the feature extraction into the classifier, and outputting a prediction result.
2. The wearable device-based epilepsia electroencephalogram feature extraction method as claimed in claim 1, wherein the computational processing of kernel principal component analysis, multi-kernel support vector machine and migration feature calculation comprises the following steps:
acquiring marked epilepsy electroencephalogram data collected by a hospital and unmarked epilepsy electroencephalogram data collected by wearable equipment in groups, and respectively preprocessing the two groups of data;
performing feature extraction on the preprocessed marked epilepsy electroencephalogram data by adopting kernel principal component analysis, and outputting a first feature parameter matrix;
performing classifier training on the marked epilepsy electroencephalogram data after feature extraction by adopting a multi-core support vector machine, and outputting a first classifier;
performing feature extraction on the preprocessed unmarked epilepsia electroencephalogram data by adopting kernel principal component analysis, and outputting a second feature parameter matrix;
performing space calculation on the first characteristic parameter matrix and the second characteristic parameter matrix, and outputting migration characteristics;
and putting the migration features into the classifier, performing migration learning, and outputting an updated second classifier.
3. The wearable device-based epilepsia electroencephalogram feature extraction method as claimed in claim 2, wherein the spatial calculation is performed on the first feature parameter matrix and the second feature parameter matrix, and the migration feature is output, comprising the following steps:
defining the Euclidean distance between the n-dimensional principal components of the marked epileptic electroencephalogram data in the first characteristic parameter matrix and the unmarked epileptic electroencephalogram data in the second characteristic parameter matrix as dis (n), and then, if dis (n) ═ PCAlabel-PCAunlabel；
Setting a distance threshold, sorting dis (n), deleting the feature dimension of which the value of dis (n) is smaller than the distance threshold, and finishing feature migration.
4. The wearable device-based epilepsia electroencephalogram feature extraction method as claimed in claim 1, wherein feature extraction is performed on unlabeled real-time epilepsia electroencephalogram data collected by the wearable device by adopting kernel principal component analysis, and the method comprises the following steps:
preprocessing the unmarked real-time epilepsia electroencephalogram data;
and performing feature extraction on the preprocessed unmarked real-time epilepsia electroencephalogram data by adopting kernel principal component analysis, and outputting a third feature parameter matrix.
5. The wearable device based epileptic brain electrical characteristic extraction method as claimed in claim 2 or 3, wherein the preprocessing comprises sequentially processing each set of data for eye electrical removal, filtering and baseline calibration.
6. The wearable device based epileptic brain electrical feature extraction method of claim 4, wherein the filtering comprises EEMD-ICA based filtering.
7. The wearable device-based epileptic electroencephalogram feature extraction method according to any one of claims 1-6, further comprising the steps of:
adding unmarked real-time epilepsia electroencephalogram data acquired by wearable equipment into the unmarked epilepsia electroencephalogram data, and outputting a classifier updated in real time during operation processing together with marked epilepsia electroencephalogram data acquired by a hospital.
8. The wearable device based epilepsia electroencephalogram feature extraction method as claimed in any one of claims 1-6, wherein feature extraction is performed by using the kernel principal component analysis, and the method comprises the following steps:
the mapping of the input space X to the feature space F, i.e. X, is realized by transforming hl＝＞h(xl)；
Principal component analysis is used in the feature space, namely, a feature solution is solved: lambda [ alpha ]iui＝Cui(ii) a 1, 2.. ·, l; wherein,is a sample covariance matrix, λ, in the feature spaceiIs a non-zero eigenvalue of C, uiIs λiThe corresponding feature vector;
and arranging the eigenvectors into a matrix from top to bottom according to the size of the corresponding eigenvalue, and taking the first k rows to form a matrix P, wherein P is the obtained eigenvector matrix.
9. The wearable device-based epileptic electroencephalogram feature extraction method as claimed in any one of claims 1-6, wherein a multi-core support vector machine is adopted for classifier training, comprising the following steps:
a kernel matrix is established for each feature using radial basis functions: based on n training samples, the feature vector of the ith sample is defined as xi＝{xi (1),...,xi (M)And y is the label corresponding to each feature vectori-1,1 }; wherein M represents M types of features;
using a weighting factor βmAnd forming a mixed kernel matrix by the multi-kernel support vector machine, wherein the mixed kernel matrix is defined as:
the decision function is
Wherein k is(m)(xi (m),xj (m))＝＜φ(xi (m)),φ(xj (m)) When 0 is not more than αiWhen the content is less than or equal to C,phi (-) represents a kernel-function-guided mapping function; k is a radical of(m)(xi (m),xj (m)) Representing a training sample xi (m)And xj (m)In the mth characteristic, the kernel matrix is a Lagrange multiplier, and represents the inner product operation, βmAnd more than or equal to 0 represents the weight factor of the mth characteristic, and C represents the number of the constraint conditions in the model parameter.
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