CN113040772A - Classification method of syphilis addicts and normal persons based on electroencephalogram signals - Google Patents
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Abstract
The invention discloses a classification method of ice-poison addicts and normal persons based on electroencephalogram signals, which comprises the steps of firstly collecting the electroencephalogram signals of the ice-poison addicts and the normal persons when the ice-poison addicts and the normal persons react to a task, preprocessing the collected electroencephalogram signals, and obtaining electroencephalogram characteristics under the reaction task; then, respectively extracting the features of the electroencephalogram to obtain space-time cluster features; then, carrying out average calculation on the voltage/energy values in each space-time cluster in a time dimension and a space dimension so as to obtain a voltage/energy average value of each space-time cluster; training the voltage/energy average value of each cluster to obtain a classifier through a machine learning algorithm; finally, the trained classifier is used for inputting electroencephalogram signals, and the distinction between the ice-toxin addict and the normal person is realized. The invention can accurately judge whether the tested person is addicted to the methamphetamine or not through the electroencephalogram signal, has better robustness and can avoid dimension disaster in the machine learning process.
Description
Technical Field
The invention relates to the field of electroencephalogram signal processing, in particular to a classification method for syphilitic addicts and normal people based on electroencephalogram signals.
Background
Methamphetamine (methamphetamine) belongs to excitatory psychotropic drugs and has strong central excitation, and a strong excited state during drug absorption and an uncomfortable mood during drug non-absorption are two extremes, the excited state is pursued by people, although the damage to nerves and bodies is great; the patient is in depression and difficulty when not taking the drug, and the patient can continue to use the drug because the patient is excited when pursuing drug taking in order to avoid the difficulty, which can lead to the addiction of the ice drug.
The electroencephalogram signal is a bioelectricity signal generated by nerve cell activity, carries corresponding physiological and pathological information of the brain, and has important research significance in medical diagnosis, scientific exploration and engineering application. However, in the prior art, the distinction between an addiction to ice toxicity and a normal person by electroencephalogram signals has not been proposed.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a classification method of ice drug addicts and normal people based on electroencephalogram signals.
The invention provides a classification method of ice-poison addicts and normal persons based on electroencephalogram signals, which comprises the following steps of S1-S4:
and S1, acquiring electroencephalogram signals of the ice drug addict and the normal person during the reaction task, and preprocessing the acquired electroencephalogram signals to obtain electroencephalogram characteristics under the reaction task.
The response task comprises an ice toxicity stimulus and a non-ice toxicity stimulus, and the electroencephalogram characteristics comprise time domain voltage and frequency domain energy;
and S2, respectively extracting the features of the electroencephalogram features in the step S1 to obtain space-time cluster features.
Further, the feature extraction process is as follows: firstly, subtracting the electroencephalogram characteristics under the ice toxicity stimulation condition and the non-ice toxicity stimulation condition, obtaining a time-frequency area with obvious difference between an ice toxicity addict and a normal person based on replacement test, then collecting the electroencephalogram signals of the whole scalp by utilizing a plurality of electrodes, and forming time-frequency information and space-time cluster characteristics of time information and space information by using the time-frequency information with the obvious difference under the two stimulation conditions through statistic test based on clusters, wherein the time-time cluster characteristics comprise time, space, voltage/energy.
S3, averaging the voltage/energy values in each space-time cluster in a time dimension and a space dimension to obtain a voltage/energy average value of each space-time cluster;
further, the method for calculating the voltage/energy average value of each space-time cluster comprises the following steps: first, the representation of all three-dimensional points in a spatio-temporal cluster is { (a)1,b1,X11),(a1,b2,X12),...,(a1,bm,X1m),(a2,b1,X21),...,(a2,bm，X2m),...,(an,bm, Xnm) Is then, according to the formula
And calculating to obtain the average value of voltage/energy of each space-time cluster.
Wherein, a1,a2,...,anRepresenting all different time points in a spatio-temporal cluster, b1,b2,...,bmRepresenting all the different spatial points, X, in a spatio-temporal cluster11,X12,...,XnmRepresenting the corresponding voltage/energy value at each point in time.
And S4, training the voltage/energy average value of each cluster into a classifier through a linear Support Vector Machine (SVM) algorithm.
Further, the step of training the classifier by the linear support vector machine algorithm is as follows:
s41, selecting P linearly separable samples { (X1, d1), (X2, d2),., (Xp, dp) }; for any input sample Xp, the desired output is dp ± 1;
wherein, Xp is expressed as the average value of voltage/energy of time-space clusters of the electroencephalograms of the ice drug addict and the normal human; dp represents two categories of identification, namely, the drug addicts and the normal persons; dp ═ 1, indicates normal, dp ═ -1, indicates viral addicts.
S42, constructing a classification hyperplane equation (W0)TX+b0＝0)。
Further, the maximum interval algorithm is used for calculating the maximum W0 and b0 values of the data intervals of the hyperplane from two sides:
s421, constructing a constraint optimization problem:
(s.t:
and alpha isi>＝0,i＝1,2,…,P)
Wherein α ═ (α)1,α2,…,αP)TIs a Lagrange multiplier vector, alphai≥0，i＝1,2,…,P。
S422, using SMO algorithm to find the corresponding value when Q (alpha) is minimum
S423, using the formula
Calculating to obtain W0;
s424, finding out all alpha satisfyingi>Sample (X) corresponding to 0i，di) (assuming a total of S such samples), then each (X) is calculatedi，di) Corresponding to
Final calculation
S43, using the hyperplane equation obtained in step S42, the discriminant function f (x) S of the classifier is constructedgn(W0TX + b0), i.e., if W0TX+b0>0, dp is + 1; if W0TX+b0<0，dp＝-1。
And S5, inputting electroencephalogram signals by using the trained classifier, and distinguishing the drug addiction person from the normal person.
The invention also protects corresponding two types of computer program products and equipment:
a computer device comprising a memory storing a computer program and a processor implementing the steps of the above method when executing the computer program.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the above-mentioned method.
The invention has the beneficial effects that: 1. a method for distinguishing an ice drug addict from a normal person is designed through a machine learning algorithm and an electroencephalogram signal, and whether a tested person is addicted to the ice drug can be accurately judged through the electroencephalogram signal; 2. the invention has better robustness and can avoid dimension disaster in the machine learning process.
Drawings
FIG. 1 is a flow chart of a classification method of ice drug addicts and normal persons based on electroencephalogram signals.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments. The embodiments of the present invention have been presented for purposes of illustration and description, and are not intended to be exhaustive or limited to the invention in the form disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiment was chosen and described in order to best explain the principles of the invention and the practical application, and to enable others of ordinary skill in the art to understand the invention for various embodiments with various modifications as are suited to the particular use contemplated.
Example 1
A classification method of ice drug addicts and normal persons based on electroencephalogram signals is disclosed, as shown in figure 1, the method comprises the following steps of S1-S4:
and S1, acquiring electroencephalogram signals of the ice drug addict and the normal person during the reaction task, and preprocessing the acquired electroencephalogram signals to obtain electroencephalogram characteristics under the reaction task.
The response task comprises an ice toxicity stimulus and a non-ice toxicity stimulus, and the electroencephalogram characteristics comprise time domain voltage and frequency domain energy;
and S2, respectively extracting the features of the electroencephalogram features in the step S1 to obtain space-time cluster features.
Specifically, the feature extraction process is as follows: firstly, subtracting the electroencephalogram characteristics under the ice toxicity stimulation condition and the non-ice toxicity stimulation condition, obtaining a time-frequency area with obvious difference between an ice toxicity addict and a normal person based on replacement test, then collecting the electroencephalogram signals of the whole scalp by utilizing a plurality of electrodes, and forming time-frequency information and space-time cluster characteristics of time information and space information by using the time-frequency information with the obvious difference under the two stimulation conditions through statistic test based on clusters, wherein the time-time cluster characteristics comprise time, space, voltage/energy.
S3, averaging the voltage/energy values in each space-time cluster in a time dimension and a space dimension to obtain a voltage/energy average value of each space-time cluster;
specifically, the method for calculating the average value of voltage/energy of each space-time cluster comprises the following steps: first, the representation of all three-dimensional points in a spatio-temporal cluster is { (a)1,b1,X11),(a1,b2,X12),...,(a1,bm,X1m),(a2,b1,X21),...,(a2,bm，X2m),...,(an,bm,X nm) Is then, according to the formula
And calculating to obtain the average value of voltage/energy of each space-time cluster.
Wherein, a1,a2,...,anRepresenting all different time points in a spatio-temporal cluster, b1,b2,...,bmRepresenting all the different spatial points, X, in a spatio-temporal cluster11,X12,...,XnmRepresenting the corresponding voltage/energy value at each point in time.
S4, training the voltage/energy average value of each cluster into a classifier through a linear Support Vector Machine (SVM) algorithm; the computational complexity of the support vector machine algorithm depends on the number of support vectors, rather than the dimensionality of the sample space, and "dimensionality disasters" can be avoided.
Specifically, the step of training the classifier by the linear support vector machine algorithm is as follows:
s41, selecting P linearly separable samples { (X1, d1), (X2, d2),., (Xp, dp) }; for any input sample Xp, the desired output is dp ± 1;
wherein, Xp is expressed as the average value of voltage/energy of time-space clusters of the electroencephalograms of the ice drug addict and the normal human; dp represents two categories of identification, namely, the drug addicts and the normal persons; dp ═ 1, indicates normal, dp ═ -1, indicates viral addicts.
S42, constructing a classification hyperplane equation (W0)TX+b0＝0)。
More specifically, the maximum interval algorithm is used for calculating the maximum values of W0 and b0 of the data interval between the hyperplane and the two sides:
s421, constructing a constraint optimization problem:
(s.t:
and alpha isi>＝0,i＝1,2,…,P)
Where α ═ α (α 1, α 2, …, α P)TIs a Lagrange multiplier vector, alphai≥0，i＝1,2,…,P。
S422, using SMO algorithm to find the corresponding value when Q (alpha) is minimum
S423, using the formula
Calculating to obtain W0;
s424, finding out all alpha satisfyingi>Sample (X) corresponding to 0i，di) (assuming a total of S such samples), then each (X) is calculatedi，di) Corresponding to
Final calculation
S43: using the hyperplane equation obtained in step S42, the discriminant function f (x) ═ sgn (W0) of the classifier is constructedTX + b0), i.e., if W0TX+b0>0, dp is + 1; if W0TX+b0<0，dp＝-1。
And S5, inputting electroencephalogram signals by using the trained classifier, and distinguishing the drug addiction person from the normal person.
Example 2
In hardware the invention is typically implemented on the basis of a computer device which typically comprises a processor, a memory, a network interface and a database. The processor is used for providing calculation and control capability, and the memory comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium may store an operating system, a computer program, and a database; the internal memory may provide an environment for the operation of an operating system and a computer program in the non-volatile storage medium, and the classification scheme of the ice drug addicts and normal persons based on the electroencephalogram signals in embodiment 1 is implemented by running the computer program.
Example 3
Accordingly, the present invention can be directly embodied in hardware in a computer-readable storage medium on which a computer program is stored, and the computer program, when executed by a processor, implements the classification scheme of the ice drug addicts and normal persons based on the electroencephalogram signals in embodiment 1.
It is to be understood that the described embodiments are merely a few embodiments of the invention, and not all embodiments. All other embodiments, which can be derived by one of ordinary skill in the art and related arts based on the embodiments of the present invention without any creative effort, shall fall within the protection scope of the present invention. It is to be understood that the described embodiments are merely a few embodiments of the invention, and not all embodiments. All other embodiments, which can be derived by one of ordinary skill in the art and related arts based on the embodiments of the present invention without any creative effort, shall fall within the protection scope of the present invention.
Claims (10)
1. A classification method of an icy drug addict and a normal person based on an electroencephalogram signal is characterized in that the classification of the icy drug addict and the normal person is realized by training the acquired electroencephalogram signal through a machine learning algorithm, and the classification method is constructed and comprises the following steps:
s1, collecting electroencephalogram signals of the ice drug addict and the normal person during a reaction task, and preprocessing the collected electroencephalogram signals to obtain electroencephalogram characteristics under the reaction task;
s2, respectively extracting the features of the electroencephalogram in the step S1 to obtain space-time cluster features;
s3, carrying out average calculation on the voltage/energy values in each space-time cluster in a time dimension and a space dimension so as to obtain the voltage/energy average value of each space-time cluster;
and S4, training the voltage/energy average value of each cluster into a classifier through a machine learning algorithm.
2. The method for classifying an addiction to an ice drug and a normal person based on electroencephalogram signals, according to claim 1, wherein the feature extraction process in the step S2 is: firstly, subtracting the electroencephalogram characteristics under the ice toxicity stimulation condition and the non-ice toxicity stimulation condition, obtaining a time-frequency area with obvious difference between an ice toxicity addict and a normal person based on replacement test, then collecting the electroencephalogram signals of the whole scalp by utilizing a plurality of electrodes, and forming time-frequency information and space-time cluster characteristics of time information and space information by using the time-frequency information with the obvious difference under the two stimulation conditions through statistic test based on clusters, wherein the time-time cluster characteristics comprise time, space, voltage/energy.
3. The method for classifying an iced drug addict and a normal person based on electroencephalogram signals according to claim 1, wherein the formula for calculating the average value in the step S3 is as follows:
4. The method for classifying an addiction to an ice drug and a normal person based on electroencephalogram signals, as claimed in claim 1, wherein the machine learning algorithm in step S4 is a support vector machine algorithm.
5. The method for classifying an addiction to an ice-poisoning person from a normal person based on electroencephalogram signals, wherein the step of training a classifier by using a support vector machine algorithm comprises the following steps:
s41, selecting P linearly separable samples { (X1, d1), (X2, d2),., (Xp, dp) }; for any input sample Xp, the desired output is dp ± 1;
wherein, Xp is expressed as the average value of voltage/energy of time-space clusters of the electroencephalograms of the ice drug addict and the normal human; dp represents two categories of identification, namely, the drug addicts and the normal persons; dp ═ 1, for normal humans, dp ═ -1, for virus addicts;
s42, constructing a classification hyperplane equation (W0)TX+b0＝0)；
S43, using the hyperplane equation obtained in step S42, the discriminant function f (x) ═ sgn (W0) of the classifier is constructedTX + b0), i.e., if W0TX+b0>0, dp is + 1; if W0TX+b0<0，dp＝-1。
6. The method for classifying an iced drug addict and a normal person according to claim 5, wherein in the step S42, a classification hyperplane equation is calculated by using a maximum interval algorithm, and the steps are as follows:
s421, constructing a constraint optimization problem:
(s.t：
and alpha isi>＝0,i＝1,2,…,P)
Wherein α ═ (α)1,α2,…,αP)TIs a Lagrange multiplier vector, alphai≥0，i＝1,2,…,P；
S422, using SMO algorithm to find the corresponding value when Q (alpha) is minimum
S433, using the formula
Calculating to obtain W0;
s434, finding out all the alpha satisfyingi>Sample (X) corresponding to 0i，di) (assuming a total of S such samples), then each (X) is calculatedi，di) Corresponding to
Final calculation
Thereby constructing a classification hyperplane equation.
7. The method for classifying an iced drug addict and a normal person based on electroencephalogram signals according to any one of claims 1 to 6, wherein the reaction task in step 1 includes an iced drug stimulus and a non-iced drug stimulus.
8. The method for classifying drug addicts and normal persons based on electroencephalogram signals according to any one of claims 1 to 6, wherein the electroencephalogram characteristics in the step 1 include time-domain voltage and frequency-domain energy.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor when executing the computer program implements the method for classifying an iced drug addict and a normal person based on electroencephalogram signals according to any one of claims 1 to 8.
10. A computer-readable storage medium on which a computer program is stored, the computer program, when being executed by a processor, implementing the method for classifying an addiction to ice drugs and a normal person based on electroencephalogram signals according to any one of claims 1 to 8.
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| CN113598794A (en) * | 2021-08-12 | 2021-11-05 | 中南民族大学 | Training method and system for detection model of ice drug addict |
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