阅读说明：本技术 基于脑电信息的自闭症风险量化方法、装置和存储介质 (Self-imposed disease risk quantification method and device based on electroencephalogram information and storage medium ) 是由 赵蕾蕾 杨铁牛 于 2019-09-18 设计创作，主要内容包括：本发明公开了一种基于脑电信息的自闭症风险量化方法、装置和存储介质,通过获取患者的患者信息和至少2个脑区的脑电信息作为数据基础,对所述脑电信息进行预处理得出过滤后的脑电数据,有利于去除杂讯,提高数据的准确度,再计算不同脑区之间脑电数据的相干性,并与服务器中存储的正常人群的脑电数据相干性进行比较,从而得出自闭症的风险量化结果,比起现有技术依靠医生人工判断的效率有了极大的提高,能够快速准确地得出初步的自闭症风险量化结果。(The invention discloses an autism risk quantification method, device and storage medium based on electroencephalogram information, which are characterized in that electroencephalogram information of a patient and electroencephalogram information of at least 2 brain areas are acquired as data bases, the electroencephalogram information is preprocessed to obtain filtered electroencephalogram data, noise is favorably removed, the accuracy of the data is improved, the coherence of the electroencephalogram data among different brain areas is calculated and compared with the coherence of the electroencephalogram data of normal people stored in a server, so that an autism risk quantification result is obtained, compared with the prior art, the autism risk quantification method based on doctor manual judgment efficiency is greatly improved, and a primary autism risk quantification result can be quickly and accurately obtained.)

1. An autism risk quantification method based on electroencephalogram information is characterized by comprising the following steps:

the method comprises the steps that a client side obtains patient information of a patient and electroencephalogram information of at least 2 brain areas, wherein the patient information comprises age information and gender information;

the client preprocesses the electroencephalogram information of the at least 2 brain areas to obtain electroencephalogram data;

the client calculates the coherence among the electroencephalogram data corresponding to each brain area, and sets the coherence as a first coherence;

the client matches a second coherence of the at least 2 brain areas from the server according to the patient information, wherein the second coherence is electroencephalogram data coherence of normal people pre-stored in the server;

and if the client detects that the value of the first coherence is smaller than the second coherence, setting the autism risk quantification result as a high risk.

2. The method for quantifying risk of autism based on electroencephalogram information as recited in claim 1, wherein: the frequency bands of the electroencephalogram information comprise a delta frequency band, a theta frequency band, an alpha frequency band, a beta frequency band and a gamma frequency band.

3. The method for quantifying risk of autism based on electroencephalogram information as recited in claim 1, wherein: the pre-processing of the electroencephalogram signals comprises filtering, baseline removing processing and noise removing.

4. The electroencephalogram information-based autism risk quantification method according to claim 1, wherein the step of calculating the coherence between the electroencephalogram data corresponding to each brain area by the client specifically comprises the following steps:

the client side obtains the cross-correlation sequence of the electroencephalogram data corresponding to each brain area;

the client acquires a sampling point from the cross-correlation sequence and calculates the Fourier coefficient of the sampling point;

and the client acquires the self-power spectrum of the electroencephalogram data, and calculates the coherence according to the self-power spectrum and the Fourier coefficient.

5. The electroencephalogram information-based autism risk quantification method according to claim 4, wherein the calculation formula of the Fourier coefficient is as follows:

wherein X and Y are electroencephalogram data of two different brain areas, R_xy(m) is the cross-correlation sequence of X and Y, m is the m-th sample point of the cross-correlation sequence, P_xy(f) Are fourier coefficients.

6. The electroencephalogram information-based autism risk quantification method according to claim 5, wherein the calculation formula of the coherence is as follows:

wherein, P_xx(f) Self-power spectrum of X, P_yy(f) Is the self-power spectrum of Y.

7. An apparatus for performing a method for quantification of risk of autism based on electroencephalogram information, comprising a CPU unit for performing the steps of:

the method comprises the steps that a client side obtains patient information of a patient and electroencephalogram information of at least 2 brain areas, wherein the patient information comprises age information and gender information;

the client preprocesses the electroencephalogram information of the at least 2 brain areas to obtain electroencephalogram data;

the client calculates the coherence among the electroencephalogram data corresponding to each brain area, and sets the coherence as a first coherence;

the client matches a second coherence of the at least 2 brain areas from the server according to the patient information, wherein the second coherence is electroencephalogram data coherence of normal people pre-stored in the server;

and if the client detects that the value of the first coherence is smaller than the second coherence, setting the autism risk quantification result as a high risk.

8. The apparatus for performing a method for quantification of risk of autism based on electroencephalogram information of claim 7, wherein said CPU unit is further configured to perform the steps of: the client side obtains the cross-correlation sequence of the electroencephalogram data corresponding to each brain area;

the client acquires a sampling point from the cross-correlation sequence and calculates the Fourier coefficient of the sampling point;

and the client acquires the self-power spectrum of the electroencephalogram data, and calculates the coherence according to the self-power spectrum and the Fourier coefficient.

9. A computer-readable storage medium characterized by: the computer-readable storage medium stores computer-executable instructions for causing a computer to execute a method for quantification of autism risk based on electroencephalogram information according to any one of claims 1 to 6.

Technical Field

The invention relates to the field of electroencephalogram signals, in particular to a self-imposed syndrome risk quantification method and device based on electroencephalogram information and a storage medium.

Background

Autism, a disease of the nervous system, is usually diagnosed with the aid of electroencephalograms. The existing scheme mainly depends on interpretation and judgment of doctors through electroencephalograms, and has high requirements on professional levels of doctors and low diagnosis efficiency.

Disclosure of Invention

In order to overcome the defects of the prior art, the invention aims to provide an autism risk quantification method, device and storage medium based on electroencephalogram information, which can automatically quantify autism risks after acquiring electroencephalogram signals.

The technical scheme adopted by the invention for solving the problems is as follows: in a first aspect, the invention provides an autism risk quantification method based on electroencephalogram information, which comprises the following steps:

the method comprises the steps that a client side obtains patient information of a patient and electroencephalogram information of at least 2 brain areas, wherein the patient information comprises age information and gender information;

the client preprocesses the electroencephalogram information of the at least 2 brain areas to obtain electroencephalogram data;

the client calculates the coherence among the electroencephalogram data corresponding to each brain area, and sets the coherence as a first coherence;

the client matches a second coherence of the at least 2 brain areas from the server according to the patient information, wherein the second coherence is electroencephalogram data coherence of normal people pre-stored in the server;

and if the client detects that the value of the first coherence is smaller than the second coherence, setting the autism risk quantification result as a high risk.

Further, the frequency bands of the electroencephalogram information comprise a delta frequency band, a theta frequency band, an alpha frequency band, a beta frequency band and a gamma frequency band.

Further, the preprocessing of the electroencephalogram signals comprises filtering, baseline removing processing and noise removing.

Further, the step of calculating the coherence between the electroencephalogram data corresponding to the brain areas by the client specifically comprises the following steps:

the client side obtains the cross-correlation sequence of the electroencephalogram data corresponding to each brain area;

the client acquires a sampling point from the cross-correlation sequence and calculates the Fourier coefficient of the sampling point;

and the client acquires the self-power spectrum of the electroencephalogram data, and calculates the coherence according to the self-power spectrum and the Fourier coefficient.

Further, the calculation formula of the fourier coefficient is:

wherein X and Y are electroencephalogram data of two different brain areas, R_xy(m) is the cross-correlation sequence of X and Y, m is the m-th sample point of the cross-correlation sequence, P_xy(f) Are fourier coefficients.

Further, the calculation formula of the coherence is as follows:

wherein, P_xx(f) Self-power spectrum of X, P_yy(f) Is the self-power spectrum of Y.

In a second aspect, the present invention provides an apparatus for performing a method for quantification of risk of autism based on electroencephalogram information, comprising a CPU unit for performing the steps of:

the method comprises the steps that a client side obtains patient information of a patient and electroencephalogram information of at least 2 brain areas, wherein the patient information comprises age information and gender information;

the client preprocesses the electroencephalogram information of the at least 2 brain areas to obtain electroencephalogram data;

the client calculates the coherence among the electroencephalogram data corresponding to each brain area, and sets the coherence as a first coherence;

the client matches a second coherence of the at least 2 brain areas from the server according to the patient information, wherein the second coherence is electroencephalogram data coherence of normal people pre-stored in the server;

and if the client detects that the value of the first coherence is smaller than the second coherence, setting the autism risk quantification result as a high risk.

Further, the CPU unit is further configured to perform the steps of:

the client side obtains the cross-correlation sequence of the electroencephalogram data corresponding to each brain area;

the client acquires a sampling point from the cross-correlation sequence and calculates the Fourier coefficient of the sampling point;

and the client acquires the self-power spectrum of the electroencephalogram data, and calculates the coherence according to the self-power spectrum and the Fourier coefficient.

In a third aspect, the invention provides a device for executing a method for quantification of risk of autism based on electroencephalogram information, comprising at least one control processor and a memory for communicative connection with the at least one control processor; the memory stores instructions executable by the at least one control processor to enable the at least one control processor to perform a method for quantification of risk of autism based on electroencephalogram information as described above.

In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions for causing a computer to execute the method for quantification of autism risk based on electroencephalogram information as described above.

In a fifth aspect, the present invention also provides a computer program product comprising a computer program stored on a computer-readable storage medium, the computer program comprising program instructions which, when executed by a computer, cause the computer to perform the method for quantification of risk of autism based on electroencephalogram information as described above.

One or more technical schemes provided in the embodiment of the invention have at least the following beneficial effects: according to the method and the device, the patient information of the patient and the electroencephalogram information of at least 2 brain areas are obtained as data bases, the electroencephalogram information is preprocessed to obtain the filtered electroencephalogram data, noise is removed beneficially, the accuracy of the data is improved, the coherence of the electroencephalogram data among different brain areas is calculated, and the electroencephalogram data are compared with the coherence of the electroencephalogram data of normal people stored in a server, so that the risk quantification result of the autism is obtained, compared with the prior art, the efficiency of manual judgment by a doctor is greatly improved, and the preliminary autism risk quantification result can be obtained quickly and accurately.

Drawings

The invention is further illustrated with reference to the following figures and examples.

FIG. 1 is a flowchart of a method for quantification of autism risk based on electroencephalogram information according to a first embodiment of the present invention;

FIG. 2 is a flowchart illustrating preprocessing of electroencephalogram signals in a method for quantifying risk of autism based on electroencephalogram information according to a first embodiment of the present invention;

fig. 3 is a schematic device diagram for performing an autism risk quantification method based on electroencephalogram information according to a second embodiment of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

It should be noted that, if not conflicted, the various features of the embodiments of the invention may be combined with each other within the scope of protection of the invention. Additionally, while functional block divisions are performed in apparatus schematics, with logical sequences shown in flowcharts, in some cases, steps shown or described may be performed in sequences other than block divisions in apparatus or flowcharts.

Referring to fig. 1, a first embodiment of the present invention provides an autism risk quantification method based on electroencephalogram information, including the following steps:

step S100, a client acquires patient information of a patient and electroencephalogram information of at least 2 brain areas, wherein the patient information comprises age information and gender information;

step S200, preprocessing the electroencephalogram information of at least 2 brain areas by a client to obtain electroencephalogram data;

step S300, the client calculates the coherence among the electroencephalogram data corresponding to each brain area and sets the coherence as a first coherence;

step S400, the client matches a second coherence of at least 2 brain areas from the server according to the patient information, wherein the second coherence is electroencephalogram data coherence of normal people pre-stored in the server;

step S500, if the client detects that the value of the first coherence is smaller than the second coherence, the autism risk quantification result is set to be a high risk.

It should be noted that, because the coherence of the electroencephalograms of different ages and genders is different, the present embodiment can obtain a quantization result with a higher reference value by using the age information and the gender information as the standard for matching data from the server. It should be noted that the EEG information may be acquired from any signal obtained by any type of device, such as a 64-lead ANTEEGOTMrt EEG signal acquired at 1000 Hz. It should be noted that the number of brain areas for acquiring the electroencephalogram information may be any, and at least includes 2. If the number of the selected brain areas is greater than 2, the coherence between every 2 brain areas is calculated in step S300 for determination. It can be understood that the electroencephalogram data coherence of the normal population in the server is pre-selected storage, and the electroencephalogram data of the normal population can be acquired by acquiring the electroencephalogram data of the normal population. It should be noted that, in step S500, the value of the first coherence being smaller than the value of the second coherence indicates that the brain motor connectivity of the patient is smaller than the normal level, so that the patient may be determined as autism, or a threshold may be set for the requirement to allow the existence of an error, that is, when the difference between the values of the first coherence and the second coherence is greater than the threshold, the risk of autism is defined as a high risk, and the specific value may be adjusted according to the actual requirement.

Further, in another embodiment of the present invention, the frequency bands of the electroencephalogram information include a delta frequency band, a theta frequency band, an alpha frequency band, a beta frequency band, and a gamma frequency band.

It should be noted that the electroencephalogram signals may be collected in any frequency band, and the delta frequency band, the theta frequency band, the alpha frequency band, the beta frequency band, and the gamma frequency band are only preferred in this embodiment, and other frequency bands may also be collected according to actual needs, so that the calculation of the electroencephalogram data may be implemented.

Further, in another embodiment of the present invention, the pre-processing of the brain electrical signal includes filtering, de-baselining, and noise removal.

It should be noted that the filtering of this embodiment includes high-pass filtering and low-pass filtering, and the combination of the baseline removal processing can be beneficial to removing the noise part in the electroencephalogram signal. It can be understood that the noise removal in this embodiment includes removing the electro-oculogram and the myoelectric noise, so as to avoid the influence of irrelevant biological signals on the data.

Referring to fig. 2, further, in another embodiment of the present invention, the step of calculating the coherence between the electroencephalogram data corresponding to each brain region by the client specifically includes the following steps:

step S310, the client acquires the cross-correlation sequence of the electroencephalogram data corresponding to each brain area;

step S320, the client acquires a sampling point from the cross-correlation sequence and calculates the Fourier coefficient of the sampling point;

and step S330, the client acquires the self-power spectrum of the electroencephalogram data, and calculates the coherence according to the self-power spectrum and the Fourier coefficient.

Further, in another embodiment of the present invention, the calculation formula of the fourier coefficient is:

wherein X and Y are electroencephalogram data of two different brain areas, R_xy(m) is the cross-correlation sequence of X and Y, m is the mth sample of the cross-correlation sequenceSample point, P_xy(f) Are fourier coefficients.

Further, in another embodiment of the present invention, the coherence is calculated as:

wherein, P_xx(f) Self-power spectrum of X, P_yy(f) Is the self-power spectrum of Y.

Referring to fig. 3, a second embodiment of the present invention further provides an apparatus for executing a method for quantifying risk of autism based on electroencephalogram information, where the apparatus is an intelligent device, such as a smart phone, a computer, a tablet computer, and the like, and can have a processor and implement a corresponding function, and the present embodiment is described by taking a computer as an example.

In the computer 3000 for executing the method for quantifying risk of autism based on electroencephalogram information, a CPU unit 3100 is included, the CPU unit 3100 is configured to perform the steps of:

the method comprises the steps that a client side obtains patient information of a patient and electroencephalogram information of at least 2 brain areas, wherein the patient information comprises age information and gender information;

the client preprocesses the electroencephalogram information of at least 2 brain areas to obtain electroencephalogram data;

the client calculates the coherence between the electroencephalogram data corresponding to each brain area, and sets the coherence as a first coherence;

the client matches a second coherence of at least 2 brain areas from the server according to the patient information, wherein the second coherence is electroencephalogram data coherence of normal people pre-stored in the server;

and if the client detects that the first coherence is smaller than the second coherence, setting the autism risk quantification result as a high risk.

Further, in another embodiment of the present invention, the CPU unit is further configured to perform the steps of:

the client acquires the cross-correlation sequence of the electroencephalogram data corresponding to each brain area;

the client acquires a sampling point from the cross-correlation sequence and calculates the Fourier coefficient of the sampling point;

the client acquires the self-power spectrum of the electroencephalogram data, and calculates the coherence according to the self-power spectrum and the Fourier coefficient.

The computer 3000 and the CPU unit 3100 may be connected via a bus or other means, and the computer 3000 further includes a memory, which is a non-transitory computer-readable storage medium and can be used for storing non-transitory software programs, non-transitory computer-executable programs, and modules, such as program instructions/modules corresponding to the apparatus for performing the method for quantifying risk of autism based on electroencephalogram information in the embodiment of the present invention. The computer 3000 controls the CPU unit 3100 to execute various functional applications and data processing for executing the method for quantifying risk of autism based on electroencephalogram information by running non-transitory software programs, instructions, and modules stored in the memory, that is, to implement the method for quantifying risk of autism based on electroencephalogram information according to the above-described method embodiment.

The memory may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the CPU unit 3100, and the like. Further, the memory may include high speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory optionally includes memory located remotely from CPU unit 3100, which may be connected to computer 3000 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The one or more modules are stored in the memory and when executed by the CPU unit 3100, perform the method for electroencephalogram information-based risk quantification of autism in the above-described method embodiments.

The embodiment of the invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are executed by the CPU 4100 to realize the self-imposed syndrome risk quantification method based on the electroencephalogram information.

The above-described embodiments of the apparatus are merely illustrative, and the apparatuses described as separate components may or may not be physically separate, may be located in one place, or may be distributed over a plurality of network apparatuses. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.

It should be noted that, since the apparatus for executing the autism risk quantification method based on electroencephalogram information in this embodiment is based on the same inventive concept as the above-mentioned autism risk quantification method based on electroencephalogram information, the corresponding contents in the method embodiment are also applicable to this apparatus embodiment, and are not described in detail here.

Through the above description of the embodiments, those skilled in the art can clearly understand that the embodiments can be implemented by software plus a general hardware platform. Those skilled in the art will appreciate that all or part of the processes in the methods of the above embodiments may be implemented by hardware related to instructions of a computer program, which may be stored in a computer-readable storage medium, and when executed, may include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read Only Memory (ROM), a Random Access Memory (RAM), or the like.

While the preferred embodiments of the present invention have been described in detail, it will be understood by those skilled in the art that the foregoing and various other changes, omissions and deviations in the form and detail thereof may be made without departing from the scope of this invention.