CN111035383A - Analysis method for electroencephalogram signals of children facing autism spectrum disorder - Google Patents
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Abstract
The invention provides an analysis method for an electroencephalogram signal of a child with autism spectrum disorder, which comprises the following steps: the method comprises the following steps of firstly, collecting resting state electroencephalogram signals by adopting a multi-lead electroencephalogram system, secondly, preprocessing the obtained image data, and thirdly, calculating the source activity of intracranial cerebral cortex from the electroencephalogram signals recorded by scalp electrodes by adopting a precise low-resolution brain electromagnetic tomography technology to obtain current density time sequences of 84 ROI areas; fourthly, respectively carrying out space-time characteristic analysis on the current density time sequence of each tested object, each frequency band and each ROI to obtain space-time characteristic parametersβ(ii) a The fifth step, detecting autism spectrum disorderTime-space characteristic parameters of two groups of tested children and normal childrenβWhether there was a significant difference between groups. The method for analyzing the electroencephalogram signals of the children facing the autism spectrum disorder can obtain electroencephalogram time-space characteristic parameters which are easy for the children with the autism spectrum disorder to diagnose, and assists medical workers to finish the primary diagnosis of the children with the autism spectrum disorder.
Description
Technical Field
The invention discloses an analysis method for an electroencephalogram signal of a child with autism spectrum disorder, and belongs to the technical field of brain image data analysis.
Background
Autism Spectrum Disorder (ASD), or "Autism Spectrum Disorder", "Autism" is a general term for a series of neurodevelopmental disorders. Autism was first discovered and named by the american physician Kanner in 1946. Patients suffering from this disease are generally characterized by the following characteristics, as defined in the handbook of diagnosis and statistics of mental Disorders (Fifth Edition), published by the American Psychiatric Association (APA) 2013: social interaction and communication impairment, which is a central feature, narrow interests and stereotyped behaviors.
Functional magnetic resonance imaging (fMRI) is widely used for neuromechanical studies of ASD, but fMRI techniques also have certain limitations. For example, the environment in which fMRI signals are acquired is relatively closed, and thus may cause fear and anxiety of claustrophobic environments in children with ASD. fMRI signal acquisition requires the subject to hold his head still, which is difficult for ASD children to achieve. Both of these factors can lead to failure of fMRI signal acquisition. Another disadvantage of fMRI techniques is their relatively poor time resolution (in seconds).
Meanwhile, the electroencephalogram technology used for ASD research also has some limitations, which are mainly reflected in that: (1) the electroencephalogram signals are weak relative to artifacts such as motion, myoelectricity, electrocardio, eye movement and the like, so that a certain algorithm is required to correct the relevant artifacts. The problem is particularly prominent because the matching of the autism patients (especially autism children) is poor and the signal artifact is generally large; (2) the selection of the reference electrode in the electroencephalogram signal analysis is still a very controversial problem; (3) due to the existence of the volume conduction effect, the spatial resolution of the electroencephalogram signal is poor.
Disclosure of Invention
In order to solve the problem of fMRI, the invention adopts an electroencephalogram technology. The electroencephalogram technology used for ASD research has the advantages that: (1) the time resolution of the electroencephalogram signal is very high (can reach 1 ms); (2) can provide rich frequency domain information from 0.1Hz to 100Hz (the intracranial brain electricity can analyze the activity above 100 Hz), and can reflect real-time neural activity and cognitive processing process.
The invention aims to provide an analysis method for an electroencephalogram signal of a child suffering from autism spectrum disorder. The electroencephalogram time-space characteristic parameters which are easy for the diagnosis of the children with the autism spectrum disorder can be obtained, and medical workers are assisted to complete the initial diagnosis of the children with the autism spectrum disorder.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
an analysis method for an electroencephalogram signal of a child with autism spectrum disorder comprises the following steps:
s1, resting state electroencephalogram signal acquisition:
adopting a multi-lead electroencephalogram system to acquire resting electroencephalogram signals and obtain image data; in a room with sound and electromagnetic shielding functions, a 128-lead brain electrical system produced by EGI of America is preferably used for static brain electrical signal acquisition, wherein Cz is selected as a reference electrode, the resistance of all electrodes is less than 40k omega, and the sampling rate is 500 Hz.
S2, data preprocessing:
the method comprises the following steps of preprocessing the obtained image data by using a program, and preparing for subsequent feature extraction and data analysis, wherein the method specifically comprises the following steps: (1) performing band-pass filtering on the original electroencephalogram signals by adopting 0.1-100 Hz; (2) removing data in a time period with large drift; (3) for the channel with larger artifact, adopting a spherical spline interpolation algorithm to perform interpolation; (4) performing 0.5-80 Hz band-pass filtering on the continuous data by using a Finite Impulse Response (FIR) filter; (5) carrying out recess filtering on 50Hz to remove mains supply interference; (6) using independent component analysis to correct blink, horizontal eye movement, myoelectricity, electrocardio and other non-physiological artifacts, and converting data after artifact correction into average reference; (7) the data is segmented (2000 ms segment).
S3, source positioning analysis:
calculating the source activity of intracranial cerebral cortex from brain electrical signal recorded by scalp electrode by accurate low-resolution brain electromagnetic tomography, obtaining the current density time sequence of ROI of interest, and filtering by band-pass filter to obtainAnd obtaining the current density time series of different frequency bands. 6239 samples of 5X 5mm size were obtained for each test using open source LORETA software in combination with MNI152 templates3Time series of current densities (in A/m) of gray brain voxels2)。
Preferably, 84 regions of interest (ROIs) are obtained based on the brudman partition method; after obtaining the current density activity of 6239 gray matter voxels, the present invention obtains a current density time series of 84 ROIs by averaging the current densities of all voxels in each ROI; for each ROI, the neural activity signals of the following six frequency bands are obtained through band-pass filtering: delta (1-4 Hz), theta (4-7 Hz), alpha (8-13 Hz), beta (13-30 Hz), low-gamma (30-55 Hz), and high-gamma (65-80 Hz).
S4, signal space-time characteristic analysis:
and respectively carrying out space-time characteristic analysis on the current density time sequence of each tested object, each frequency band and each ROI to obtain space-time characteristic parameters β, wherein the steps are summarized as follows:
(1) let X (t) be the current density time series of a certain ROI in a certain frequency band, and obtain its analysis signal X (t) + X by using Hilbert transformH(t) i, and obtaining the instantaneous amplitude time sequence thereof
(2) Calculating a signal waveform S (t) of a certain instantaneous amplitude time sequence A (t);
(3) a series of window lengths T are defined in the interval 1 to 15 seconds. They are equidistant after log transformation of their lengths. For each window length τ in the set T, s (T) is divided into a series of windows of length τ and overlapping 50%. The standard deviation is calculated after each window has its linear trend removed by least squares fitting. For each window length tau, calculating the mean value of the standard deviations of all windows under the window length as a 'fluctuation function' < F (tau) >;
(4) the relationship between the ripple function and the window length in this dual logarithmic coordinate system appears as a linear relationship, and in this figure, the slope of the least squares line of the ripple function and the window length is referred to as the spatio-temporal characteristic parameter β.
S5, statistical analysis, wherein independent sample t test is respectively carried out on each ROI and each frequency band in order to detect whether the space-time characteristic parameters β of the two groups of tested ASD and normal children have significant interclass difference.
Has the advantages that: compared with the prior art, the method has the following advantages:
(1) the invention provides an analysis method for an electroencephalogram signal of an autism spectrum disorder child, which avoids the defect that the time domain and space domain information is not comprehensively considered in the traditional electroencephalogram signal analysis method.
(2) The invention deeply researches the problem of the retest reliability of results influenced by different parameter combinations, and determines the parameter combination with the highest retest reliability and the space-time characteristic index through a series of researches, thereby avoiding the problem that the retest reliability is not considered in the traditional electroencephalogram signal analysis method.
In conclusion, the method is simple in structure, wide in applicability and excellent in performance, provides an analysis method for the electroencephalogram signals of the children facing the autism spectrum disorder, can obtain electroencephalogram time-space characteristic parameters which are easy for the ASD diagnosis of the children, assists medical workers in completing the initial diagnosis of the ASD of the children, and has a good market prospect.
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FIG. 1 is a block diagram of the present invention.
Detailed Description
The technical solution of the present invention will be described in detail with reference to the accompanying drawings and examples.
As shown in figure 1, the method for analyzing the electroencephalogram signals of children facing the autism spectrum disorder comprises the following steps of firstly, preferably, collecting resting electroencephalogram signals by using a 128-lead electroencephalogram system produced by American EGI company, wherein a reference electrode is Cz, the resistance of all electrodes is less than 40k omega, the sampling rate is 500Hz, secondly, preprocessing the obtained image data by using a program, preparing for subsequent feature extraction and data analysis, thirdly, calculating the source activity of an intracranial cerebral cortex by using an accurate low-resolution brain electromagnetic tomography technology from the electroencephalogram signals recorded by scalp electrodes, preferably, obtaining 84 interesting Regions (ROI) based on a Brudeman partition, obtaining current density time sequences of 84 ROI by using open-source LORETA software and combining an MNI152 template, obtaining neural activity signals of six frequency bands through band-pass filtering for each ROI, and respectively carrying out time-space-time analysis on delta (1-4 Hz), theta (4 Hz), alpha (8-7 Hz), beta (13) and gamma (13 Hz), carrying out time sequence analysis on the five-time-frequency bands of which are corrected by using a time-space-time-frequency-range analysis, and carrying out a statistical test on whether the characteristic statistics of the difference of the four frequency bands of delta (FDI-80 Hz, and the characteristics of the three-four-time-frequency-four-frequency.
The present invention will be described in further detail with reference to specific embodiments thereof, which should be considered as illustrative and not restrictive.
Example (b):
the example comprises five steps, namely resting state electroencephalogram signal acquisition, data preprocessing, source localization analysis, signal space-time characteristic analysis and statistical analysis.
Firstly, collecting resting state electroencephalogram signals.
In a room with sound and electromagnetic shielding functions, a 128-lead brain electrical system produced by EGI of America is preferably used for static brain electrical signal acquisition, wherein Cz is selected as a reference electrode, the resistance of all electrodes is less than 40k omega, and the sampling rate is 500 Hz.
And secondly, preprocessing data.
The method comprises the following steps of preprocessing the obtained image data by using a program, and preparing for subsequent feature extraction and data analysis, wherein the method specifically comprises the following steps: (1) performing band-pass filtering on the original electroencephalogram signals by adopting 0.1-100 Hz; (2) removing data in a time period with large drift; (3) for the channel with larger artifact, adopting a spherical spline interpolation algorithm to perform interpolation; (4) performing 0.5-80 Hz band-pass filtering on the continuous data by using a Finite Impulse Response (FIR) filter; (5) carrying out recess filtering on 50Hz to remove mains supply interference; (6) using independent component analysis to correct blink, horizontal eye movement, myoelectricity, electrocardio and other non-physiological artifacts, and converting data after artifact correction into average reference; (7) the data is segmented (2000 ms segment).
And thirdly, source positioning analysis.
Calculating the source activity of intracranial cerebral cortex from brain electrical signals recorded by scalp electrodes by adopting accurate low-resolution brain electromagnetic tomography technology, and obtaining 6239 samples with the size of 5 multiplied by 5mm by each sample by using open source LORETA software and combining MNI152 template3Time series of current densities (in A/m) of gray brain voxels2)。
Preferably, 84 regions of interest (ROIs) are obtained according to the brudman partition method; after obtaining the current density activity of 6239 gray matter voxels, the present invention obtains a current density time series of 84 ROIs by averaging the current densities of all voxels in each ROI; for each ROI, the neural activity signals of the following six frequency bands are obtained through band-pass filtering: delta (1-4 Hz), theta (4-7 Hz), alpha (8-13 Hz), beta (13-30 Hz), low-gamma (30-55 Hz), and high-gamma (65-80 Hz).
And fourthly, analyzing the space-time characteristics of the signals.
And respectively performing space-time characteristic analysis on the current density time sequence of each tested object, each frequency band and each ROI, wherein the steps are summarized as follows:
(1) let X (t) be the current density time series of a certain ROI in a certain frequency band, and obtain its analysis signal X (t) + X by using Hilbert transformH(t) i, and obtaining the instantaneous amplitude time sequence thereof
(2) Calculating a signal waveform S (t) of a certain instantaneous amplitude time sequence A (t);
(3) a series of window lengths T are defined in the interval 1 to 15 seconds. They are equidistant after log transformation of their lengths. For each window length τ in the set T, s (T) is divided into a series of windows of length τ and overlapping 50%. The standard deviation is calculated after each window has its linear trend removed by least squares fitting. For each window length tau, calculating the mean value of the standard deviations of all windows under the window length as a 'fluctuation function' < F (tau) >;
(4) the relationship between the ripple function and the window length in this dual logarithmic coordinate system appears as a linear relationship, and in this figure, the slope of the least squares line of the ripple function and the window length is referred to as the spatio-temporal characteristic parameter β.
And fifthly, carrying out statistical analysis.
To detect whether there are significant inter-group differences in the two tested spatio-temporal characteristic parameters β for ASD and normal children, we performed independent sample t-tests for each ROI, each frequency band separately.
The present invention can be realized in light of the above.
Claims (8)
1. The invention provides an analysis method for an electroencephalogram signal of a child with autism spectrum disorder, which comprises the following steps:
s1, resting state electroencephalogram signal acquisition: adopting a multi-lead electroencephalogram system to acquire resting electroencephalogram signals and obtain image data;
s2, data preprocessing: preprocessing the obtained image data to prepare for subsequent feature extraction and data analysis;
s3, source positioning analysis: calculating the source activity of intracranial cerebral cortex from an electroencephalogram signal recorded by a scalp electrode by adopting a precise low-resolution brain electromagnetic tomography technology to obtain a current density time sequence of an ROI (region of interest), and filtering by a band-pass filter to further obtain current density time sequences of different frequency bands;
s4, signal space-time characteristic analysis, namely performing space-time characteristic analysis on the current density time sequence of each tested frequency band and each ROI respectively to obtain space-time characteristic parameters β;
s5, carrying out statistical analysis to detect whether the two groups of tested space-time characteristic parameters β of the autism spectrum disorder children and the normal children have significant group-to-group differences.
2. The method for analyzing the electroencephalogram signals for the children with the autism spectrum disorder as claimed in claim 1, wherein sound and electromagnetic shielding processing is performed during the acquisition of the electroencephalogram signals in S1, a 128-wire electroencephalogram system is used, wherein Cz is selected as a reference electrode, the resistance of all the electrodes is less than 40 kOmega, and the sampling rate is 500 Hz.
3. The method for analyzing the electroencephalogram signals of the children with the autism spectrum disorder as claimed in claim 1, wherein the preprocessing of the data in the step S2 specifically comprises the following steps: (1) performing band-pass filtering on the original electroencephalogram signals by adopting 0.1-100 Hz; (2) removing data in a time period with large drift; (3) for the channel with larger artifact, adopting a spherical spline interpolation algorithm to perform interpolation; (4) carrying out 0.5-80 Hz band-pass filtering on the continuous data by using a finite impulse response filter; (5) carrying out recess filtering on 50Hz to remove mains supply interference; (6) using independent component analysis to correct blink, horizontal eye movement, myoelectricity, electrocardio and other non-physiological artifacts, and converting data after artifact correction into average reference; (7) the data is segmented.
4. The method for analyzing the electroencephalogram signals for the autism spectrum disorder children as claimed in claim 1, wherein the S3 source localization analysis adopts a precise low-resolution brain electromagnetic tomography (EMT) techniqueIntraoperative, and using open source LORETA software in combination with MNI152 templates yielded 6239 samples of 5X 5mm in size per test3Current density time series of grey brain matter voxels.
5. The method for analyzing electroencephalogram signals for children with autism spectrum disorders according to claim 4, wherein 84 regions of interest are obtained by using the Brucemann-based partition in the S3 source localization analysis.
6. The method for analyzing electroencephalogram signals for children with autism spectrum disorders according to claim 5, wherein in the source localization analysis, the neural activity signals of the following six frequency bands are obtained by band-pass filtering 84 regions of interest: delta (1-4 Hz), theta (4-7 Hz), alpha (8-13 Hz), beta (13-30 Hz), low-gamma (30-55 Hz), and high-gamma (65-80 Hz).
7. The method for analyzing the electroencephalogram signals of children facing to the autism spectrum disorder as claimed in claim 1, wherein the analysis of the space-time characteristics of the S4 signals comprises the following steps: (1) let X (t) be the current density time series of a certain ROI in a certain frequency band, and obtain its analysis signal X (t) + X by using Hilbert transformH(t) i, and obtaining the instantaneous amplitude time sequence thereof
(2) Calculating a signal waveform S (t) of a certain instantaneous amplitude time sequence A (t); (3) defining a series of window lengths T within the interval of 1-15 seconds, carrying out logarithmic transformation on the lengths and equidistant spacing, dividing the window lengths T into a series of windows with the lengths of tau and overlapping 50 percent for each window length tau in the set T, calculating the standard deviation of each window after eliminating the linear trend of each window through least square fitting, and calculating the mean value of the standard deviations of all windows under the window length as a 'fluctuation function' under the window length for each window length tau "<F(τ)>(ii) a (4) After the computation of the ripple function at all window lengths is complete,the window length and the fluctuation function are respectively subjected to logarithmic transformation, the relation between the fluctuation function and the window length in the dual logarithmic coordinate system is represented as a linear relation, and the slope of the least square straight line of the fluctuation function and the window length is called as a space-time characteristic parameter β.
8. The method for analyzing EEG signals of children with autism spectrum disorder as claimed in claim 1, wherein in S5, the age is taken as covariate in statistical test, and FDR program is used to correct p value to control multiple comparison problem.
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| CN116898401A (en) * | 2023-07-17 | 2023-10-20 | 燕山大学 | Autism spectrum disorder subtype classification method and device based on combined recursion quantification |
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