CN116421187A - Attention deficit hyperactivity disorder analysis system based on speech hierarchy sequence - Google Patents
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Abstract
The invention discloses an analysis system for attention deficit hyperactivity disorder based on a speech hierarchy sequence, which comprises: the voice sequence generation module is used for constructing a voice sequence with syllable and vocabulary levels; the electroencephalogram signal acquisition module is used for presenting a voice sequence to a tested person by using audio input equipment and synchronously acquiring electroencephalogram signals of the tested person by using electroencephalogram recording equipment; and the electroencephalogram signal analysis module is used for processing the electroencephalogram signals to obtain electroencephalogram response frequency spectrums, calculating the phase consistency among the test times of the electroencephalogram responses at different frequencies based on the electroencephalogram response frequency spectrums, and judging whether the tested person has attention deficit hyperactivity disorder or not according to the peak value of the phase consistency of the electroencephalogram responses at syllable and vocabulary frequencies. The system provided by the invention has the characteristics of convenience and high efficiency, provides good auxiliary effect for diagnosis and intervention of attention deficit hyperactivity disorder through the electroencephalogram signals, and has wide application scenes and applicable crowds.
Description
Technical Field
The invention relates to the technical field of medicine, in particular to an analysis system for attention deficit hyperactivity disorder based on a speech hierarchy sequence.
Background
Attention Deficit Hyperactivity Disorder (ADHD) is a common mental disorder in children characterized by attention deficit, hyperactivity and/or impulsivity, and cognitive dysfunction, commonly known as hyperactivity disorder. Investigation showed that attention deficit hyperactivity disorder prevalence is as high as 3% to 7% worldwide (Faraone, s.v., assson, p., banaschewski, t., et al 2015. Attention-to-defect/hyperactivity disorder Reviews Disease Primers, 1-23) and a continuously rising trend (thanar, a., cooper, m.,2016.Attention deficit hyperactivity disorder.The Lancet 387,10024,1240-1250). ADHD patients can have symptoms of inattention, short attention time, excessive activity, unstable emotion and the like, influence learning, work and social interaction of the patients, seriously damage physical and mental health of the patients, and cause medical and economic burden to families and society of the patients. Therefore, the accurate diagnosis of ADHD patients, especially ADHD children in early development stage, can provide positive and effective intervention and treatment for patients in time, is beneficial to improving the illness state of the patients, avoiding the malignant development of symptoms and relieving the medical and economic burden of families and society of the patients.
Traditional ADHD diagnostic methods are often performed using clinical scales. In the diagnosis process, doctors or evaluators communicate with the testee and family members thereof, the on-site performance of the testee and the daily conditions of the testee fed back by the family members are rated and scored according to various contents and indexes listed in the rating scale, and ADHD diagnosis is carried out according to the scoring result. Currently available scales include parental ratings such as Conners Parent Symptom Questionnaire, professional ratings such as SNAP IV, and pediatric self-ratings such as Problems Scales in Youth Self-Report, etc. However, the rating scale is a subjective evaluation process by which doctors, evaluators and relatives score the performance of the tested person, so that when the test is aimed at the young child, the evaluators may influence the evaluation of the attention level of the evaluators because the functions such as perception, movement and cognition may not be developed perfectly, and the diagnosis result may be inaccurate. In addition, the scores and results of the clinical scale are also greatly affected by the personal professional ability of the evaluator.
With the research and development of cognitive science, some computer test methods based on the task of cognitive science experiments have also been applied in ADHD diagnosis, such as the sustained job test (Continuous Performance Test, CPT) proposed by Rosvold et al (Rosvold, h.e., mirsky, a.f., sarson, i., et al 1956.a continuous performance test of brain damage. Journal of consulting psychology 20,5,343.). The test requires that the tester keep paying attention to the numbers or letters randomly appearing in the center of the computer's black screen. When the target number or letter appears, the tester needs to press the reaction button as soon as possible. CPT characterizes the attention of the tested person when participating in the test by recording the results of the response time, the miss rate and the like of the tested person, thereby evaluating the attention level of the tested person. The means of attention assessment in this type of test provides an objective behavioral indicator for diagnosis of ADHD. However, such tests require a certain cognitive ability of the tested person, and require a corresponding response to the stimulus presented on the computer screen, which is a relatively complex task. As a subject, an young child whose cognitive level is still at a developmental stage may not understand the content of the test. In addition, the test often requires the testee to perform repeated operation of engraving property for a long time, and the low-age children may not be willing to actively cooperate to complete the test, and the test result is greatly influenced by the current state of the testee participating in the test.
In addition to the traditional attention assessment approaches described above, the advent of the current emerging eye movement capture technology also provides new means and methods for ADHD diagnosis. The patent "method of measuring attention" (Hansen Stper. Method of measuring attention; chinese patent: CN104254281A, 2014-12-31.) proposes an attention assessment method based on eye movement. The method uses an eye position tracking sensor to detect the condition of eye gaze stimulus while presenting one or more visual stimuli on an electronic screen, and evaluates the level of attention by measuring the gaze and convergence angle of the subject's eyes on the stimulus. This method requires directing the person under test to look at a specific position of the screen before the test to correct the eye position tracker and requires the person under test to keep his head and eyes from moving widely throughout the test, which is highly demanding and is not met by some young children.
At the same time, researchers in the field of cognitive neuroscience are exploring attention-related brain activities through electromagnetic physiological signals of the human brain. Li Gexin et al (Li Gexin, wu, chang Shu) in 2001. A620 brain electrical biofeedback instrument is used for diagnosing and treating attention deficit and hyperactivity disorder in children. Beijing biomedical engineering 20,3,235-236.) after comparing brain electrical signals of attention deficit children with those of normal children, it is found that in the task of looking at numbers and following the number of the brain electrical signals of normal children, the power spectrum ratio of 4-8 Hz to 13-21 Hz frequency bands is significantly lower than the corresponding power ratio of the brain electrical signals of attention deficit children, so that the power spectrum ratio of 4-8 Hz to 13-21 Hz frequency bands can be used as a neurophysiologic index for diagnosing ADHD. The method requires the tested person to perform an active default task, and the evaluation result is greatly influenced by the matching degree of the tested person. The patent "a system for analyzing the hyperactivity of children based on functional nuclear magnetic resonance images" (Yudongchuan, a system for analyzing the hyperactivity of children based on functional nuclear magnetic resonance images, chinese patent CN107967943A, 2021-11-12.) proposes a system for analyzing the hyperactivity of children based on functional nuclear magnetic resonance images, which utilizes a pattern recognition technology to realize the evaluation of the risk of ADHD illness by comparing the difference of the functional nuclear magnetic resonance images of the attention-deficient children and the normal children in a resting state. The method needs to image the brain of the tested person by adopting functional magnetic resonance equipment, the equipment cost is high, and a strong magnetic field in the imaging process can possibly cause a certain harm to the human body.
In summary, the following problems exist in the prior art for diagnosis of attention deficit hyperactivity disorder:
1. according to the traditional scale evaluation method, the performances of the tested person are evaluated and scored by clinicians, evaluation staff and family relatives of the tested person, evaluation results are easily influenced by subjective factors such as professional ability of the individual evaluator and impression of the family relatives, and evaluation results given by different evaluators to the same tested person can be different, so that the reliability of ADHD diagnosis is reduced.
2. ADHD diagnosis method based on behavior task requires the tested person to execute complex behavior task or respond to presented stimulus in real time, which requires the tested person to have higher initiative coordination will, cognition level and behavior ability, and is not suitable for the young children whose cognition level is still in development stage.
3. ADHD diagnosis methods based on eye movement capturing technology or brain imaging technology generally require that a tested person completely cooperates with a test to perform eye, gesture and behavior reactions in the evaluation process, have low degree of freedom and single application scene. Some young children cannot cooperate with the test for a long time, resulting in a decrease in test accuracy. The resting state of functional magnetic resonance measurement is expensive and may be harmful to the human body.
Disclosure of Invention
The invention aims at overcoming the defects of the prior art and provides an analysis system for attention deficit hyperactivity disorder based on a speech hierarchy sequence.
In order to achieve the above purpose, the technical scheme of the invention is as follows: an analysis system for attention deficit hyperactivity disorder based on a speech hierarchy sequence, the system comprising:
the voice sequence generation module is used for constructing a voice sequence with syllable and vocabulary levels;
the electroencephalogram signal acquisition module is used for presenting a voice sequence to a tested person by using audio input equipment and synchronously acquiring electroencephalogram signals of the tested person by using electroencephalogram recording equipment;
and the electroencephalogram signal analysis module is used for processing the electroencephalogram signals to obtain electroencephalogram response frequency spectrums, calculating the phase consistency among the test times of the electroencephalogram responses at different frequencies based on the electroencephalogram response frequency spectrums, and judging whether the tested person has attention deficit hyperactivity disorder or not according to the peak value of the phase consistency of the electroencephalogram responses at syllable and vocabulary frequencies.
Compared with the prior art, the invention has the beneficial effects that: the invention provides an analysis system for attention deficit hyperactivity disorder based on a speech level sequence, which is characterized in that an electroencephalogram signal is collected by presenting a corpus sequence with syllables and vocabularies at two different levels to a tested person, preprocessing is carried out on the electroencephalogram signal, band-pass filtering of a high frequency band, hilbert transformation and discrete Fourier transformation are carried out to obtain an electroencephalogram response spectrum, the phase consistency among test times of electroencephalogram responses at different frequencies is calculated based on the electroencephalogram response spectrum, and whether the attention deficit hyperactivity disorder exists in the tested person is judged through peaks of the electroencephalogram response phase consistency at syllable and vocabularies. The system provides objective basis for diagnosis of attention deficit hyperactivity disorder.
Meanwhile, the system directly correlates the brain activities with the attention situation, can realize objective evaluation of the attention deficit hyperactivity disorder situation of the tested person without depending on the evaluation personnel and the family members of the tested person, has a standardized evaluation system, and can objectively and scientifically reflect the attention deficit hyperactivity disorder situation of the tested person.
The evaluation task related by the system is a voice hearing task, is simple and direct passive input for a tested person, and has low requirements on the cognitive level and the capability of the tested person; in addition, the evaluation corpus related by the invention can be customized according to different testees, and has wide applicable scenes and applicable crowds, including testees of low-age children.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort to a person skilled in the art.
FIG. 1 is a schematic diagram of a system provided by an embodiment of the present invention;
fig. 2 is a schematic diagram of a system flow provided in an embodiment of the present invention.
FIG. 3 is a schematic diagram of corpus and speech constructed in accordance with an embodiment of the present invention;
fig. 4 is a schematic diagram of high-frequency electroencephalogram amplitude extraction according to an embodiment of the present invention;
FIG. 5 is a graph of an electroencephalogram response phase consistency spectrum provided by an embodiment of the present invention;
fig. 6 is a graph showing the correlation between high-frequency electroencephalogram and ADHD scale scores according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The features of the following examples and embodiments may be combined with each other without any conflict.
The basic principle of the invention is that nerve tracking activity of the brain is stimulated through a hierarchical voice sequence, and the brain electrical response phase consistency of the nerve tracking activity of the crowd with attention deficit hyperactivity disorder with different degrees is different. Therefore, according to the consistency of the brain electrical response phase of the tested person, whether the tested person has the symptoms of attention deficit hyperactivity disorder or not can be judged, and the test device can be used for auxiliary diagnosis of the attention deficit hyperactivity disorder.
As shown in fig. 1 and 2, the present invention proposes an analysis system for attention deficit hyperactivity disorder based on a speech hierarchy sequence, the system comprising: the device comprises a voice sequence generation module, an electroencephalogram signal acquisition module and an electroencephalogram signal analysis module.
And the voice sequence generation module is used for constructing a voice sequence with syllable and vocabulary levels.
Specifically, the speech sequence is synthesized from a corpus sequence. Each vocabulary in the corpus contains the same number of syllables, so that the language structure of each corpus is the same and the length is the same.
Preferably, no repeated vocabulary exists in each corpus; preferably, proper vocabulary is selected for testees with different ages and different cognitive levels, and a proper corpus sequence is constructed.
Specifically, in the embodiment of the invention, a Neospeech voice synthesizer is adopted to convert and synthesize the corpus sequence into the voice sequence.
Further, the sound intensity and duration of all syllables in the speech sequence are identical.
Preferably, the presentation duration of a single syllable in the speech sequence is 100 milliseconds to 1000 milliseconds; preferably, no pauses or beats are inserted between syllables in the speech sequence.
And the electroencephalogram signal acquisition module is used for presenting a voice sequence to a tested person by using the audio input equipment and synchronously acquiring the electroencephalogram signal of the tested person by using the electroencephalogram recording equipment.
In this example, the audio input device employs an in-ear earphone; the electroencephalograph adopts a multichannel electroencephalograph, and preferably the multichannel electroencephalograph needs to have a sampling frequency of 500 Hz or more. Wherein the number of the electrodes for recording the brain electrical signals is 1-256, and at least 1 electrode is central-rear She Dianji.
It should be noted that, the audio input device is used to present the voice sequence to the tested person, and each section of voice sequence can be presented to the tested person in turn; and a certain section of voice sequence can be repeatedly presented to a tested person for a plurality of times so as to obtain stable brain electrical signals.
The electroencephalogram signal analysis module is used for preprocessing an electroencephalogram signal, carrying out band-pass filtering on a high frequency band, hilbert transformation and discrete Fourier transformation to obtain an electroencephalogram response frequency spectrum, calculating the phase consistency among test times of electroencephalogram responses at different frequencies based on the electroencephalogram response frequency spectrum, and judging whether a tested person has attention deficit hyperactivity disorder or not through the phase consistency peak values of the electroencephalogram responses at syllable and vocabulary frequencies.
The preprocessing of the electroencephalogram signal comprises the preprocessing operations of downsampling, re-referencing, artifact removal and the like, so that the signal-to-noise ratio of data is improved.
Carrying out high-frequency band-pass filtering and Hilbert transform on the preprocessed electroencephalogram signals to obtain the narrow-band amplitude of the high-frequency electroencephalogram signals; the method specifically comprises the following steps:
firstly, carrying out band-pass filtering of a high frequency band on the preprocessed electroencephalogram signals to obtain the electroencephalogram signals of the high frequency band. The frequency range of the high-frequency electroencephalogram response is preferably 70 to 160 hertz.
Then, the high-frequency electroencephalogram signal is divided into a plurality of sections of narrow-band frequency bands. Preferably, each segment of the narrowband is spaced from 1 to 40 hertz.
And finally, extracting the amplitude of each narrow-band signal through Hilbert transformation, normalizing each amplitude to be the average value of each wave band, and obtaining the narrow-band amplitude of the high-frequency electroencephalogram signal.
And performing discrete Fourier transform on the narrow-band amplitude of the high-frequency electroencephalogram signal to obtain an electroencephalogram response frequency spectrum of the tested person. The method specifically comprises the following steps:
the brain electrical signal after Hilbert transformation is recorded as X (N), N is a time domain point sequence number, the brain electrical spectrum is recorded as X (m), m is a frequency point sequence number, N is a sampling point number of brain electrical data, and the expression of brain electrical response spectrum is as follows:
and calculating the phase consistency among the test times of the electroencephalogram response, and judging whether the tested person has the symptoms of attention deficit hyperactivity disorder or not through the peak value of the phase consistency of the electroencephalogram response at syllable and vocabulary frequency.
When the brain electrical response of the tested person has obvious ITC peaks at syllable and vocabulary frequency, the attention of the tested person is normal; when the brain electrical response of the tested person only has a remarkable ITC peak value at syllable frequency, if the remarkable ITC peak value does not exist at vocabulary frequency or is lower than normal mode level, the attention level of the tested person is abnormal; when the brain electrical response of the tested person does not have obvious ITC peak values at syllable and vocabulary frequency, the abnormal test system is indicated, repeated test is needed, and the analysis result is output again.
The method for calculating the inter-test phase consistency of the brain electrical response comprises the following steps:
firstly, converting a discrete Fourier transform formula of an electroencephalogram response frequency spectrum X (m) into a rectangular coordinate form, wherein the expression is as follows:
where j is an imaginary number.
Further converting the rectangular coordinate form into complex form, the expression is as follows:
X(m)＝X real (m)+j·X imag (m)
wherein X is real (m) is the real part at the frequency point m, X imag (m) is the imaginary part at the frequency point m.
Calculating the phase angle theta of the electroencephalogram response of each frequency point m m The expression is as follows:
calculating the phase consistency among test times of brain electrical responses at different frequencies, and recording the brain electrical signals of the ith section of voice sequence as test times i and theta m Is the phase of the electroencephalogram response at frequency m in trial i, the inter-trial phase consistency ITC is a scalar measure between 0 and 1, and the calculation formula is as follows:
example 1
In embodiment 1, a BOGLIA in-ear noise reduction earphone is used as a sound presenting device, a 32-channel EGI electroencephalograph is used as an electroencephalograph recording device, and a computer is provided for voice presentation, data storage and data analysis. The computer is equipped with an Intel i7-4800 central processing unit, an associative 16G DDR3 memory, an InRui-to-2 TB SSD solid state disk, a Lynx 2 sound card and an associative T24A-10 liquid crystal display. The invention provides an analysis system for attention deficit hyperactivity disorder based on a speech hierarchy sequence.
And the voice sequence generation module is used for constructing a voice sequence with syllable and vocabulary levels.
In this embodiment, 50 common double-pitch vocabularies (e.g., apples, books, etc.) are selected as corpus sequences to construct a hierarchical speech sequence. And randomly extracting 20 double-syllable vocabularies from the 50 double-syllable vocabularies, and arranging the 20 double-syllable vocabularies in a random sequence to construct each section of corpus sequence. In the embodiment, 35 sections of corpus sequences are constructed in total, and the same vocabulary cannot be repeated in each section of corpus sequence. The corpus sequence is composed of independent double-syllable vocabularies, so that constant double-syllable vocabularies structures can be formed, and each structure comprises syllables and vocabularies in two levels.
The corpus is synthesized and converted into a speech sequence by using a Neospeech speech synthesizer. In this example, all syllables in the hierarchical corpus are independently synthesized by using a Neospeech speech synthesizer (http:// www.neospeech.com/; male voices, beams), the sound intensities of all syllables are adjusted to be the same, the duration is 400 milliseconds, and all syllables are synthesized and spliced to generate the hierarchical speech sequence, as shown in FIG. 3. No acoustic gaps are interposed between syllables in the hierarchical speech sequence. At this time, syllables of the hierarchical phonetic sequence are presented at a rhythm of 400 milliseconds, namely at a rhythm of 2.5 hertz; the double pitch vocabulary is presented at a rhythm of 800 milliseconds, i.e. at a rhythm of 1.25 hz. Each voice sequence has a duration of 8 seconds.
And the electroencephalogram signal acquisition module is used for presenting a voice sequence to a tested person by using the audio input equipment and synchronously acquiring the electroencephalogram signal of the tested person by using the electroencephalogram recording equipment.
Specifically, a hierarchical voice sequence is presented to a tested person through an in-ear noise reduction earphone, and an electroencephalogram signal of the tested person is synchronously acquired through an EGI electroencephalogram instrument in the time of presenting the voice sequence. The EGI electroencephalograph is provided with 32 electroencephalogram electrodes which are distributed on a reticular electroencephalogram cap. During analysis and test, the brain electrical cap is worn on the head of a tested person, and the brain electrical electrode is contacted with the scalp surface of the tested person through physiological saline, so that the brain electrical signal of the tested person is collected. The sampling frequency of the EGI electroencephalograph is 500 Hz, namely 500 data points are acquired per second. Each segment of the electroencephalogram signal acquired in synchronization with each segment of the voice sequence is called a test time. For each tested person, 35 sections of voice sequences are played in the embodiment, and 35 electroencephalogram signals are recorded.
The electroencephalogram signal analysis module is used for preprocessing an electroencephalogram signal, carrying out band-pass filtering on a high frequency band, hilbert transformation and discrete Fourier transformation to obtain an electroencephalogram response frequency spectrum, calculating the phase consistency among test times of electroencephalogram responses at different frequencies based on the electroencephalogram response frequency spectrum, and judging whether a tested person has attention deficit hyperactivity disorder or not through the phase consistency peak values of the electroencephalogram responses at syllable and vocabulary frequencies.
Wherein, brain electricity data preprocessing includes: and downsampling the electroencephalogram signal to 320 Hz so as to improve the efficiency of electroencephalogram data analysis. The reference signal is subtracted from the downsampled electroencephalogram data, and the reference signal is an average signal of 32 electroencephalogram electrodes. The amplitude in the signal is set to 0, which is higher than 1000 microvolts, to remove artifacts.
Extracting the amplitude of the high-frequency electroencephalogram signal comprises the following steps: as shown in fig. 4, the high-band brain electrical signal is divided into 9 narrow-band signals by a FIR band-pass filter according to a passband width of 10 hz. The passband ranges of the 9 narrowband signals are respectively: 70 to 80 hertz, 80 to 90 hertz, 90 to 100 hertz, 100 to 110 hertz, 110 to 120 hertz, 120 to 130 hertz, 130 to 140 hertz, 140 to 150 hertz, 150 to 160 hertz. The amplitude of each narrowband signal can be extracted through Hilbert transform, then each amplitude is normalized to the average value of each band, and all the amplitudes are averaged to obtain the amplitude of the high-frequency electroencephalogram signal.
Extracting the phase of the electroencephalogram response by discrete fourier transform includes: for each test, discrete Fourier transform is performed on the high-frequency electroencephalogram signal amplitude to obtain a frequency spectrum of response amplitude, and response phases on the response frequency spectrum are further extracted.
The formula of the discrete fourier transform is:
wherein N is the sampling point number of the electroencephalogram data, N is the time domain point number of the electroencephalogram data, and m is the frequency domain point number of the electroencephalogram data.
The Fourier transform formula can be converted into a rectangular coordinate form and is used for extracting the phase of the frequency domain point, and the expression is as follows:
can be further converted into:
X(m)＝X real (m)+j·X imag (m)
the phase theta of the frequency domain sequence number m point of the brain electrical data m The method comprises the following steps:
calculating the phase consistency of the electroencephalogram response comprises: based on different test times, calculating the phase consistency among the test times on the electroencephalogram response frequency spectrum, wherein the formula is as follows:
wherein θ m Is the phase of the electroencephalogram response at frequency m in the ith trial, and the inter-trial phase consistency ITC is a scalar measure between 0 and 1.
The output analysis result according to the electroencephalogram response phase consistency condition comprises: if the brain electrical response of the tested person has obvious ITC peaks at syllable (2.5 Hz) and vocabulary (1.25 Hz) frequencies, the attention of the tested person is normal; if the brain electrical response of the tested person only has a significant ITC peak value at syllable frequency, and no significant ITC peak value exists at vocabulary frequency or the ITC peak value is lower than a normal mode level, the attention of the tested person is abnormal; if the brain electrical response of the tested person does not have obvious ITC peak values at syllable and vocabulary frequency, the current test system or test flow is abnormal, repeated test is needed, and the analysis result is output again.
In this example, the ITC spectra of 11 normal children and 11 ADHD children are shown in fig. 5, and at the population level, the ITC peaks at syllable frequency are similar to those of ADHD children for normal children, and at lexical frequency are significantly higher than those of ADHD children. To further verify the effectiveness of the method, this example uses the SNAP-IV diagnostic scale to evaluate all children and calculates pearson correlation between the diagnostic scale evaluation and the children's brain electrical response ITC peak. The SNAP-IV diagnostic scale is primarily used to evaluate ADHD symptoms in typically developing children. The manifest contains three atypical behavior subsets: distraction (INATT, 9), hyperactivity/impulsivity (HYP/IMP, 9) and oppositional defiant behavior (ODD, 8). Each item was rated from 0 to 3, with higher scores indicating elevated levels of ADHD symptoms. The sub-table scores are calculated by averaging the score of the items in each subset. FIG. 6 shows the SNAP-IV sub-scale score versus the ITC peak at lexical frequency for 22 children tested. Wherein, the scores of the two sub-scales of Inattention (INATT) and multi-motor/impulse (HYP/IMP) are in a significant negative correlation with the ITC peak value at the vocabulary frequency (INATT: r= -0.576, p=0.006; HYP/IMP: r= -0.496, p=0.022), namely, the lower the ITC peak value of the brain electrical response of the tested children at the vocabulary frequency, the higher the diagnostic scores of the INATT and HYP/IMP sub-scales, the more obvious the ADHD symptom.
This example demonstrates that by presenting a hierarchical speech sequence to a child under test, and analyzing the ITC peaks of its high-band electroencephalogram response in real time, the level of ADHD symptoms in the child under test can be assessed.
In conclusion, the brain electrical response phase consistency of the nerve tracking activities of the crowd with attention deficit hyperactivity disorder with different degrees is different. The system provided by the invention excites nerve tracking activity of the brain through a hierarchical voice sequence, acquires an electroencephalogram response frequency spectrum, calculates the phase consistency among the test times of electroencephalogram responses at different frequencies based on the electroencephalogram response frequency spectrum, and judges whether the tested person has attention deficit hyperactivity disorder or not through the peak value of the phase consistency of the electroencephalogram responses at syllable and vocabulary frequencies. The invention directly correlates the brain activities with the attention situation, can realize objective evaluation of the attention deficit hyperactivity disorder situation of the tested person without depending on the evaluation personnel and the family members of the tested person, has a standardized evaluation system, and can objectively and scientifically reflect the attention deficit hyperactivity disorder situation of the tested person.
Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the application pertains. The specification and examples are to be regarded in an illustrative manner only.
It is to be understood that the present application is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof.
Claims (10)
1. An analysis system for attention deficit hyperactivity disorder based on a speech hierarchy sequence, the system comprising:
the voice sequence generation module is used for constructing a voice sequence with syllable and vocabulary levels;
the electroencephalogram signal acquisition module is used for presenting a voice sequence to a tested person by using audio input equipment and synchronously acquiring electroencephalogram signals of the tested person by using electroencephalogram recording equipment;
and the electroencephalogram signal analysis module is used for processing the electroencephalogram signals to obtain electroencephalogram response frequency spectrums, calculating the phase consistency among the test times of the electroencephalogram responses at different frequencies based on the electroencephalogram response frequency spectrums, and judging whether the tested person has attention deficit hyperactivity disorder or not according to the peak value of the phase consistency of the electroencephalogram responses at syllable and vocabulary frequencies.
2. The speech-level sequence-based attention deficit hyperactivity disorder analysis system of claim 1, wherein constructing a speech sequence having two levels of syllables and vocabulary comprises:
selecting a corpus, wherein each vocabulary in the corpus contains syllables with the same number, so that the language structures of each corpus are identical and the lengths of the vocabularies are identical;
the corpus sequence is converted and synthesized into a voice sequence.
3. The speech-level-sequence-based attention deficit hyperactivity disorder analysis system of claim 1 or 2, wherein the presentation duration of individual syllables in a speech sequence is 100 ms to 1000 ms; no pauses are inserted between syllables in the speech sequence.
4. The system for analyzing attention deficit hyperactivity disorder based on speech hierarchy sequences as claimed in claim 1, wherein the electroencephalograph is a multichannel electroencephalograph with a sampling frequency of 500 hz and above; the number of electrodes for recording brain electrical signals is 1-256, and at least 1 is the center-back She Dianji.
5. The system for analyzing attention deficit hyperactivity disorder based on speech hierarchy sequences as claimed in claim 1, wherein processing the electroencephalogram signal to obtain an electroencephalogram response spectrum comprises: preprocessing the electroencephalogram signals, carrying out band-pass filtering of high frequency bands, hilbert transformation and discrete Fourier transformation to obtain electroencephalogram response frequency spectrums.
6. The speech hierarchy sequence based attention deficit hyperactivity disorder analysis system of claim 5, wherein preprocessing of the brain electrical signals comprises: and (5) downsampling, re-referencing and removing artifacts on the electroencephalogram signals.
7. The speech hierarchy sequence based attention deficit hyperactivity disorder analysis system of claim 5, wherein high-band bandpass filtering of the preprocessed brain electrical signals comprises:
carrying out high-frequency band-pass filtering and Hilbert transform on the preprocessed electroencephalogram signals to obtain the narrow-band amplitude of the high-frequency electroencephalogram signals; the frequency range of the high-frequency electroencephalogram response is 40 to 200 Hz;
dividing the high-frequency electroencephalogram signal into a plurality of sections of narrow-band frequency bands; wherein the interval of each section of narrow band is 1 to 40 Hz.
8. The system for analyzing attention deficit hyperactivity disorder based on speech hierarchy sequences as claimed in claim 7, wherein performing discrete fourier transform on the narrowband amplitude of the high-band electroencephalogram signal to obtain the electroencephalogram response spectrum comprises:
the brain electrical signal after Hilbert transformation is recorded as X (N), N is a time domain point sequence number, the brain electrical spectrum is recorded as X (m), m is a frequency point sequence number, N is a sampling point number of brain electrical data, and the expression of brain electrical response spectrum is as follows:
9. the system for analyzing attention deficit hyperactivity disorder based on speech hierarchy sequences of claim 1, where calculating inter-trial phase consistency of the brain electrical response at different frequencies based on the brain electrical response spectrum comprises:
the discrete Fourier transform formula of the electroencephalogram response frequency spectrum X (m) is converted into a rectangular coordinate form, and the expression is as follows:
wherein j is an imaginary number;
the rectangular coordinate form is converted into a complex form, and the expression is as follows:
X(m)＝X real (m)+j·X imag (m)
wherein X is real (m) is the real part at the frequency point m, X imag (m) is the imaginary part at frequency point m;
calculating the phase angle theta of the electroencephalogram response of each frequency point m m The expression is as follows:
calculating the phase consistency among test times of brain electrical responses at different frequencies, and recording the brain electrical signals of the ith section of voice sequence as test times i and theta m Is the phase of the electroencephalogram response at frequency m in trial i, and the expression is as follows:
where inter-trial phase consistency ITC is a scalar measure between 0 and 1.
10. The speech hierarchy sequence-based attention deficit hyperactivity disorder analysis system of claim 1, wherein determining whether the subject has symptoms of attention deficit hyperactivity disorder by syllable, phase consistency peaks of brain electrical response at lexical frequencies, comprises:
when the brain electrical response of the tested person has obvious ITC peaks at syllable and vocabulary frequency, the attention of the tested person is normal;
when the brain electrical response of the tested person only has a remarkable ITC peak value at syllable frequency, if the remarkable ITC peak value does not exist at vocabulary frequency or is lower than normal mode level, the attention level of the tested person is abnormal;
when the brain electrical response of the tested person does not have significant ITC peak value at syllable and vocabulary frequency, the test is needed again.
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