KR20170050150A - Method for diagnosis brain health state of child young people and apparatus executing the method - Google Patents

Abstract

Disclosed are a method for diagnosing the brain health state of a child or adolescent, and a device for performing the same. The method for diagnosing the brain health state of a child or adolescent according to an embodiment of the present invention comprises: a step of obtaining brain wave data from reference children and adolescents who have performed a task; a step of obtaining and storing information related to an average(s) of the brain wave data based on either or both of gender and age of the reference children and adolescents; a step of obtaining brain wave data from a subject who has performed the task; a step of retrieving average-related information corresponding to the gender and age of the subject; a step of comparing the brainwave data of the subject with the average-related information retrieved; and a step of diagnosing the brain health state of the subject based on the comparison result.

Description

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a method for diagnosing brain health in children and adolescents,

An embodiment of the present invention relates to a brain health diagnostic technique.

It is called brain waves that consecutively record the potential fluctuation between two points on the scalp caused by the electrical activity of the brain. EEG is a bio-signal reflecting the state of human consciousness, and studies are being conducted to measure the mental health state of a subject using brain waves.

Korean Patent Laid-Open No. 10-2012-0106339 (September 26, 2012)

Korean Patent Publication No. 10-2007-0061311 (Jun. 13, 2007)

An embodiment of the present invention is to provide a method for diagnosing a brain health condition of a child and a device for performing the same, which can diagnose the brain health state of a subject using the brain wave information of a standard child and adolescent youth.

According to an exemplary embodiment of the present invention, there is provided a method for diagnosing a brain health condition of a child and adolescents, the method comprising: a first brain wave data in which a task is not performed by a reference child and a second brain wave data Respectively; The apparatus for diagnosing brain health may further comprise means for obtaining average related information of first brain wave data according to at least one of gender and age of the reference child and adolescents using the acquired first brain wave data and second brain wave data, Calculating and storing the average related information, respectively; Acquiring first brain wave data in a state in which the task of the subject is not performed and second EEG data in a state in which the task is performed, in the brain health state diagnostic apparatus; Extracting average related information of first EEG data and average related information of second EEG data corresponding to gender and age of the subject in the brain health state diagnostic apparatus; The apparatus for diagnosing brain health states may further comprise means for comparing the first EEG data of the subject with the average related information of the first EEG data and comparing the second EEG data of the subject with the average related information of the second EEG data ; And diagnosing the brain health state of the subject according to the comparison result in the brain health state diagnostic apparatus.

The tasks for acquiring the second brain wave data include a NoGo Suppression task, a NoGo Monitoring task, a P3b task, a Cue P3 task, a preparation ability (CNV) task, An adaptability capability (Novelty) task, and a non-vision (Ignore) task.

The average related information of the first EEG data includes at least one of an average value of the amplitude of the first EEG, a standard deviation of a size of the first EEG, and an average development curve of a size of the first EEG according to age And the average related information of the second EEG data includes at least one of an average value of the second EEG amplitude, an average value of the latency of the second EEG, a standard deviation of a size of the second EEG, The standard deviation of the latency of the brain waves, the average developmental curve of the age of the second brain waves, and the average developmental curve of the latency of the second brain waves.

Wherein the first brain wave data includes at least one of delta wave, theta wave, alpha wave, SMR (Sensory Motor Rhythm), beta wave, and high beta wave, And checking whether the neurotransmitter is deficient in the subject according to the comparison result of the first EEG data of the subject and the average related information of the first EEG data.

And generating the prescription information according to the type and deficiency degree of the deficient neurotransmitter in the brain health state diagnostic apparatus when deficiency occurs in the neurotransmitter of the subject.

The second brain wave data includes at least one of a NoGo Suppression task, a NoGo Monitoring task, a P3b task, a Cue P3 task, a CNV task, Wherein the step of diagnosing the brain health state of the subject comprises the steps of: measuring the second brain wave data of the subject and the second brain wave data of the subject; And checking the prefrontal development state of the subject according to the comparison result of the average related information of the second EEG data.

And generating the prescription information according to the abnormality and the abnormality in the brain health condition diagnosis apparatus if the prefrontal development state of the subject is abnormal as a result of the checking.

According to another exemplary embodiment of the present invention, there is provided a method for diagnosing a brain health condition of a child and adolescents, the method comprising: acquiring brain wave data in a state in which a task of a reference child adolescent is performed; Calculating and storing average related information of brain wave data according to at least one of gender and age of the reference child and adolescent youth using the acquired brain wave data; Acquiring brain wave data in a state in which the task of the subject is performed in the brain health state diagnostic apparatus; Extracting average related information of brain wave data corresponding to gender and age of the subject in the brain health state diagnostic apparatus; Comparing the brain wave data of the subject with the average related information of the extracted brain wave data in the brain health state diagnostic apparatus; And diagnosing the brain health state of the subject according to the comparison result in the brain health state diagnostic apparatus.

The task for acquiring the brain wave data includes a NoGo Suppression task, a NoGo Monitoring task, a P3b task, a Cue P3 task, a CNV task, A Novelty task, and an Ignore task.

The average related information of the EEG data includes at least one of an average value of the EEG amplitude, an average value of the EEG latency, a standard deviation of the EEG amplitude, a standard deviation of the EEG latency, An average development curve by age, and an age-specific average development curve of the latency of the EEG.

The EEG data may include at least one of a NoGo Suppression task, a NoGo Monitoring task, a P3b task, a Cue P3 task, a CNV task, Wherein the step of diagnosing the brain health condition of the subject comprises the steps of: measuring the EEG data of the subject and the extracted EEG data; And checking the prefrontal development state of the subject according to the comparison result of the average related information.

And generating the prescription information according to the abnormality and the abnormality in the brain health condition diagnosis apparatus if the prefrontal development state of the subject is abnormal as a result of the checking.

An apparatus according to one exemplary embodiment includes one or more processors; Memory; And one or more programs stored in the memory and configured to be executed by the one or more processors, the program comprising: first EEG data in a state in which the task of the subject is not performed; Acquiring second brain wave data in a state in which the task is performed; Extracting average related information of first EEP data and average related information of second EEP data corresponding to gender and age of the subject; Comparing the first EEG data of the subject with the average related information of the first EEG data and comparing the second EEG data of the subject with the average related information of the second EEG data; And diagnosing a brain health state of the subject according to the comparison result.

The tasks for acquiring the second brain wave data include a NoGo Suppression task, a NoGo Monitoring task, a P3b task, a Cue P3 task, a preparation ability (CNV) task, An adaptability capability (Novelty) task, and a non-vision (Ignore) task.

The program checks the development state of the frontal lobe of the subject in accordance with the comparison result of the second brain wave data of the subject and the average related information of the second brain wave data in the step of diagnosing the brain health state of the subject And may include instructions for executing the steps.

The program may further include instructions for further performing the step of generating prescription information according to the anomaly and the anomaly if there is an abnormality in the prefrontal development state of the subject as a result of the confirmation.

An apparatus according to another exemplary embodiment includes one or more processors; Memory; And one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, the program comprising the steps of: obtaining EEG data in a state in which a task of a subject is performed; ; Extracting average related information of brain wave data corresponding to sex and age of the subject; Comparing brain wave data of the subject with average related information of the extracted brain wave data; And diagnosing a brain health state of the subject according to the comparison result.

The task for acquiring the brain wave data includes a NoGo Suppression task, a NoGo Monitoring task, a P3b task, a Cue P3 task, a CNV task, A Novelty task, and an Ignore task.

The step of checking the frontal lobe development state of the subject according to the comparison result of the EEG data of the subject and the average related information of the extracted EEG data at the step of diagnosing the brain health state of the subject And < / RTI >

The program may further include instructions for further performing the step of generating prescription information according to the anomaly and the anomaly if there is an abnormality in the prefrontal development state of the subject as a result of the confirmation.

The embodiment of the present invention compares the average related information of the first brain wave data of the reference child adolescents whose gender and age correspond to the first brain wave of the subject and the subject to be measured to determine whether the neurotransmitter deficiency of the subject is diagnosed And it is possible to provide a solution according to the type and deficiency of the deficient neurotransmitter.

In addition, it is possible to diagnose the frontal executive state (or developmental state) of the subject by comparing the average related information of the second brain wave data of the reference child adolescents whose sex and age correspond to the second brain wave of the subject and the subject, , It is possible to provide a solution according to the degree of abnormality and the abnormality in the development of the frontal lobe.

1 is a block diagram showing a configuration of a brain health condition diagnosis apparatus according to an exemplary embodiment;
FIG. 2 is a diagram for explaining respective tasks for measuring a second brain wave in the brain health condition diagnosis system according to the exemplary embodiment; FIG.
3 is a flowchart illustrating a method of diagnosing brain health conditions according to an exemplary embodiment;
4 is a graph showing an average development curve of the second EEG according to the suppression task (NoGo Suppression) in the exemplary embodiment
FIG. 5 is a graph showing an average development curve of the second brain wave according to the non-repetitive task (NoGo Monitoring) in the exemplary embodiment
6 is a graph showing an average development curve of the second brain wave according to the task performance (P3b) task in the exemplary embodiment
FIG. 7 is a graph showing an average development curve of a second EEG according to a task of performing an atmospheric condition task performance (Cue P3) in the exemplary embodiment
8 is a graph showing an average development curve for the second brain wave according to the preparation ability (CNV) task in the exemplary embodiment
9 is a graph showing an average development curve of the second EEG according to an adaptive ability (Novelty) task in the exemplary embodiment
10 is a graph showing an average development curve of a second brain wave according to a non-vision task in the exemplary embodiment
Figure 11 depicts a computing environment including an exemplary computing device suitable for use in the exemplary embodiments

Hereinafter, specific embodiments of the present invention will be described with reference to the drawings. The following detailed description is provided to provide a comprehensive understanding of the methods, apparatus, and / or systems described herein. However, this is merely an example and the present invention is not limited thereto.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings. In the following description, well-known functions or constructions are not described in detail since they would obscure the invention in unnecessary detail. The following terms are defined in consideration of the functions of the present invention, and may be changed according to the intention or custom of the user, the operator, and the like. Therefore, the definition should be based on the contents throughout this specification. The terms used in the detailed description are intended only to describe embodiments of the invention and should in no way be limiting. Unless specifically stated otherwise, the singular form of a term includes plural forms of meaning. In this description, the expressions "comprising" or "comprising" are intended to indicate certain features, numbers, steps, operations, elements, parts or combinations thereof, Should not be construed to preclude the presence or possibility of other features, numbers, steps, operations, elements, portions or combinations thereof.

In the following description, terms such as " transmission ", "transmission "," transmission ", "reception ", and the like, of a signal or information refer not only to the direct transmission of signals or information from one component to another But also through other components. In particular, "transmitting" or "transmitting" a signal or information to an element is indicative of the final destination of the signal or information and not a direct destination. This is the same for "reception" of a signal or information. Also, in this specification, the fact that two or more pieces of data or information are "related" means that when one piece of data (or information) is acquired, at least a part of the other data (or information) can be acquired based thereon.

Also, the terms first, second, etc. may be used to describe various components, but the components should not be limited by the terms. The terms may be used for the purpose of distinguishing one component from another. For example, without departing from the scope of the present invention, the first component may be referred to as a second component, and similarly, the second component may also be referred to as a first component.

1 is a block diagram showing a configuration of a brain health condition diagnosis apparatus according to an exemplary embodiment.

Referring to FIG. 1, the brain health condition diagnosis apparatus 100 may include an EEG measurement unit 102, a data processing unit 104, a database 106, and a diagnostic module 108.

The brain health condition diagnosis apparatus 100 may be implemented as one apparatus, but some configurations may be implemented in another apparatus (or implemented in another apparatus). For example, the brain wave measuring unit 102, the data processing unit 104, and the diagnostic module 108 may be implemented as one device, and the database 106 may be implemented as an external server. The database 106 may be a cloud-based server. Further, the brain wave measuring unit 102 may be implemented as a separate apparatus, and the data processing unit 104 and the diagnostic module 108 may be implemented as a single apparatus. At this time, the data processing unit 104 may acquire EEG data from the EEG measuring unit 102 by wire or wirelessly.

The brain health condition diagnosis apparatus 100 measures a first EEG when the task is not performed and a second EEG when the task is performed on the children and adolescents, (For example, mean value of EEG amplitude, standard deviation of EEG amplitude, average development curve of EEG amplitude, etc.) and average related information of second EEG data (for example, EEG amplitude and EEG latency The average value of the latency, the standard deviation of the brain wave size and the brain latency, the average developmental curve of the brain wave size and the brain wave latency, etc.).

Next, the brain health condition diagnosis apparatus 100 calculates the first and second EEG waves of the subject (a person who diagnoses the brain health state) with the average of the pre-stored EEG data corresponding to the sex and age of the corresponding child adolescents It is possible to diagnose the brain health condition of the subject in comparison with the information.

Hereinafter, the configuration and operation of the brain health condition diagnosis apparatus 100 will be described in detail.

The EEG measuring unit 102 measures the first EEG on the scalp of children and adolescents. The first EEG is an EEG measured in a state where pediatric adolescents do not perform tasks (ie, natural state). The EEG measuring unit 102 may measure the amplitude of the first EEG. The first EEG includes Delta wave, Theta wave, Alphapa, Sensory Motor Rhythm (SMR), Beta wave, and High Beta wave depending on the frequency. The delta wave is an EEG with a frequency of 0.5 to 4 Hz, which appears in a state of sleep. Theta wave is an EEG with a frequency of 4 to 7 Hz and appears in drowsy or distracted or daydream. The alpha wave is an EEG with a frequency of 8 to 12 Hz and appears when the external concentration is relaxed in a relaxed state. SMR (Sensory Motor Rhythm) is an EEG with a frequency of 12 to 15 Hz, which appears when you remain focused while not moving. The beta wave is an EEG with a frequency of 15 to 18 Hz, which appears when you are in an active state of thinking and maintaining concentration. The high beta wave is an EEG with a frequency of 18 Hz or higher, which appears in a state of tension or anxiety.

In addition, the EEG measuring unit 102 measures the second EEG on the scalp of children and adolescents. The second EEG is an EEG measured by children and adolescents performing tasks. The EEG measuring unit 102 measures the amplitude of the second EEG and the time from the occurrence of the second EEG to the maximum amplitude of the second EEG (hereinafter, may be referred to as latency) can do.

More specifically, the EEG measuring unit 102 provides the children and adolescents with tasks such as a NoGo Suppression task, a NoGo Monitoring task, a P3b task, a Cue P3 task, ) Task, an adaptability ability (Novelty task), and a non-visual (Ignore) task, and can measure the brain waves (ie, second brain waves) of children and adolescents while performing each task. A detailed description of each task will be given later with reference to Fig.

The EEG measurer 102 can measure the first EEG and the second EEG on a male and female adolescents of various ages (for example, 6 to 17 years old). At this time, the EEG measurer 102 may measure the first EEG and the second EEG on a normal child's brain health state (hereinafter, referred to as a "reference child and adolescent"). At this time, the measured first brain wave data and second brain wave data are used as reference brain wave data. The EEG measuring unit 102 may be configured to be wearable on the head of the subject. The EEG measuring unit 102 amplifies the EEG signal measured on the scalp of the subject and can remove the noise.

The data processing unit 104 receives the first brain wave data and the second brain wave data from the brain wave measuring unit 102. In addition, the data processing unit 104 receives the body information (for example, the sex of the subject and the age of the subject) of the reference child and adolescents. The body information of the reference child and adolescent can be inputted from the user interface (not shown). The data processing unit 104 generates first brain wave data (for example, delta wave, theta wave, alpha wave, SMR (Sensory Motor Rhythm), beta wave, high beta wave, etc.) according to at least one of sex and age ) Can be calculated. In addition, the data processing unit 104 may generate second brain wave data (for example, an inhibitory task, a reflexive task, a task performing task, an atmospheric condition task performing task, a preparation ability task, an adjustment task, Ability task, and visual acuity task). The average and standard deviation of the EEG data can be calculated.

The data processing unit 104 may calculate the average development curve of the first EEG data according to sex and age of the reference child and adolescent youth. At this time, the data processing unit 104 may calculate the average development curve according to each index (e.g., delta wave, theta wave, alpha wave, SMR, beta wave, high beta wave, etc.) of the first brain wave data. In addition, the data processing unit 104 may calculate the average development curve of the second EEG data according to sex and age of the reference child and adolescent youth. At this time, the data processing unit 104 may be configured to classify each task indicator (e.g., inhibitory task, introspection task, task performing task, waiting condition task performing task, preparatory ability task, adaptive capacity task, , The average development curve can be calculated.

The database 106 may include a first database 106-1 and a second database 106-2. The first database 106-1 may store the first brain wave data measured by the brain-wave measuring unit 102. [ The first database 106-1 may store the average and standard deviation of the first brain wave data according to at least one of sex and age. The first database 106-1 may store the average development curve of the first brain wave data according to sex and age. The second database 106-2 may store the second brain wave data measured by the brain wave measuring unit 102. [ The second database 106-2 may store the average and standard deviation of the second brain wave data according to at least one of sex and age. The second database 106-2 may store an average development curve of the second brain wave data according to sex and age.

The diagnostic module 108 may measure the first brain wave of the subject to be diagnosed through the brain wave measuring unit 102. The diagnostic module 108 compares the first brain wave data of the subject with the average related information of the first stored EEG data corresponding to the gender and the age of the subject to determine the brain health state (or brain development degree) of the subject Can be detected. For example, the diagnostic module 108 may detect the brain health state of the subject by comparing the first EEG data of the subject with the average development curve of the previously stored first EEG data.

In an exemplary embodiment, the diagnostic module 108 compares the first EEG data of the subject with the average value of the first stored EEG data or the average development curve of the first EEG data to determine the neurotransmitter of the subject (e.g., , Dopamine, norepinephrine, guar, serotonin, etc.) can be detected. The diagnostic module 108 may provide a solution depending on the degree of neurotransmitter deficiency if a deficiency of neurotransmitter is predicted. For example, if the subject is predicted to be dopamine deficient, the diagnostic module 108 may prescribe neurofeedback training to prescribe drugs that enhance dopamine (e.g., vitamins, minerals, herbs, etc.) can do.

The diagnostic module 108 may measure the second brain wave of the subject to be diagnosed through the brain wave measuring unit 102. The diagnostic module 108 compares the second EEG data of the subject with the average related information of the second EEG data corresponding to the gender and the age of the subject to determine the brain health state (or brain development degree) of the subject Can be detected. For example, the diagnostic module 108 can detect the brain health state of the subject by comparing the second brain wave data of the subject with the average development curve of the previously stored second brain wave data.

In an exemplary embodiment, the diagnostic module 108 compares the mean value of the second EEG data of the subject and the previously stored second EEG data or the average development curve of the second EEG data to determine the prefrontal development state of the subject State) can be diagnosed. The diagnostic module 108 may provide a solution for frontal lobe development if the subject's frontal lobe development state is below average.

2 is a diagram for explaining each task for measuring a second brain wave in the brain health condition diagnosis system according to the exemplary embodiment.

Referring to FIG. 2, the subject may be given two stimuli (e.g., picture or picture) at regular time intervals (for example, 1000 ms). The first stimulus and the second stimulus may be seen for 100 ms each. There may be a time interval of 300ms until the first stimulus is given. Subjects are trained to press the button when the first stimulus is the animal (A) and the second stimulus is the animal (A), and if the first stimulus is the animal (A) and the second stimulus is the plant (P) You can be educated not to enjoy.

Here, the NoGo Suppression task can measure the EEG according to the response of the subject while showing the animal (A) as the first stimulus and the plant (P) as the second stimulus. At this time, the subjects should not enjoy the button when the plant (P) comes out with the second stimulus.

The NoGo Monitoring task measures the EEG according to the self-recognition of the subject while performing the NoGo Suppression task. That is, when the subject measures the second stimulus P, the EEG according to the recognition of whether or not he / she has acted correctly (that is, the button is not pressed) or has acted properly (that is, the button is pressed) .

The task performance (P3b) task can measure the EEG according to the response of the subject while showing the animal (A) as the first stimulus and the second stimulus as the animal (A). At this time, the subjects should press the button when the animal (A) comes out as the second stimulus.

Atmospheric Condition Task Performing Ability (Cue P3) Task can measure EEG according to the subject's reaction when the animal (A) is shown as the first stimulus. In other words, the atmospheric condition task performance task measures the EEG of the subjects after the first stimulus when the animal (A) is shown as the first stimulus.

The preparation ability (CNV) task can display the animal (A) as the first stimulus and measure the brain waves in the prepared state for the second stimulus of the subjects accordingly. That is, when the animal (A) is shown as the first stimulus, the subjects are in a state of reacting to the second stimulus. The preparation task shows the animal (A) as the first stimulus, The EEG of the subject immediately before the reaction can be measured.

The Novelty task shows the plant (P) as the first stimulus and when the second stimulus shows a new picture (eg, a person) that was not mentioned in the previous training, Can be measured.

The Ignore task can measure the EEG according to the responses of the subjects when the first stimulus shows the plant (P) and the second stimulus shows the plant (P).

3 is a flowchart illustrating a method of diagnosing a brain health condition according to an exemplary embodiment. In the illustrated flow chart, the method is described as being divided into a plurality of steps, but at least some of the steps may be performed in reverse order, combined with other steps, performed together, omitted, divided into detailed steps, One or more steps may be added and performed. Also, one or more steps not shown in the method according to the embodiment may be performed with the method.

Referring to FIG. 3, the EEG measuring unit 102 measures a first EEG in a case where a task is not performed on a normal adolescent child with normal brain health (i.e., a reference child) (S 101). For example, the EEG measuring unit 102 may measure the delta wave, theta wave, alpha wave, SMR (sensory motor rhythm), beta wave, and high beta wave of the reference child youth. The EEG measuring unit 102 may measure the amplitude of the first EEG and the latency from the time of the first EEG to the maximum amplitude of the first EEG.

Next, the EEG measuring unit 102 measures the second EEG when the task is performed on the reference child adolescents (S103). For example, the brain wave measuring unit 102 may be configured to perform tasks such as a NoGo Suppression task, a NoGo Monitoring task, a P3b task, a Cue P3 task, (CNV) task, an adaptive ability (Novelty) task, and a non-visual (Ignore) task. The EEG measuring unit 102 may measure the amplitude of the second EEG and the time from the occurrence of the second EEG to the maximum amplitude of the second EEG.

Next, the data processing unit 104 calculates the average of the first brain wave data and the average of the second brain wave data according to at least one of the gender and the age of the reference child and adolescents (S 105). For example, the data processing unit 104 may calculate the average of the first brain wave data according to the gender and the age of the reference child and adolescents, and store the average in the database 106. The data processing unit 104 may calculate the average of the second brain wave data according to sex and age of the reference child and adolescents and store the average in the database 106. The data processing unit 104 may also calculate the standard deviation of the first brain wave data and the standard deviation of the second brain wave data. In addition, the data processing unit 104 may calculate the average development curve of the first EEG data and the average development curve of the second EEG data according to gender and age of the reference child and adolescents.

Next, the EEG measuring unit 102 measures the first EEG when the subject is not performing the task and the second EEG when the task is performed on the subject (the child and adolescent to diagnose the brain health condition) (S 107).

Next, the diagnostic module 108 extracts the average of the first brain wave data corresponding to the gender and the age of the subject in the database 106 (S 109).

Next, the diagnostic module 108 compares the average of the first brain wave data of the subject with the extracted first brain wave data to check whether the neurotransmitter of the subject is deficient (S 111). If it is determined in step S 111 that there is a deficiency in the neurotransmitter of the subject, the diagnostic module 108 generates prescription information according to the type and deficiency of the deficient neurotransmitter (S 113).

Next, the diagnostic module 108 extracts the average of the second brain wave data corresponding to the gender and the age of the subject in the database 106 (S 115).

Next, the diagnostic module 108 compares the average of the second brain wave data of the subject and the extracted second brain wave data to check whether the frontal executive state (or the frontal lobe development state) of the subject is abnormal (S 117 ). As a result of the check in step S117, if there is an abnormality in the subject's frontal lobe execution state, the diagnostic module 108 generates prescription information according to the capability and abnormality of the abnormality (S119).

4 is a graph showing an average development curve of the second EEG according to the suppression task (NoGo Suppression) in the exemplary embodiment. FIG. 4A is a graph showing an average development curve for the amplitude of the second EEG according to the suppression task, FIG. 4B is a graph showing the latency of the second EEG according to the suppression task, The graph shows the average development curve. Here, the x axis represents the age of the standard pediatric adolescents and the y axis represents the normalized mean value.

Referring to FIG. 4 (a), it can be seen that the mean value of the second EEG according to the restraining task increases as the age of the reference child and adolescents increases. 4A is a graph showing the magnitude of the second brain wave according to the suppression task. Here, the x-axis represents the time (ms) and the y-axis represents the potential (.). The magnitude of the second EEG according to the suppression task may mean the maximum amplitude value in the graph (SG 1).

Referring to FIG. 4 (b), it can be seen that the latency average value of the second EEG according to the restraining task decreases as the age of the reference child and adolescents increases. 4 (b), the graph (SG 2) is a graph showing the latency of the second EEG according to the suppression task. Here, the x-axis represents the time (ms) and the y-axis represents the potential (.). The latency of the second EEG according to the suppression task may mean the period from the reaction time of the reference pediatric adolescent to the time when the maximum amplitude appears.

5 is a graph showing an average development curve of a second EEG according to a non-repetitive task (NoGo Monitoring) in the exemplary embodiment. FIG. 5A is a graph showing an average development curve for the amplitudes of the second EEG according to the introspection task, FIG. 5B is a graph showing the latency of the second EEG according to the introspection task, The graph shows the average development curve. Here, the x axis represents the age of the standard pediatric adolescents and the y axis represents the normalized mean value.

Referring to FIG. 5 (a), the average size of the second EEG according to the introspection task is found to be constant from 7 to 12 years of age and increase from 13 years of age or older. 5 (a) is a graph showing the magnitude of the second brain wave according to the introspection task. Here, the x-axis represents the time (ms) and the y-axis represents the potential (.). The magnitude of the second brain wave according to the introspection task may mean the maximum amplitude value in the graph (SG 3).

Referring to FIG. 5 (b), it can be seen that the latency average value of the second EEG according to the introspection task becomes smaller as the age of the reference child and adolescent becomes higher. 5 (b) is a graph showing the latency of the second EEG according to the introspection task. Here, the x-axis represents the time (ms) and the y-axis represents the potential (.). The latency of the second EEG according to the introspection task may mean the period from the reaction time of the reference pediatric adolescent to the time when the maximum amplitude appears.

FIG. 6 is a graph showing an average development curve of the second brain wave according to the task performance (P3b) task in the exemplary embodiment. FIG. 6A is a graph showing an average development curve for the amplitude of the second EEG according to the task performance task, FIG. 6B is a graph showing the latency of the second EEG according to the task performance task, And the average development curve of FIG. Here, the x axis represents the age of the standard pediatric adolescents and the y axis represents the normalized mean value.

6 (a), the average size of the second EEG according to the task performance task is increased from 7 to 8 years of age in the reference pediatric adolescents, and decreases slightly from 9 years of age . 6 (a) is a graph showing the magnitude of the second brain wave according to the task performance task. Here, the x-axis represents the time (ms) and the y-axis represents the potential (.). The size of the second EEG according to task performance task may mean the maximum amplitude value in the graph (SG 5).

6B, the latency average value of the second EEG according to the task performance task increases as the age of the reference pediatric adolescents increases from 7 to 8 years, decreases from 9 to 13 years, Can be seen. 6 (b) is a graph showing the latency of the second EEG according to task performance task. Here, the x-axis represents the time (ms) and the y-axis represents the potential (.). The latency of the second EEG according to the task performance task may mean the period from the reaction time of the reference pediatric adolescent to the time when the maximum amplitude appears.

FIG. 7 is a graph showing an average development curve of a second EEG according to a task of performing an atmospheric condition task performance (Cue P3) in the exemplary embodiment. FIG. 7A is a graph showing an average development curve for amplitude of a second EEG according to an atmospheric condition task performance task, and FIG. 7B is a graph showing a second EEG according to an atmospheric condition task performance task. The graph shows the average development curve for latency. Here, the x axis represents the age of the standard pediatric adolescents and the y axis represents the normalized mean value.

Referring to FIG. 7 (a), the average size of the second EEG according to the atmospheric condition task performance task increases with age from 7 to 9 years, and decreases with age from 10 years . 7A is a graph showing the magnitude of the second EEG according to the atmospheric condition task performance task. Here, the x-axis represents the time (ms) and the y-axis represents the potential (.). The magnitude of the second brain wave according to the atmospheric condition task performance task can mean the maximum amplitude value in the graph (SG 7).

Referring to FIG. 7 (b), the latency average value of the second EEG according to the atmospheric condition task performance task is slightly increased as the age of the reference child and adolescents increases. However, it can be seen that the latency average value of the second EEG according to the atmospheric condition task performance task is not significantly different according to the age. The graph (SG 8) in FIG. 7 (b) is a graph showing the latency of the second EEG according to the atmospheric condition task performance task. Here, the x-axis represents the time (ms) and the y-axis represents the potential (.). The latency of the second EEG according to the atmospheric condition task performance task may mean the period from the reaction time of the reference pediatric adolescent to the time when the maximum amplitude appears.

8 is a graph showing an average development curve for the second brain wave according to the preparation ability (CNV) task in the exemplary embodiment. Here, the x axis represents the age of the standard pediatric adolescents and the y axis represents the normalized mean value. In the case of the second brain wave according to the preparation ability task, the area of the electric potential of the brain EEG measured in the scalp of the reference child and adolescent during a certain period (1200 ms to 1400 ms in FIG. 8) immediately before the response to the second stimulus Area).

Referring to FIG. 8, the area average value of the second EEG according to the preparation ability task varies depending on the age of the reference child and adolescents. In other words, the mean area of the second EEG according to the preparation ability task is increased from 7 to 9 years of age, decreases from 10 to 13 years, and increases again from 14 to 16 years. 8, a graph (SG9) is a graph showing the area below the reference potential with reference to the reference potential for the second brain wave according to the preparation ability task. Here, the x-axis represents the time (ms) and the y-axis represents the potential (.).

9 is a graph showing an average development curve of a second brain wave according to an adaptability ability (Novelty) task in the exemplary embodiment. FIG. 9A is a graph showing an average development curve for the amplitude of the second EEG according to the adaptive capability task, FIG. 9B is a graph showing the latency of the second EEG according to the adaptive capability task, And the average development curve of FIG. Here, the x axis represents the age of the standard pediatric adolescents and the y axis represents the normalized mean value.

Referring to FIG. 9 (a), the average size of the second EEG according to the adaptive ability task is slightly decreased from 9 to 12 years of age and increases with age from 13 years old. 9A is a graph showing the magnitude of the second EEG according to the adaptive capability task. Here, the x-axis represents the time (ms) and the y-axis represents the potential (.). The magnitude of the second EEG according to the adaptive ability task may mean the maximum amplitude value in the graph (SG 10).

Referring to FIG. 9B, it can be seen that the latency average value of the second EEG according to the adaptive ability task decreases slightly as the age of the reference child and adolescents increases. 9 (b) is a graph showing the latency of the second EEG according to the adaptive capability task. Here, the x-axis represents the time (ms) and the y-axis represents the potential (.). The latency of the second EEG according to the adaptive ability task may mean the period from the reaction time of the reference pediatric adolescent to the time when the maximum amplitude appears.

10 is a graph showing an average development curve of a second brain wave according to a non-vision task in the exemplary embodiment. FIG. 10A is a graph showing an average development curve for the magnitude of the second EEG according to the non-visual task, FIG. 10B is a graph showing the latency of the second EEG according to the non- And the average development curve of FIG. Here, the x axis represents the age of the standard pediatric adolescents and the y axis represents the normalized mean value.

Referring to FIG. 10 (a), it can be seen that the mean value of the second EEG according to the non-visual task decreases as the age of the reference child and adolescents increases. In FIG. 10 (a), the graph (SG 12) is a graph showing the magnitude of the second brain wave according to the non-vision task. Here, the x-axis represents the time (ms) and the y-axis represents the potential (.). The magnitude of the second brain wave according to the non-visual task can mean the maximum amplitude value in the graph (SG 12).

Referring to FIG. 10 (b), the latency average value of the second EEG according to the non-visual task is slightly increased as the age of the reference pediatric adolescents increases, and then decreases slightly at the age of 12 years. The graph (SG 13) in FIG. 10 (b) is a graph showing the latency of the second EEG according to the non-visual task. Here, the x-axis represents the time (ms) and the y-axis represents the potential (.). The latency of the second EEG according to the non-visual task can mean the period from the reaction time of the pediatric adolescent to the time when the maximum amplitude appears.

11 illustrates a computing environment including an exemplary computing device suitable for use in the exemplary embodiments.

The exemplary computing environment 200 shown in FIG. 11 includes a computing device 210. Typically, each configuration may have different functions and capabilities, and may additionally include components that are appropriate for the configuration, even if not described below. The computing device 210 may be an apparatus or system (e.g., brain health condition diagnostic apparatus 100) that measures brain waves to diagnose brain health conditions.

The computing device 210 includes at least one processor 212, a computer readable storage medium 214, and a bus 260. The processor 212 is coupled to the bus 260 and the bus 260 includes a computer readable storage medium 214 to connect the various other components of the computing device 210 to the processor 212.

The processor 212 may cause the computing device 210 to operate in accordance with the exemplary embodiment discussed above. For example, the processor 212 may execute computer-executable instructions stored in the computer-readable storage medium 214, and computer-executable instructions stored in the computer-readable storage medium 214 may be executed by the processor 212 The computing device 210 may be configured to perform operations in accordance with certain exemplary embodiments.

Computer readable storage medium 214 may store computer-executable instructions or program code (e.g., instructions contained in application 230), program data (e.g., data used by application 230), and / As shown in FIG. The application 230 stored in the computer-readable storage medium 214 includes a predetermined set of instructions executable by the processor 212. [

The memory 216 and the storage device 218 shown in FIG. 11 are examples of a computer-readable storage medium 214. The memory 216 may be loaded with computer executable instructions that may be executed by the processor 212. Also, the program data may be stored in the memory 216. [ For example, such memory 216 may be volatile memory, such as random access memory, non-volatile memory, or any suitable combination thereof. As another example, the storage device 218 may include one or more removable or non-removable components for storage of information. For example, the storage device 218 may be a hard disk, flash memory, magnetic disk, optical disk, any other form of storage medium that can be accessed by the computing device 210 and store the desired information, or any suitable combination thereof.

The computing device 210 may also include one or more input / output interfaces 220 that provide an interface for one or more input / output devices 270. The input / output interface 220 is connected to the bus 260. The input / output device 270 may be connected to (other components of) the computing device 210 via the input / output interface 220. The input / output device 270 includes an input device such as a pointing device, a keyboard, a touch input device, a voice input device, a sensor device and / or a photographing device and / or an output device such as a display device, printer, speaker and / can do.

On the other hand, certain embodiments may include a computer readable storage medium comprising a program for performing the procedures described herein on a computer. Such computer-readable storage media may include program instructions, local data files, local data structures, etc., alone or in combination. The computer-readable storage medium may be those specially designed and constructed for the present invention. Examples of computer-readable storage media include magnetic media such as hard disks, floppy disks and magnetic tape, optical recording media such as CD-ROMs and DVDs, magneto-optical media such as floppy disks, and ROMs, And hardware devices specifically configured to store and execute the same program instructions. Examples of program instructions may include machine language code such as those generated by a compiler, as well as high-level language code that may be executed by a computer using an interpreter or the like.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the invention is not limited to the disclosed exemplary embodiments, but, on the contrary, . Therefore, the scope of the present invention should not be limited to the above-described embodiments, but should be determined by equivalents to the appended claims, as well as the appended claims.

100: Brain health condition diagnosis device
102: EEG measurement unit
104:
106: Database
108: Diagnostic module

Claims (20)

Acquiring first brain wave data in a state in which the task of the reference child and adolescents is not performed and second brain wave data in a state in which the task is performed, in the brain health state diagnostic apparatus;
The apparatus for diagnosing brain health may further comprise means for obtaining average related information of first brain wave data according to at least one of gender and age of the reference child and adolescents using the acquired first brain wave data and second brain wave data, Calculating and storing the average related information, respectively;
Acquiring first brain wave data in a state in which the task of the subject is not performed and second EEG data in a state in which the task is performed, in the brain health state diagnostic apparatus;
Extracting average related information of first EEG data and average related information of second EEG data corresponding to gender and age of the subject in the brain health state diagnostic apparatus;
The apparatus for diagnosing brain health states may further comprise means for comparing the first EEG data of the subject with the average related information of the first EEG data and comparing the second EEG data of the subject with the average related information of the second EEG data ; And
And diagnosing the brain health state of the subject according to the comparison result in the brain health state diagnostic apparatus.
The method according to claim 1,
Wherein the task for acquiring the second brain wave data comprises:
NoGo Suppression task, NoGo Monitoring task, P3b task, Cue P3 task, CNV task, Novelty task, and Ignore ) Tasks, including at least one of the following:
The method according to claim 1,
The average related information of the first EEG data includes at least one of an average value of the amplitude of the first EEG, a standard deviation of a size of the first EEG, and an average development curve of a size of the first EEG according to age and,
The average related information of the second EEG data may be an average value of the amplitude of the second EEG, an average value of the latency of the second EEG, a standard deviation of a size of the second EEG, A standard deviation of the latency, an average development curve of the second brain wave size according to the age, and an average development curve of the latency of the second brain wave.
The method according to claim 1,
Wherein the first brain wave data includes at least one of delta wave, theta wave, alpha wave, SMR (Sensory Motor Rhythm), beta wave, and high beta wave,
The step of diagnosing the brain health state of the subject includes checking whether the neurotransmitter is deficient in the subject according to the comparison result of the first brain wave data of the subject and the average related information of the first brain wave data A method of diagnosing brain health in children and adolescents, including.
The method of claim 4,
Further comprising the step of generating prescription information according to the type of the deficient neurotransmitter and the degree of deficiency in the brain health state diagnostic apparatus when deficiency occurs in the neurotransmitter of the subject, How to diagnose your health condition.
The method according to claim 1,
The second brain wave data includes at least one of a NoGo Suppression task, a NoGo Monitoring task, a P3b task, a Cue P3 task, a CNV task, A Novelty task, and an Ignore task, which are measured on the basis of the measured EEG,
Wherein the step of diagnosing the brain health state of the subject includes a step of confirming a state of development of the frontal lobe of the subject according to a result of comparison between the second brain wave data of the subject and the average related information of the second brain wave data Diagnosis of brain health in children and adolescents.
The method of claim 6,
Further comprising the step of generating the prescription information according to the abnormal ability and the abnormal condition in the brain health condition diagnostic apparatus when abnormality occurs in the frontal lobe development state of the subject as a result of the checking, Methods for diagnosing brain health conditions.
Acquiring brain wave data in a state of performing a task of a reference child and adolescent child in a brain health state diagnostic apparatus;
Calculating and storing average related information of brain wave data according to at least one of gender and age of the reference child and adolescent youth using the acquired brain wave data;
Acquiring brain wave data in a state in which the task of the subject is performed in the brain health state diagnostic apparatus;
Extracting average related information of brain wave data corresponding to gender and age of the subject in the brain health state diagnostic apparatus;
Comparing the brain wave data of the subject with the average related information of the extracted brain wave data in the brain health state diagnostic apparatus; And
And diagnosing the brain health state of the subject according to the comparison result in the brain health state diagnostic apparatus.
The method of claim 8,
Wherein the task for acquiring the brain wave data comprises:
NoGo Suppression task, NoGo Monitoring task, P3b task, Cue P3 task, CNV task, Novelty task, and Ignore ) Tasks, including at least one of the following:
The method of claim 8,
Wherein the average related information of the brain wave data includes:
The mean value of the EEG amplitude, the mean value of the EEG latency, the standard deviation of the size of the EEG, the standard deviation of the EEG latency, the average development curve of the EEG size according to age, A method for diagnosing brain health conditions in children and adolescents, comprising at least one of average developmental curves by latency.
The method according to claim 1,
The EEG data may include at least one of a NoGo Suppression task, a NoGo Monitoring task, a P3b task, a Cue P3 task, a CNV task, Novelty task, and an Ignore task, respectively,
Wherein the step of diagnosing the brain health state of the subject comprises the step of checking the developmental state of the frontal lobe of the subject in accordance with the comparison result of the brain wave data of the subject and the average related information of the extracted brain wave data, A method for diagnosing a brain health condition.
The method of claim 11,
Further comprising the step of generating the prescription information according to the abnormal ability and the abnormal condition in the brain health condition diagnostic apparatus when abnormality occurs in the frontal lobe development state of the subject as a result of the checking, Methods for diagnosing brain health conditions.
One or more processors;
Memory; And
An apparatus comprising one or more programs,
Wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors,
The program includes:
Acquiring first brain wave data in a state in which a task of a subject is not performed and second brain wave data in a state in which a task is performed;
Extracting average related information of first EEP data and average related information of second EEP data corresponding to gender and age of the subject;
Comparing the first EEG data of the subject with the average related information of the first EEG data and comparing the second EEG data of the subject with the average related information of the second EEG data; And
And diagnosing a brain health condition of the subject according to the comparison result.
14. The method of claim 13,
Wherein the task for acquiring the second brain wave data comprises:
NoGo Suppression task, NoGo Monitoring task, P3b task, Cue P3 task, CNV task, Novelty task, and Ignore ) Tasks.
15. The method of claim 14,
The program may further comprise, in the step of diagnosing the brain health state of the subject,
And checking the frontal lobe development state of the subject according to a comparison result of the second EEG data of the subject and the average related information of the second EEG data.
16. The method of claim 15,
The program includes:
And generating the prescription information according to the anomaly and the anomaly if the determination results in an abnormality in the prefrontal development state of the subject.
One or more processors;
Memory; And
An apparatus comprising one or more programs,
Wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors,
The program includes:
Acquiring brain wave data in a state in which a task of a subject is performed;
Extracting average related information of brain wave data corresponding to sex and age of the subject;
Comparing brain wave data of the subject with average related information of the extracted brain wave data; And
And diagnosing the brain health state of the subject according to the comparison result
18. The method of claim 17,
Wherein the task for acquiring the brain wave data comprises:
NoGo Suppression task, NoGo Monitoring task, P3b task, Cue P3 task, CNV task, Novelty task, and Ignore ) Tasks.
19. The method of claim 18,
The program may further comprise, in the step of diagnosing the brain health state of the subject,
And checking the frontal development status of the subject according to a comparison result of the subject's brain wave data and the average related information of the extracted brain wave data.
The method of claim 19,
The program includes:
And generating the prescription information according to the anomaly and the anomaly if the determination results in an abnormality in the prefrontal development state of the subject.

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