KR20190059377A - Traumatic brain injury evaluation system and method using brain wave analysis - Google Patents

Abstract

The present invention relates to a traumatic brain injury evaluation system through brainwave analysis capable of detecting a slight traumatic brain injury, and a method thereof. The traumatic brain injury evaluation system through brainwave analysis comprises: a measurement unit measuring a brainwave of a stable state; an analysis unit removing noise from the brainwave measured from the measurement unit, analyzing connectivity and a network index, and comparing the network index and a standard network index of a healthy person classified by gender and age to calculate a degree of a traumatic brain injury; and an output unit outputting an analysis result of the analysis unit.

Description

[0001] The present invention relates to a system and a method for evaluating a traumatic brain injury,

The present invention relates to a system and a method for evaluating traumatic brain injury through EEG analysis, and more particularly, to a system and method for evaluating traumatic brain injury by analyzing EEG in a stable state and confirming functional connectivity, and comparing the distribution of a healthy standard brain network constructed with ages and genders To a method and system for determining a traumatic brain injury.

In general, traumatic brain injury, which is caused by an artificial shock and causing damage to the brain, is frequent. In particular, the number of patients complaining of chronic pain due to accidents resulting from traffic accidents is rapidly increasing. However, when the symptoms of traumatic brain injury due to such accidents are mild, no abnormality is found in brain imaging, There is a problem in that it is not provided.

The most commonly used method for evaluating and diagnosing traumatic brain injury in the past is a method of photographing a brain image and examining the image by a doctor such as a doctor to determine the degree of damage and degree of brain damage. However, it is difficult to detect a slight brain damage .

Unlike the above method, a body-worn device such as the patent 10-2017-0071951 (Traumatic Brain Injury Detection Apparatus and Traumatic Brain Injury Detection Method Utilizing Same, published June 26, 2017) has been developed.

However, the above apparatus has a problem that external impact is applied to the wearer, change in the magnitude and duration of the external impact is detected, and it is not applicable to patients suspected of traumatic brain injury.

Therefore, there is a need for a diagnostic system that diagnoses the presence or severity of traumatic brain injury through an objective diagnostic method and allows the patient to receive appropriate treatment according to the condition.

SUMMARY OF THE INVENTION The present invention has been made in view of the above problems, and it is an object of the present invention to provide a system and a method for evaluating traumatic brain injury through EEG analysis capable of detecting a slight traumatic brain injury.

Another problem to be solved by the present invention is to provide a more systematic and reliable traumatic brain injury assessment system and method using the difference in characteristics of brain networks according to gender.

According to an aspect of the present invention, there is provided a system for assessing traumatic brain injury through EEG analysis, comprising: a measurement unit for measuring a stable state EEG; a noise canceling unit for removing noise from the EEG measured by the measurement unit; An analyzing unit for analyzing the network index and for comparing the degree of traumatic brain injury with the health standard network index classified by gender and age and an output unit for outputting the analysis result of the analyzing unit.

According to an embodiment of the present invention, the analysis unit may include a filter unit for removing noise from the EEG signals measured by the measurement unit, a correlation unit for analyzing the association and the network of noise- A comparator for comparing the analysis results of the association and network index calculator with a brain network of a health person stored in a network index database; And calculating a result to calculate a degree of traumatic brain injury.

According to an embodiment of the present invention, the association and network index calculator performs functional connectivity and network analysis of the brain, and the functional connectivity includes a default mode network (DMN), a front- functional connectivity between the constituent areas of the network defined by the fronto-parietal network (FPN).

According to an embodiment of the present invention, the functional connectivity can be expressed by the following equation (1).

Equation 1

In the above equation (1), f is a frequency, Sxy (f) is a cross-spectrum between X and Y, Sxx (f) and Syy (f) are X and Y spectrums, respectively. im denotes the imaginary part of coherence, and () is the interval average in ().

According to an embodiment of the present invention, the comparison unit can compare the default mode network and the alpha-wave region of the front-port parity network to check the location and degree of brain damage.

According to one embodiment of the present invention, in the above-described alpha-wave region, the connectivity between the left brain and the right brain at the same position in the traumatic brain injury patient group is larger than that in the healthy control group and the connectivity between the left brain and the right brain is lower .

The calculation unit can calculate the degree of brain damage through the following equation (2).

Equation 2

는 건강인 표준 뇌 네트워크 강도로서 동일 연령대 및 성별 데이터베이스 평균값이고, σ는 건강인 표준 뇌 네트워크 강도의 표준편차값X is the detected brain network intensity,

Is the standardized brain network intensity of the healthy person and is the mean value of the same age and gender database and σ is the standard deviation value

According to an embodiment of the present invention, the output unit may tag the text data to the visualized image of the traumatic brain damage data of the measurement subject and output the text data together.

According to another aspect of the present invention, there is provided a method for evaluating traumatic brain injury through EEG analysis, comprising the steps of: a) measuring stable EEG data; b) filtering EEG data to remove noise; c) Calculating an indicator; d) comparing the result of step c) with a standard brain network index of the same gender, and calculating the degree of brain damage; e) And comparing the individual indices of the brain network to identify the location and degree of brain damage.

According to an embodiment of the present invention, the step c) performs functional connectivity and network analysis of the brain, wherein the functional connectivity includes a default mode network (DMN), a fronto- parietal network (FPN), which is a network-based network.

According to an embodiment of the present invention, the functional connectivity can be expressed by the following equation (1).

In the above equation (1), f is a frequency, Sxy (f) is a cross-spectrum between X and Y, Sxx (f) and Syy (f) are X and Y spectrums, respectively. im denotes the imaginary part of coherence, and () is the interval average in ().

According to an embodiment of the present invention, the default mode network and the alpha-wave region of the frontal parity network may be compared to determine the position and degree of brain damage.

According to one embodiment of the present invention, in the above-described alpha-wave region, the connectivity between the left brain and the right brain at the same position in the traumatic brain injury patient group is larger than that in the healthy control group, and the connectivity between the left brain and the right brain is lower .

According to an embodiment of the present invention, the step d) may calculate the degree of brain damage through the following equation (2).

Equation 2

는 건강인 표준 뇌 네트워크 강도로서 동일 연령대 및 성별 데이터베이스 평균값이고, σ는 건강인 표준 뇌 네트워크 강도의 표준편차값X is the detected brain network intensity,

Is the standardized brain network intensity of the healthy person and is the mean value of the same age and gender database and σ is the standard deviation value

According to an embodiment of the present invention, e) tagging the text data on the visualized image of the traumatic brain injury data of the measurement subject and outputting together.

The present invention relates to a method and apparatus for measuring a brain injury by measuring EEG in a stable state, confirming functional connectivity, realizing a stable state brain network by using functional connectivity, comparing with a healthy standard brain network, .

In addition, the present invention has the effect of determining more objective and reliable traumatic brain injury considering the specificity of the brain network according to sex and age.

1 is a configuration diagram of a traumatic brain injury evaluation system through EEG analysis according to a preferred embodiment of the present invention.
2 is a flowchart of a method for evaluating traumatic brain injury through EEG analysis according to a preferred embodiment of the present invention.
3 is an electrode arrangement diagram of the measurement sensor.
4 is network configuration area information.
FIG. 5 is a graph of the network connection strength between the traumatic brain injury patient group (mTBI) and the healthy control group (HC) in the DMN alpha network.
FIGS. 6 and 7 are diagrams comparing the connectivity of the DMN alpha network, respectively.
FIG. 8 is a graph of the network connection strength between the traumatic brain injury patient group (mTBI) and the health control group (HC) in the FPN alpha network.
9 and 10 are graphs showing the connectivity of the FPN alpha network, respectively.
11 is a graph of the network connection strength between a patient with a traumatic brain injury and a healthy control group during a connection in an AN network.
12 is a graph of the network connection strength between a patient with traumatic brain injury and a healthy control group in the SMN network.

Hereinafter, a system and a method for evaluating traumatic brain injury through EEG analysis according to a preferred embodiment of the present invention will be described in detail with reference to the accompanying drawings.

The embodiments of the present invention can make various changes and have various embodiments, and specific embodiments will be described in detail with reference to the drawings. It should be understood, however, that the embodiments of the present invention are not limited to specific embodiments, but include all modifications, equivalents, and alternatives falling within the spirit and scope of embodiments of the present invention.

Terms including ordinals, such as first, second, etc., may be used to describe various elements, but the elements are not limited to these terms. The terms are used only for the purpose of distinguishing one component from another. For example, without departing from the scope of the embodiments 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. And / or < / RTI > includes any combination of a plurality of related listed items or any of a plurality of related listed items.

The terminology used in the embodiments of the present invention is used only to describe a specific embodiment and is not intended to limit the embodiments of the present invention. The singular expressions include plural expressions unless the context clearly dictates otherwise. In the embodiments of the present invention, terms such as " comprise " or " comprise ", etc. designate the presence of stated features, integers, steps, operations, elements, parts, or combinations thereof, It should be understood that the foregoing does not preclude the presence or addition of other features, numbers, steps, operations, elements, parts, or combinations thereof.

Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which the embodiments of the present invention belong. Terms such as those defined in commonly used dictionaries are to be interpreted as having a meaning consistent with the meaning of the context in the related art and, unless explicitly defined in the embodiments of the present invention, are intended to mean ideal or overly formal .

1 is a configuration diagram of a traumatic brain injury evaluation system through EEG analysis according to a preferred embodiment of the present invention.

Referring to FIG. 1, a traumatic brain injury evaluation system through EEG analysis according to a preferred embodiment of the present invention includes a measurement unit 10 for measuring an EEG in a stable state, a noise measurement unit 10 for measuring noise in the EEG measured by the measurement unit 10, An output unit 30 for outputting the analysis result of the analysis unit 20, an analysis unit 20 for analyzing the connectivity, the network index, .

Hereinafter, the configuration and operation of the traumatic brain injury evaluation system through the EEG analysis according to the preferred embodiment of the present invention will be described in detail. The EEG analysis according to the preferred embodiment of the present invention shown in FIG. Refer to the flow chart of the traumatic brain injury assessment method.

First, the measurement unit 10 includes a measurement sensor 11 for measuring brain waves, and an amplification unit 12 for amplifying a measurement signal of the measurement sensor 11.

EEG is an electrical signal generated when a signal is transmitted between brain cells. A measurement sensor (also referred to as a recording electrode 11) is attached to the scalp (S10).

The attachment position of the measurement sensor 11 is in accordance with the international standard 10-20 system (Nuwer, 1987).

Fig. 3 shows an electrode arrangement diagram of the measurement sensor 11. Fig.

And 19 electrodes (Fp1, Fp2, F7, F8, F3, F4, Fz, T3, T4, C3, C4, Cz, T5, T6, P3, P4, Pz, O1, O2) Since the EEG is a signal containing various frequency components, the EEG is divided into frequency bands in order to observe the characteristics of the constituent frequency components.

In the present invention, a delta (delta, 1 to 4 Hz), ata (4 to 8 Hz), alpha (alpha, 8 to 13 Hz) ~ 21 Hz), Beta 2 (Beta 2, β 2, 21 ~ 30 Hz) can be used.

Electroencephalogram (EGG), which is an electroencephalogram (EGG) which is normally stable, has an amplitude of about 50,, amplifies it through the amplification unit 12, and makes it easy to analyze in the analysis unit 20 including the averaging process.

The electrical signals amplified by the measured EEG signals are input to the analyzer 20 in step S20 of FIG.

The analysis unit 20 includes a filter unit 21 for removing noise from the signals input from the measurement unit 10 and performs an EEG preprocessing in step S30.

The analysis unit 20 may be a computer such as a PC or a notebook computer, or may use hardware produced separately. The filter unit 21 for performing noise cancellation may be hardware or software that can be filtered. The filter unit 21 filters an electrical signal of the input unit 10 and extracts only valid signals.

The filter unit 21 may simply remove the noise of the signal, but it may remove other components except the EEG through the deep neural network analysis.

Each node layer of the neural network can extract different features from measured EEG data and different layer features can be learned from layer to layer. The characteristics of the lower level can be simple and concrete, and the higher the level, the more complex and abstract the character can be.

The EEG signal including the noise measured by the measuring unit 10 can be classified into a pure EEG component, a horizontal eye motion component, a vertical eye motion component, a muscle motion component, and other noise components using the features of the deep neural network have.

Then, based on the classified signal, the remaining noise components other than the EEG component are removed from the input EEG data, and the EEG elimination noise can be finally output.

The in-depth neural network that can be used for the depth neural network analysis may be Convolution Neural Network (CNN), Recurrent Neural Network (RNN) or Hybrid Neural Network (HNN).

The analysis unit 20 further includes a connectivity and network index calculation unit 22, a network index database 23, a comparison unit 24, and a calculation unit 25.

The electrical signals obtained by amplifying the EEG filtered by the filter unit 21 are used to calculate the functional connectivity of the brain in the connectivity and network index calculator 22 as in step S40.

Different regions of the brain or neuronal cells cooperate or compete through the complex network of the brain and process complex information efficiently. Analyzes the connections between specific regions of the brain and calculates the strength of the connections.

The functional connectivity of the brain is calculated by calculating the EEG sources at 5810 positions in the cerebral cortex and then using the default mode network (DMN), the attention network (AN), the fronto- the functional connectivity between the constituent areas of the network defined by the parietal network (FPN) and the sensorimotor network (SMN).

The network strength of each network is divided by frequency band (delta (1 ~ 4Hz), theta (4 ~ 8Hz), alpha (8 ~ 13Hz), beta 1 (13 ~ 21Hz) ).

Various indicators can be used as functional connectivity indicators, and imaginary coherence (iCoh) is used in the present invention. The functional connectivity index can be calculated by the following equation (1).

F is the frequency, Sxy (f) is the cross-spectrum between X and Y, Sxx (f) and Syy (f) are the spectra of X and Y, respectively. im denotes the imaginary part of coherence, and () is the interval average in (). The value of iCo _xy is determined between 0 and 1.

If the value of iCoh _xy is 0, it means that the two signals at the positions of X and Y at a given frequency are linearly independent. Conversely, a value of 1 means that the two signals are at maximum correlated at a given frequency.

The computation of the brain network is a total connection strength index that calculates the mean value of the total connectivity index after calculating the brain connectivity between each network constitution area. The clustering coefficient, the path length, , And a centrality indicator.

The connectivity and network index calculator 22 of the analyzer 20 analyzes the connectivity and the network and the comparison unit 24 compares the indicators stored in the network index database 23 with the indicators stored in the network index database 23, And the analysis results of the connectivity and network index calculation unit 22 are compared.

Then, the calculating unit 25 of the analyzing unit 20 calculates the intensity of each index, performs an average, and calculates the degree of brain damage Z by using the average.

The degree of brain damage can be expressed by the following equation (2).

는 건강인 표준의 네트워크 지표 데이터베이스로부터 산출한 동일 연령대 및 성별 데이터베이스 평균값이고, σ는 건강인 표준 네트워크 지표 데이터베이스로부터 산출한 동일 연령대 및 성별 데이터베이스 표준편차값이다.In Equation (2), X is the intensity of the detected index,

Is the same age and gender database mean value as calculated from the network index database of the health standard and σ is the same age and gender database standard deviation value calculated from the healthy standard network index database.

Then, the brain damage degree (Z) value calculated as in step S60 is determined to determine whether or not the brain is damaged.

The degree of brain damage (Z) is zero or a positive real or negative real value, and the brain damage level is determined using the positive real or negative real value.

If the degree of brain damage (Z) is 0, it is determined that there is no damage since it is the same as the standard network index of the healthy person, and the degree of brain damage can be judged according to the magnitude of the real number. If the absolute value of the degree of brain damage (Z) is less than 2.5 in consideration of the individual deviation of the calculated brain damage degree (Z), it can be judged that there is no brain damage.

Then, if the absolute value of the degree of brain damage (Z) exceeds 2.5 as a result of the determination in step S60, the degree of damage is diagnosed by comparing the network indicators of the same age, gender, health, and the detected network index as in step S70.

In step S70, the damage connectivity region and the degree of damage may be estimated by separately comparing the brain connectivity (network configuration connectivity) between the healthy person group and the brain damage patient.

4 is network configuration area information.

Referring to FIG. 4, an example of the network configuration connectivity may be a connectivity value between MFG_L and MFG_R, and in step S70, all the connectivity is compared to determine the damage location and degree.

The brain network connection strength of the brain damage patient in a certain position of the brain may be stronger or weaker than the brain network connection strength of the health person, and the position and degree of the damage are judged by considering the difference of the network by the position.

Applicants of the present invention compared the steady state brain networks of twenty patients with traumatic brain injury and twenty healthy persons of the same age and sex.

FIG. 5 is a graph of the network connection strength between the traumatic brain injury patient group (mTBI) and the healthy control group (HC) in the DMN alpha network.

Referring to FIG. 5, the difference in the network intensity between the traumatic brain injury patient group (mTBI) and the health control group (HC) in the DMN alpha network is remarkable. In other words, the intensity of the DMN alpha network can be compared to determine the traumatic brain injury.

FIG. 6 shows that the healthy control group (HC) exhibits stronger connectivity than the traumatic brain injury patient group (mTBI) in the DMN alpha network. FIG. 7 shows that the traumatic brain injury patient group (mTBI) HC), as compared with the case of the non-HC.

6 and 7, in the case of traumatic brain injury patients (mTBI), the connectivity between the left brain and the right brain (MFG _L -MFG _R ) is larger than that of the healthy control group (HC) Lt; RTI ID = 0.0 > connectivity. ≪ / RTI >

In this same brain network, the connectivity of the traumatic brain injury patient (mTBI) may show a stronger or weaker connection than the HC (healthy control group) The occurrence of damage can be detected, and the degree of damage can be confirmed using the difference in strength of connectivity.

FIG. 8 is a graph of the network connection strength between the traumatic brain injury patient group (mTBI) and the health control group (HC) in the FPN alpha network.

In FIG. 8, as in the DMN alpha network, the network intensity of the traumatic brain injury group (mTBI) and the healthy control group (HC) are significantly different, and the traumatic brain injury can be judged by using the intensity of the DMN alpha network.

FIG. 9 shows the case where the health control group (HC) exhibits stronger connectivity than the traumatic brain injury patient group (mTBI) in the FPN alpha network, and FIG. 10 shows that the traumatic brain injury patient group (mTBI) HC), as compared with the case of the non-HC.

Referring to FIGS. 9 and 10, it is found that the connectivity between the left brain and the right brain (DLPFC _L -DLPFC _R, etc.) is greater in the traumatic brain injury patient group (mTBI) than in the healthy control group (HC) Lt; RTI ID = 0.0 > connectivity. ≪ / RTI >

On the other hand, in the AN network shown in FIG. 11, the intensity differences between the health control group (HC) and the traumatic brain damage patient group (mTBI) are not clearly distinguished from each frequency band, Traumatic brain injury is difficult to judge.

This also applies to the SMN network shown in Fig.

As described above, the degree of brain damage analyzed by the analysis unit 20 is output through the output unit 30 as in step S80. Here, the term "output" refers to an output of a report that can be easily understood by a person to be measured, which may be a concept including display on a display device or direct printing on paper, etc., and includes transmission to another device using communication Should be understood.

The present invention proposes an automated method for performing step S80.

More specifically, in the process of outputting from the output unit 30, the contents can be tagged and outputted together with the brain wave data.

For example, the text data determined according to the degree of damage may be stored in the output unit 30 and the text data determined according to the degree of brain damage analyzed by the analysis unit 20 may be output together. That is, text can be added to a visualized image of traumatic brain injury data and output.

The content may be text data describing each brain wave data, and the output unit 30 may tag the text data to the visualized image of the brain wave data of the measurement subject and output it together.

In addition, the content may include clinical interpretation information according to the assessed degree of traumatic brain injury.

It will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the spirit and scope of the invention will be.

10: Measuring section 11: Measuring sensor
12: amplification unit 20:
21: Filter part 22: Connectivity and network index calculation part
23: network index database 24: comparison section
25: operation unit 30: output unit

Claims (15)

A measurement unit for measuring stable state brain waves;
An analysis unit for calculating the degree of traumatic brain injury by removing noise from the EEG measured at the measurement unit, analyzing the connectivity and network index, and comparing with the health standard network index classified by gender and age; And
And an output unit for outputting an analysis result of the analysis unit. The method according to claim 1,
However,
A filter unit for removing noise from the EEG signals measured by the measuring unit;
A correlation and network index calculator for analyzing a correlation and a network of EEG signals from which noise has been removed from the filter unit;
Network index database to store the brain network strength of the health person;
A comparing unit comparing the analysis results of the association and network index calculator with a brain network of a health person stored in a network index database; And
And calculating a comparison result of the comparison unit to calculate a degree of traumatic brain injury. 3. The method of claim 2,
The association and network index calculation unit calculates,
Perform functional connectivity and network analysis of the brain,
The functional connectivity is characterized by calculating functional connectivity between the configuration areas of the network defined as a default mode network (DMN), a fronto-parietal network (FPN) Traumatic Brain Injury Assessment System Using EEG Analysis. The method of claim 3,
Preferably,
Wherein the system is characterized by being expressed by the following equation (1).
Equation 1

(F) is a cross-spectrum between X and Y, Sxx (f) and Syy (f) are a spectrum of X and Y respectively, im is an imaginary part coherent () Denotes an imaginary part of coherence, The method of claim 3,
Wherein,
Wherein the default mode network is compared with the alpha region of the frontal parity network to determine the location and degree of brain damage. 6. The method of claim 5,
In the alpha wave region,
The traumatic brain injury evaluation system through brain wave analysis is characterized in that the connectivity between the left brain and the right brain in the traumatic brain injury group is larger than that in the healthy control group, and the connectivity between the left brain and the other right brain regions is lower. The method of claim 3,
The operation unit,
Wherein the degree of brain damage is calculated through Equation (2) below.
Equation 2

X is the detected brain network intensity,

Is the standardized brain network intensity of the healthy person and is the mean value of the same age and gender database and σ is the standard deviation value The method according to claim 1,
The output unit includes:
Wherein the text data is tagged on the visualized image of the traumatic brain injury data of the subject to be measured and is output together. a) measuring stable brain wave data;
b) filtering EEG data to remove noise;
c) calculating connectivity and network indicators;
d) comparing the result of step c) with a standard brain network index of the same gender, and calculating the degree of brain damage;
e) comparing the individual indicators of the brain network to determine the location and extent of brain damage if the absolute value of the brain damage degree exceeds a set value. 10. The method of claim 9,
The step c)
Perform functional connectivity and network analysis of the brain,
The functional connectivity is characterized by calculating functional connectivity between the configuration areas of the network defined as a default mode network (DMN), a fronto-parietal network (FPN) To evaluate the traumatic brain injury. 11. The method of claim 10,
Preferably,
Wherein the system is characterized by being expressed by the following equation (1).
Equation 1

(F) is a cross-spectrum between X and Y, Sxx (f) and Syy (f) are a spectrum of X and Y respectively, im is an imaginary part coherent () Denotes an imaginary part of coherence, 11. The method of claim 10,
Wherein the default mode network is compared with an alpha wave region of the frontal parity network to verify the location and extent of brain damage. 13. The method of claim 12,
In the alpha wave region,
The traumatic brain injury evaluation system through brain wave analysis is characterized in that the connectivity between the left brain and the right brain in the traumatic brain injury group is larger than that in the healthy control group, and the connectivity between the left brain and the other right brain regions is lower. 10. The method of claim 9,
The step d)
Wherein the degree of brain damage is calculated through Equation (2) below.
Equation 2

X is the detected brain network intensity,

Is the standardized brain network intensity of the healthy person and is the mean value of the same age and gender database and σ is the standard deviation value 10. The method of claim 9,
e) tagging the text data to a visualized image of traumatic brain injury data of the subject to be measured and outputting together.

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