WO2019103188A1 - Système et procédé d'évaluation de lésion cérébrale traumatique par analyse d'ondes cérébrales - Google Patents

Système et procédé d'évaluation de lésion cérébrale traumatique par analyse d'ondes cérébrales Download PDF

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WO2019103188A1
WO2019103188A1 PCT/KR2017/013455 KR2017013455W WO2019103188A1 WO 2019103188 A1 WO2019103188 A1 WO 2019103188A1 KR 2017013455 W KR2017013455 W KR 2017013455W WO 2019103188 A1 WO2019103188 A1 WO 2019103188A1
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network
brain
connectivity
unit
analysis
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Korean (ko)
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강승완
진승현
홍슬기
윤혜진
배영우
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주식회사 아이메디신
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/25—Bioelectric electrodes therefor
    • A61B5/279—Bioelectric electrodes therefor specially adapted for particular uses
    • A61B5/291—Bioelectric electrodes therefor specially adapted for particular uses for electroencephalography [EEG]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/369—Electroencephalography [EEG]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/369—Electroencephalography [EEG]
    • A61B5/372—Analysis of electroencephalograms
    • A61B5/374—Detecting the frequency distribution of signals, e.g. detecting delta, theta, alpha, beta or gamma waves
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7225—Details of analog processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/74—Details of notification to user or communication with user or patient ; user input means
    • A61B5/742—Details of notification to user or communication with user or patient ; user input means using visual displays
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/22—Social work or social welfare, e.g. community support activities or counselling services
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment

Definitions

  • 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.
  • traumatic brain injury which is caused by an artificial shock and causing damage to the brain, is frequent.
  • the number of patients complaining of chronic pain due to accidents resulting from traffic accidents is rapidly increasing.
  • 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.
  • a doctor such as a doctor to determine the degree of damage and degree of brain damage.
  • it is difficult to detect a slight brain damage .
  • 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.
  • 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.
  • 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.
  • 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.
  • 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).
  • DNN default mode network
  • FPN fronto-parietal network
  • the functional connectivity can be expressed by the following 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
  • () is the interval average in ().
  • 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.
  • 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).
  • 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
  • ⁇ is the standard deviation value
  • 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.
  • 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.
  • 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.
  • DDN default mode network
  • FPN fronto- parietal network
  • the functional connectivity can be expressed by the following 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
  • () is the interval average in ().
  • 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.
  • 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 step d) may calculate the degree of brain damage through the following 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
  • ⁇ is the standard deviation value
  • 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, .
  • 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.
  • FIG. 1 is a configuration diagram of a traumatic brain injury evaluation system through EEG analysis according to a preferred embodiment of the present invention.
  • FIG. 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.
  • 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.
  • FIG. 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.
  • Measuring section 11 Measuring sensor
  • 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.
  • first component may be referred to as a second component, and similarly, the second component may also be referred to as a first component.
  • / or < / RTI &gt includes any combination of a plurality of related listed items or any of a plurality of related listed items.
  • FIG. 1 is a configuration diagram of a traumatic brain injury evaluation system through EEG analysis according to a preferred embodiment of the present invention.
  • a traumatic brain injury evaluation system through EEG analysis 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, .
  • 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.
  • 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
  • S10 the scalp
  • 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.
  • EEG 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.
  • a delta (delta, 1 to 4 Hz), ata (4 to 8 Hz), alpha (alpha, 8 to 13 Hz) ⁇ 21 Hz), Beta 2 (Beta 2, ⁇ 2, 21 ⁇ 30 Hz) can be used.
  • Electroencephalogram 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.
  • 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).
  • CNN Convolution Neural Network
  • RNN Recurrent Neural Network
  • HNN Hybrid Neural Network
  • 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).
  • DNN default mode network
  • AN attention network
  • FPN parietal network
  • SSN sensorimotor network
  • 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
  • () is the interval average in ().
  • the value of iCo xy is determined between 0 and 1.
  • iCoh xy 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 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 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.
  • 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).
  • 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 ⁇ is the same age and gender database standard deviation value calculated from the healthy standard network index database.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • the connectivity of the traumatic brain injury patient 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.
  • 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
  • FIG. 10 shows that the traumatic brain injury patient group (mTBI) HC), as compared with the case of the non-HC.
  • DLPFC L -DLPFC R the connectivity between the left brain and the right brain
  • mTBI traumatic brain injury patient group
  • HC healthy control group
  • RTI ID 0.0 &gt
  • 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.
  • the degree of brain damage analyzed by the analysis unit 20 is output through the output unit 30 as in step S80.
  • 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.
  • the contents can be tagged and outputted together with the brain wave data.
  • 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
  • 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.
  • the content may include clinical interpretation information according to the assessed degree of traumatic brain injury.
  • the present invention can be used industrially because it can determine the objective and reliable traumatic brain injury degree by calculating the functional relevance and network strength of each brain position and comparing with the standard network strength of health of the same age and sex.

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Abstract

La présente invention concerne un système et un procédé d'évaluation d'une lésion cérébrale traumatique par analyse d'ondes cérébrales, comprenant : une unité de mesure pour mesurer des ondes cérébrales dans un état stable ; une unité d'analyse qui élimine le bruit des ondes cérébrales mesurées par l'unité de mesure, analyse les indicateurs de connectivité et de réseau et les compare à des indicateurs de réseau standard de personnes normales classées par sexe et âge, calculant ainsi l'amplitude d'une lésion cérébrale traumatique ; et une unité de sortie pour délivrer en sortie le résultat d'analyse de l'unité d'analyse.
PCT/KR2017/013455 2017-11-23 2017-11-24 Système et procédé d'évaluation de lésion cérébrale traumatique par analyse d'ondes cérébrales WO2019103188A1 (fr)

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