WO2022149789A1 - Procédé de prédiction de délire postopératoire au moyen d'une analyse d'électroencéphalogramme, et appareil d'analyse - Google Patents

Procédé de prédiction de délire postopératoire au moyen d'une analyse d'électroencéphalogramme, et appareil d'analyse Download PDF

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WO2022149789A1
WO2022149789A1 PCT/KR2021/020104 KR2021020104W WO2022149789A1 WO 2022149789 A1 WO2022149789 A1 WO 2022149789A1 KR 2021020104 W KR2021020104 W KR 2021020104W WO 2022149789 A1 WO2022149789 A1 WO 2022149789A1
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eeg
delirium
analysis
operative
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PCT/KR2021/020104
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Korean (ko)
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심용수
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가톨릭대학교 산학협력단
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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
    • A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
    • 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
    • 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
    • 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
    • G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders

Definitions

  • the technique to be described below is a technique for predicting postoperative delirium using EEG analysis.
  • Delirium is a neurological disease in which symptoms such as restlessness, hallucinations, and hallucinations appear due to confusion of a mental state that appears temporarily and very suddenly.
  • Post-operative delirium refers to delirium experienced by patients after surgery. Postoperative delirium can lead to a decrease in cognitive and physical function over a long period of time.
  • Postoperative delirium is reported to be associated with an increase in the incidence of dementia and mortality in elderly patients. However, postoperative delirium is currently only diagnosed through clinical diagnosis after surgery.
  • the technique to be described below is intended to provide a technique for predicting postoperative delirium for a surgical patient before surgery.
  • the post-operative delirium prediction method using EEG analysis includes the steps of: the analysis device receiving the pre-operative EEG data of the subject and the analysis device predicting whether the subject will develop post-operative delirium based on the intensity of each frequency band of the EEG data include
  • the post-operative delirium prediction method using EEG analysis includes the steps of: an analysis device receiving pre-operative EEG data of a subject; extracting features by the analysis device for each frequency band of the EEG data; and predicting whether the subject will develop post-operative delirium based on a value that is input to the predictive model built in advance, and the predictive model outputs.
  • the analysis device for predicting postoperative delirium using EEG analysis includes an input device that receives preoperative EEG data of a subject, a storage device that stores a program for predicting postoperative delirium using EEG data, and the inputted using the program. It analyzes the EEG data and includes a computing device for predicting whether the subject's post-operative delirium occurs.
  • the technology to be described below predicts post-operative delirium for the patient before surgery, so that post-operative treatment and management can be fully prepared. Through this, the technology to be described below can improve the prognosis and health function of the patient after surgery.
  • 1 is a result of comparing the EEG of the postoperative delirium patient group and the control group.
  • Figure 2 is another result of comparing the EEG of the postoperative delirium patient group and the control group.
  • 3 is an example of a postoperative delirium prediction system using EEG analysis.
  • 5 is another example of the process of building a model for predicting post-operative delirium.
  • 6 is an example of an analysis device for predicting post-operative delirium by analyzing EEG.
  • first, second, A, and B may be used to describe various components, but the components are not limited by the above terms, and only for the purpose of distinguishing one component from other components.
  • a first component may be named as a second component, and similarly, the second component may also be referred to as a first component without departing from the scope of the technology to be described below. and/or includes a combination of a plurality of related listed items or any of a plurality of related listed items.
  • each constituent unit is responsible for. That is, two or more components to be described below may be combined into one component, or one component may be divided into two or more for each more subdivided function.
  • each of the constituent units to be described below may additionally perform some or all of the functions of other constituent units in addition to the main function it is responsible for. Of course, it can also be performed by being dedicated to it.
  • each process constituting the method may occur differently from the specified order unless a specific order is clearly described in context. That is, each process may occur in the same order as specified, may be performed substantially simultaneously, or may be performed in the reverse order.
  • EEG is widely used as a functional test tool of the nervous system, because EEG is a tool that can directly measure an electromagnetic signal related to a function of the nervous system. EEG measures the difference in electric potential caused by the interaction between neurons in the cerebral cortex, and it can be seen that it reflects the electrochemical process of the brain.
  • EEG data is EEG information about a subject and consists of a constant value. Research on brain function evaluation or disease diagnosis by quantifying EEG data and extracting specific parameters is ongoing.
  • Quantitative electroencephalogram refers to a process of obtaining specific information or characteristics through a mathematical process of EEG data stored as digital signals. This process includes signal analysis including seizure wave detection, signal location analysis and frequency analysis, brain mapping showing geographic distribution, and statistical analysis.
  • the researcher collected the EEG of the patients before surgery and analyzed the correlation between the possibility of postoperative delirium and the EEG measured before surgery.
  • the subjects used by the researcher for EEG analysis are shown in Table 1 below.
  • the postoperative delirium group was 10 patients who developed delirium after surgery, and the control group (no POD) was 16 patients who did not develop delirium after surgery. Whether the subjects had postoperative delirium was confirmed through postoperative symptoms and clinical judgment.
  • age, sex, education level, and the time of onset of delirium after surgery all represent the average values of the patients.
  • Investigators measured EEG before surgery for the subjects. EEG was measured by contacting electrodes to each of the normalized areas of the head. In general, EEG is measured according to the international standard 10-20 electrode placement method. Each electrode corresponds to one channel, and the signal strength may be expressed as a value of spectral power. Each channel of the EEG receives a signal through two input terminals.
  • EEG corresponds to a value expressed over time by the voltage difference coming from each channel to the two input terminals. According to the international convention, the waveform is directed upward when the input 1 of the amplifier is relatively negative compared to the input 2, and, conversely, when the waveform is positive, the waveform is regulated downward.
  • EEG may be divided into alpha, beta, theta, delta, and gamma bands according to frequency bands.
  • Each frequency band can generally be divided into delta (1-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), beta (12-30 Hz) and gamma (30-45 Hz) .
  • Alpha can be divided into alpha1 (8-10 Hz) and alpha2 (10-12 Hz).
  • Beta can be divided into beta1 (12-15 Hz), beta2 (15-20 Hz) and beta3 (20-30 Hz).
  • G1 is a control group (no POD)
  • G2 is a postoperative delirium patient group (POD).
  • the postoperative delirium patient group (G2) showed higher spectral power in delta and theta bands compared to G1 (p ⁇ 0.05).
  • the postoperative delirium patient group (G2) showed lower spectral power in alpha 1, beta 2 and beta 3 compared to G1 (p ⁇ 0.05).
  • FIG. 2 is another result of comparing the EEG of the postoperative delirium patient group and the control group. 2 is a result of analyzing the ratio of EEG bands.
  • G1 denotes a control group (no POD)
  • G2 denotes a postoperative delirium patient group (POD). 2 is a result showing a ratio of a slow first frequency band (slow frequency) and a relatively fast second frequency band (fast frequency).
  • TBR theta/beta ratio
  • TBR2 theta/beta2 ratio
  • DAR delta/alpha ratio
  • TAR theta/alpha ratio
  • the preoperative EEG characteristics of the postoperative delirium patient group show high spectral power in delta and theta bands, and low spectral power in alpha 1, beta 2 and beta 3 compared to normal subjects. Therefore, the preoperative EEG characteristics of the postoperative delirium patient group have higher values in the slow frequency band than in the fast frequency band, so the ratio of the slow frequency band to the fast frequency band is high compared to normal people. Therefore, it can be seen that a certain characteristic (pattern) is observed in the EEG measured before surgery in postoperative delirium patients. Using these EEG characteristics, it is possible to predict whether a patient will develop postoperative delirium before surgery.
  • the medical prescription may include surgery, drug prescription, and the like. That is, the techniques described below can be applied to predicting delirium that occurs after a patient's surgery as well as other medical prescriptions. The technology described below can predict whether delirium will occur after medical prescription by measuring the patient's EEG before medical prescription.
  • the analysis device analyzes the subject's EEG signals to predict post-operative delirium.
  • the analysis device may be implemented as various devices capable of data processing.
  • the analysis device may be implemented as a PC, a server on a network, a smart device, a chipset in which a dedicated program is embedded, or the like.
  • 3 is an example of the postoperative delirium prediction system 100 using EEG analysis. 3 shows an example in which the analysis device is a computer terminal 130 and a server 140 .
  • the EEG measuring device 110 measures an EEG signal of the subject.
  • EEG data of the subject may be stored in an Electronic Medical Record (EMR, 120).
  • EMR Electronic Medical Record
  • the user A may predict postoperative delirium by analyzing the subject's EEG data using the computer terminal 130 .
  • the computer terminal 130 receives the brain wave data of the subject.
  • the computer terminal 130 may receive the subject's brain wave data from the brain wave measuring device 110 or the EMR 120 through a wired or wireless network.
  • the computer terminal 130 may be a device physically connected to the EEG measuring device 110 .
  • the computer terminal 130 may pre-process the subject's EEG data in a constant manner.
  • the computer terminal 130 may extract EEG data features from EEG data, and predict postoperative delirium for a subject based on the extracted features. User A can check the analysis result.
  • the server 140 may receive the subject's brain wave data from the brain wave measuring device 110 or the EMR 120 .
  • the server 140 may constantly process the brain wave data of the subject.
  • the server 140 may extract EEG data features from EEG data, and predict postoperative delirium for the subject based on the extracted features.
  • the server 140 may transmit the analysis result to the terminal of user A. User A can check the analysis result.
  • the computer terminal 130 and/or the server 140 may store the analysis result in the EMR 120 .
  • the analysis device may predict postoperative delirium by classifying and analyzing the subject's EEG data by frequency band. For example, if the analyzer shows high spectral power above the first threshold in delta and theta bands, and at the same time shows low spectral power below the second threshold in alpha 1, beta 2 and beta 3, the corresponding It can be predicted that the subject is more likely to develop delirium after surgery.
  • the analysis apparatus may use a prediction model prepared in advance, not based on values for each frequency band.
  • the predictive model may be a machine learning model.
  • machine learning models such as decision trees, random forests, K-nearest neighbor (KNN), naive Bayes, support vector machine (SVM), and artificial neural network (ANN).
  • FIG. 4 is an example of a process 200 of building a model for predicting post-operative delirium.
  • the predictive model building process may be performed through a separate computer device instead of an analysis device.
  • the computer device builds a predictive model.
  • the EEG measuring device measures the EEG of the subject before surgery ( 210 ).
  • the subject is a patient with postoperative delirium.
  • EEG data is measured from multiple subjects.
  • the computer device may routinely pre-process the EEG signal ( 220 ).
  • EEG signal preprocessing may include at least one of various preprocessing processes utilized in EEG signal processing.
  • the computer device may filter a signal of a specific frequency band (1 to 100 Hz) from the previously acquired EEG signal.
  • the computer device may remove noise included in the EEG signal.
  • the computer device may remove signals such as a safety level signal, an EMG signal, and an electrocardiogram signal included in the signal by using independent component analysis (ICA).
  • ICA independent component analysis
  • the computer device may extract a certain feature from the EEG signal ( 230 ).
  • the computer device may distinguish a signal epoch of a predetermined length, which is a feature extraction unit. The length of the fragment may be determined in consideration of the experimental environment or the size of data.
  • the computer device may convert the EEG signal into a frequency band (Fast Fourier Transform) and classify the signal for each frequency band. For example, the computer device may extract characteristics of the signal for each delta, theta, alpha, beta, and gamma band.
  • the model building process generally uses EEG signals obtained from a plurality of subjects. Accordingly, the computer device may uniformly normalize EEG signals between individual subjects.
  • the computer device may select the extracted features and perform regression analysis using information on whether the subject has postoperative delirium (240).
  • the computer device may derive a linear model to which a stepwise variable selection method is applied to the frequency characteristics of the subject's brain waves.
  • the computer device can build a predictive model (multiple linear regression model) that predicts the possibility of postoperative delirium using the subject's EEG signals.
  • the predictive model may be built through a machine learning model such as a support vector machine (SVM), a deep learning model, or the like.
  • SVM support vector machine
  • 5 is another example of the process 300 of building a model for predicting post-operative delirium. 5 corresponds to a process of learning a deep learning model such as a convolutional neural network (CNN).
  • CNN convolutional neural network
  • the EEG measuring device measures the EEG of the subjects before surgery (310).
  • the subject includes patients with post-operative delirium.
  • the learning data consists of the subject's brain wave data and the subject's label value (postoperative delirium or not).
  • the computer device may routinely pre-process the EEG signal ( 320 ).
  • EEG signal preprocessing may include at least one of various preprocessing processes utilized in EEG signal processing.
  • the computer device may filter a signal of a specific frequency band (1 to 100 Hz) from the previously acquired EEG signal. Also, the computer device may remove noise included in the EEG signal.
  • Processes 310 and 320 correspond to the process of preparing learning data.
  • the computer device inputs the EEG data from the training data to the deep learning model, and repeats the process of updating the parameters of the model while comparing the probability value and the label value output by the deep learning model ( 330 ).
  • the computer device can repeat the model training process until the deep learning model shows a certain performance (accurate prediction probability).
  • the computer device may input the brain wave data into the deep learning model in the form of image data.
  • the computer device may one-hot encode the EEG data and input it to the deep learning model in the form of a one-dimensional array or a multi-dimensional array.
  • convolutional layers extract features from input data, and pre-connected layers are trained to receive the extracted features and calculate a probability value for postoperative delirium.
  • the analysis device 400 corresponds to the above-described analysis devices 130 and 140 in FIG. 3 .
  • the analysis device 400 may be physically implemented in various forms.
  • the analysis device 400 may have the form of a computer device such as a PC, a server of a network, a chipset dedicated to data processing, and the like.
  • the analysis device 400 may include a storage device 410 , a memory 420 , an arithmetic device 430 , an interface device 440 , a communication device 450 , and an output device 460 .
  • the storage 410 may store the aforementioned predictive model (regression analysis model or other machine learning model).
  • the storage device 410 may store a program for predicting post-operative delirium by analyzing input EEG data.
  • the storage device 410 may store EEG data of the subject.
  • the storage device 410 may store a program or code for processing brain wave data of a subject.
  • the storage device 410 may store the analysis result.
  • the memory 420 stores data and information generated in the process of the analysis device 400 processing the brain wave data, the process of predicting post-operative delirium using the EEG data, and the process of predicting the post-operative delirium using the prediction model.
  • the interface device 440 is a device that receives predetermined commands and data from the outside.
  • the interface device 440 may receive EEG data to be analyzed from a physically connected EEG measuring device or an external storage device.
  • the interface device 440 may transmit the analysis result to an external object.
  • the communication device 450 refers to a configuration that receives and transmits certain information through a wired or wireless network.
  • the communication device 450 may receive EEG data of an analysis target from an external object.
  • the communication device 450 may transmit the analysis result to an external object such as a user terminal.
  • the interface device 440 and the communication device 450 are components for exchanging certain data from a user or other physical object, they may be collectively referred to as an input/output device. If the function of receiving EEG data is limited, the interface device 440 and the communication device 450 may be referred to as input devices.
  • the output device 460 is a device that outputs certain information.
  • the output device 460 may output an interface necessary for a data processing process, an analysis result, and the like.
  • the arithmetic unit 430 may predict whether the subject has post-operative delirium by analyzing the subject's EEG data using a command or a program code stored in the storage unit 410 .
  • the computing device 430 may uniformly pre-process the EEG data using the above-described process and a corresponding program. The computing device 430 may then perform an analysis process on the preprocessed data.
  • the computing device 430 may calculate a spectral value of the brain wave data for each frequency band using the above-described process and a corresponding program.
  • the computing unit 430 exhibits high spectral power above the first threshold in delta and theta bands, and simultaneously obtains low spectral power below the second threshold in alpha 1, beta 2, and beta 3 If so, it can be predicted that the subject is more likely to develop postoperative delirium.
  • the first threshold value and the second threshold value may be preset values for post-operative delirium prediction. This value can be determined based on the value of preoperative EEG data of multiple postoperative delirium patients during the development process.
  • the computing device 430 may predict that the subject is highly likely to have post-operative delirium.
  • the ratio may be at least one of TBR, TBR2, DAR, and TAR described with reference to FIG. 2 .
  • the computing device 430 extracts features for each frequency band from the EEG data, and inputs the features for each frequency band into the regression model to calculate a predicted value for postoperative delirium.
  • the computing device 430 may calculate a predicted value (probability value) for post-operative delirium by inputting the EEG data into a machine learning model (eg, a deep learning model) learned in advance.
  • the computing device 430 may finally predict whether the subject has post-operative delirium based on the value calculated by the predictive model. For example, the computing device 430 may predict whether the subject has postoperative delirium by comparing the value calculated by the predictive model with the reference value.
  • the computing device 430 may be a device such as a processor, an AP, or a program embedded chip that processes data and processes a predetermined operation.
  • the EEG analysis method or postoperative delirium prediction method as described above may be implemented as a program (or application) including an executable algorithm that can be executed in a computer.
  • the program may be provided by being stored in a temporary or non-transitory computer readable medium.
  • the non-transitory readable medium refers to a medium that stores data semi-permanently, rather than a medium that stores data for a short moment, such as a register, cache, memory, etc., and can be read by a device.
  • the various applications or programs described above are CD, DVD, hard disk, Blu-ray disk, USB, memory card, ROM (read-only memory), PROM (programmable read only memory), EPROM (Erasable PROM, EPROM)
  • ROM read-only memory
  • PROM programmable read only memory
  • EPROM Erasable PROM, EPROM
  • it may be provided while being stored in a non-transitory readable medium such as an EEPROM (Electrically EPROM) or a flash memory.
  • Temporarily readable media include: Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDR SDRAM), Enhanced SDRAM (Enhanced) SDRAM, ESDRAM), Synchronous DRAM (Synclink DRAM, SLDRAM) and Direct Rambus RAM (Direct Rambus RAM, DRRAM) refers to a variety of RAM.

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Abstract

La présente invention concerne un procédé de prédiction de délire postopératoire au moyen d'une analyse d'électroencéphalogramme comprend les étapes dans lesquelles : un appareil d'analyse reçoit des données d'électroencéphalogramme préopératoires d'un sujet ; et l'appareil d'analyse prédit si le sujet va développer un délire postopératoire sur la base de l'intensité de chaque bande de fréquence des données d'électroencéphalogramme.
PCT/KR2021/020104 2021-01-07 2021-12-29 Procédé de prédiction de délire postopératoire au moyen d'une analyse d'électroencéphalogramme, et appareil d'analyse WO2022149789A1 (fr)

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KR10-2021-0001725 2021-01-07
KR20210001725 2021-01-07
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KR1020210183423A KR20220099898A (ko) 2021-01-07 2021-12-21 뇌파 분석을 이용한 수술후섬망 예측 방법 및 분석장치

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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5230346A (en) * 1992-02-04 1993-07-27 The Regents Of The University Of California Diagnosing brain conditions by quantitative electroencephalography
KR20080068003A (ko) * 2005-08-02 2008-07-22 브레인스코프 컴퍼니 인코퍼레이티드 뇌기능 평가를 위한 방법 및 휴대용 자동 뇌기능 평가 장치
JP2016536086A (ja) * 2013-09-19 2016-11-24 ユーエムシー ユトレヒト ホールディング ベーフェーＵｍｃ Ｕｔｒｅｃｈｔ Ｈｏｌｄｉｎｇ Ｂ．Ｖ． 患者がせん妄状態か否かを示すパラメータを決定するための方法及びシステム
JP2019500939A (ja) * 2015-12-04 2019-01-17 ユニバーシティー オブ アイオワ リサーチ ファウンデーション 脳症／せん妄のスクリーニングおよびモニタリングのための装置、システムおよび方法
KR20200031496A (ko) * 2018-09-14 2020-03-24 주식회사 아이메디신 인지 장애 진단 방법 및 컴퓨터 프로그램

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5230346A (en) * 1992-02-04 1993-07-27 The Regents Of The University Of California Diagnosing brain conditions by quantitative electroencephalography
KR20080068003A (ko) * 2005-08-02 2008-07-22 브레인스코프 컴퍼니 인코퍼레이티드 뇌기능 평가를 위한 방법 및 휴대용 자동 뇌기능 평가 장치
JP2016536086A (ja) * 2013-09-19 2016-11-24 ユーエムシー ユトレヒト ホールディング ベーフェーＵｍｃ Ｕｔｒｅｃｈｔ Ｈｏｌｄｉｎｇ Ｂ．Ｖ． 患者がせん妄状態か否かを示すパラメータを決定するための方法及びシステム
JP2019500939A (ja) * 2015-12-04 2019-01-17 ユニバーシティー オブ アイオワ リサーチ ファウンデーション 脳症／せん妄のスクリーニングおよびモニタリングのための装置、システムおよび方法
KR20200031496A (ko) * 2018-09-14 2020-03-24 주식회사 아이메디신 인지 장애 진단 방법 및 컴퓨터 프로그램

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