WO2024101759A1 - Procédé, programme et dispositif d'analyse de données d'électrocardiogramme - Google Patents

Procédé, programme et dispositif d'analyse de données d'électrocardiogramme Download PDF

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WO2024101759A1
WO2024101759A1 PCT/KR2023/017193 KR2023017193W WO2024101759A1 WO 2024101759 A1 WO2024101759 A1 WO 2024101759A1 KR 2023017193 W KR2023017193 W KR 2023017193W WO 2024101759 A1 WO2024101759 A1 WO 2024101759A1
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data
ecg
electrocardiogram
features
principal component
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Korean (ko)
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권준명
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주식회사 메디컬에이아이
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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
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
    • A61B5/346—Analysis of electrocardiograms
    • 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
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7253—Details of waveform analysis characterised by using transforms
    • 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
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7271—Specific aspects of physiological measurement analysis
    • A61B5/7275—Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
    • 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
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • 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

Definitions

  • the present disclosure relates to data processing technology in the medical field, and specifically relates to a method of facilitating analysis by reducing the dimension of feature data extracted from electrocardiogram signals.
  • An electrocardiogram is a graphical recording of potentials related to heartbeat on the surface of the body.
  • the electrocardiogram has the advantage of being relatively inexpensive, non-invasive, and easily repeatable recording.
  • An electrocardiogram refers to a recording of electrical signals occurring in the heart, and can usually be measured at a length of about 10 seconds.
  • the heart is recorded beating several times in 10 seconds, and the recording of one beat of the heart can be called 1 beat.
  • One beat of an electrocardiogram may include a P wave corresponding to atrial depolarization, a QRS wave corresponding to ventricular depolarization, and a T wave corresponding to ventricular repolarization.
  • the extracted features are 'PR INTERVAL', which is the interval from the start point of the P wave to the start point of the QRS wave, 'QRS duration', which is the interval of the QRS wave, 'P AREA', which is the area of the + area among the P waves, and the + value among the P waves.
  • 'PR INTERVAL' which is the interval from the start point of the P wave to the start point of the QRS wave
  • 'QRS duration' which is the interval of the QRS wave
  • 'P AREA' which is the area of the + area among the P waves
  • This may include 'P AMP', which is the voltage value of the highest point.
  • the present disclosure was developed in response to the above-described background technology, and its purpose is to provide a method of reducing the dimension of ECG feature data extracted from ECG signals and converting them into data in a form that is easy to analyze.
  • a method of analyzing electrocardiogram data which is performed by a computing device, is disclosed according to an embodiment of the present disclosure for realizing the above-described problem.
  • the method includes obtaining an electrocardiogram signal, generating electrocardiogram feature data including a plurality of features based on the electrocardiogram signal, and reducing the dimension of the electrocardiogram feature data based on one or more principal components to obtain electrocardiogram principal component data. It may include the step of generating.
  • the one or more main components are characterized in that the plurality of features are merged based on the correlation between the plurality of features.
  • generating the ECG feature data may include extracting n features (n is a natural number) based on a preset feature extraction algorithm.
  • generating the ECG feature data may include inferring n features by inputting the ECG signal into a pre-trained first neural network model.
  • the principal component may be characterized as including a first principal component corresponding to the dimensional axis in which the variance of the ECG principal component data is greatest.
  • the method may further include visualizing the first principal component and the ECG principal component data in a first graph.
  • the first graph may include first disease area information.
  • the main component may further include a second main component orthogonal to the first main component.
  • the method may further include visualizing the ECG feature data in a second graph based on the first principal component and the second principal component.
  • the step of predicting a disease corresponding to the ECG main component data by inputting the ECG main component data into a pre-trained second neural network model may be further included.
  • a computer program stored in a computer-readable storage medium When the computer program runs on one or more processors, it performs operations for analyzing electrocardiogram data. At this time, the operations include acquiring an ECG signal, generating ECG feature data including a plurality of features based on the ECG signal, and reducing the dimension of the ECG feature data based on one or more main components, An operation for generating principal component data may be included.
  • the one or more main components are characterized in that the plurality of features are merged based on the correlation between the plurality of features.
  • a computing device for inputting electrocardiogram data through an image projection method.
  • the device includes a processor including at least one core; a memory containing program codes executable on the processor; And it may include a network unit for acquiring electrocardiogram data.
  • the processor acquires an ECG signal, generates ECG feature data including a plurality of features based on the ECG signal, reduces the dimension of the ECG feature data based on one or more main components, and generates ECG main component data. creates .
  • the one or more main components are characterized in that the plurality of features are merged based on the correlation between the plurality of features.
  • the present disclosure can convert ECG feature data including a plurality of features into a form that is easy to analyze by using a data dimension reduction method including principal component analysis.
  • the present disclosure enables intuitive analysis by visualizing ECG feature data with reduced dimensionality.
  • the present disclosure can predict a user's disease based on dimensionally reduced ECG feature data.
  • 1 is a diagram explaining ECG feature data extracted from an ECG signal.
  • FIG. 2 is a block diagram of a computing device according to an embodiment of the present disclosure.
  • Figure 3 is a block diagram illustrating an electrocardiogram data analysis device according to an embodiment of the present disclosure.
  • Figure 4 is a block diagram illustrating a neural network model according to an embodiment of the present disclosure.
  • FIG. 5 is a diagram illustrating a method of reducing the dimension of ECG feature data in the ECG data analysis method according to an embodiment of the present disclosure.
  • Figure 6 is a diagram visualizing the result of reducing the dimension of ECG data according to an embodiment of the present disclosure.
  • FIG. 7 is a diagram illustrating a method for visualizing ECG data according to an embodiment of the present disclosure.
  • Figure 8 is a block diagram illustrating a neural network model according to an embodiment of the present disclosure.
  • FIG. 9 is a flowchart explaining an ECG data analysis method according to an embodiment of the present disclosure.
  • Figure 10 is a flowchart explaining an ECG data analysis method according to an embodiment of the present disclosure.
  • the term “or” is intended to mean an inclusive “or” and not an exclusive “or.” That is, unless otherwise specified in the present disclosure or the meaning is not clear from the context, “X uses A or B” should be understood to mean one of natural implicit substitutions. For example, unless otherwise specified in the present disclosure or the meaning is not clear from the context, “X uses A or B” means that It can be interpreted as one of the cases where all B is used.
  • N is a natural number
  • N is a natural number
  • components performing different functional roles may be distinguished as first components or second components.
  • components that are substantially the same within the technical spirit of the present disclosure but must be distinguished for convenience of explanation may also be distinguished as first components or second components.
  • acquisition used in this disclosure is understood to mean not only receiving data through a wired or wireless communication network with an external device or system, but also generating data in an on-device form. It can be.
  • module refers to a computer-related entity, firmware, software or part thereof, hardware or part thereof.
  • the “module” or “unit” can be understood as a term referring to an independent functional unit that processes computing resources, such as a combination of software and hardware.
  • the “module” or “unit” may be a unit composed of a single element, or may be a unit expressed as a combination or set of multiple elements.
  • a “module” or “part” in the narrow sense is a hardware element or set of components of a computing device, an application program that performs a specific function of software, a process implemented through the execution of software, or a program. It can refer to a set of instructions for execution, etc.
  • module or “unit” may refer to the computing device itself constituting the system, or an application running on the computing device.
  • module or “unit” may be defined in various ways within a range understandable to those skilled in the art based on the contents of the present disclosure.
  • model refers to a system implemented using mathematical concepts and language to solve a specific problem, a set of software units to solve a specific problem, or a process to solve a specific problem. It can be understood as an abstract model of a process.
  • a neural network “model” may refer to an overall system implemented as a neural network that has problem-solving capabilities through learning. At this time, the neural network can have problem-solving capabilities by optimizing parameters connecting nodes or neurons through learning.
  • a neural network “model” may include a single neural network or a neural network set in which multiple neural networks are combined.
  • FIG. 2 is a block diagram of a computing device according to an embodiment of the present disclosure.
  • the computing device 100 may be a hardware device or part of a hardware device that performs comprehensive processing and calculation of data, or may be a software-based computing environment connected to a communication network.
  • the computing device 100 may be a server that performs intensive data processing functions and shares resources, or it may be a client that shares resources through interaction with the server.
  • the computing device 100 may be a cloud system in which a plurality of servers and clients interact to comprehensively process data. Since the above description is only an example related to the type of computing device 100, the type of computing device 100 may be configured in various ways within a range understandable to those skilled in the art based on the contents of the present disclosure.
  • the computing device 100 may include a processor 110, a memory 120, and a network unit 130. However, since FIG. 2 is only an example, the computing device 100 may include other components for implementing a computing environment. Additionally, only some of the configurations disclosed above may be included in computing device 100.
  • the processor 110 may be understood as a structural unit including hardware and/or software for performing computing operations.
  • the processor 110 may read a computer program to extract features of an ECG signal and perform principal component analysis on the extracted features.
  • the processor 110 can read a computer program and perform data processing for machine learning.
  • the processor 110 can process computational processes such as processing input data for machine learning, extracting features for machine learning, and calculating errors based on backpropagation.
  • the processor 110 for performing such data processing includes a central processing unit (CPU), a general purpose graphics processing unit (GPGPU), a tensor processing unit (TPU), and a custom processing unit (TPU).
  • CPU central processing unit
  • GPU general purpose graphics processing unit
  • TPU tensor processing unit
  • TPU custom processing unit
  • processor 110 may include a semiconductor (ASIC: application specific integrated circuit), or a field programmable gate array (FPGA: field programmable gate array). Since the type of processor 110 described above is only an example, the type of processor 110 may be configured in various ways within a range understandable to those skilled in the art based on the contents of the present disclosure.
  • ASIC application specific integrated circuit
  • FPGA field programmable gate array
  • the processor 110 may generate ECG feature data based on the ECG signal.
  • the processor 110 may reduce the dimension of ECG feature data. Additionally, the processor 110 may visualize the dimensionally reduced ECG feature data or predict the health status of the ECG measurement target using the dimensionally reduced ECG feature data.
  • the processor 110 may learn a deep learning model that extracts ECG features by using ECG signals as input. Additionally, the processor 110 may use the learned deep learning model to generate ECG feature data including a user-specified number of ECG features based on the ECG signal, which is the inference target data. The processor 110 may extract ECG features using a rule-based algorithm.
  • the processor 110 may reduce the dimensionality of ECG feature data using principal component analysis. Additionally, the processor 110 can visualize the ECG feature data with reduced dimensions to be expressed in a virtual space. Through this data dimension reduction and visualization, the processor 110 can process the data into a form that allows even people without expert knowledge of electrocardiograms to intuitively understand the analysis results. In addition, the processor 110 can learn a deep learning model that diagnoses the type, onset, and course of a disease for the measurement target or predicts the onset of the disease, based on electrocardiogram data whose dimensions have been reduced through principal component analysis. there is. The processor 110 can use dimensionality reduction data to enable a deep learning model to easily perform data interpretation and learning. In addition, the processor 110 can predict, diagnose, or classify the health status of the subject of electrocardiogram measurement based on the dimensionality reduction data that is the subject of inference using the learned deep learning model.
  • the memory 120 may be understood as a structural unit including hardware and/or software for storing and managing data processed in the computing device 100. That is, the memory 120 can store any type of data generated or determined by the processor 110 and any type of data received by the network unit 130.
  • the memory 120 may be a flash memory type, hard disk type, multimedia card micro type, card type memory, or random access memory (RAM). ), SRAM (static random access memory), ROM (read-only memory), EEPROM (electrically erasable programmable read-only memory), PROM (programmable read-only memory), magnetic memory , may include at least one type of storage medium among a magnetic disk and an optical disk.
  • the memory 120 may include a database system that controls and manages data in a predetermined system. Since the type of memory 120 described above is only an example, the type of memory 120 may be configured in various ways within a range understandable to those skilled in the art based on the contents of the present disclosure.
  • the memory 120 can manage data required for the processor 110 to perform operations, a combination of data, and program code executable on the processor 110 by structuring and organizing them.
  • the memory 120 may store ECG data received through the network unit 130, which will be described later.
  • the memory 120 includes program code for mathematical operations that generate ECG feature data and perform dimensionality reduction, program code that operates the neural network model to perform learning by receiving ECG data, and a computing device that receives ECG data from the neural network model.
  • Program code that operates to perform inference according to the purpose of use of (100) and processed data generated as the program code is executed may be stored.
  • the network unit 130 may be understood as a structural unit that transmits and receives data through any type of known wired or wireless communication system.
  • the network unit 130 is a local area network (LAN), wideband code division multiple access (WCDMA), long term evolution (LTE), and WiBro (wireless).
  • broadband internet 5th generation mobile communication (5G), ultra wide-band wireless communication, ZigBee, radio frequency (RF) communication, wireless LAN, wireless fidelity ), data transmission and reception can be performed using a wired or wireless communication system such as near field communication (NFC), or Bluetooth. Since the above-described communication systems are only examples, the wired and wireless communication systems for data transmission and reception of the network unit 130 may be applied in various ways other than the above-described examples.
  • the network unit 130 may receive data necessary for the processor 110 to perform operations through wired or wireless communication with any system or client. Additionally, the network unit 130 may transmit data generated through the calculation of the processor 110 through wired or wireless communication with any system or any client. For example, the network unit 130 may receive ECG data through communication with a database in a hospital environment, a cloud server that performs tasks such as standardization of medical data, or the computing device 100. The network unit 130 transmits the output data of the neural network model, intermediate data derived from the calculation process of the processor 110, processed data, etc. through communication with the above-described database, server, or computing device 100. You can.
  • Figure 3 is a block diagram illustrating an electrocardiogram data analysis device according to an embodiment of the present disclosure.
  • the ECG data analysis device 200 may include an input module 210, a feature extraction module 220, and a main component data generation module 230.
  • the ECG data analysis device 200 may further include a visualization module 240 or an analysis module 250.
  • the input module 210 may be an electrocardiogram measurement device that directly measures the user's electrocardiogram signal or a communication module for receiving the electrocardiogram signal transmitted through a network. If the input module 210 is a communication module for receiving an electrocardiogram signal, the input module 210 may correspond to the network unit 130 of FIG. 1.
  • the feature extraction module 220 may extract one or more features from the user's ECG signal based on a preset feature extraction algorithm.
  • the feature extraction module 220 may include a first neural network model for inferring n features by inputting an ECG signal.
  • the first neural network model may be learned based on the ECG signal and a plurality of features extracted from the ECG signal using a preset algorithm.
  • the principal component data generation module 230 may reduce the dimension of ECG feature data and generate ECG principal component data based on principal component analysis. For example, the principal component data generation module 230 may convert a plurality of feature variables into one principal component. The specific principal component analysis method is explained in FIG. 5.
  • the visualization module 240 may display ECG main component data including one, two, or three main components on a graph and provide it to the user.
  • the specific visualization method is explained in Figure 7.
  • the analysis module 250 can predict diseases corresponding to ECG main component data using statistical methods.
  • the analysis module 250 may include a second neural network model learned with ECG principal component data and disease information determined based on the ECG principal component data.
  • the second neural network model for predicting the user's disease can generate disease prediction data by inputting electrocardiogram main component data.
  • Figure 4 is a block diagram illustrating a neural network model according to an embodiment of the present disclosure.
  • the neural network model 320 may be learned based on the ECG signal and a plurality of features extracted from the ECG signal by a preset algorithm.
  • the neural network model 320 for inferring the characteristics of the ECG signal may generate ECG characteristic data 330 by inputting the measured ECG signal 310.
  • the neural network model 320 can input electrocardiogram data as learning data into a convolutional neural network, extract and interpret electrocardiogram features from the electrocardiogram data.
  • the neural network model 320 can calculate the loss between the analysis result of the convolutional neural network and the ground truth (GT) using loss functions such as mean square error (MSE), cross entropy, etc.
  • MSE mean square error
  • the neural network model 320 can optimize the parameters of the convolutional neural network by minimizing loss through optimization techniques such as backpropagation and stochastic gradient descent (SGD).
  • SGD stochastic gradient descent
  • the neural network model 320 can output a numerical value regarding the future health state based on the standardized data that is the subject of inference using the convolutional neural network learned in this way.
  • the type of neural network described above is only an example, and the type of neural network can be configured in various ways, such as a recurrent neural network and a multi-layer perceptron (MLP) neural network, depending on the characteristics of the ECG data.
  • FIG. 5 is a diagram illustrating a method of reducing the dimension of ECG feature data in the ECG data analysis method according to an embodiment of the present disclosure.
  • the ECG data analysis method can reduce the dimension of ECG feature data using Principal Component Analysis (PCA).
  • ECG feature data may include a first feature (FT1) and a second feature (FT2).
  • the ECG data analysis method can convert the first feature (FT1) and the second feature (FT2) into the first principal component (PC1), which is a new linearly independent variable.
  • Graph (a) is a graph showing ECG feature data with the first feature (FT1) on the X-axis and the first feature (FT1) on the Y-axis.
  • the first ECG feature data (DT_1), the second ECG feature data (DT_2), and the Nth ECG feature data (DT_N) are graphed (a) according to the first feature (FT1) and second feature (FT2) values, respectively. ) can be displayed.
  • the first principal component (PC1) axis displayed in graph (a) may be the axis where the dispersion of the first ECG feature data (DT_1), the second ECG feature data (DT_2), and the Nth ECG feature data (DT_N) is preserved to a maximum.
  • the first principal component (PC1) may be one variable that contains as much of the information contained in the two variables as the first feature (FT1) and the second feature (FT2).
  • the ECG data analysis method uses principal component analysis to convert first ECG feature data (DT_1) into first ECG principal component data (PDT_1) and convert second ECG feature data (DT_2) into second ECG principal component data (PDT_2). It can be understood in this way.
  • ECG principal component data can be displayed in graph (b).
  • ECG principal component data may include only one first principal component (PC1) variable, which is a variable that includes all information of the ECG feature data. Therefore, users can intuitively determine disease status, etc. through graph (b).
  • PC1 principal component
  • N is an integer greater than 1
  • M is It can be converted to the principal components of an integer greater than 0 and less than N.
  • a second main component may be extracted in addition to the first main component.
  • the first principal component may be in the form of an eigenvector having a direction with the greatest variance
  • the second principal component may be in the form of an eigenvector that is orthogonal to the first principal component and has the next largest variance after the first principal component.
  • the ECG data analysis device can enable intuitive and easy interpretation of the ECG by reducing the dimension of the highly complex ECG characteristic data.
  • Figure 6 is a diagram visualizing the result of reducing the dimension of ECG data according to an embodiment of the present disclosure.
  • Table (a) is an example of ECG feature data
  • Table (b) may be an example of principal component data obtained by reducing the dimension of the ECG feature data in Table (a) using principal component analysis.
  • the first feature (FT1) included in table (a) is heart rate
  • the second feature (FT2) is QRS duration
  • the third feature (FT3) is PR interval
  • the fourth feature (FT4) is ) may be the QRS axis.
  • the first data (DATA1), second data (DATA2), and third data (DATA3) included in table (a) may be ECG characteristic data of different users, and ECG characteristic data measured at different times of the same user. It may be.
  • the first principal component (PC1) included in Table (b) is a variable that combines the first feature (FT1), the second feature (FT2), the third feature (FT3), and the fourth feature (FT4), and is a variable that combines the first feature (FT1), the second feature (FT2), the third feature (FT3), and the fourth feature (FT4). It may be a variable that maximizes variance to include as much information as possible.
  • the second principal component (PC2) included in Table (b) may be a variable of a component orthogonal to the first principal component (PC1).
  • Table (b) illustrates two main components by way of example, but in the ECG data analysis method, only one main component may be extracted, or two or more main components may be extracted, considering the features included in the ECG characteristic data.
  • FIG. 7 is a diagram illustrating a method for visualizing ECG data according to an embodiment of the present disclosure.
  • the ECG data analysis device extracts the first main component (PC1) from the ECG characteristic data and displays first data (DATA1), second data (DATA2), and third data ( DATA3) can be displayed.
  • Graph (a) may be a one-dimensional graph with the first principal component (PC1) as a variable.
  • the ECG data analysis device may set a first region (MI) where the first disease (eg, myocardial infarction) occurs with a high probability through statistical analysis or a preset algorithm. The user may determine that the third data (DATA3) in graph (a) has a high probability of corresponding to the first disease.
  • MI first region
  • the first disease eg, myocardial infarction
  • the ECG data analysis device extracts the first main component (PC1) and the second main component (PC2) from the ECG characteristic data and displays the first data (DATA1), second data (DATA2), and third data (DATA3) in graph (b). can be displayed.
  • Graph (b) may be a two-dimensional graph with the first principal component (PC1) and the second principal component (PC2) as variables.
  • the ECG data analysis device may set a first region (MI) where the first disease (eg, myocardial infarction) occurs with a high probability through statistical analysis or a preset algorithm.
  • the user may determine that the third data (DATA3) in graph (b) has a high probability of corresponding to the first disease.
  • the ECG data analysis device can visually express the position of the ECG and its relationship with other ECGs in virtual space.
  • the ECG data analysis device may select the number of main components in consideration of data characteristic preservation rate or data operation speed during the analysis of ECG characteristic data.
  • the ECG data analysis device can increase the number of main components to increase the accuracy of the analysis results, or decrease the number of main components to speed up the analysis.
  • the ECG data analysis device may select the number of main components so that the accumulated variance ratio of the data is more than a preset ratio (for example, 90%).
  • Figure 8 is a block diagram illustrating a neural network model according to an embodiment of the present disclosure.
  • the second neural network model 420 may use ECG main component data and disease information determined based on the ECG main component data as learning data.
  • the second neural network model 420 for predicting the user's disease may generate disease prediction data 330 by inputting the ECG main component data 410.
  • FIG. 9 is a flowchart explaining an ECG data analysis method according to an embodiment of the present disclosure.
  • the computing device 100 may acquire an electrocardiogram signal (S110).
  • the computing device 100 may be connected to an ECG measurement device and use a measured ECG signal or an ECG signal transmitted through a network.
  • the computing device 100 may generate ECG feature data by extracting features from the acquired ECG signal (S120).
  • the computing device 100 may extract n features (n is a natural number) based on a preset feature extraction algorithm.
  • the features extracted from the ECG signal are 'PR INTERVAL', which is the interval from the start point of the P wave to the start point of the QRS wave, 'QRS duration', which is the interval of the QRS wave, and 'QRS duration', which is the area of the + region among the P waves. It can include various features such as 'P AREA' and 'P AMP', which is the voltage value at the point with the highest + value among P waves.
  • the computing device 100 may infer n features by inputting the ECG signal into a pre-trained first neural network model.
  • the first neural network model may be learned based on the ECG signal and a plurality of features extracted from the ECG signal using a preset algorithm.
  • the computing device 100 may reduce the dimension of ECG feature data and generate ECG main component data based on principal component analysis (S130). For example, the computing device 100 may reduce the dimension of ECG feature data and generate ECG main component data using the principal component analysis described in FIG. 5 .
  • the principal component may include a first principal component corresponding to the dimensional axis with the largest variance of the ECG principal component data, and more principal components may be extracted according to the ECG feature data.
  • the computing device 100 uses independent component analysis (ICA), multidimensional scaling, etc. in addition to principal component analysis to reduce the dimension of the ECG characteristic data. The number can be reduced.
  • ICA independent component analysis
  • multidimensional scaling etc.
  • the computing device 100 provides ECG feature data with reduced dimensions, thereby making it easier to analyze the ECG feature data.
  • Figure 10 is a flowchart explaining an ECG data analysis method according to an embodiment of the present disclosure.
  • the computing device 100 may reduce the dimension of ECG feature data and generate ECG main component data based on principal component analysis (S210).
  • S210 principal component analysis
  • the computing device 100 can visualize ECG main component data (S220).
  • the result of visualizing ECG main component data in the computing device 100 may be graph (a) or graph (b) shown in FIG. 7 .
  • the computing device 100 extracts one main component from the ECG feature data and shows it in a one-dimensional graph, extracts two main components from the ECG feature data and shows them in a two-dimensional graph, or extracts three main components from the ECG feature data and shows them in a two-dimensional graph. It can be shown on a 3D graph.
  • the computing device 100 can predict a disease corresponding to ECG main component data (S230).
  • the computing device 100 may predict a disease corresponding to ECG main component data using a statistical method. Referring to FIG. 7, when the ECG main component data corresponding to the first disease is located with a high probability in the first area (MI), if the user's ECG data is located in the first area (MI), it is determined to be the first disease. You can.
  • the computing device 100 may train a second neural network model using ECG main component data and disease information determined based on the ECG main component data as learning data. The second neural network model for predicting the user's disease can generate disease prediction data by inputting electrocardiogram main component data.
  • the computing device 100 may visualize dimensionally reduced ECG feature data and provide it to a user. Users can intuitively understand health status and changes in health status through visualized data.

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Abstract

Un procédé d'analyse de données d'électrocardiogramme, selon un mode de réalisation de la présente divulgation, peut comprendre les étapes consistant à : acquérir un signal d'électrocardiogramme ; générer des données de caractéristique d'électrocardiogramme comprenant une pluralité de caractéristiques sur la base du signal d'électrocardiogramme ; et générer des données de composant d'électrocardiogramme principal par réduction de la dimension des données de caractéristique d'électrocardiogramme sur la base d'un ou plusieurs composants principaux. Le ou les composants principaux sont caractérisés en ce que la pluralité de caractéristiques est fusionnée sur la base de la corrélation entre la pluralité de caractéristiques.
PCT/KR2023/017193 2022-11-09 2023-11-01 Procédé, programme et dispositif d'analyse de données d'électrocardiogramme WO2024101759A1 (fr)

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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR101788803B1 (ko) * 2016-10-12 2017-10-20 조선대학교 산학협력단 심전도를 이용한 개인 식별 정보 생성방법 및 그 개인 식별 정보를 이용한 개인 식별 방법
CN109171712A (zh) * 2018-09-28 2019-01-11 东软集团股份有限公司 心房颤动识别方法、装置、设备及计算机可读存储介质
KR20220087113A (ko) * 2020-12-17 2022-06-24 포항공과대학교 산학협력단 위전도를 활용한 스트레스 수준 분석 시스템 및 방법
KR20220104583A (ko) * 2021-01-18 2022-07-26 주식회사 바디프랜드 인공지능을 통한 2차원 심전도의 3차원 심전도로의 변환 전처리 및 심장질환 예측 시스템
CN115204297A (zh) * 2022-07-14 2022-10-18 中国人民解放军南部战区总医院 一种基于多通道卷积神经网络的心电图分类方法及系统

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR102417949B1 (ko) 2022-03-24 2022-07-06 (주)나눔테크 인공지능 기반 자동심장충격기용 심전도 분석 및 적용 방법

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR101788803B1 (ko) * 2016-10-12 2017-10-20 조선대학교 산학협력단 심전도를 이용한 개인 식별 정보 생성방법 및 그 개인 식별 정보를 이용한 개인 식별 방법
CN109171712A (zh) * 2018-09-28 2019-01-11 东软集团股份有限公司 心房颤动识别方法、装置、设备及计算机可读存储介质
KR20220087113A (ko) * 2020-12-17 2022-06-24 포항공과대학교 산학협력단 위전도를 활용한 스트레스 수준 분석 시스템 및 방법
KR20220104583A (ko) * 2021-01-18 2022-07-26 주식회사 바디프랜드 인공지능을 통한 2차원 심전도의 3차원 심전도로의 변환 전처리 및 심장질환 예측 시스템
CN115204297A (zh) * 2022-07-14 2022-10-18 中国人民解放军南部战区总医院 一种基于多通道卷积神经网络的心电图分类方法及系统

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