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WO2025220874A1 - Procédé et système de classification de signal d'électrocardiogramme - Google Patents

Procédé et système de classification de signal d'électrocardiogramme

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Publication number
WO2025220874A1
WO2025220874A1 PCT/KR2025/002776 KR2025002776W WO2025220874A1 WO 2025220874 A1 WO2025220874 A1 WO 2025220874A1 KR 2025002776 W KR2025002776 W KR 2025002776W WO 2025220874 A1 WO2025220874 A1 WO 2025220874A1
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WO
WIPO (PCT)
Prior art keywords
node
qrs
graph data
present
classification model
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Pending
Application number
PCT/KR2025/002776
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English (en)
Korean (ko)
Inventor
김진국
이명훈
임지우
정성훈
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Huinno Co Ltd
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Huinno Co Ltd
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Publication date
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Publication of WO2025220874A1 publication Critical patent/WO2025220874A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

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Classifications

    • 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
    • A61B5/349—Detecting specific parameters of the electrocardiograph cycle
    • 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
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
    • A61B5/346—Analysis of electrocardiograms
    • A61B5/349—Detecting specific parameters of the electrocardiograph cycle
    • A61B5/353—Detecting P-waves
    • 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
    • A61B5/349—Detecting specific parameters of the electrocardiograph cycle
    • A61B5/355—Detecting T-waves
    • 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
    • A61B5/349—Detecting specific parameters of the electrocardiograph cycle
    • A61B5/366—Detecting abnormal QRS complex, e.g. widening
    • 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
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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 invention relates to a method and system for classifying an electrocardiogram signal.
  • ECG electrocardiogram
  • CNNs convolutional neural networks
  • cardiologists analyze electrocardiogram signals by considering the relationship between beats and/or the relationship between P wave - QRS complex - T wave within a beat.
  • conventional CNN-based electrocardiogram analysis methods or conventional GCN-based electrocardiogram analysis methods have limitations in that they cannot analyze electrocardiograms by considering these relationships because they use the entire electrocardiogram signal as input.
  • the inventor(s) of the present invention propose a technology for converting an electrocardiogram signal into first graph data including a P node corresponding to a P wave, a QRS node corresponding to a QRS complex, and a T node corresponding to a T wave, processing the first graph data using a GCN-based classification model to output a classification result of the electrocardiogram signal, thereby enabling analysis of the electrocardiogram signal by considering the relationship between beats and/or the relationship between P wave - QRS complex - T wave within a beat.
  • Non-patent Document 1 H. Ma and L. Xia, "Atrial Fibrillation Detection Algorithm Based on Graph Convolution Network," in IEEE Access, vol. 11, pp. 67191-67200, 2023
  • the purpose of the present invention is to solve all of the problems of the above-mentioned prior art.
  • another object of the present invention is to convert an electrocardiogram signal into first graph data including a P node corresponding to a P wave, a QRS node corresponding to a QRS complex, and a T node corresponding to a T wave, and to output a classification result of an electrocardiogram signal by processing the first graph data using a GCN-based classification model.
  • Another object of the present invention is to enable analysis of an electrocardiogram signal by considering the relationship between beats and/or the relationship between P wave - QRS complex - T wave within a beat.
  • Another object of the present invention is to enable analysis of electrocardiogram signals on a bit-by-bit basis.
  • another object of the present invention is to enable more efficient diagnosis by analyzing electrocardiogram signals (particularly, long-term electrocardiogram signals measured for a long period of time of 10 seconds or more) to classify a patient's arrhythmia on a beat-by-beat basis and providing the results to a cardiologist.
  • a representative configuration of the present invention to achieve the above purpose is as follows.
  • a method including a step of converting an electrocardiogram signal into first graph data including a P node corresponding to a P wave, a QRS node corresponding to a QRS complex, and a T node corresponding to a T wave, and a step of outputting a classification result of the electrocardiogram signal by processing the first graph data using a classification model based on a Graph Convolutional Network (GCN).
  • GCN Graph Convolutional Network
  • a system including a graph data management unit that converts an electrocardiogram signal into first graph data including a P node corresponding to a P wave, a QRS node corresponding to a QRS complex, and a T node corresponding to a T wave, and a classification model management unit that processes the first graph data using a classification model based on a GCN (Graph Convolutional Network) to output a classification result of the electrocardiogram signal.
  • a graph data management unit that converts an electrocardiogram signal into first graph data including a P node corresponding to a P wave, a QRS node corresponding to a QRS complex, and a T node corresponding to a T wave
  • GCN Graph Convolutional Network
  • non-transitory computer-readable recording medium recording another method for implementing the present invention, another system, and a computer program for executing the method are further provided.
  • an electrocardiogram signal is converted into first graph data including a P node corresponding to a P wave, a QRS node corresponding to a QRS complex, and a T node corresponding to a T wave, and the first graph data is processed using a GCN-based classification model, thereby outputting a classification result of the electrocardiogram signal.
  • an electrocardiogram signal (particularly, a long-term electrocardiogram signal measured for a long period of time of 10 seconds or more), a patient's arrhythmia can be classified on a beat-by-beat basis, and the results can be provided to a cardiologist, thereby supporting more efficient diagnosis.
  • FIG. 1 is a diagram schematically illustrating the configuration of an entire system for classifying an electrocardiogram signal according to one embodiment of the present invention.
  • FIG. 2 is a drawing showing in detail the internal configuration of a signal processing system according to one embodiment of the present invention.
  • FIG. 3 and FIG. 4 are diagrams exemplarily showing a process of converting an electrocardiogram signal into first graph data according to one embodiment of the present invention.
  • FIG. 5 is a diagram exemplifying the structure of an artificial intelligence-based signal processing model for embedding an electrocardiogram signal according to one embodiment of the present invention.
  • FIG. 6 is a diagram visually illustrating a matrix representation of first graph data according to one embodiment of the present invention.
  • FIG. 7 is a diagram exemplarily showing the result of converting an electrocardiogram signal into second graph data according to one embodiment of the present invention.
  • FIG. 8 is a diagram visually illustrating a matrix representation of second graph data according to one embodiment of the present invention.
  • FIG. 9 is a diagram exemplarily showing a process in which pooling is performed based on a QRS node according to one embodiment of the present invention.
  • FIG. 10 is a diagram exemplarily showing the structure of a GCN-based classification model according to one embodiment of the present invention.
  • FIG. 11 is a diagram exemplarily showing a process for generating augmented graph data according to one embodiment of the present invention.
  • FIG. 12 is a diagram exemplarily showing the structure of a GCN-based classification model according to one embodiment of the present invention.
  • a beat refers to a unit that distinguishes an electrocardiogram signal, and generally, one beat is composed of a P wave, a QRS complex, and a T wave.
  • FIG. 1 is a diagram schematically illustrating the configuration of an entire system for classifying an electrocardiogram signal according to one embodiment of the present invention.
  • the entire system may include a communication network (100), a signal processing system (200), and a device (300).
  • the communication network (100) can be configured regardless of the communication mode such as wired communication or wireless communication, and can be configured with various communication networks such as a local area network (LAN), a metropolitan area network (MAN), and a wide area network (WAN).
  • the communication network (100) referred to herein may be the well-known Internet or the World Wide Web (WWW).
  • WWW World Wide Web
  • the communication network (100) is not necessarily limited thereto, and may include at least a portion of a well-known wired or wireless data communication network, a well-known telephone network, or a well-known wired or wireless television communication network.
  • the communication network (100) may be a wireless data communication network that implements conventional communication methods such as WiFi communication, WiFi-Direct communication, Long Term Evolution (LTE) communication, 5G communication, Bluetooth communication (including Bluetooth Low Energy (BLE) communication), infrared communication, ultrasonic communication, etc., at least in part.
  • the communication network (100) may be an optical communication network that implements conventional communication methods such as LiFi (Light Fidelity), etc., at least in part.
  • the signal processing system (200) can perform communication with the device (300) described later through the communication network (100).
  • the signal processing system (200) according to one embodiment of the present invention can perform a function of converting an electrocardiogram signal into first graph data including a P node corresponding to a P wave, a QRS node corresponding to a QRS complex, and a T node corresponding to a T wave, and outputting a classification result of the electrocardiogram signal by processing the first graph data using a GCN-based classification model.
  • the signal processing system (200) may be a digital device equipped with a memory means and a microprocessor to have a computing capability, and may be, for example, a server system operated on the communication network (100).
  • a device (300) is a digital device that includes a function for communicating after being connected to a signal processing system (200).
  • such a device (300) may be a wearable monitoring device including a sensing means (e.g., a contact electrode, etc.) for measuring a predetermined biosignal (e.g., an electrocardiogram signal) from a human body. Furthermore, such a device (300) may further include a display means for providing a user with various information regarding the measurement of the biosignal.
  • a sensing means e.g., a contact electrode, etc.
  • a predetermined biosignal e.g., an electrocardiogram signal
  • the device (300) may include an application (not shown) that supports a user to receive services from the signal processing system (200).
  • an application may be downloaded from the signal processing system (200) or an external application distribution server (not shown).
  • the nature of such an application may be generally similar to the graph data management unit (210), classification model management unit (220), communication unit (230), and control unit (240) of the signal processing system (200), which will be described later.
  • at least a part of the application may be replaced with a hardware device or firmware device that can perform functions substantially identical to or equivalent thereto, as necessary.
  • FIG. 2 is a drawing showing in detail the internal configuration of a signal processing system (200) according to one embodiment of the present invention.
  • a signal processing system (200) may be configured to include a graph data management unit (210), a classification model management unit (220), a communication unit (230), and a control unit (240).
  • the graph data management unit (210), the classification model management unit (220), the communication unit (230), and the control unit (240) may be program modules that communicate with an external system (not shown).
  • These program modules may be included in the signal processing system (200) in the form of an operating system, an application program module, or other program modules, and may be physically stored in various known memory devices.
  • these program modules may be stored in a remote memory device that can communicate with the signal processing system (200).
  • these program modules include, but are not limited to, routines, subroutines, programs, objects, components, data structures, etc. that perform specific tasks or execute specific abstract data types, which will be described later, according to the present invention.
  • the signal processing system (200) has been described as above, this description is exemplary, and it is obvious to those skilled in the art that at least some of the components or functions of the signal processing system (200) may be realized within a device (300) or a server (not shown) or included within an external system (not shown) as needed.
  • the graph data management unit (210) can perform a function of converting an electrocardiogram signal into first graph data including a P node corresponding to a P wave, a QRS node corresponding to a QRS complex, and a T node corresponding to a T wave.
  • the electrocardiogram signal may be measured for a predetermined period of time by the sensing means of the device (300).
  • a long-term electrocardiogram signal an electrocardiogram signal measured for a long period of time of 10 seconds or more
  • measured through a single-lead electrocardiogram test using a patch-type device (300) may correspond to such an electrocardiogram signal.
  • the graph data management unit (210) can segment an electrocardiogram signal into a P wave, a QRS complex, and a T wave according to its waveform in order to convert the P wave, the QRS complex, and the T wave into corresponding nodes, respectively.
  • segmentation can be performed using rule-based segmentation software, or using an artificial intelligence, for example, a CNN-based model, or can also be performed by an expert (for example, a cardiologist).
  • an expert for example, a cardiologist
  • the segmentation according to one embodiment of the present invention is not limited to the method described above, and can be variously changed within the scope that can achieve the purpose of the present invention.
  • the graph data management unit (210) can convert the P wave, QRS complex, and T wave included in the electrocardiogram signal into corresponding nodes after performing the above segmentation, which are referred to herein as P node, QRS node, and T node, respectively.
  • Each of these nodes may include information about the signal (i.e., P wave, QRS complex, or T wave) corresponding thereto or may be associated with such information, and may be included in the first graph (meaning a graph in data structure theory) data.
  • such first graph data may also include edges defined based on the connection relationship between the P node, the QRS node, and the T node.
  • a lower weight may be assigned to an edge as the distance between the P node, the QRS node, and the T node becomes greater, and a higher weight may be assigned to an edge as the distance between the P node, the QRS node, and the T node becomes closer.
  • a higher weight assigned to an edge may mean a higher degree of correlation between nodes (or signals corresponding thereto) connected by the edge, and a lower weight may mean a lower degree of correlation between nodes (or signals corresponding thereto) connected by the edge.
  • Figures 3 and 4 are diagrams exemplarily showing a process of converting an electrocardiogram signal into first graph data.
  • the graph data management unit (210) can segment an electrocardiogram (ECG) signal into a P wave, a QRS complex, and a T wave according to its waveform.
  • ECG electrocardiogram
  • Figure 4 shows an example of the result of converting the electrocardiogram signal shown in Figure 3 into first graph data.
  • the first graph (PQRST Graph) data may include nodes (410p, 410qrs, and 410t) and edges (421, 422, and 423).
  • the types of nodes may be any one of a P node (410p), a QRS node (410qrs), and a T node (410t), and these nodes may be connected by edges.
  • the types of edges may be any one of edges connecting nodes (421, 422, and 423) and edges connecting each node to itself (i.e., self-loop edges; not shown).
  • edges (421) may be connected between adjacent nodes.
  • edges (422) may be connected between adjacent QRS nodes, and edges (423) may also be connected between nearby QRS nodes located within a predetermined distance (or window size).
  • edges (423) are connected between QRS nodes with a unit distance (or window size) of 2, but the unit distance is not limited thereto, and may be variously changed within a range that can achieve the purpose of the present invention.
  • the graph data management unit (210) can associate values obtained by embedding signals corresponding to each of the P node, QRS node, and T node with each of the P node, QRS node, and T node using an artificial intelligence-based signal processing model.
  • FIG. 5 is a diagram exemplarily showing the structure of an artificial intelligence-based signal processing model that embeds an electrocardiogram signal.
  • the signal processing model may follow the structure of an auto-encoder model.
  • such a signal processing model may be learned by compressing (or embedding) an actual electrocardiogram signal and then restoring it, and the graph data management unit (210) according to one embodiment of the present invention may associate the embedded value (embedded signal) with each node using only the encoder after learning is completed.
  • the structure or hyperparameters (e.g., number of blocks, input/output dimensions, etc.) of the signal processing model according to one embodiment of the present invention are not limited to those illustrated in FIG. 5, and may be variously changed within a range that can achieve the purpose of the present invention.
  • Fig. 6 is a diagram visually illustrating a matrix representation of the first graph data. Specifically, Fig. 6 (a) represents an adjacency matrix of the first graph data, and Fig. 6 (b) represents a feature matrix of the first graph data.
  • the adjacency matrix can express whether nodes are connected to each other and the degree of that connection.
  • the degree of connection may be a concept corresponding to the weight assigned to the edge (or the degree of association between connected nodes) described above.
  • this adjacency matrix may be a symmetric matrix.
  • the value of the diagonal element of the adjacency matrix (indicating the degree of connection; for example, 1) is the largest, and when two nodes are not connected in the adjacency matrix, the element indicating whether the two nodes are connected and the degree of the connection (for example, the adjacency matrix A[i][j] when node i and node j are not connected) may have the smallest value (for example, 0).
  • the value of the element indicating whether the two nodes are connected and the degree of the connection may be smaller as the distance between the two nodes is farther (for example, a value closer to 0 than 1), and larger as the two nodes are closer (for example, a value closer to 1 than 0).
  • the color is displayed darker as the value of the corresponding element is larger, and the color is displayed lighter as the value of the corresponding element is smaller.
  • the graph data management unit (210) can convert first graph data, which is an electrocardiogram signal or a result of converting the electrocardiogram signal, into second graph data that includes a QRS node but does not include a P node or a T node.
  • the graph data management unit (210) can generate second graph data including only the QRS node in addition to first graph data including all of the P node, QRS node, and T node.
  • FIG. 7 is a diagram exemplarily showing the result of converting the electrocardiogram signal illustrated in FIG. 3 into second graph data according to one embodiment of the present invention.
  • the second graph (QRS Graph) data may include nodes (710qrs) and edges (721).
  • all types of nodes included in the second graph data are QRS nodes (410qrs), and each node may be connected by an edge.
  • the type of edge included in the second graph data may be any one of an edge (721) connecting adjacent nodes and an edge connecting each node itself (i.e., a self-loop edge; not shown).
  • the second graph data may only include edges connecting adjacent QRS nodes.
  • Fig. 8 is a diagram visually illustrating a matrix representation of the second graph data. Specifically, Fig. 8 (a) illustrates an adjacency matrix of the second graph data, and Fig. 8 (b) illustrates a feature matrix of the second graph data.
  • the description of the adjacency matrix and feature matrix illustrated in Fig. 8 is identical to that described above regarding the adjacency matrix and feature matrix illustrated in Fig. 6, and therefore, any redundant description will be omitted.
  • the second graph data can be used to output the classification result of the electrocardiogram signal, and details thereof will be described later.
  • the classification model management unit (220) can perform a function of outputting a classification result of an electrocardiogram signal by processing the first graph data using a GCN-based classification model.
  • the classification model management unit (220) can classify an electrocardiogram signal in bit units by processing the first graph data using a GCN-based classification model and output the classification result.
  • one QRS node (QRS complex) or one bit can be classified into any one class of Normal Beat (hereinafter, N bit), Supraventricular Ectopic Beat (hereinafter, S bit), and Ventricular Ectopic Beat (hereinafter, V bit) (since one QRS complex is included in one bit, the classification for the QRS node and the classification for the bit may refer to the same thing).
  • N bit Normal Beat
  • S bit Supraventricular Ectopic Beat
  • V bit Ventricular Ectopic Beat
  • the class according to one embodiment of the present invention is not limited to those listed above, and may be variously changed within a scope that can achieve the purpose of the present invention.
  • the classification model management unit (220) can output the classification result for each QRS node included in the first graph data as the classification result of the electrocardiogram signal.
  • the classification model management unit (220) can output the classification result for each of the five QRS nodes included in the first graph data as the classification result of the electrocardiogram signal.
  • the classification model management unit (220) classifies the electrocardiogram signal by bit unit (or QRS node unit) and allows a cardiologist to refer to the result.
  • the classification model management unit (220) can perform pooling based on each QRS node included in the first graph data using a GCN-based classification model so that classification results for each QRS node are output as described above.
  • FIG. 9 is a diagram exemplarily showing a process in which pooling is performed based on a QRS node according to one embodiment of the present invention.
  • the classification model management unit (220) can perform pooling on information associated with surrounding nodes based on each QRS node (or centered on each QRS node) based on the index of the QRS node (910; QRS-Centered Pooling).
  • such pooling can be performed according to the Weighted Average Pooling technique, but is not limited thereto, and various pooling techniques can be used within the scope that can achieve the purpose of the present invention.
  • FIG. 10 is a diagram exemplarily showing the structure of a GCN-based classification model according to one embodiment of the present invention.
  • the classification model management unit (220) can output a classification result of an electrocardiogram signal by processing the first graph (PQRST Graph) data in the above-described manner using the classification model.
  • the output value (Output) can indicate which of the N bits, S bits, and V bits each QRS node included in the first graph data corresponds to.
  • the structure or hyperparameters (e.g., number of blocks, input/output dimensions, etc.) of the classification model according to one embodiment of the present invention are not limited to those illustrated in FIG. 10, and may be variously changed within a range that can achieve the purpose of the present invention.
  • the above classification model may be learned using augmented graph data generated by cutting graph data in which an electrocardiogram signal associated with arrhythmia is converted.
  • FIG. 11 is a diagram exemplarily showing a process for generating augmented graph data according to one embodiment of the present invention.
  • the graph data (Original Graph) converted from an electrocardiogram signal associated with an arrhythmia may include a P node, a QRS node, and a T node, like the first graph data.
  • the electrocardiogram signal associated with an arrhythmia may mean that at least one of the QRS nodes included in the graph data converted from the corresponding signal is classified into a class associated with an arrhythmia (e.g., the S bit or V bit described above).
  • augmented graph data when the above graph data is cut, augmented graph data is generated, and since the graph data is associated with an arrhythmia, at least one QRS node included in this augmented graph data may also be classified into a class associated with an arrhythmia.
  • the augmented graph data generated as described above can be used for training the above-described classification model, thereby improving the performance of the classification model.
  • the reason for generating such augmented graph data is that, in general, in electrocardiogram signals (training data) collected for training a classification model, there are overwhelmingly more bits classified into classes not associated with arrhythmia (e.g., the above-described S bit or V bit) than bits classified into classes not associated with arrhythmia (e.g., the above-described N bit), so if a classification model is trained using only the collected electrocardiogram signals, the performance of the model may deteriorate due to class imbalance.
  • the important point is that the augmented graph data can be generated in the above-described manner and used for training the classification model because the classification model has a structure capable of outputting the classification result for each QRS node included in the first graph data as the classification result of the electrocardiogram signal. That is, as described above, since the conventional artificial neural network-based electrocardiogram analysis method treats the entire electrocardiogram signal as a single input and outputs a single classification result, it is not possible to generate augmented graph data in the manner according to one embodiment of the present invention and use it for training the model.
  • the classification model management unit (220) can output a classification result of an electrocardiogram signal by further processing the second graph data described above using a GCN-based classification model.
  • FIG. 12 is a diagram exemplarily showing the structure of a GCN-based classification model according to one embodiment of the present invention.
  • the classification model management unit (220) can output a classification result of an electrocardiogram signal by processing the first graph (PQRST Graph) data and the second graph (QRS Graph) data in the above-described manner using the classification model.
  • the classification model management unit (220) can output a classification result of an electrocardiogram signal by processing the second graph data through a QRS block, concatenating it with the pooling result of the first graph data, and then processing it.
  • a classification result that considers the importance of the QRS node higher than that of the P node or the T node can be obtained as an output value.
  • the structure or hyperparameters (e.g., number of blocks, input/output dimensions, etc.) of the classification model according to one embodiment of the present invention are not limited to those illustrated in FIG. 12, and may be variously changed within a range that can achieve the purpose of the present invention.
  • the communication unit (230) can perform a function that enables data transmission and reception from/to the graph data management unit (210) and the classification model management unit (220).
  • control unit (240) can perform a function of controlling the flow of data between the graph data management unit (210), the classification model management unit (220), and the communication unit (230). That is, the control unit (240) according to one embodiment of the present invention can control the flow of data from/to the outside of the signal processing system (200) or the flow of data between each component of the signal processing system (200), thereby controlling the graph data management unit (210), the classification model management unit (220), and the communication unit (230) to perform their respective unique functions.
  • the embodiments of the present invention described above may be implemented in the form of program commands that can be executed through various computer components and recorded on a computer-readable recording medium.
  • the computer-readable recording medium may include program commands, data files, data structures, etc., either singly or in combination.
  • the program commands recorded on the computer-readable recording medium may be specially designed and configured for the present invention or may be known and available to those skilled in the art of computer software.
  • Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical recording media such as CD-ROMs and DVDs, magneto-optical media such as floptical disks, and hardware devices specifically configured to store and execute program commands, such as ROMs, RAMs, and flash memories.
  • Examples of program commands include not only machine language codes generated by a compiler, but also high-level language codes that can be executed by a computer using an interpreter, etc. Hardware devices may be changed into one or more software modules to perform processing according to the present invention, and vice versa.

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Abstract

Selon un aspect de la présente invention, l'invention concerne un procédé de classification d'un signal d'électrocardiogramme, le procédé comprenant les étapes consistant à : convertir un signal d'électrocardiogramme en premières données de graphe comprenant un nœud P correspondant à une onde P, un nœud QRS correspondant à un complexe QRS, et un nœud T correspondant à une onde T ; et délivrer le résultat de classification du signal d'électrocardiogramme par traitement des premières données de graphe à l'aide d'un modèle de classification basé sur un réseau convolutif graphique (GCN).
PCT/KR2025/002776 2024-04-18 2025-02-27 Procédé et système de classification de signal d'électrocardiogramme Pending WO2025220874A1 (fr)

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KR1020240051844A KR20250153409A (ko) 2024-04-18 2024-04-18 심전도 신호를 분류하기 위한 방법 및 시스템
KR10-2024-0051844 2024-04-18

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WO2025220874A1 true WO2025220874A1 (fr) 2025-10-23

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