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WO2025023629A1 - Procédé et système d'estimation du risque d'arrêt cardiaque et de mort - Google Patents

Procédé et système d'estimation du risque d'arrêt cardiaque et de mort Download PDF

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Publication number
WO2025023629A1
WO2025023629A1 PCT/KR2024/010396 KR2024010396W WO2025023629A1 WO 2025023629 A1 WO2025023629 A1 WO 2025023629A1 KR 2024010396 W KR2024010396 W KR 2024010396W WO 2025023629 A1 WO2025023629 A1 WO 2025023629A1
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real
time information
classification model
subject
risk
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English (en)
Korean (ko)
Inventor
성시백
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Huinno Aim Co Ltd
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Huinno Aim Co Ltd
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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
    • A61B5/01—Measuring temperature of body parts ; Diagnostic temperature sensing, e.g. for malignant or inflamed tissue
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
    • A61B5/0205—Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
    • A61B5/021—Measuring pressure in heart or blood vessels
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
    • A61B5/024—Measuring pulse rate or heart rate
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/08—Measuring devices for evaluating the respiratory organs
    • 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]
    • 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
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/044—Recurrent networks, e.g. Hopfield networks
    • 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
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • 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
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/0464—Convolutional networks [CNN, ConvNet]
    • 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 estimating the risk of cardiac arrest and death.
  • the purpose of the present invention is to solve all of the problems of the above-mentioned prior art.
  • the present invention has another purpose of allowing the risk of cardiac arrest and death of a subject to be estimated with high accuracy by allowing non-real-time information and real-time information related to the subject's bio-signals to be input as continuous information into a deep learning or artificial intelligence-based model, and further allowing the estimation results to be clinically meaningfully utilized.
  • a representative configuration of the present invention to achieve the above purpose is as follows.
  • a method for estimating a risk of cardiac arrest and death comprising the steps of: obtaining non-real-time information and real-time information associated with a bio-signal of a subject; applying a first classification model and a second classification model to the non-real-time information and the real-time information, respectively, to generate output information based on the non-real-time information and output information based on the real-time information; and combining the output information based on the non-real-time information and the output information based on the real-time information to estimate a risk of at least one of cardiac arrest and death of the subject.
  • a system for estimating a risk of cardiac arrest and death comprising: an acquisition unit for acquiring non-real-time information and real-time information associated with a bio-signal of a subject; a generation unit for applying a first classification model and a second classification model to the non-real-time information and the real-time information, respectively, to generate output information based on the non-real-time information and output information based on the real-time information; and an estimation unit for estimating a risk of at least one of cardiac arrest and death of the subject by combining the output information based on the non-real-time information and the output information based on the real-time information.
  • the subject's risk of cardiac arrest and death can be estimated with high accuracy, and furthermore, the estimation results can be clinically meaningfully utilized.
  • FIG. 1 is a diagram schematically illustrating the configuration of an entire system for estimating the risk of cardiac arrest and death according to one embodiment of the present invention.
  • FIG. 2 is a drawing detailing the internal configuration of an estimation system according to one embodiment of the present invention.
  • FIG. 3 is a drawing exemplarily showing a process for estimating the risk of cardiac arrest and death according to the present invention.
  • FIG. 1 is a diagram schematically illustrating the configuration of an entire system for estimating the risk of cardiac arrest and death according to one embodiment of the present invention.
  • the entire system may include a communication network (100), an estimation system (200), a server (300), and a device (400).
  • a communication network (100) may be configured regardless of a communication mode such as wired communication or wireless communication, and may 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 in the present specification 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 part of a well-known wired and wireless data communication network, a well-known telephone network, or a well-known wired and wireless television communication network.
  • the communication network (100) may be a wireless data communication network that implements, at least in part, a conventional communication method such as WiFi communication, WiFi-Direct communication, Long Term Evolution (LTE) communication, 5G communication, Bluetooth communication (including Bluetooth Low Energy (BLE) communication), infrared communication, or ultrasonic communication.
  • a conventional communication method such as WiFi communication, WiFi-Direct communication, Long Term Evolution (LTE) communication, 5G communication, Bluetooth communication (including Bluetooth Low Energy (BLE) communication), infrared communication, or ultrasonic communication.
  • the estimation system (200) can perform communication with the server (300) and the device (400) described later through the communication network (100).
  • the estimation system (200) can obtain non-real-time information and real-time information related to the bio-signal of the subject of measurement, apply a first classification model and a second classification model to the non-real-time information and the real-time information, respectively, to generate output information based on the non-real-time information and output information based on the real-time information, and perform a function of estimating the risk of at least one of cardiac arrest and death of the subject of measurement by combining the output information based on the non-real-time information and the output information based on the real-time information.
  • the estimation system (200) may be a digital device equipped with a memory means and equipped with a microprocessor to have a computational ability, and may be, for example, a server system operating on the communication network (100).
  • a server (300) may be installed within a medical institution such as a hospital or health center, and may perform a function of managing various events occurring within the medical institution.
  • a server (300) can perform a function of storing and managing (e.g., updating) an electronic medical record (EMR) of a subject.
  • EMR electronic medical record
  • a subject's heart rate (HR), respiratory rate (RR), body temperature (BT), blood pressure (BP), and other bio-signals may be recorded in an electronic medical record (EMR).
  • HR heart rate
  • RR respiratory rate
  • BT body temperature
  • BP blood pressure
  • bio-signals may be recorded in the electronic medical record (EMR) at predetermined time intervals, but may not be recorded in real time, and thus, bio-signals recorded in the electronic medical record (EMR) may include measurement gaps.
  • the server (300) can communicate with the estimation system (200) through the communication network (100), and during this communication process, an electronic medical record (EMR) (or a bio-signal recorded in the electronic medical record (EMR)) can be transmitted from the server (300) to the estimation system (200).
  • EMR electronic medical record
  • EMR bio-signal recorded in the electronic medical record
  • a device (400) may be configured to include a sensing means (not shown) (e.g., a contact electrode, etc.) attached to the body of a subject to measure a biosignal (e.g., an electrocardiogram (ECG), oxygen saturation (SpO 2 ; saturation of percutaneous oxygen), etc.) from the subject in real time, or may be linked with the sensing means.
  • a sensing means e.g., a contact electrode, etc.
  • a biosignal e.g., an electrocardiogram (ECG), oxygen saturation (SpO 2 ; saturation of percutaneous oxygen), etc.
  • a device (400) may be configured to include a sensing means for measuring an electrocardiogram (ECG) based on a single or multiple leads by being attached to the body of a subject, or may be linked to the sensing means.
  • ECG electrocardiogram
  • such a device (400) may be configured in the form of a patient monitor (PM), a Holter, a patch, a wristwatch, etc.
  • a device (400) may be configured to include a sensing means for measuring oxygen saturation (SpO 2 ) and the like based on a photoplethysmogram (PPG) by being attached to the body of a subject (specifically, a peripheral body part such as a fingertip) or may be linked with the sensing means.
  • a device (400) may be configured in the form of a clip or the like.
  • the device (400) can communicate with the estimation system (200) through the communication network (100), and during this communication process, a biosignal measured by the sensing means can be transmitted from the device (400) to the estimation system (200).
  • FIG. 2 is a drawing showing in detail the internal configuration of an estimation system (200) according to one embodiment of the present invention.
  • FIG. 3 is a drawing showing an example of a process for estimating the risk of cardiac arrest and death according to the present invention.
  • the estimation system (200) may include an acquisition unit (210), a generation unit (220), an estimation unit (230), a communication unit (240), and a control unit (250).
  • the acquisition unit (210), the generation unit (220), the estimation unit (230), the communication unit (240), and the control unit (250) of the estimation system (200) may be program modules that communicate with an external system (not shown).
  • Such program modules may be included in the estimation 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.
  • program modules may be stored in a remote memory device that can communicate with the estimation 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 according to the present invention.
  • estimation 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 estimation system (200) may be realized within the server (300) and/or device (400) or included within an external system (not shown) as needed.
  • the acquisition unit (210) can perform a function of acquiring non-real-time information (I1) and real-time information (I2) associated with the bio-signal of the subject.
  • the acquisition unit (210) can acquire non-real-time information (I1) associated with a bio-signal of a subject from a server (300).
  • the non-real-time information (I1) may include a bio-signal recorded in an electronic medical record (EMR) of the subject.
  • the bio-signal recorded in the electronic medical record (EMR) of the subject may include, as described above, the heart rate (HR), respiratory rate (RR), body temperature (BT), blood pressure (BP), etc. of the subject.
  • HR heart rate
  • RR respiratory rate
  • BT body temperature
  • BP blood pressure
  • bio-signals since such bio-signals may include measurement gaps in the recording process as described above, they may be understood to lack real-time properties and thus be included in the non-real-time information (I1).
  • the acquisition unit (210) can acquire real-time information (I2) associated with a bio-signal of a subject from the device (400).
  • the real-time information (I2) may include a bio-signal of the subject measured by the device (400).
  • the bio-signal of the subject measured by the device (400) may include, as described above, an electrocardiogram (ECG), oxygen saturation (SpO 2 ), etc. of the subject.
  • ECG electrocardiogram
  • SpO 2 oxygen saturation
  • the acquisition unit (210) may receive non-real-time information (I1) and real-time information (I2) from the server (300) and the device (400), respectively.
  • the acquisition unit (210) receives the non-real-time information (I1) and real-time information (I2) as a single module, but it may also be understood that the first acquisition unit (not shown) and the second acquisition unit (not shown) included in the acquisition unit (210) independently receive the non-real-time information (I1) and real-time information (I2) as separate modules.
  • the non-real-time information (I1) is received by the first acquisition unit
  • the real-time information (I2) is received by the second acquisition unit.
  • non-real-time information (I1) may include measurement gaps, which may be replaced with valid information (or valid values) through a predetermined processing process (which processing process may correspond to a preprocessing process).
  • the acquisition unit (210) can replace the measurement gap with valid information using at least one statistical method in response to the inclusion of a measurement gap in the non-real-time information (I1).
  • the acquisition unit (210) may replace a measurement gap with valid information by using a method such as using a trend of a plurality of bio-signals recorded at a time point prior to the time point at which a measurement gap occurred, a trend of a plurality of bio-signals recorded at a time point after the time point at which a measurement gap occurred, an average of a plurality of bio-signals recorded within a predetermined time interval including the time point at which a measurement gap occurred, etc.
  • a method such as using a trend of a plurality of bio-signals recorded at a time point prior to the time point at which a measurement gap occurred, a trend of a plurality of bio-signals recorded at a time point after the time point at which a measurement gap occurred, an average of a plurality of bio-signals recorded within a predetermined time interval including the time point at which a measurement gap occurred, etc.
  • the method of replacing a measurement gap with valid information is not necessarily limited to what has been listed above, and it is to be noted that various methods may be used, such as a method of replacing a measurement gap with information identical to a bio-signal recorded at a point in time immediately before the occurrence of the measurement gap (forward fill), and a method of replacing a measurement gap with information identical to a bio-signal recorded at a point in time immediately after the occurrence of the measurement gap (backward fill).
  • the generation unit (220) can apply the first classification model (M1) and the second classification model (M2) to the non-real-time information (I1) and the real-time information (I2) obtained as described above, respectively, to generate output information based on the non-real-time information (I1) and output information based on the real-time information (I2).
  • the generation unit (220) can allow non-real-time information (I1) to be input as input data for the first classification model (M1), and can allow real-time information (I2) to be input as input data for the second classification model (M2).
  • the generation unit (220) when real-time information (I2) is input to a second classification model (M2), the generation unit (220) according to one embodiment of the present invention can divide the real-time information (I2) (e.g., divide it into fixed sizes) into time-series elements (sequences) and input these time-series elements into the second classification model (M2).
  • the time-series elements can be configured in a lower dimension than the real-time information (I2).
  • the first classification model (M1) and the second classification model (M2) may correspond to different deep learning (or artificial intelligence)-based classification models.
  • the first classification model (M1) may be a classification model based on at least one of a recurrent neural network (RNN) and a convolutional neural network (CNN)
  • the second classification model (M2) may be a classification model based on at least one of a convolutional neural network (CNN) and a vision transformer (ViT).
  • the first classification model (M1) and the second classification model (M2) may correspond to different deep learning-based classification models as described above, but may be models learned in common to generate (or output) a risk of cardiac arrest and death.
  • output information based on non-real-time information (I1) may be generated
  • output information based on real-time information (I2) may be generated
  • output information based on real-time information (I2) may be generated.
  • Each of the output information generated by the first classification model (M1) and the second classification model (M2) may include a risk level regarding cardiac arrest and death.
  • the first classification model (M1) and the second classification model (M2) can receive information having different attributes (i.e., non-real-time or real-time) and generate information having the same attributes.
  • the estimation unit (230) can perform a function of finally estimating the risk of at least one of cardiac arrest and death of the subject by combining output information based on non-real-time information (I1) and output information based on real-time information (I2).
  • the estimation unit (230) can allow an ensemble model (M3) that combines each output information generated from the first classification model (M1) and the second classification model (M2) (or combines the first classification model (M1) and the second classification model (M2)) to estimate the risk of at least one of cardiac arrest and death of the subject.
  • M3 an ensemble model that combines each output information generated from the first classification model (M1) and the second classification model (M2) (or combines the first classification model (M1) and the second classification model (M2)) to estimate the risk of at least one of cardiac arrest and death of the subject.
  • the estimation unit (230) may estimate the risk of at least one of cardiac arrest and death of the subject by using a method such as voting on information output from the first classification model (M1) (i.e., output information based on non-real-time information (I1)) and information output from the second classification model (M2) (i.e., output information based on real-time information (I2)).
  • M1 i.e., output information based on non-real-time information (I1)
  • M2 i.e., output information based on real-time information (I2)
  • the ensemble model (M3) is depicted as being placed after the first classification model (M1) and the second classification model (M2) in FIG. 3, it may be understood that the ensemble model (M3) includes the first classification model (M1) and the second classification model (M2).
  • the estimation unit (230) can convert each risk estimated from the ensemble model (M3) into a score within a predetermined range and output it.
  • each risk estimated in the ensemble model (M3) can be expressed in the form of a probability
  • the estimation unit (230) can convert each risk expressed in the form of a probability into the form of a score and output it as a score (S1) regarding the risk of cardiac arrest and a score (S2) regarding the risk of death.
  • the score converted by the estimation unit (230) can be converted to a specific value within a range set between 0 and 100, but it should be noted that the range is not necessarily limited to the above-described range and can be variously changed within a range that can achieve the purpose of the present invention.
  • the communication unit (240) can perform a function that enables data transmission and reception from/to the acquisition unit (210), the generation unit (220), and the estimation unit (230).
  • control unit (250) can perform a function of controlling the flow of data between the acquisition unit (210), the generation unit (220), the estimation unit (230), and the communication unit (240). That is, the control unit (250) according to one embodiment of the present invention can control the flow of data from/to the outside of the estimation system (200) or the flow of data between each component of the estimation system (200), thereby controlling the acquisition unit (210), the generation unit (220), the estimation unit (230), and the communication unit (240) to perform their own 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., alone or in combination.
  • the program commands recorded on the computer-readable recording medium may be those specially designed and configured for the present invention or those known and available to those skilled in the art of computer software.
  • Examples of the computer-readable recording medium 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 specially configured to store and execute program commands, such as ROMs, RAMs, and flash memories.
  • Examples of the 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.
  • the 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 d'estimation du risque d'arrêt cardiaque et de mort, comprenant les étapes consistant à : obtenir des informations non en temps réel et des informations en temps réel associées à un bio-signal d'un sujet ; appliquer un premier modèle de classification et un second modèle de classification aux informations en temps non réel et aux informations en temps réel pour générer des informations de sortie sur la base des informations en temps non réel et des informations de sortie sur la base des informations en temps réel, respectivement ; et combiner les informations de sortie sur la base des informations en temps non réel et des informations de sortie sur la base des informations en temps réel pour estimer le risque d'arrêt cardiaque et/ou de mort du sujet.
PCT/KR2024/010396 2023-07-25 2024-07-18 Procédé et système d'estimation du risque d'arrêt cardiaque et de mort Pending WO2025023629A1 (fr)

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KR10-2023-0097000 2023-07-25
KR1020230097000A KR102681050B1 (ko) 2023-07-25 2023-07-25 심정지 및 사망에 관한 위험도를 추정하기 위한 방법 및 시스템

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KR102681050B1 (ko) * 2023-07-25 2024-07-05 주식회사 휴이노에임 심정지 및 사망에 관한 위험도를 추정하기 위한 방법 및 시스템

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