WO2023058946A1 - Système et procédé de prédiction de pronostic de maladie respiratoire par des mesures en série chronologique de sons de toux, de sons respiratoires, de sons de récitation et de sons vocaux - Google Patents

Système et procédé de prédiction de pronostic de maladie respiratoire par des mesures en série chronologique de sons de toux, de sons respiratoires, de sons de récitation et de sons vocaux Download PDF

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WO2023058946A1
WO2023058946A1 PCT/KR2022/014058 KR2022014058W WO2023058946A1 WO 2023058946 A1 WO2023058946 A1 WO 2023058946A1 KR 2022014058 W KR2022014058 W KR 2022014058W WO 2023058946 A1 WO2023058946 A1 WO 2023058946A1
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sound
respiratory disease
data
disease prognosis
time
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PCT/KR2022/014058
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English (en)
Korean (ko)
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전진희
김경남
민충기
김태진
한상훈
문경민
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주식회사 웨이센
울산대학교 산학협력단
전진희
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Priority claimed from KR1020220037496A external-priority patent/KR102624637B1/ko
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Publication of WO2023058946A1 publication Critical patent/WO2023058946A1/fr

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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
    • A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B7/00—Instruments for auscultation
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
    • G10L25/48—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use
    • G10L25/51—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use for comparison or discrimination
    • G10L25/66—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use for comparison or discrimination for extracting parameters related to health condition
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
    • 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
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients

Definitions

  • the present invention relates to a system and method for predicting the prognosis of respiratory diseases, and more particularly, by time-series analysis of voice data through time-series cough sound, breathing sound, reading sound, and vocalization sound measurement, and based on the analyzed information, the diagnosis of respiratory disease
  • it relates to a method for predicting the prognosis of respiratory diseases through the measurement of time-series cough sounds, breathing sounds, reading sounds, and vocalization sounds so that users can respond in advance.
  • Respiratory disease is classically diagnosed by a physician's chest auscultation, imaging tests, and the like. Coughing and wheezing accompany most respiratory diseases, and as an early detectable symptom, it is an important factor in a doctor's diagnosis. Depending on the respiratory disease, there are differences in the presence or absence of wheezing, the location, type, and time of coughing, and the presence or absence of sputum.
  • machine learning models based on deep learning have been continuously researched and developed, and such machine learning models are used in various voice analysis fields. Since there is a limit to directly hearing and analyzing abnormal breathing sounds by medical staff, it can play a role in supporting (assisting) the doctor's decision (diagnosis) for the patient by distinguishing them through a machine learning model.
  • a highly contagious disease such as COVID19
  • self-isolation there is no way to confirm whether the prognosis worsens or improves, so an opportunity to respond in a timely manner may be missed.
  • Patent Document 1 discloses a "cough sound analysis method using a disease signature for diagnosing respiratory diseases", thereby diagnosing one or more diseases of the patient's airways.
  • a method for doing this includes acquiring a cough sound from the patient; processing the cough sound to generate cough sound feature signals representing one or more cough sound features from cough segments; obtaining one or more disease signatures based on the cough sound feature signals; and classifying the one or more disease signatures to consider that the cough segments represent one or more of the diseases, wherein obtaining the one or more disease signatures based on the cough sound feature signals comprises: applying cough sound features to each of one or more pre-trained disease signature determination machines, each of which classifies the cough sound features as corresponding to a particular disease or non-disease state or to a first particular disease or It is characterized in that it is trained in advance to classify as corresponding to a second specific disease different from the first specific disease.
  • the cough sound features are applied to disease signature decision machines, and each decision machine is trained to classify the cough sound features as those corresponding to a specific disease or non-disease state, so that the patient's cough sound
  • each decision machine uses only the feature signal of the patient's cough sound, so other sounds of the patient, such as breathing sounds, reading sounds, or vocalizations, can be used to diagnose the respiratory tract. Predicting the prognosis of a disease contains problems that are difficult to apply.
  • the present invention was created in consideration of the above matters comprehensively, and analyzes voice data in a time series through periodic measurement of the user's cough sound, breathing sound, reading sound, and pronunciation sound, and based on the analyzed information, respiratory
  • An object of the present invention is to provide a respiratory disease prognosis prediction system and method through time-series coughing, breathing, reading, and vocalization measurements that allow users to respond appropriately in advance by predicting the prognosis of the disease.
  • the respiratory disease prognosis prediction system through the measurement of time-series cough sound, breathing sound, reading sound, and vocalization sound according to the present invention
  • a voice measuring unit that periodically measures cough sounds, breath sounds, reading sounds, and vocal sounds from the user
  • a data collection unit for collecting learning data obtained by evaluating a respiratory disease severity score with respect to the data measured by the voice measurement unit
  • model learning that trains a respiratory disease prognosis prediction model that takes the time series measurement data at any K time as an input value of the respiratory disease prognosis prediction model and uses the respiratory disease severity evaluation score after M hours as an output value. wealth;
  • a respiratory disease prognosis prediction unit that inputs the time-series measurement data at time K to the learned respiratory disease prognosis prediction model and predicts a respiratory disease prognosis based on a respiratory disease prognosis prediction value output by the respiratory disease prognosis prediction model after M time ;
  • the status of the data collection unit, the model learning unit, and the respiratory disease prognosis prediction unit are checked, the respiratory disease severity is evaluated by the data collection unit and classified into a specific class, and the respiratory disease prognosis prediction model is learned by the model learning unit. And, it is characterized in that it includes a control unit that transmits each control command for predicting the respiratory disease prognosis by the respiratory disease prognosis prediction unit.
  • the cough sound measured by the voice measurement unit can be obtained by repeatedly recording the patient's cough sound multiple times using a recording function of a smartphone.
  • the breathing sound can be obtained by repeatedly recording the patient's inhalation and exhalation multiple times using a recording function of a smartphone.
  • the reading sound may be obtained by recording a sound read aloud in a normal tone of speech using a recording function of a smartphone.
  • the vocalized sound can be obtained by recording a voice through vocalization produced while singing a pitched sound according to a familiar melody using a recording function of a smartphone.
  • the cough sound, breath sound, reading sound, and vocal sound can be measured using a smartphone at a period of 1 hour to N hours.
  • the data collection unit may evaluate the severity of respiratory disease with respect to the measured data, and classify it into normal (0), caution (1), and borderline (2) classes.
  • the model learning unit learns the respiratory disease prognosis prediction model, fine dust (PM10, PM2.5), temperature / humidity, CO 2 , Personal environment data and public environment data including VOCs (volatile organic compounds), and blood pressure/heart rate data may be learned together.
  • the model learning unit extracts the characteristics of the measured cough sound, breathing sound, reading sound, and vocalization sound using Mel-spectrogram, and extracts voice features from it. It can be trained using CNN (Convolutional Neural Network) algorithm by attaching data and public environment data.
  • CNN Convolutional Neural Network
  • M_(x ⁇ m ⁇ n) multi-channel data is formed by configuring x number of channels for cough sound, breath sound, reading sound, and vocal sound data and each data of the personal biometric, environmental data, and public environment data created, and the output of the CNN can be configured as a regression model of the respiratory disease severity evaluation score.
  • the method for predicting the prognosis of respiratory diseases through the measurement of time-series cough sound, breathing sound, reading sound, and vocalization sound includes a voice measurement unit, a data collection unit, a model learning unit, and a respiratory disease prognosis
  • a respiratory disease prognosis prediction method based on a respiratory disease prognosis prediction system through time series cough sound, breathing sound, reading sound, and vocalization sound measurement including a prediction unit,
  • Respiratory disease prognosis prediction in which the model learning unit takes time-series measurement data at any K time as an input value of a respiratory disease prognosis prediction model based on the collected learning data, and the respiratory disease severity evaluation score after M time as an output value training the model;
  • the respiratory disease prognosis prediction unit inputs the time-series measurement data of the K time to the learned respiratory disease prognosis prediction model, and the respiratory disease prognosis is based on the respiratory disease prognosis prediction value output by the respiratory disease prognosis prediction model after M time Its feature is that it includes the step of predicting .
  • the cough sound can be obtained by repeatedly recording the patient's cough sound multiple times using a recording function of a smart phone.
  • the breathing sound can be obtained by repeatedly recording the patient's inhalation and exhalation multiple times using a recording function of a smartphone.
  • the reading sound may be obtained by recording a sound read aloud in a normal tone of speech using a recording function of a smartphone.
  • the vocalized sound can be obtained by recording a voice through vocalization produced while singing a pitched sound according to a familiar melody using a recording function of a smartphone.
  • the cough sound, breath sound, reading sound, and vocal sound can be measured using a smartphone at a period of 1 hour to N hours.
  • the respiratory disease severity may be evaluated for the data measured in step b), and classified into normal (0), caution (1), and borderline (2) classes.
  • fine dust PM10, PM2.5
  • temperature / humidity in the same time zone as the cough sound, breathing sound, reading sound, and pronunciation sound measurement time
  • CO 2 personal environment data and public environment data including VOCs (volatile organic compounds), and blood pressure/heart rate data may be learned together.
  • the measured cough sound, breathing sound, reading sound, and vocalization sound are extracted using the Mel-spectrogram, and the characteristics of the voice are extracted, and personal biopsies, It can be trained using CNN (Convolutional Neural Network) algorithm by attaching environmental data and public environment data.
  • CNN Convolutional Neural Network
  • M_(x ⁇ m ⁇ n) multi-channel data is formed by configuring x number of channels for cough sound, breath sound, reading sound, and vocal sound data and each data of the personal biometric, environmental data, and public environment data and the output of the CNN can be configured as a regression model of the respiratory disease severity evaluation score.
  • the voice data is analyzed in a time series through periodic measurement of cough sounds, breath sounds, reading sounds, and pronunciation sounds, and the prognosis of respiratory diseases is predicted based on the analyzed information, so that the user can It has the advantage of being able to respond appropriately.
  • FIG. 1 is a diagram schematically showing the configuration of a respiratory disease prognosis prediction system through time-series cough sound, breathing sound, reading sound, and vocal sound measurement according to the present invention.
  • FIG. 2 is a flowchart showing the execution process of the method for predicting the prognosis of respiratory diseases through the measurement of time-series cough sounds, breathing sounds, reading sounds, and vocal sounds according to the present invention.
  • FIG. 3 is a diagram showing an outline of measuring cough sound, breathing sound, reading sound, and vocalization sound using a recording function of a smartphone.
  • Figure 4 is a diagram showing the evaluation of the severity of respiratory diseases and classification into normal, caution, and alert classes.
  • FIG. 5 is a diagram showing personal environment data, public environment data, and blood pressure/heart rate data that a respiratory disease prognosis prediction model learns together.
  • FIG. 6 is a diagram showing an overview of extracting characteristics of measured cough sounds, breath sounds, reading sounds, and pronunciation sounds, and learning by attaching personal biometric data, environmental data, and public environment data.
  • FIG. 1 is a diagram schematically showing the configuration of a respiratory disease prognosis prediction system through time-series cough sound, breathing sound, reading sound, and vocal sound measurement according to an embodiment of the present invention.
  • the respiratory disease prognosis prediction system 100 through the measurement of time-series cough sound, breathing sound, reading sound, and vocalization sound according to the present invention includes a voice measurement unit 110, a data collection unit 120, and model learning It is configured to include a unit 130, a respiratory disease prognosis prediction unit 140, and a control unit 150.
  • the voice measurement unit 110 periodically measures cough sounds, breathing sounds, reading sounds, and vocal sounds from the user.
  • a smartphone may be used as such a voice measurement unit 110 .
  • the cough sound measured by the voice measurement unit 110 as described above is a patient's cough sound multiple times (eg, 3 to 5 times) can be obtained by repeatedly recording.
  • the breathing sound can be obtained by repeatedly recording the patient's inhalation and exhalation multiple times (eg, 3 to 5 times) using a recording function of a smartphone.
  • the reading sound is a sentence presented using the recording function of the smartphone (eg, 'I agree to record voices for a healthier society') in a normal tone. It can be obtained by recording the sound you read.
  • the voiced sound is a pitched sound (for example, a pitched sound such as 'Do Re Mi Fa Sol La Si Do', a global common scale) using the recording function of a smartphone to a familiar melody. It can be obtained by recording the voice through vocalization while singing along.
  • cough sounds, breath sounds, reading sounds, and vocal sounds as described above can be measured using a smartphone at intervals of 1 hour to N hours.
  • the data collection unit 120 collects learning data by evaluating the data measured by the voice measurement unit 110 as a respiratory disease severity score.
  • the data collection unit 120 evaluates the respiratory disease severity as a score for the measured data.
  • the model learning unit 130 sets the time-series measurement data of any K time as an input value of the respiratory disease prognosis prediction model 160 as shown in FIG. 4, and the respiratory disease after M time A respiratory disease prognosis prediction model 160 having a disease severity evaluation score as an output value is trained.
  • the model learning unit 130 learns the respiratory disease prognosis prediction model 160, as shown in FIG.
  • personal environment data and public environment data including fine dust (PM10, PM2.5), temperature/humidity, CO 2 , VOCs (volatile organic compounds) of the time zone, and blood pressure/heart rate data can be learned together.
  • the model learning unit 140 as described above, as shown in FIG.
  • M_(x ⁇ m ⁇ n) multi-channel data is formed by configuring x number of channels for cough sound, breath sound, reading sound, and vocal sound data and each data of the personal biometric, environmental data, and public environment data created, and the output of the CNN can be configured as a respiratory disease severity evaluation score regression model.
  • the respiratory disease prognosis prediction unit 140 inputs the time-series measurement data of the K time to the learned respiratory disease prognosis prediction model 160, and after M time, the respiratory disease prognosis prediction model ( 160) predicts the respiratory disease prognosis based on the respiratory disease prognosis prediction value output.
  • the respiratory disease prognosis prediction unit 140 compares the respiratory disease prognosis prediction value output by the respiratory disease prognosis prediction model 160 with a preset reference value (reference range) so that the predicted value is a reference value (reference range) (eg , 0 to 1 point of the reference value), "steady state", if the predicted value belongs to the first threshold value (for example, 2 to 4 points of the reference value) for the reference value (reference range), "attention state", If it falls within 2 thresholds (eg, 5 points of the reference value), the respiratory disease prognosis is predicted as "alert state".
  • a respiratory disease prognosis prediction unit 140 may be configured with a microprocessor or microcontroller.
  • the control unit 150 checks the status of the data collection unit 120, the model learning unit 130, and the respiratory disease prognosis prediction unit 140 and controls their operations, and determines the severity of respiratory disease by the data collection unit 120.
  • Such control unit 150 may be composed of a microprocessor or microcontroller.
  • the data collection unit 120, the model learning unit 130, the respiratory disease prognosis prediction unit 140, and the control unit 150 as described above may be integrated into one computer system.
  • FIG. 2 is a flowchart illustrating an execution process of a method for predicting the prognosis of a respiratory disease through measurement of time-series coughing sound, breathing sound, reading sound, and vocalization sound according to an embodiment of the present invention.
  • the method for predicting the prognosis of respiratory diseases through the measurement of time-series cough sound, breathing sound, reading sound, and vocalization sound includes the voice measurement unit 110, the data collection unit 120, Respiratory disease based on respiratory disease prognosis prediction system 100 through measurement of time-series cough sound, breathing sound, reading sound, and vocalization sound including model learning unit 130, respiratory disease prognosis prediction unit 140, and control unit 150
  • the voice measuring unit 110 ie, using a user terminal (eg, a smartphone)
  • the cough sound can be obtained by repeatedly recording the patient's cough sound multiple times (eg, 3 to 5 times) using a recording function of a smartphone, as shown in FIG. 3 (a).
  • the breathing sound can be obtained by repeatedly recording the patient's inhalation and exhalation multiple times (eg, 3 to 5 times) using a recording function of a smartphone.
  • the reading sound is a sentence presented using the recording function of the smartphone (eg, 'I agree to record voices for a healthier society') in a normal tone. It can be obtained by recording the sound you read.
  • the voiced sound is a pitched sound (for example, a pitched sound such as 'Do Re Mi Fa Sol La Si Do', a global common scale) using the recording function of a smartphone to a familiar melody. It can be obtained by recording the voice through vocalization while singing along.
  • cough sounds, breath sounds, reading sounds, and vocal sounds as described above can be measured using a smartphone at intervals of 1 hour to N hours.
  • the data collection unit 120 evaluates the measured data as a respiratory disease severity score and collects learning data.
  • a data collection unit 120 as shown in Figure 4, can evaluate the respiratory disease severity score with respect to the measured data.
  • the model learning unit 130 sets the time series measurement data of arbitrary K hours as an input value of the respiratory disease prognosis prediction model 160 based on the collected learning data, and M hours later A respiratory disease prognosis prediction model 160 having a respiratory disease severity evaluation score as an output value is trained (step S203).
  • fine dust PM10, PM2
  • temperature/humidity CO 2
  • blood pressure/heart rate data can be learned together.
  • the model learning unit 130 learns the respiratory disease prognosis prediction model 160, as shown in FIG. 6, the measured cough sound, breathing sound, reading sound, and vocalization sound are Mel-spectrogram It is possible to extract features of voice using , and attach personal biometric data, environmental data, and public environment data to it, and learn them using a Convolutional Neural Network (CNN) algorithm.
  • CNN Convolutional Neural Network
  • the personal biometric data, environment data, and public environment data all data of 24 to 48 hours can be used.
  • the time point influencing symptoms can affect the current time or the situation N hours ago can affect the present, data of 24 to 48 hours It is preferable to use all of them.
  • M_(x ⁇ m ⁇ n) multi-channel data is formed by configuring x number of channels for cough sound, breath sound, reading sound, and vocal sound data and each data of the personal biometric, environmental data, and public environment data created, and the output of the CNN can be configured as a regression model that outputs respiratory disease severity evaluation scores.
  • the respiratory disease prognosis prediction unit 140 inputs the time-series measurement data of time K into the learned respiratory disease prognosis prediction model 160, and predicts the respiratory disease prognosis after M time.
  • a respiratory disease prognosis is predicted based on the respiratory disease prognosis prediction value output by the model 160 (step S204).
  • the respiratory disease prognosis prediction unit 140 compares the respiratory disease prognosis prediction value output by the respiratory disease prognosis prediction model 160 with a preset reference value (reference range) so that the predicted value is a reference value (reference range) (eg , 0 to 1 point of the reference value), "steady state”, if the predicted value belongs to the first threshold value (for example, 2 to 4 points of the reference value) for the reference value (reference range), "attention state", If it falls within 2 thresholds (eg, 5 points of the reference value), the respiratory disease prognosis is predicted as "alert state".
  • a reference value reference range
  • the system and method for predicting the prognosis of respiratory diseases through the measurement of time-series cough sound, breathing sound, reading sound, and vocalization sound provide voice through periodic measurement of cough sound, breathing sound, reading sound, and vocalization sound.

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Abstract

L'invention concerne un procédé de prédiction de pronostic de maladie respiratoire par des mesures en série chronologique de sons de toux, de sons respiratoires, de sons de récitation et de sons vocaux, comprenant des étapes dans lesquelles : une unité de mesure de la voix mesure périodiquement des sons de toux, des sons respiratoires, des sons de récitation et des sons vocaux d'un utilisateur ; une unité de collecte de données collecte des données d'apprentissage dans lesquelles un score de gravité de maladie respiratoire est évalué par rapport aux données de mesure ; une unité d'apprentissage de modèle entraîne, sur la base des données d'apprentissage collectées, un modèle de prédiction de pronostic de maladie respiratoire dont une valeur d'entrée est constituée par les données de mesure de série chronologique de K heures aléatoires et dont une valeur de sortie est un score d'évaluation de gravité de maladie respiratoire après M heures ; et une unité de prédiction de pronostic de maladie respiratoire entre les données de mesure de série chronologique de K heures dans le modèle de prédiction de pronostic de maladie respiratoire entraîné et prédit un pronostic de maladie respiratoire sur la base de la valeur de prédiction de pronostic de maladie respiratoire délivrée par le modèle de prédiction de pronostic de maladie respiratoire après M heures.
PCT/KR2022/014058 2021-10-06 2022-09-20 Système et procédé de prédiction de pronostic de maladie respiratoire par des mesures en série chronologique de sons de toux, de sons respiratoires, de sons de récitation et de sons vocaux WO2023058946A1 (fr)

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KR1020220037496A KR102624637B1 (ko) 2021-10-06 2022-03-25 시계열 기침음, 호흡음, 낭독음, 발성음 측정을 통한 호흡기 질환 예후 예측시스템 및 방법

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