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WO2001091691A1 - Systeme d'incubateur a intelligence artificielle et procede de commande de celui-ci - Google Patents

Systeme d'incubateur a intelligence artificielle et procede de commande de celui-ci Download PDF

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Publication number
WO2001091691A1
WO2001091691A1 PCT/KR2001/000767 KR0100767W WO0191691A1 WO 2001091691 A1 WO2001091691 A1 WO 2001091691A1 KR 0100767 W KR0100767 W KR 0100767W WO 0191691 A1 WO0191691 A1 WO 0191691A1
Authority
WO
WIPO (PCT)
Prior art keywords
nurture
treatment equipment
setpoint value
value
life body
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/KR2001/000767
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English (en)
Inventor
Gun-Bok Lee
Sang-Hyun Byun
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
P & M Co Ltd
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P & M Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by P & M Co Ltd filed Critical P & M Co Ltd
Publication of WO2001091691A1 publication Critical patent/WO2001091691A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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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
    • A61B5/72—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
    • A61G—TRANSPORT, PERSONAL CONVEYANCES, OR ACCOMMODATION SPECIALLY ADAPTED FOR PATIENTS OR DISABLED PERSONS; OPERATING TABLES OR CHAIRS; CHAIRS FOR DENTISTRY; FUNERAL DEVICES
    • A61G11/00—Baby-incubators; Couveuses
    • 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
    • A61B5/02055—Simultaneously evaluating both cardiovascular condition and temperature
    • 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
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
    • A61M16/00—Devices for influencing the respiratory system of patients by gas treatment, e.g. ventilators; Tracheal tubes
    • A61M16/021—Devices for influencing the respiratory system of patients by gas treatment, e.g. ventilators; Tracheal tubes operated by electrical means
    • A61M16/022—Control means therefor
    • A61M16/024—Control means therefor including calculation means, e.g. using a processor
    • A61M16/026—Control means therefor including calculation means, e.g. using a processor specially adapted for predicting, e.g. for determining an information representative of a flow limitation during a ventilation cycle by using a root square technique or a regression analysis
    • 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 an intelligent type incubator system and a control method thereof; and more particularly, to an intelligent type incubator system and a control method thereof, in which various kinds of information data concerning checked state of a patient are processed as control data of various kinds of treatment equipments for maintaining a normal state of a corresponding patient, and the outputs of the corresponding treatment equipments are automatically controlled, to thereby make a state monitoring of the patient and its diagnosis and treatment operation, closed-loop automatic controlled.
  • an incubator system is provided as an important medical equipment having a function of temporarily nursing a patient under an abnormal state like an immature infant, and of recovering to a normal state of the patient through an appropriate treatment.
  • Fig. 1 is a block diagram of a conventional incubator system, and this system is constructed by an incubator 12 for providing a proper environmental standard such as temperature, humidity and oxygen etc. within a closed space where there is a patient 11; an artificial respiratory machine 13 for controlling the breathing number and breathing pressure of the patient 11 etc.
  • a therapeutic medicine injector 14 for injecting various therapeutic medicines to the patient 11; other equipments (not shown) for a treatment of the patient; and several kinds of measuring equipments 15 for measuring an electrocardiogram (ECG), blood pressure and the breathing number of the patient 11, etc.
  • ECG electrocardiogram
  • the present invention is directed to an intelligent type incubator system and a control method thereof that substantially obviates one or more of the limitations and disadvantages of the related art.
  • a primary object of the present invention is to provide an intelligent type incubator system and a control method thereof, which is capable of making a state monitoring of a patient and its diagnosis and treatment operation, closed-loop automatic controlled.
  • the intelligent type incubator system is constructed by a nurture/treatment equipment which includes an environment controlling part for controlling an air environment containing temperature, humidity and an oxygen amount within a given space according to a corresponding setpoint value, an artificially breathing part for artificially breathing of a lungs-breath life body according to the corresponding setpoint value, and an injection part for injecting various kinds of injection agents onto the life body according to the corresponding setpoint value; a measuring unit for measuring a state of the life body like a medical and biological metabolism etc., the life body being as the object body under a nurture and/or a treatment through the nurture/treatment equipment; and an artificial intelligence controlling unit for processing state data of the life body measured by the measuring unit, as a control variable value for various setpoint values of the nurture/treatment equipment, on the basis of a neural network driven-type fuzzy inference model based on statistical information for a correlation between a state of the life body and an output of the nurture/treatment equipment, and for controlling the output of the nurture/treatment equipment
  • the nurture/treatment equipment in a controlling method of the intelligent type incubator system having the nurture/treatment equipment and manually varying a setpoint value of the nurture/treatment equipment according to a state of a corresponding life body, the nurture/treatment equipment containing an incubator for controlling an air environment containing temperature, humidity and an oxygen amount within a given space according to the setpoint value, an artificial respiratory machine for artificially breathing of a lungs-breath life body according to the setpoint value, and an injecting apparatus for injecting various kinds of injection agents onto the life body according to the setpoint value; the method includes the steps of: constructing a plural number of fuzzy inference rules on the basis of a statistical distribution of the setpoint value of the nurture/treatment equipment on the state data of the life body, and forming the plural number of fuzzy inference rules as the structure of the neural network; dividing the state data of the life body and its based setpoint value of the nurture/treatment equipment into a plurality of groups corresponding to the plural number of fuzzy inference rules and establishing them,
  • a treatment experience of a corresponding expert doctor and a special knowledge for the life body are established as a knowledge -based system of an artificial intelligence so that system itself has a judgment function, thereby, a corresponding nurture/treatment equipment is appropriately controlled so as to lead the life body to be normally nurtured, treated and restored to health.
  • Fig. 1 is a block diagram of a conventional incubator system
  • Fig. 2 indicates a block diagram of an intelligent type incubator system in one embodiment of the present invention
  • Fig. 3 depicts a constructive diagram for an algorithm of a neural network driven-type fuzzy inference model of an artificial intelligence controller shown in Fig. 2;
  • Fig. 4 is a drawing showing, on a two dimensional plane, group data classified under an assumption that there are two inputs to embody a neural network driven-type fuzzy inference model of an artificial intelligence controller shown in Fig. 2;
  • Fig. 5 sets forth a drawing showing another constructive example for embodying a inference rule of an artificial intelligence controller shown in Fig. 2.
  • an intelligent type incubator system and a controlling method thereof are described in detail, as follows.
  • Fig. 2 is a block diagram of an intelligent type incubator system in one embodiment of the present invention
  • the intelligent type incubator system includes a nurture/treatment equipment 20 which has an incubator 21 for outputting temperature of air, humidity, oxygen, pressure, and air controlled by each setpoint value for an air flowing quantity, and controlling a quality of air supplied into the inside of a given space, an artificial respiratory machine 22 for outputting oxygen controlled according to each setpoint value for expiration/inspiration pressure, oxygen content, the breathing number per unit time, humidity and temperature, and for artificially breathing of the lungs breathing life body (hereinafter, referred to as "patient") 30, a therapeutic medicine injector 23 for controlling and outputting an actual injection amount according to a setpoint value concerning the injection amount and for injecting various kinds of a therapeutic medicine to the patient 30; a measuring equipment 40 for measuring and inputting various kinds of state data which contains an electrocardiogram (ECG) having irregular pulse and the pulse number representing a state of a medical and biological metabolism etc.
  • ECG electrocardiogram
  • an artificial intelligence controller 50 for processing various kinds of state data of the patient 30 measured by the measuring equipment 40, as a control variable value for various setpoint values of the nurture/treatment equipment 20, on the basis of a neural network driven-type fuzzy inference model based on statistical information for a correlation between the state data of the patient 30 and an output of the nurture/treatment equipment 20 on the state data, and for controlling various outputs of the nurture/treatment equipment 20 on the basis of the processed control variable value.
  • a closed-loop system constructed by the nurture/treatment equipment 20, the patient 30, the measuring equipment 40 and the artificial intelligence controller 50.
  • the artificial intelligence controller 50 receives in feedback a state of the patient 30 through the measuring equipment 40, and processes and outputs the inputted state data of the patient, as the control variable value for various setpoint values, on the basis of the neural network driven-type fuzzy inference model, and sends out it as an input signal of the nurture/treatment equipment 20 containing the incubator 21, the artificial respiratory machine 22 and/or the injector 23, to thereby control the control object like the patient 30 through various kinds of nurture/treatment equipments 20.
  • the artificial controller 50 performs a function of recovering the patient to a stabilized balance state, namely, a normal state, through an appropriate procedure, on the basis of knowledge for a current state and a peculiarity of the patient 30, namely, treatment experience of a doctor and medical knowledge for the human body etc.
  • An input of the incubator 21 become a setpoint values for humidity, oxygen partial pressure and temperature of air supplied to the incubator 21 inside, and an air flowing amount, and an output thereof becomes the air controlled by the setpointed value.
  • An input of the artificial respiratory machine 22 becomes a setpoint value for expiration/inspiration pressure, an oxygen content, the breathing number per unit time, humidity and temperature, and an output thereof becomes the oxygen controlled by the setpoint value.
  • An input of the therapeutic medicine injector 23 becomes a setpoint value for the amount of the therapeutic medicine injection, and an output thereof becomes an actual injection amount based on the setpoint value.
  • An input of the patient 30 becomes various kinds of outputs of the nurture/treatment equipment 20 which contains the incubator 21, the respiratory machine 22 and the injector 23, and an output thereof becomes, a state of the patient, the electrocardiogram (ECG) having the irregular pulse and the pulse number, blood pressure, the breathing number, the oxygen saturation degree, the percutaneous oxygen partial pressure, the percutaneous dioxide partial pressure, the body temperature, the weight and age, etc.
  • ECG electrocardiogram
  • the artificial intelligence controller 50 gains information for value of the above-mentioned input/output signal, and processes the current state data of the patient 30 measured by the measuring equipment 40, as the control variable value for various setpoint values of the nurture/treatment equipment 20, on the basis of the neural network driven-type fuzzy inference model established by the expert knowledge and statistical information for the correlation between various state data of the patient 30 and a selective output of the nurture/treatment equipment 20, and also, controls various outputs of the nurture/treatment equipment 20 on the basis of the processed control variable value.
  • the establishment of the neural network driven-type fuzzy inference model and its inventive control method are described in detail, as follows.
  • Fig. 3 is a constructive diagram for an algorithm of the neural network driven-type fuzzy inference model of the artificial intelligence controller 50 shown in Fig. 2.
  • the fuzzy inference model constructs a fuzzy inference rule by using the expert knowledge of a doctor and the statistic distribution of the setpoint value of the nurture/treatment equipment 20 for various kinds of state data of a patient, and the state data of the patient for a learning is divided into groups by the number of rules, and it is decided a coupling coefficient of units in each hierarchy of the neural network NNm through the learning.
  • the membership value OtFl, ⁇ 2, . . . , ⁇ fr) of actual state data (xil, xi2, xin) is decided.
  • Xj n is obtained from the learned neural network model (NN ⁇ NN r ), and is combined with the previously obtained membership value ( Fi, 2, • • ⁇ , flr), thus a setpoint value (y * ) of the nurture/treatment equipment 20 to control the state of the patient 30 is gained.
  • a driving algorithm of the neural network driven-type fuzzy inference model of such artificial intelligence controller 50 is described in detail step by step, as follows.
  • an input variable value concerning an output y is decided. For example, if the blood pressure, the breathing number and the body temperature of the patient becomes close to temperature within the incubator, an input variable Xi of the controller 50 becomes the blood pressure, the breathing number and the body temperature measured and fed back from the patient, and an output of the controller 50 becomes setpoint temperature of the incubator 21.
  • the coupling coefficient of a network is decided from a neural network structure NN m of a membership function of Fig. 3 and the data divided into the groups of the r number through repeated learning. For instances, in case that the input data Xi belongs to a group G 2 , an output of NN m becomes (0, 1, 0, 0, . . . , 0).
  • the coupling coefficient of the network is decided through the repeated learning from a neural network structure NNi. NN2, . . . , NN r of Fig. 3 and a divided data group, namely, (xr 1 , yr 1 ). For example, if the input data is the data belonging to the G2 group, the coupling coefficient is decided through an application to the NN2 structure.
  • a final fifth step data measured in real time and extracted, namely, the state data of the patient, will be generally distributed randomly within a given range.
  • an output value of the controller 50 is decided from the inference network structure decided in the fourth step on the following numerical expression 1.
  • Fig. 5 presents another constructive example for realizing a inference rule of the artificial intelligence controller shown in Fig. 2, and herewith, an output y * is obtained as follows.
  • a fuzzy inference rule is decided by the r number, from the statistical distribution of the input/output data.
  • r (the number of inference rules), and A ⁇ .i and A12.2 represent a membership function of the input variables xi, X 2 , and ii, 1 2 indicates a sort of a membership function for the respective input variables xi and X 2 , and ⁇ ; is an inference value corresponding to each inference rule decided by the learning.
  • an inference value y * of an output is decided on the basis of the following numerical expression 3.
  • the existing open loop incubator system adjusted manually and operated by a setpoint value is constructed as a closed-loop system by realizing an artificial intelligence controller based on a neural network driven-type fuzzy inference model, to whereby control a state of a patient automatically and recover to a normal state. Accordingly, according to an establishment of the inventive closed-loop automatic control system, there are advantages of remarkably lessening an economic waste caused by numerous manpower commitment to the existing incubator system and reducing an error in a diagnosis and treatment to be caused by a frequent monitoring on a state of the patient.

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  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Veterinary Medicine (AREA)
  • Public Health (AREA)
  • General Health & Medical Sciences (AREA)
  • Animal Behavior & Ethology (AREA)
  • Physics & Mathematics (AREA)
  • Artificial Intelligence (AREA)
  • Pregnancy & Childbirth (AREA)
  • Pediatric Medicine (AREA)
  • Gynecology & Obstetrics (AREA)
  • Biophysics (AREA)
  • Signal Processing (AREA)
  • Biomedical Technology (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Medical Informatics (AREA)
  • Molecular Biology (AREA)
  • Surgery (AREA)
  • Pathology (AREA)
  • Psychiatry (AREA)
  • Physiology (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Mathematical Physics (AREA)
  • Fuzzy Systems (AREA)
  • Evolutionary Computation (AREA)
  • Accommodation For Nursing Or Treatment Tables (AREA)

Abstract

La présente invention concerne un système d'incubateur à intelligence artificielle et un procédé de commande de celui-ci. Ce système est constitué de divers types de matériels (20) de soins/traitement: un incubateur (21), un respirateur artificiel (22) et un injecteur (23) médical thérapeutique, ces matériels étant destinés aux soins et/ou au traitement d'un patient. Ce système comprend également un matériel de mesure destiné à mesurer et entrer diverses données d'états du patient, et un contrôleur (50) à intelligence artificielle destiné à traiter divers types de données d'état de ce patient (30) mesurées par le matériel de mesure (40), sous forme de valeur de variable de commande de diverses valeurs de consigne du matériel (20) de soins/traitement, à partir d'un modèle d'inférence flou conduit par réseau neuronal fondé sur des informations statistiques. Ces informations sont destinées à effectuer une corrélation entre les données d'état du patient (30) et une sortie du matériel de soins/traitement (20) relative à des données d'état, et à commander diverses sorties de ce matériel (20) de soins/traitement à partir de cette valeur de variable de commande traitée.
PCT/KR2001/000767 2000-06-01 2001-05-12 Systeme d'incubateur a intelligence artificielle et procede de commande de celui-ci Ceased WO2001091691A1 (fr)

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KR10-2000-0030129A KR100367908B1 (ko) 2000-06-01 2000-06-01 지능형 인큐베이터 시스템 및 그 제어방법
KR2000/30129 2000-06-01

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Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6884211B2 (en) 2002-07-12 2005-04-26 Pontifica Universidad Catolica Del Peru Neonatal artificial bubble
CN103309370A (zh) * 2013-06-01 2013-09-18 中南林业科技大学 一种基于bp神经网络的孵房湿度控制方法
CN103631285A (zh) * 2013-11-28 2014-03-12 马从国 一种基于can总线的鸡舍环境温度控制系统
CN104536474A (zh) * 2014-11-13 2015-04-22 无锡悟莘科技有限公司 一种水产养殖增氧的模糊控制方法
WO2021195138A1 (fr) * 2020-03-24 2021-09-30 Vyaire Medical, Inc. Système et procédé d'évaluation d'états de patients ventilés
CN115050454A (zh) * 2022-05-26 2022-09-13 深圳先进技术研究院 预测机械通气脱机的方法、装置、设备及存储介质

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20140115129A (ko) * 2013-03-20 2014-09-30 제이더블유중외메디칼 주식회사 사용자 인터페이스가 개선된 보육기
KR101592123B1 (ko) 2014-11-26 2016-02-04 세명대학교 산학협력단 산소 챔버를 구비하는 산소 치료 시스템
KR102347888B1 (ko) 2020-05-08 2022-01-05 인제대학교 산학협력단 신생아 인큐베이터용 차광장치
US12154673B2 (en) * 2021-08-02 2024-11-26 Mozarc Medical Us Llc Artificial intelligence assisted home therapy settings for dialysis

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4509505A (en) * 1982-03-12 1985-04-09 La Calhene Societe Anonyme Isolator for confining and transporting human beings in a sterile atmosphere
US5316542A (en) * 1992-02-14 1994-05-31 Dragerwerk Aktiengesellschaft Coupled control of operating parameters of an incubator
US5446934A (en) * 1993-11-30 1995-09-05 Frazier; Richard K. Baby monitoring apparatus

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4509505A (en) * 1982-03-12 1985-04-09 La Calhene Societe Anonyme Isolator for confining and transporting human beings in a sterile atmosphere
US5316542A (en) * 1992-02-14 1994-05-31 Dragerwerk Aktiengesellschaft Coupled control of operating parameters of an incubator
US5446934A (en) * 1993-11-30 1995-09-05 Frazier; Richard K. Baby monitoring apparatus

Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6884211B2 (en) 2002-07-12 2005-04-26 Pontifica Universidad Catolica Del Peru Neonatal artificial bubble
CN103309370A (zh) * 2013-06-01 2013-09-18 中南林业科技大学 一种基于bp神经网络的孵房湿度控制方法
CN103631285A (zh) * 2013-11-28 2014-03-12 马从国 一种基于can总线的鸡舍环境温度控制系统
CN104536474A (zh) * 2014-11-13 2015-04-22 无锡悟莘科技有限公司 一种水产养殖增氧的模糊控制方法
WO2021195138A1 (fr) * 2020-03-24 2021-09-30 Vyaire Medical, Inc. Système et procédé d'évaluation d'états de patients ventilés
CN115551579A (zh) * 2020-03-24 2022-12-30 维亚埃尔医疗股份有限公司 用于评估通风患者状况的系统和方法
CN115551579B (zh) * 2020-03-24 2024-04-12 维亚埃尔医疗股份有限公司 用于评估通风患者状况的系统和方法
CN115050454A (zh) * 2022-05-26 2022-09-13 深圳先进技术研究院 预测机械通气脱机的方法、装置、设备及存储介质
CN115050454B (zh) * 2022-05-26 2023-04-07 深圳先进技术研究院 预测机械通气脱机的方法、装置、设备及存储介质

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Publication number Publication date
KR100367908B1 (ko) 2003-01-14
KR20010109443A (ko) 2001-12-10

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