WO2023022519A1 - 2유도 심전도 데이터를 이용한 복수개의 표준 심전도 데이터 생성 시스템 - Google Patents
2유도 심전도 데이터를 이용한 복수개의 표준 심전도 데이터 생성 시스템 Download PDFInfo
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- A61B5/25—Bioelectric electrodes therefor
- A61B5/279—Bioelectric electrodes therefor specially adapted for particular uses
- A61B5/28—Bioelectric electrodes therefor specially adapted for particular uses for electrocardiography [ECG]
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Definitions
- the present invention relates to a system for generating a plurality of standard ECG data using two-lead ECG data, capable of generating a plurality of standard-lead ECG data from ECG data of two or more leads using a principle-based algorithm.
- AI algorithms can detect subtle changes in electrocardiogram waveforms, and furthermore, electrocardiogram interpretation can be improved.
- the electrocardiogram used in the medical field is a 12-lead electrocardiogram, which is measured by attaching 10 electrodes of three limb electrodes, six chest electrodes, and one ground electrode, and the measured electrocardiogram data can be remotely transmitted.
- the single-lead electrocardiogram measured in this way can be used for diagnosing arrhythmia, but has limitations in diagnosing a disease requiring ECG information of various leads, such as myocardial infarction.
- a technical problem to be achieved by the spirit of the present invention is to provide a system for generating a plurality of standard ECG data using two-lead ECG data, which can generate a plurality of standard-lead ECG data from ECG data of two or more leads using a principle-based algorithm. is to do
- an embodiment of the present invention is an electrocardiogram measurement unit for generating electrocardiogram data by measuring two or more standard lead electrocardiograms;
- An electrocardiogram generating unit that identifies and calculates characteristic information of the two or more standard lead electrocardiogram data through a pre-learned principle-based algorithm, synthesizes a plurality of residual lead electrocardiogram data in a three-dimensional space, and generates multiple lead electrocardiogram data.
- an output unit for outputting the actual ECG data measured by the ECG measurement unit and the multi-lead ECG data generated by the ECG generator; including a plurality of standard ECG data generation systems using two-lead ECG data to provide.
- the principle-based algorithm analyzes the 2-lead electrocardiogram data accumulated in the medical institution server and the direction information of each potential vector of the 2-lead electrocardiogram data to determine two coordinate axes corresponding to the potential vectors, respectively, and A potential vector is calculated to generate an additional potential vector to create the 3-dimensional space, and the electrocardiogram data is projected in the direction of the potential vector of the residual induced electrocardiogram data on the 3-dimensional space, so that the potential vector direction of each standard lead electrocardiogram data Corresponding multi-lead electrocardiogram data may be generated.
- the principle-based algorithm may generate the additional potential vector by subtracting the two potential vectors to generate the three-dimensional space composed of three potential vectors.
- the principle-based algorithm can generate the 3-dimensional space by separating 3-dimensional components from the two potential vectors.
- the principle-based algorithm may generate 4-dimensional electrocardiogram data by adding a time axis to the 3-dimensional space using time-series information included in the electrocardiogram data.
- the principle-based algorithm visualizes the coordinate axes of the three-dimensional space by the potential vector and the additional potential vector, or obtains numerical information of the additional potential vector through a formula algorithm for extracting the additional potential vector from the potential vector. outputting, a coordinate axis corresponding to a potential vector of the residual induced electrocardiogram data may be calculated, and electrocardiogram data corresponding to the coordinate axis may be generated.
- the plurality of induced ECG data may be synchronously induced ECG data, asynchronously induced ECG data, or induced ECG data generated without considering synchronization.
- a plurality of standard lead electrocardiograms are generated in a three-dimensional space using only the two-lead electrocardiograms measured from the subject, and a plurality of standard lead electrocardiograms used for diagnosis and prediction of disease are generated with only a small number of electrocardiogram information to inform medical staff. It is possible to provide, and by using a small number of electrocardiogram information to generate a plurality of standard lead electrocardiograms, there is an effect of measuring, diagnosing, examining, and predicting health conditions more accurately.
- FIG. 1 is a block diagram of a system for generating a plurality of standard ECG data using two-lead ECG data according to an embodiment of the present invention.
- FIG. 2 schematically illustrates electrocardiogram data generation by a plurality of standard electrocardiogram data generation systems using the two-lead electrocardiogram data of FIG. 1 .
- FIG. 3 illustrates a flow chart of electrocardiogram generation by a plurality of standard electrocardiogram data generating systems using the two-lead electrocardiogram data of FIG. 1 .
- the system for generating a plurality of standard ECG data using 2-lead ECG data includes an electrocardiogram measuring unit 110 that generates ECG data by measuring 2 or more standard-lead ECGs, a pre-learned principle-based algorithm ( 121), an electrocardiogram generating unit 120 that identifies and calculates characteristic information of two or more standard lead electrocardiogram data, synthesizes a plurality of residual lead electrocardiogram data in a 3-dimensional space, and generates multiple lead electrocardiogram data, and Using the principle-based algorithm 121, including the output unit 130 that outputs the actual ECG data measured by the ECG measurement unit 110 and the multi-guided ECG data generated by the ECG generator 120 The main point is to generate a plurality of standard lead electrocardiogram data from electrocardiogram data of two or more leads.
- the electrocardiogram measuring unit 110 is a component that generates electrocardiogram data by measuring two or more standard lead electrocardiograms, and is equipped with two electrodes to contact two or more electric axes by contacting two body parts of the examinee. Electrocardiogram data of two or more leads to be obtained is transmitted to the electrocardiogram generating unit 120 through wired and wireless short-distance communication.
- a plurality of electrocardiograms of two or more different electrical axes may be respectively measured by measuring corresponding conduction II electrocardiograms.
- the electrocardiogram measuring unit 110 is a wearable electrocardiogram patch 111 capable of measuring an electrocardiogram during contact or non-contact during daily life, a smart watch 112, a 6-lead electrocardiogram bar that is measured in a short time, or an electrocardiogram of more than 2 leads installed in a medical institution synchronous electrocardiogram or asynchronous electrocardiogram may be measured.
- the electrocardiogram measuring unit 110 may measure the examinee's continuous electrocardiogram and transmit the electrocardiogram generator 120, or may measure two electrocardiograms at a time interval and transmit the electrocardiogram generator 120.
- the electrocardiogram generation unit 120 identifies and calculates characteristic information of two or more standard lead electrocardiogram data through the pre-learned principle-based algorithm 121, synthesizes a plurality of residual lead electrocardiogram data in a three-dimensional space, Generate multi-lead electrocardiogram data.
- the principle-based algorithm 121 analyzes 2-lead ECG data, which is big data accumulated in a medical institution server, and direction information of each potential vector, which is characteristic information of the 2-lead ECG data, to obtain two coordinate axes corresponding to the potential vectors. Each is determined, and two potential vectors are calculated geometrically or mathematically to generate an additional potential vector, thereby generating a three-dimensional space identical to a space in which the actual electrocardiogram is measured from the coordinate axes of the three potential vectors.
- multiple lead ECG data corresponding to the direction of the potential vector of each standard lead ECG data can be generated by projecting the ECG data in the direction of the potential vector of the remaining lead ECG data on the three-dimensional space where the two lead ECG data exists.
- the principle-based algorithm 121 generates an electrocardiogram that can be recorded in a plurality of different potential vector directions based on the electrocardiogram information determined in a three-dimensional space.
- the corresponding ECG is obtained from the 2-lead ECG data input from the ECG measurement unit 110.
- Identifies the characteristic information of the data identifies which type of ECG data it is, creates a 3-dimensional space, and selects a plurality of ECG data of a previously unidentified type or selects a plurality of electrocardiogram data to be generated in the direction of various potential vectors.
- Electrocardiogram data that can be recorded in each direction of the induced electrocardiogram potential vector can be generated by projecting the potential recording of the induced electrocardiogram on a three-dimensional space.
- the principle-based algorithm 121 geometrically subtracts the two potential vectors corresponding to the ECG data of Induction 1 and Induction 2 to generate an additional potential vector corresponding to the ECG data of Induction 3.
- a three-dimensional space composed of two potential vectors can be created.
- the principle-based algorithm 121 can generate a 3-dimensional space by separating 3-dimensional components from two potential vectors, respectively, by identifying the characteristic information of the corresponding electrocardiogram data and figuring out which type of electrocardiogram data it is, and then identifying the corresponding potential vector.
- a 3D space can be created from the extracted 3D information by extracting the 3D components of .
- the principle-based algorithm 121 generates 4-dimensional electrocardiogram data by adding a time axis to the previously created 3-dimensional space using the time-series information included in the electrocardiogram data, and analyzes the electrocardiogram time-series to obtain a 3-dimensional electrocardiogram. It is also possible to analyze and predict the trend of occurrence of diseases and the trend of deterioration or relief of diseases through time-series changes.
- the principle-based algorithm 121 geometrically calculates and visualizes the coordinate axes of the three-dimensional space constructed by the two potential vectors provided from the electrocardiogram measurement unit 110 and the additionally generated potential vectors, and displays them, or from the potential vectors.
- the numerical information of the additional potential vector is mathematically output through a specific formula algorithm for extracting the additional potential vector, the coordinate axis corresponding to the potential vector of the residual induced electrocardiogram data is calculated, and the potential record is projected onto the coordinate axis to record the corresponding coordinate axis.
- Electrocardiogram data can be generated.
- the electrocardiogram data measured by the electrocardiogram measurement unit 110 or the multi-induction electrocardiogram data generated by the electrocardiogram generator 120 may be synchronous induction electrocardiogram data, asynchronous induction electrocardiogram data, or induction generated without considering synchronization. It may be electrocardiogram data.
- the noise removal unit 150 minimizes the noise of the ECG data provided from the ECG measurement unit 110 to the ECG generator 120 to further increase the reliability of the standard-guided ECG data generated by the ECG generator 120.
- the noise removal unit 150 may further include, for example, the noise removal unit 150 may include a plurality of electrocardiogram data, for example, based on standard 12-lead electrocardiogram data accumulated in medical institutions, electrocardiogram data for each conduction with less noise and electrocardiogram data for each induction
- the deep learning algorithm 121 built by pre-learning the learning dataset of the unique style, the characteristics of the examinee and the measurement method are reflected, and the corresponding unique style is obtained from the ECG data measured by the ECG measuring unit 110. It can be created by extracting and converting it into electrocardiogram data of a specific induction style that does not include noise through the extracted unique style.
- the noise removal unit 150 reflects the characteristics of the examinee's age, gender, disease, etc., electrode attachment position, and measurement method characteristics due to the ECG device, etc., so that the unique style for each ECG data induction is high It can be identified with accuracy, and based on this, electrocardiogram data for each induction can be more accurately converted and generated.
- the output unit 130 may output the actual ECG data measured by the ECG measurement unit 110 and the multi-guided ECG data generated by the ECG generator 120 to provide to medical personnel.
- the electrocardiogram data output from the output unit 130 is input to the diagnosis prediction unit 140, which is built from the standard-guided electrocardiogram data and predicts a disease, and corresponds to the electrocardiogram data measured by the electrocardiogram measurement unit 110. You can diagnose and predict your health condition.
- the diagnosis prediction unit 140 includes diseases of the circulatory system, endocrine, nutritional and metabolic diseases, neoplastic diseases, mental and behavioral disorders, diseases of the nervous system, diseases of the eye and appendages, ears and mastoids Diseases of the respiratory system, diseases of the digestive system, diseases of the skin and skin tissues, diseases of the musculoskeletal system and connective tissue, diseases of the genitourinary system, pregnancy, childbirth and postpartum diseases, congenital Deformities, transformations, and chromosomal abnormalities can be diagnosed and predicted.
- diagnosis prediction unit 140 damage due to physical trauma can be confirmed, prognosis can be confirmed, pain can be measured, the risk of death or aggravation due to trauma can be predicted, and concurrent complications can be captured or It can be predicted, and certain conditions that appear before and after birth can be identified.
- diagnosis prediction unit 140 as a healthcare area, aging, sleep, weight, blood pressure, blood sugar, oxygen saturation, metabolism, stress, tension, fear, drinking, smoking, problem behavior, lung capacity, exercise, pain Management, obesity, body mass, body composition, diet, type of exercise, life pattern recommendation, emergency management, chronic disease management, medication prescription, examination recommendation, examination recommendation, nursing care, remote health management, remote medical treatment, vaccination and post-inoculation management, etc. It may be possible to measure, diagnose, examine, and predict the health status of the examinee, which can lead to the service of
- FIG. 3 illustrates a flowchart of electrocardiogram generation by a plurality of standard electrocardiogram data generating systems using the two-lead electrocardiogram data of FIG.
- electrocardiogram measurement unit 110 by contacting two or more body parts of the examinee, electrocardiogram data of two or more inductions corresponding to two or more electrical axes is obtained, and wired and wireless short-range communication is performed with the electrocardiogram generator 120. It transmits through (S110).
- the electrocardiogram generation unit 120 equipped with the pre-learned principle-based algorithm 121, the characteristic information of two or more standard lead electrocardiogram data is identified and calculated to generate a plurality of residual lead electrocardiogram data in a 3-dimensional space.
- Multi-lead electrocardiogram data is generated by synthesizing (S120).
- the principle-based algorithm 121 analyzes 2-lead ECG data, which is big data accumulated in a medical institution server, and direction information of each potential vector, which is characteristic information of the 2-lead ECG data, to obtain two coordinate axes corresponding to the potential vectors. Each is determined, and two potential vectors are calculated geometrically or mathematically to generate an additional potential vector, thereby generating a three-dimensional space identical to a space in which the actual electrocardiogram is measured from the coordinate axes of the three potential vectors.
- the noise removal unit 150 minimizes the noise of the ECG data provided from the ECG measurement unit 110 to the ECG generator 120 to further increase the reliability of the standard-guided ECG data generated by the ECG generator 120.
- the noise removal unit 150 removes noise based on a plurality of electrocardiogram data, for example, standard 12-lead electrocardiogram data accumulated in medical institutions.
- the electrocardiogram measuring unit A corresponding unique style may be extracted from the ECG data measured in step 110 and converted into ECG data of a specific induction style that does not contain noise through the extracted unique style.
- the actual electrocardiogram data measured by the electrocardiogram measurement unit 110 and the multi-guided electrocardiogram data generated by the electrocardiogram generator 120 may be output and provided to medical personnel. S (140).
- the electrocardiogram data output from the output unit 130 is constructed from the standard-guided electrocardiogram data to predict the disease (S150), to the electrocardiogram measurement unit 110 through the diagnosis prediction unit 140. It is also possible to diagnose and predict the health condition corresponding to the electrocardiogram data measured by (S150).
- a plurality of standard-lead ECGs are generated in a three-dimensional space using only the 2-lead ECGs measured from the examinee, thereby reducing the number of ECGs.
- Multiple standard lead ECGs used for disease diagnosis and prediction can be generated and provided to medical staff using only information, and multiple standard lead ECGs can be generated using a small number of ECG information to measure, diagnose, check, and predict health conditions can be performed more precisely.
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Abstract
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Claims (7)
- 2유도 이상의 표준유도 심전도를 측정하여 심전도 데이터를 생성하는 심전도 측정부;미리학습된 원칙기반 알고리즘을 통해, 상기 2유도 이상의 표준유도 심전도 데이터의 특성정보를 식별하고 연산하여 3차원공간 상에서의 복수의 잔여 유도 심전도 데이터를 합성하여 복수 유도 심전도 데이터를 생성하는, 심전도 생성부; 및상기 심전도 측정부에 의해 측정된 실제 심전도 데이터와, 상기 심전도 생성부에 의해 생성된 복수 유도 심전도 데이터를 출력하는 출력부;를 포함하는,2유도 심전도 데이터를 이용한 복수개의 표준 심전도 데이터 생성 시스템.
- 제1항에 있어서,상기 원칙기반 알고리즘은,의료기관서버에 축적된 2유도 심전도 데이터와, 상기 2유도 심전도 데이터의 각 전위벡터의 방향정보를 분석하여 상기 전위벡터에 상응하는 2개의 좌표축을 각각 결정하고, 2개의 전위벡터를 연산하여 추가 전위벡터를 생성하여 상기 3차원공간을 생성하고, 상기 3차원공간 상에서 상기 심전도 데이터를 상기 잔여 유도 심전도 데이터의 전위벡터 방향으로 투사하여 각 표준유도 심전도 데이터의 전위벡터 방향에 상응하는 상기 복수 유도 심전도 데이터를 생성하는 것을 특징으로 하는,2유도 심전도 데이터를 이용한 복수개의 표준 심전도 데이터 생성 시스템.
- 제2항에 있어서,상기 원칙기반 알고리즘은 상기 2개의 전위벡터를 차감하여 상기 추가 전위벡터를 생성하여 3개의 전위벡터로 구성되는 상기 3차원공간을 생성하는 것을 특징으로 하는,2유도 심전도 데이터를 이용한 복수개의 표준 심전도 데이터 생성 시스템.
- 제2항에 있어서,상기 원칙기반 알고리즘은 상기 2개의 전위벡터로부터 각각 3차원성분을 분리하여 상기 3차원공간을 생성하는 것을 특징으로 하는,2유도 심전도 데이터를 이용한 복수개의 표준 심전도 데이터 생성 시스템.
- 제3항 또는 제4항에 있어서,상기 원칙기반 알고리즘은 상기 심전도 데이터에 포함된 시계열정보를 이용하여 상기 3차원공간에 시간축을 추가하여 4차원의 심전도 데이터를 생성하는 것을 특징으로 하는,2유도 심전도 데이터를 이용한 복수개의 표준 심전도 데이터 생성 시스템.
- 제2항에 있어서,상기 원칙기반 알고리즘은 상기 전위벡터와 상기 추가 전위벡터에 의한 상기 3차원공간의 좌표축을 시각화하거나, 상기 전위벡터로부터 상기 추가 전위벡터를 추출하는 수식 알고리즘을 통해 상기 추가 전위벡터의 수치정보를 출력하여서, 상기 잔여 유도 심전도 데이터의 전위벡터에 해당하는 좌표축을 산출하고, 상기 좌표축에 상응하는 심전도 데이터를 생성하는 것을 특징으로 하는,2유도 심전도 데이터를 이용한 복수개의 표준 심전도 데이터 생성 시스템.
- 제1항에 있어서,상기 복수 유도 심전도 데이터는 동기 유도 심전도 데이터이거나, 비동기 유도 심전도 데이터이거나, 동기화를 고려하지 않고 생성된 유도 심전도 데이터인 것을 특징으로 하는,2유도 심전도 데이터를 이용한 복수개의 표준 심전도 데이터 생성 시스템.
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Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP2004505658A (ja) * | 2000-08-03 | 2004-02-26 | シーメンス メディカル ソリューションズ ユーエスエー インコーポレイテッド | 誘導を合成し精度の尺度を形成するための心電図システム |
JP2008093264A (ja) * | 2006-10-13 | 2008-04-24 | Fukuda Denshi Co Ltd | 心電図自動解析装置、心電図自動解析方法および心電図自動解析プログラム |
KR102142841B1 (ko) * | 2019-11-06 | 2020-08-10 | 메디팜소프트(주) | Ai 기반 심전도 판독 시스템 |
KR20210061769A (ko) * | 2019-11-20 | 2021-05-28 | 연세대학교 산학협력단 | 심전도 재구축 시스템 및 방법 |
JP6914910B2 (ja) * | 2015-04-03 | 2021-08-04 | ユニヴェルシテ・ドゥ・ロレーヌUniversite De Lorraine | 3次元マップにおいて峡部を特定するための方法およびシステム |
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Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP2004505658A (ja) * | 2000-08-03 | 2004-02-26 | シーメンス メディカル ソリューションズ ユーエスエー インコーポレイテッド | 誘導を合成し精度の尺度を形成するための心電図システム |
JP2008093264A (ja) * | 2006-10-13 | 2008-04-24 | Fukuda Denshi Co Ltd | 心電図自動解析装置、心電図自動解析方法および心電図自動解析プログラム |
JP6914910B2 (ja) * | 2015-04-03 | 2021-08-04 | ユニヴェルシテ・ドゥ・ロレーヌUniversite De Lorraine | 3次元マップにおいて峡部を特定するための方法およびシステム |
KR102142841B1 (ko) * | 2019-11-06 | 2020-08-10 | 메디팜소프트(주) | Ai 기반 심전도 판독 시스템 |
KR20210061769A (ko) * | 2019-11-20 | 2021-05-28 | 연세대학교 산학협력단 | 심전도 재구축 시스템 및 방법 |
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