CN113994437A - 用于检测尖端扭转型室性心动过速的风险的方法 - Google Patents

用于检测尖端扭转型室性心动过速的风险的方法 Download PDF

Info

Publication number
CN113994437A
CN113994437A CN202080041388.3A CN202080041388A CN113994437A CN 113994437 A CN113994437 A CN 113994437A CN 202080041388 A CN202080041388 A CN 202080041388A CN 113994437 A CN113994437 A CN 113994437A
Authority
CN
China
Prior art keywords
patient
risk
pointes
data
ecg
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.)
Pending
Application number
CN202080041388.3A
Other languages
English (en)
Chinese (zh)
Inventor
J-E·萨朗
E·普里夫蒂
A·A·普利尼
J-D·楚克尔
C·丰克-布伦塔诺
A·莱纳特
I·当茹瓦
F·克塞特尔默阿纳
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.)
Assistance Publique Hopitaux de Paris APHP
Institut National de la Sante et de la Recherche Medicale INSERM
Institut de Recherche pour le Developpement IRD
Sorbonne Universite
Universite Paris Cite
Original Assignee
Assistance Publique Hopitaux de Paris APHP
Institut National de la Sante et de la Recherche Medicale INSERM
Institut de Recherche pour le Developpement IRD
Sorbonne Universite
Universite de Paris
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 Assistance Publique Hopitaux de Paris APHP, Institut National de la Sante et de la Recherche Medicale INSERM, Institut de Recherche pour le Developpement IRD, Sorbonne Universite, Universite de Paris filed Critical Assistance Publique Hopitaux de Paris APHP
Publication of CN113994437A publication Critical patent/CN113994437A/zh
Pending legal-status Critical Current

Links

Images

Classifications

    • 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

Landscapes

  • Health & Medical Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Medical Informatics (AREA)
  • Public Health (AREA)
  • Biomedical Technology (AREA)
  • Data Mining & Analysis (AREA)
  • Databases & Information Systems (AREA)
  • Pathology (AREA)
  • Epidemiology (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)
CN202080041388.3A 2019-06-05 2020-06-04 用于检测尖端扭转型室性心动过速的风险的方法 Pending CN113994437A (zh)

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
EP19305730.4 2019-06-05
EP19305730.4A EP3748647A1 (en) 2019-06-05 2019-06-05 Method for detecting risk of torsades de pointes
PCT/EP2020/065562 WO2020245322A1 (en) 2019-06-05 2020-06-04 Method for detecting risk of torsades de pointes

Publications (1)

Publication Number Publication Date
CN113994437A true CN113994437A (zh) 2022-01-28

Family

ID=67070776

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202080041388.3A Pending CN113994437A (zh) 2019-06-05 2020-06-04 用于检测尖端扭转型室性心动过速的风险的方法

Country Status (6)

Country Link
US (1) US20220230758A1 (https=)
EP (2) EP3748647A1 (https=)
JP (1) JP2022535574A (https=)
CN (1) CN113994437A (https=)
CA (1) CA3142552A1 (https=)
WO (1) WO2020245322A1 (https=)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116269417A (zh) * 2023-03-30 2023-06-23 中山大学孙逸仙纪念医院 建立scd风险预测模型的方法、装置、电子设备及介质

Families Citing this family (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR102362678B1 (ko) * 2021-06-02 2022-02-14 주식회사 뷰노 생체신호 분석 방법
WO2023057200A1 (en) * 2021-10-04 2023-04-13 Biotronik Se & Co. Kg Computer implemented method for determining a medical parameter, training method and system
KR102653258B1 (ko) * 2022-02-18 2024-04-01 주식회사 뷰노 심전도 기술 및 결과 통합 방법
CN117915834A (zh) * 2022-08-18 2024-04-19 美迪科诶爱有限公司 利用复数个心电图的基于深度学习的健康状态预测系统
WO2025010429A1 (en) * 2023-07-06 2025-01-09 The General Hospital Corporation Method and apparatus for evaluating cardiac function
EP4497386A1 (en) 2023-07-28 2025-01-29 Assistance Publique - Hôpitaux de Paris Method for detecting risk of torsades de pointes in long qt patients
US20250064406A1 (en) * 2023-08-22 2025-02-27 Synergy A.I. Co. Ltd. Method, server, and computer program for generating heart diseases prediction model

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20060264769A1 (en) * 2005-05-13 2006-11-23 Cardiocore Lab, Inc. Method and apparatus for rapid interpretive analysis of electrocardiographic waveforms
CN1953705A (zh) * 2003-12-19 2007-04-25 阿尔堡大学 分析ecg曲线获得长qt综合症和药物影响的系统和方法
US20150196770A1 (en) * 2014-01-16 2015-07-16 Sorin Crm Sas Neural network system for the evaluation and the adaptation of antitachycardia therapy by an implantable defibrillator
US20190059764A1 (en) * 2016-04-13 2019-02-28 Assistance Publique - Hopitaux De Paris Method for determining the likelihood of torsades de pointes being induced

Family Cites Families (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7593764B2 (en) * 2006-10-03 2009-09-22 General Electric Company System and method of serial comparison for detection of long QT syndrome (LQTS)
US8529448B2 (en) * 2009-12-31 2013-09-10 Cerner Innovation, Inc. Computerized systems and methods for stability—theoretic prediction and prevention of falls
US8437839B2 (en) * 2011-04-12 2013-05-07 University Of Utah Research Foundation Electrocardiographic assessment of arrhythmia risk
US9408543B1 (en) * 2012-08-17 2016-08-09 Analytics For Life Non-invasive method and system for characterizing cardiovascular systems for all-cause mortality and sudden cardiac death risk
US10517494B2 (en) * 2014-11-14 2019-12-31 Beth Israel Deaconess Medical Center, Inc. Method and system to access inapparent conduction abnormalities to identify risk of ventricular tachycardia
WO2016077786A1 (en) * 2014-11-14 2016-05-19 Zoll Medical Corporation Medical premonitory event estimation
EP3318184B1 (en) * 2016-11-08 2024-01-10 Heart2Save Oy System for determining a probability for a person to have arrhythmia
US10849587B2 (en) * 2017-03-17 2020-12-01 Siemens Healthcare Gmbh Source of abdominal pain identification in medical imaging

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1953705A (zh) * 2003-12-19 2007-04-25 阿尔堡大学 分析ecg曲线获得长qt综合症和药物影响的系统和方法
US20060264769A1 (en) * 2005-05-13 2006-11-23 Cardiocore Lab, Inc. Method and apparatus for rapid interpretive analysis of electrocardiographic waveforms
US20150196770A1 (en) * 2014-01-16 2015-07-16 Sorin Crm Sas Neural network system for the evaluation and the adaptation of antitachycardia therapy by an implantable defibrillator
US20190059764A1 (en) * 2016-04-13 2019-02-28 Assistance Publique - Hopitaux De Paris Method for determining the likelihood of torsades de pointes being induced

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
ZACHI I. ATTIA等: "《Noninvasive assessment of dofetilide plasma concentration using a deep learning (neural network) analysis of the surface electrocardiogram: A proof of concept study》", PLOS ONE, vol. 13, no. 8, 31 August 2018 (2018-08-31), pages 0201059 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116269417A (zh) * 2023-03-30 2023-06-23 中山大学孙逸仙纪念医院 建立scd风险预测模型的方法、装置、电子设备及介质

Also Published As

Publication number Publication date
EP3981011A1 (en) 2022-04-13
WO2020245322A1 (en) 2020-12-10
EP3748647A1 (en) 2020-12-09
CA3142552A1 (en) 2020-12-10
US20220230758A1 (en) 2022-07-21
JP2022535574A (ja) 2022-08-09

Similar Documents

Publication Publication Date Title
CN113994437A (zh) 用于检测尖端扭转型室性心动过速的风险的方法
Bos et al. Use of artificial intelligence and deep neural networks in evaluation of patients with electrocardiographically concealed long QT syndrome from the surface 12-lead electrocardiogram
Prifti et al. Deep learning analysis of electrocardiogram for risk prediction of drug-induced arrhythmias and diagnosis of long QT syndrome
Al-Alshaikh et al. Comprehensive evaluation and performance analysis of machine learning in heart disease prediction
Hygrell et al. An artificial intelligence–based model for prediction of atrial fibrillation from single-lead sinus rhythm electrocardiograms facilitating screening
Exarchos et al. Artificial intelligence techniques in asthma: a systematic review and critical appraisal of the existing literature
Brady et al. The HEART score: a guide to its application in the emergency department
Johnson et al. Artificial intelligence for direct-to-physician reporting of ambulatory electrocardiography
Shrivastava et al. Artificial intelligence-enabled electrocardiography to screen patients with dilated cardiomyopathy
Aufiero et al. A deep learning approach identifies new ECG features in congenital long QT syndrome
WO2022125806A1 (en) Predicting fractional flow reserve from electrocardiograms and patient records
WO2018158430A1 (en) Method for providing a wearable device, method for predicting an acute exacerbation and system for predicting an acute exacerbation
Moses et al. Time-domain heart rate variability features for automatic congestive heart failure prediction
Al-Alusi et al. A deep learning digital biomarker to detect hypertension and stratify cardiovascular risk from the electrocardiogram
Siegersma et al. Deep neural networks reveal novel sex-specific electrocardiographic features relevant for mortality risk
Huang et al. ECG restitution analysis and machine learning to detect paroxysmal atrial fibrillation: insight from the equine athlete as a model for human athletes
Friedman et al. Unsupervised deep learning of electrocardiograms enables scalable human disease profiling
Pierre-Jean et al. Phenotyping of heart failure with preserved ejection faction using electronic health records and echocardiography
Molenaar et al. Explainable machine learning using echocardiography to improve risk prediction in patients with chronic coronary syndrome
Stasenko et al. Using machine learning algorithms to determine the post-COVID state of a person by their rhythmogram
Yousefimehr et al. Improving adhd detection with cost-sensitive lightgbm
Koscova et al. From sleep patterns to heart rhythm: Predicting atrial fibrillation from overnight polysomnograms
Liang et al. Integrated genetic analysis of diabetic complications: Bioinformatics insights into foot ulcers, neuropathy and peripheral artery disease
Fengade et al. Development of enhanced machine learning models for predicting type 2 diabetes mellitus using heart rate variability: a retrospective study
Doneda et al. An ECG-based machine-learning approach for mortality risk assessment in a large European population

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
CB02 Change of applicant information
CB02 Change of applicant information

Address after: Paris France

Applicant after: ASSISTANCE PUBLIQUE - HOPITAUX DE PARIS

Applicant after: INSTITUT NATIONAL DE LA SANTE ET DE LA RECHERCHE MEDICALE (INSERM)

Applicant after: University of Western dais, Paris

Applicant after: INSTITUT DE RECHERCHE POUR LE DEVELOPPEMENT (I.R.D.)

Applicant after: SORBONNE UNIVERSITE

Address before: Paris France

Applicant before: ASSISTANCE PUBLIQUE - HOPITAUX DE PARIS

Applicant before: INSTITUT NATIONAL DE LA SANTE ET DE LA RECHERCHE MEDICALE (INSERM)

Applicant before: University of Paris

Applicant before: INSTITUT DE RECHERCHE POUR LE DEVELOPPEMENT (I.R.D.)

Applicant before: SORBONNE UNIVERSITE

WD01 Invention patent application deemed withdrawn after publication
WD01 Invention patent application deemed withdrawn after publication

Application publication date: 20220128