AR121808A1 - Análisis de electrocardiogramas - Google Patents
Análisis de electrocardiogramasInfo
- Publication number
- AR121808A1 AR121808A1 ARP210100944A ARP210100944A AR121808A1 AR 121808 A1 AR121808 A1 AR 121808A1 AR P210100944 A ARP210100944 A AR P210100944A AR P210100944 A ARP210100944 A AR P210100944A AR 121808 A1 AR121808 A1 AR 121808A1
- Authority
- AR
- Argentina
- Prior art keywords
- ecg
- acquired
- patient
- trace segments
- representative
- Prior art date
Links
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
- A61B5/346—Analysis of electrocardiograms
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
- A61B5/346—Analysis of electrocardiograms
- A61B5/349—Detecting specific parameters of the electrocardiograph cycle
- A61B5/352—Detecting R peaks, e.g. for synchronising diagnostic apparatus; Estimating R-R interval
-
- 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
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- 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
- G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
- G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
- G16H40/63—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
-
- 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
Landscapes
- Health & Medical Sciences (AREA)
- Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Biomedical Technology (AREA)
- Public Health (AREA)
- Medical Informatics (AREA)
- Physics & Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- Artificial Intelligence (AREA)
- Pathology (AREA)
- Cardiology (AREA)
- Molecular Biology (AREA)
- Biophysics (AREA)
- Heart & Thoracic Surgery (AREA)
- Veterinary Medicine (AREA)
- Surgery (AREA)
- Animal Behavior & Ethology (AREA)
- Mathematical Physics (AREA)
- Evolutionary Computation (AREA)
- Epidemiology (AREA)
- Primary Health Care (AREA)
- Psychiatry (AREA)
- Physiology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Fuzzy Systems (AREA)
- Signal Processing (AREA)
- Data Mining & Analysis (AREA)
- Databases & Information Systems (AREA)
- Business, Economics & Management (AREA)
- General Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Computational Linguistics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Software Systems (AREA)
- Computing Systems (AREA)
- Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)
- Investigating Or Analysing Biological Materials (AREA)
- Investigating Or Analysing Materials By The Use Of Chemical Reactions (AREA)
Abstract
Un método informático para facilitar el análisis de electrocardiogramas (ECG) involucra recibir uno o más trazados del ECG adquiridos para un paciente, siendo que cada uno de los trazados del ECG adquiridos representa la actividad cardiaca del paciente detectada durante un período de tiempo adquirido, y, para cada uno de los uno o más trazados del ECG adquiridos: identificar una pluralidad de correspondientes segmentos del trazado del ECG adquiridos del ECG, siendo que cada uno de los segmentos del trazado del ECG adquiridos representa la actividad cardiaca del paciente detectada para el paciente durante un segmento del período de tiempo de adquisición, y determinar un trazado del ECG representativo basándose en al menos uno de los correspondientes segmentos del trazado del ECG adquiridos identificados. El método involucra causar que al menos un clasificador de la red neuronal sea aplicado a los uno o más trazados del ECG representativos determinados para determinar uno o más valores útiles para el diagnóstico en relación con al menos un diagnóstico del paciente. Se dan a conocer otros métodos, sistemas, y medios de lectura informáticos.
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US202063007496P | 2020-04-09 | 2020-04-09 | |
EP20169028.6A EP3893246B1 (en) | 2020-04-09 | 2020-04-09 | Electrocardiogram analysis |
Publications (1)
Publication Number | Publication Date |
---|---|
AR121808A1 true AR121808A1 (es) | 2022-07-13 |
Family
ID=70285496
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
ARP210100944A AR121808A1 (es) | 2020-04-09 | 2021-04-09 | Análisis de electrocardiogramas |
Country Status (16)
Country | Link |
---|---|
US (1) | US11179088B2 (es) |
EP (1) | EP3893246B1 (es) |
JP (1) | JP2023520944A (es) |
KR (1) | KR20220166812A (es) |
CN (1) | CN115552545A (es) |
AR (1) | AR121808A1 (es) |
AU (1) | AU2021252645A1 (es) |
BR (1) | BR112022020303A2 (es) |
CA (1) | CA3174101A1 (es) |
ES (1) | ES2924025T3 (es) |
IL (1) | IL296987A (es) |
MX (1) | MX2022012682A (es) |
PT (1) | PT3893246T (es) |
TW (1) | TW202143915A (es) |
UY (1) | UY39168A (es) |
WO (1) | WO2021205355A1 (es) |
Families Citing this family (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2024019500A1 (ko) * | 2022-07-22 | 2024-01-25 | 주식회사 메디컬에이아이 | 심전도 세그먼트를 이용한 건강 상태의 예측 방법, 프로그램 및 장치 |
Family Cites Families (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US10602942B2 (en) * | 2017-08-25 | 2020-03-31 | Cambridge Heartwear Limited | Method of detecting abnormalities in ECG signals |
-
2020
- 2020-04-09 PT PT201690286T patent/PT3893246T/pt unknown
- 2020-04-09 EP EP20169028.6A patent/EP3893246B1/en active Active
- 2020-04-09 ES ES20169028T patent/ES2924025T3/es active Active
-
2021
- 2021-04-07 AU AU2021252645A patent/AU2021252645A1/en active Pending
- 2021-04-07 IL IL296987A patent/IL296987A/en unknown
- 2021-04-07 WO PCT/IB2021/052886 patent/WO2021205355A1/en active Application Filing
- 2021-04-07 CA CA3174101A patent/CA3174101A1/en active Pending
- 2021-04-07 JP JP2022562128A patent/JP2023520944A/ja active Pending
- 2021-04-07 MX MX2022012682A patent/MX2022012682A/es unknown
- 2021-04-07 KR KR1020227035866A patent/KR20220166812A/ko unknown
- 2021-04-07 BR BR112022020303A patent/BR112022020303A2/pt not_active Application Discontinuation
- 2021-04-07 CN CN202180032087.9A patent/CN115552545A/zh active Pending
- 2021-04-08 TW TW110112702A patent/TW202143915A/zh unknown
- 2021-04-08 US US17/225,410 patent/US11179088B2/en active Active
- 2021-04-09 UY UY0001039168A patent/UY39168A/es unknown
- 2021-04-09 AR ARP210100944A patent/AR121808A1/es unknown
Also Published As
Publication number | Publication date |
---|---|
PT3893246T (pt) | 2022-08-05 |
CA3174101A1 (en) | 2021-10-14 |
KR20220166812A (ko) | 2022-12-19 |
EP3893246B1 (en) | 2022-05-04 |
UY39168A (es) | 2021-10-29 |
ES2924025T3 (es) | 2022-10-04 |
BR112022020303A2 (pt) | 2022-12-06 |
IL296987A (en) | 2022-12-01 |
JP2023520944A (ja) | 2023-05-22 |
AU2021252645A1 (en) | 2022-11-03 |
CN115552545A (zh) | 2022-12-30 |
US20210315506A1 (en) | 2021-10-14 |
WO2021205355A1 (en) | 2021-10-14 |
EP3893246A1 (en) | 2021-10-13 |
MX2022012682A (es) | 2022-11-30 |
US11179088B2 (en) | 2021-11-23 |
TW202143915A (zh) | 2021-12-01 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Rajpurkar et al. | Cardiologist-level arrhythmia detection with convolutional neural networks | |
Robb et al. | Postexercise electrocardiogram in arteriosclerotic heart disease: its value in diagnosis and prognosis | |
Steinberg et al. | Pattern recognition in the clinical electrocardiogram | |
US8543194B2 (en) | System and method of detecting abnormal movement of a physical object | |
Simoons et al. | On-line processing of orthogonal exercise electrocardiograms | |
Walinjkar et al. | Personalized wearable systems for real-time ECG classification and healthcare interoperability: Real-time ECG classification and FHIR interoperability | |
JP2018500982A5 (es) | ||
Johannesen | Assessment of ECG quality on an Android platform | |
CN109754877B (zh) | 一种基于人工智能的十二导联标准心电图急性心肌梗死智能判别系统 | |
US20200187807A1 (en) | Method and device for detecting stress using beat-to-beat ecg features | |
AR121808A1 (es) | Análisis de electrocardiogramas | |
Gomes et al. | Are standard heart rate variability measures associated with the self-perception of stress of firefighters in action? | |
Lee et al. | Automatic detection of electrocardiogram ST segment: Application in ischemic disease diagnosis | |
Horoba et al. | Recognition of atrial fibrilation episodes in heart rate variability signals using a machine learning approach | |
Vollmer | Arrhythmia classification in long-term data using relative RR intervals | |
Han et al. | Automatic detection of ECG lead-wire interchange for conventional and Mason-Likar lead systems | |
Bengherbia et al. | Real-time smart system for ecg monitoring using a one-dimensional convolutional neural network | |
Domnik et al. | Moving average and standard deviation thresholding (MAST): a novel algorithm for accurate R-wave detection in the murine electrocardiogram | |
Wahyu Kusuma et al. | Design of arrhythmia detection device based on fingertip pulse sensor | |
Bhowmick et al. | HRV performance analysis in photoplethysmography and electrocardiography | |
GB2590556A (en) | Systems and methids of QT interval analysis | |
Merdjanovska et al. | Patient-specific heartbeat classification in single-lead ECG using convolutional neural network | |
Gregg et al. | Detection of left arm and left leg lead-wire interchange based on serial ECGs | |
Conway et al. | Identification of premature ventricular contraction (PVC) caused by disturbances in calcium and potassium ion concentrations using artificial neural networks | |
Sayem et al. | A novel 1D generative adversarial network-based framework for atrial fibrillation detection using restored wrist photoplethysmography signals |