Automatic diagnosis of the 12-lead ECG using a deep neural network

The role of automatic electrocardiogram (ECG) analysis in clinical practice is limited by the accuracy of existing models. In that context, the authors present a Deep Neural Network (DNN) that recognizes different abnormalities in ECG recordings which matches or outperform cardiology and emergency r...

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Autores principales:	Antônio H. Ribeiro, Manoel Horta Ribeiro, Gabriela M. M. Paixão, Derick M. Oliveira, Paulo R. Gomes, Jéssica A. Canazart, Milton P. S. Ferreira, Carl R. Andersson, Peter W. Macfarlane, Wagner Meira Jr., Thomas B. Schön, Antonio Luiz P. Ribeiro
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/6298880e5e2a47198ce31ab9d3b78bdf
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spelling	oai:doaj.org-article:6298880e5e2a47198ce31ab9d3b78bdf2021-12-02T15:39:18ZAutomatic diagnosis of the 12-lead ECG using a deep neural network10.1038/s41467-020-15432-42041-1723https://doaj.org/article/6298880e5e2a47198ce31ab9d3b78bdf2020-04-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-15432-4https://doaj.org/toc/2041-1723The role of automatic electrocardiogram (ECG) analysis in clinical practice is limited by the accuracy of existing models. In that context, the authors present a Deep Neural Network (DNN) that recognizes different abnormalities in ECG recordings which matches or outperform cardiology and emergency resident medical doctors.Antônio H. RibeiroManoel Horta RibeiroGabriela M. M. PaixãoDerick M. OliveiraPaulo R. GomesJéssica A. CanazartMilton P. S. FerreiraCarl R. AnderssonPeter W. MacfarlaneWagner Meira Jr.Thomas B. SchönAntonio Luiz P. RibeiroNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-9 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Antônio H. Ribeiro Manoel Horta Ribeiro Gabriela M. M. Paixão Derick M. Oliveira Paulo R. Gomes Jéssica A. Canazart Milton P. S. Ferreira Carl R. Andersson Peter W. Macfarlane Wagner Meira Jr. Thomas B. Schön Antonio Luiz P. Ribeiro Automatic diagnosis of the 12-lead ECG using a deep neural network
description	The role of automatic electrocardiogram (ECG) analysis in clinical practice is limited by the accuracy of existing models. In that context, the authors present a Deep Neural Network (DNN) that recognizes different abnormalities in ECG recordings which matches or outperform cardiology and emergency resident medical doctors.
format	article
author	Antônio H. Ribeiro Manoel Horta Ribeiro Gabriela M. M. Paixão Derick M. Oliveira Paulo R. Gomes Jéssica A. Canazart Milton P. S. Ferreira Carl R. Andersson Peter W. Macfarlane Wagner Meira Jr. Thomas B. Schön Antonio Luiz P. Ribeiro
author_facet	Antônio H. Ribeiro Manoel Horta Ribeiro Gabriela M. M. Paixão Derick M. Oliveira Paulo R. Gomes Jéssica A. Canazart Milton P. S. Ferreira Carl R. Andersson Peter W. Macfarlane Wagner Meira Jr. Thomas B. Schön Antonio Luiz P. Ribeiro
author_sort	Antônio H. Ribeiro
title	Automatic diagnosis of the 12-lead ECG using a deep neural network
title_short	Automatic diagnosis of the 12-lead ECG using a deep neural network
title_full	Automatic diagnosis of the 12-lead ECG using a deep neural network
title_fullStr	Automatic diagnosis of the 12-lead ECG using a deep neural network
title_full_unstemmed	Automatic diagnosis of the 12-lead ECG using a deep neural network
title_sort	automatic diagnosis of the 12-lead ecg using a deep neural network
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/6298880e5e2a47198ce31ab9d3b78bdf
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Automatic diagnosis of the 12-lead ECG using a deep neural network

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