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Comparative Study of Convolutional Neural Networks for ECG Quality Assessment

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Comparative Study of Convolutional Neural Networks for ECG Quality Assessment

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Huerta, A.; Martinez-Rodrigo, A.; Puchol, A.; Pachon, MI.; Rieta, JJ.; Alcaraz, R. (2020). Comparative Study of Convolutional Neural Networks for ECG Quality Assessment. IEEE. 1-4. https://doi.org/10.22489/CinC.2020.370

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/178588

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Title: Comparative Study of Convolutional Neural Networks for ECG Quality Assessment
Author: Huerta, Alvaro Martinez-Rodrigo, Arturo Puchol, Alberto Pachon, Marta, I Rieta, J J Alcaraz, Raul
UPV Unit: Universitat Politècnica de València. Departamento de Ingeniería Electrónica - Departament d'Enginyeria Electrònica
Issued date:
Abstract:
[EN] In the last years, convolutional neural networks (CNNs) have become popular in ECG analysis, since they do not require pre-processing stages, nor specific pre-training. However, their ability for ECG quality assessment ...[+]
Copyrigths: Reconocimiento (by)
Source:
Computing in Cardiology 2020; Vol 47. (issn: 2325-887X )
DOI: 10.22489/CinC.2020.370
Publisher:
IEEE
Publisher version: https://doi.org/10.22489/CinC.2020.370
Conference name: 47th Computing in Cardiology Conference (CinC 2020)
Conference place: Rimini, Italia
Conference date: Septiembre 13-16,2020
Project ID:
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/DPI2017-83952-C3-1-R/ES/ESTUDIO MULTICENTRICO PARA LA EVALUACION DEL SUSTRATO ARRITMOGENICO EN PACIENTES CON FIBRILACION AURICULAR. APLICACION A LA ABLACION POR CATETER/
info:eu-repo/grantAgreement/JCCM//SBPLY%2F17%2F180501%2F000411//Caracterización del sustrato auricular mediante análisis de señal como herramienta de asistencia procedimental en ablación por catéter de fibrilación auricular/
info:eu-repo/grantAgreement///AICO%2F2019%2F036//METODOS DE DIAGNOSTICO Y TERAPIA PERSONALIZADA EN ABLACION POR CATETER DE ARRITMIAS CARDIACAS/
Thanks:
This research has been supported by the grants DPI2017-83952-C3 from MINECO/AEI/FEDER EU, SBPLY/17/180501/000411 from Junta de Comunidades de Castilla-La Mancha and AICO/2019/036 from Generalitat Valenciana.
Type: Comunicación en congreso Artículo

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