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Artificial Neural Network for Atrial Fibrillation Identification in Portable Devices

Atrial fibrillation (AF) is a common cardiac disorder that can cause severe complications. AF diagnosis is typically based on the electrocardiogram (ECG) evaluation in hospitals or in clinical facilities. The aim of the present work is to propose a new artificial neural network for reliable AF ident...

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Autores principales: Marinucci, Daniele, Sbrollini, Agnese, Marcantoni, Ilaria, Morettini, Micaela, Swenne, Cees A., Burattini, Laura
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7348709/
https://www.ncbi.nlm.nih.gov/pubmed/32599796
http://dx.doi.org/10.3390/s20123570
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author Marinucci, Daniele
Sbrollini, Agnese
Marcantoni, Ilaria
Morettini, Micaela
Swenne, Cees A.
Burattini, Laura
author_facet Marinucci, Daniele
Sbrollini, Agnese
Marcantoni, Ilaria
Morettini, Micaela
Swenne, Cees A.
Burattini, Laura
author_sort Marinucci, Daniele
collection PubMed
description Atrial fibrillation (AF) is a common cardiac disorder that can cause severe complications. AF diagnosis is typically based on the electrocardiogram (ECG) evaluation in hospitals or in clinical facilities. The aim of the present work is to propose a new artificial neural network for reliable AF identification in ECGs acquired through portable devices. A supervised fully connected artificial neural network (RSL_ANN), receiving 19 ECG features (11 morphological, 4 on F waves and 4 on heart-rate variability (HRV)) in input and discriminating between AF and non-AF classes in output, was created using the repeated structuring and learning (RSL) procedure. RSL_ANN was created and tested on 8028 (training: 4493; validation: 1125; testing: 2410) annotated ECGs belonging to the “AF Classification from a Short Single Lead ECG Recording” database and acquired with the portable KARDIA device by AliveCor. RSL_ANN performance was evaluated in terms of area under the curve (AUC) and confidence intervals (CIs) of the received operating characteristic. RSL_ANN performance was very good and very similar in training, validation and testing datasets. AUC was 91.1% (CI: 89.1–93.0%), 90.2% (CI: 86.2–94.3%) and 90.8% (CI: 88.1–93.5%) for the training, validation and testing datasets, respectively. Thus, RSL_ANN is a promising tool for reliable identification of AF in ECGs acquired by portable devices.
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spelling pubmed-73487092020-07-20 Artificial Neural Network for Atrial Fibrillation Identification in Portable Devices Marinucci, Daniele Sbrollini, Agnese Marcantoni, Ilaria Morettini, Micaela Swenne, Cees A. Burattini, Laura Sensors (Basel) Article Atrial fibrillation (AF) is a common cardiac disorder that can cause severe complications. AF diagnosis is typically based on the electrocardiogram (ECG) evaluation in hospitals or in clinical facilities. The aim of the present work is to propose a new artificial neural network for reliable AF identification in ECGs acquired through portable devices. A supervised fully connected artificial neural network (RSL_ANN), receiving 19 ECG features (11 morphological, 4 on F waves and 4 on heart-rate variability (HRV)) in input and discriminating between AF and non-AF classes in output, was created using the repeated structuring and learning (RSL) procedure. RSL_ANN was created and tested on 8028 (training: 4493; validation: 1125; testing: 2410) annotated ECGs belonging to the “AF Classification from a Short Single Lead ECG Recording” database and acquired with the portable KARDIA device by AliveCor. RSL_ANN performance was evaluated in terms of area under the curve (AUC) and confidence intervals (CIs) of the received operating characteristic. RSL_ANN performance was very good and very similar in training, validation and testing datasets. AUC was 91.1% (CI: 89.1–93.0%), 90.2% (CI: 86.2–94.3%) and 90.8% (CI: 88.1–93.5%) for the training, validation and testing datasets, respectively. Thus, RSL_ANN is a promising tool for reliable identification of AF in ECGs acquired by portable devices. MDPI 2020-06-24 /pmc/articles/PMC7348709/ /pubmed/32599796 http://dx.doi.org/10.3390/s20123570 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Marinucci, Daniele
Sbrollini, Agnese
Marcantoni, Ilaria
Morettini, Micaela
Swenne, Cees A.
Burattini, Laura
Artificial Neural Network for Atrial Fibrillation Identification in Portable Devices
title Artificial Neural Network for Atrial Fibrillation Identification in Portable Devices
title_full Artificial Neural Network for Atrial Fibrillation Identification in Portable Devices
title_fullStr Artificial Neural Network for Atrial Fibrillation Identification in Portable Devices
title_full_unstemmed Artificial Neural Network for Atrial Fibrillation Identification in Portable Devices
title_short Artificial Neural Network for Atrial Fibrillation Identification in Portable Devices
title_sort artificial neural network for atrial fibrillation identification in portable devices
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7348709/
https://www.ncbi.nlm.nih.gov/pubmed/32599796
http://dx.doi.org/10.3390/s20123570
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