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Article

Artificial Neural Network for Atrial Fibrillation Identification in Portable Devices

1
Department of Information Engineering, Università Politecnica delle Marche, via Brecce Bianche 12, 60131 Ancona, Italy
2
Cardiology Department, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(12), 3570; https://doi.org/10.3390/s20123570
Received: 15 May 2020 / Revised: 17 June 2020 / Accepted: 21 June 2020 / Published: 24 June 2020
(This article belongs to the Special Issue ECG Sensors)
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. View Full-Text
Keywords: atrial fibrillation; machine learning algorithms; artificial neural networks; portable devices atrial fibrillation; machine learning algorithms; artificial neural networks; portable devices
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MDPI and ACS Style

Marinucci, D.; Sbrollini, A.; Marcantoni, I.; Morettini, M.; Swenne, C.A.; Burattini, L. Artificial Neural Network for Atrial Fibrillation Identification in Portable Devices. Sensors 2020, 20, 3570. https://doi.org/10.3390/s20123570

AMA Style

Marinucci D, Sbrollini A, Marcantoni I, Morettini M, Swenne CA, Burattini L. Artificial Neural Network for Atrial Fibrillation Identification in Portable Devices. Sensors. 2020; 20(12):3570. https://doi.org/10.3390/s20123570

Chicago/Turabian Style

Marinucci, Daniele; Sbrollini, Agnese; Marcantoni, Ilaria; Morettini, Micaela; Swenne, Cees A.; Burattini, Laura. 2020. "Artificial Neural Network for Atrial Fibrillation Identification in Portable Devices" Sensors 20, no. 12: 3570. https://doi.org/10.3390/s20123570

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