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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Veröffentlicht in:Sensors (Basel, Switzerland) Jg. 20; H. 12; S. 3570
Hauptverfasser: Marinucci, Daniele, Sbrollini, Agnese, Marcantoni, Ilaria, Morettini, Micaela, Swenne, Cees A., Burattini, Laura
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland MDPI AG 24.06.2020
MDPI
Schlagworte:
Algorithms
artificial neural networks
atrial fibrillation
Atrial Fibrillation - diagnosis
Cardiac arrhythmia
Datasets
Electrocardiography
Electrocardiography - instrumentation
Heart
Heart Rate
Humans
Identification
machine learning algorithms
Medical equipment
Morphology
Neural networks
Neural Networks, Computer
Noise
Population
portable devices
Signal processing
artificial neural networks
atrial fibrillation
portable devices
machine learning algorithms
ISSN:1424-8220, 1424-8220
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Zusammenfassung: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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ISSN:1424-8220
1424-8220
DOI:10.3390/s20123570