Artificial Neural Network for Atrial Fibrillation Identification in Portable Devices

• Published in: Sensors (Basel, Switzerland) Vol. 20; no. 12; p. 3570
• Main Authors: Marinucci, Daniele, Sbrollini, Agnese, Marcantoni, Ilaria, Morettini, Micaela, Swenne, Cees A., Burattini, Laura
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 24.06.2020; MDPI
• Subjects: 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
• DOI: 10.3390/s20123570

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.