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Electronics 2018, 7(9), 199; https://doi.org/10.3390/electronics7090199

Automatic Detection of Atrial Fibrillation and Other Arrhythmias in ECG Recordings Acquired by a Smartphone Device

1
Institute of Clinical Physiology, National Research Council of Italy, via Moruzzi 1, 56124 Pisa, Italy
2
Cardioline S.p.A., via Linz 19-21-21, 38121 Spini di Gardolo (TN), Italy
3
Dedalus S.p.A. Via Gaetano Malasoma, 24, 56121 Pisa, Italy
*
Author to whom correspondence should be addressed.
Received: 9 August 2018 / Revised: 6 September 2018 / Accepted: 14 September 2018 / Published: 16 September 2018
(This article belongs to the Special Issue Data Processing and Wearable Systems for Effective Human Monitoring)
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Abstract

Atrial fibrillation (AF) is the most common cardiac disease and is associated with other cardiac complications. Few attempts have been made for discriminating AF from other arrhythmias and noise. The aim of this study is to present a novel approach for such a classification in short ECG recordings acquired using a smartphone device. The implemented algorithm was tested on the Physionet Computing in Cardiology Challenge 2017 Database and, for the purpose of comparison, on the MIT-BH AF database. After feature extraction, the stepwise linear discriminant analysis for feature selection was used. The Least Square Support Vector Machine classifier was trained and cross-validated on the available dataset of the Challenge 2017. The best performance was obtained with a total of 30 features. The algorithm produced the following performance: F1 Normal rhythm = 0.92; F1 AF rhythm: 0.82; F1 Other rhythm = 0.75; Global F1 = 0.83, obtaining the third best result in the follow-up phase of the Physionet Challenge. On the MIT-BH ADF database the algorithm gave the following performance: F1 Normal rhythm = 0.98; F1 AF rhythm: 0.99; Global F1 = 0.98. Since the algorithm reliably detect AF and other rhythms in smartphone ECG recordings, it could be applied for personal health monitoring systems. View Full-Text
Keywords: electrocardiogram; smartphone; atrial fibrillation; arrhythmias; support vector machine electrocardiogram; smartphone; atrial fibrillation; arrhythmias; support vector machine
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Billeci, L.; Costi, M.; Lombardi, D.; Chiarugi, F.; Varanini, M. Automatic Detection of Atrial Fibrillation and Other Arrhythmias in ECG Recordings Acquired by a Smartphone Device. Electronics 2018, 7, 199.

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