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Automated detection of atrial fibrillation using RR intervals and multivariate-based classification

Kennedy, Alan, Finlay, Dewar D., Guldenring, Daniel, Bond, Raymond R., Moran, Kieran orcid logoORCID: 0000-0003-2015-8967 and McLaughlin, James (2016) Automated detection of atrial fibrillation using RR intervals and multivariate-based classification. Journal of Electrocardiology, 49 (6). pp. 871-876. ISSN 0022-0736

Automated detection of AF from the electrocardiogram (ECG) still remains a challenge. In this study, we investigated two multivariate-based classification techniques, Random Forests (RF) and k-nearest neighbor (k-nn), for improved automated detection of AF from the ECG. We have compiled a new database from ECG data taken from existing sources. R-R intervals were then analyzed using four previously described R-R irregularity measurements: (1) the coefficient of sample entropy (CoSEn), (2) the coefficient of variance (CV), (3) root mean square of the successive differences (RMSSD), and (4) median absolute deviation (MAD). Using outputs from all four R-R irregularity measurements, RF and k-nn models were trained. RF classification improved AF detection over CoSEn with overall specificity of 80.1% vs. 98.3% and positive predictive value of 51.8% vs. 92.1% with a reduction in sensitivity, 97.6% vs. 92.8%. k-nn also improved specificity and PPV over CoSEn; however, the sensitivity of this approach was considerably reduced (68.0%).
Item Type:Article (Published)
Uncontrolled Keywords:Atrial fibrillation; R-R intervals; Algorithms
Subjects:Medical Sciences > Sports sciences
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Science and Health > School of Health and Human Performance
Research Institutes and Centres > INSIGHT Centre for Data Analytics
Official URL:https://doi.org/10.1016/j.jelectrocard.2016.07.033
Copyright Information:© 2016 Published by Elsevier Inc.
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:EU’s Horizon 2020 Framework Programme for Research and Innovation Action under Grant no. 643491
ID Code:21920
Deposited On:25 Aug 2017 10:05 by Giulia Migliorato . Last Modified 26 May 2022 13:28

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