Detection of atrial fibrillation using a machine learning approach

Liaqat, S., Dashtipour, K., Zahid, A., Assaleh, K., Arshad, K. and Ramzan, N. (2020) Detection of atrial fibrillation using a machine learning approach. Information, 11(12), 549. (doi: 10.3390/info11120549)

[img] Text
220084.pdf - Published Version
Available under License Creative Commons Attribution.



The atrial fibrillation (AF) is one of the most well-known cardiac arrhythmias in clinical practice, with a prevalence of 1–2% in the community, which can increase the risk of stroke and myocardial infarction. The detection of AF electrocardiogram (ECG) can improve the early detection of diagnosis. In this paper, we have further developed a framework for processing the ECG signal in order to determine the AF episodes. We have implemented machine learning and deep learning algorithms to detect AF. Moreover, the experimental results show that better performance can be achieved with long short-term memory (LSTM) as compared to other algorithms. The initial experimental results illustrate that the deep learning algorithms, such as LSTM and convolutional neural network (CNN), achieved better performance (10%) as compared to machine learning classifiers, such as support vectors, logistic regression, etc. This preliminary work can help clinicians in AF detection with high accuracy and less probability of errors, which can ultimately result in reduction in fatality rate.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Zahid, Mr Adnan and Dashtipour, Dr Kia
Authors: Liaqat, S., Dashtipour, K., Zahid, A., Assaleh, K., Arshad, K., and Ramzan, N.
College/School:College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
Journal Name:Information
ISSN (Online):2078-2489
Copyright Holders:Copyright © 2020 The Authors
First Published:First published in 11(12):549
Publisher Policy:Reproduced under a Creative Commons licence

University Staff: Request a correction | Enlighten Editors: Update this record

Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
172865EPSRC DTP 16/17 and 17/18Tania GalabovaEngineering and Physical Sciences Research Council (EPSRC)EP/N509668/1Research and Innovation Services