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http://bura.brunel.ac.uk/handle/2438/21661
Title: | ECG arrhythmia classification by using a recurrence plot and convolutional neural network |
Authors: | Mathunjwa, BM Lin, Y-T Lin, C-H Abbod, M Shieh, I-S |
Keywords: | arrhythmia;recurrence plot;convolutional neural network;electrocardiography |
Issue Date: | 19-Oct-2020 |
Publisher: | Elsevier |
Citation: | Mathunjwa, B.M. et al. (2021) 'ECG arrhythmia classification by using a recurrence plot and convolutional neural network', Biomedical Signal Processing and Control, 64, February 2021, 102262 (15 pp.). doi: 10.1016/j.bspc.2020.102262. |
Abstract: | Cardiovascular diseases affect approximately 50 million people worldwide; thus, heart disease prevention is one of the most important tasks of any health care system. Despite the high popularity electrocardiography, superior automatic electrocardiography (ECG) signal analysis methods are required. The aim of this research was to design a new deep learning method for effectively classifying arrhythmia by using 2-second segments of 2D recurrence plot images of ECG signals. In the first stage, the noise and ventricular fibrillation (VF) categories were distinguished. In the second stage, the atrial fibrillation (AF), normal, premature AF, and premature VF categories were distinguished. Models were trained and tested using ECG databases publicly available at the website of PhysioNet. The MIT-BIH Arrhythmia Database, Creighton University Ventricular Tachyarrhythmia Database, MIT-BIH Atrial Fibrillation Database, and MIT-BIH Malignant Ventricular Ectopy Database were used to compare six types of arrhythmia. Testing accuracies of up to 95.3 % ± 1.27 % and 98.41 % ± 0.11 % were achieved for arrhythmia detection in the first and second stage, respectively, after five-fold cross-validation. In conclusion, this study provides clinicians with an advanced methodology for detecting and discriminating between different arrhythmia types. |
Description: | Supplementary data are available online at: https://www.sciencedirect.com/science/article/pii/S174680942030389X?casa_token=ZdWwd1gPF2MAAAAA:iBy5NLmoTn6n_Jxc-L-UDEs6iAChfCA2xqBB1V1B9PybdiYlE701shcVBrCYJz1nxonkknJedA#sec0135 . |
URI: | https://bura.brunel.ac.uk/handle/2438/21661 |
DOI: | https://doi.org/10.1016/j.bspc.2020.102262 |
ISSN: | 1746-8094 |
Other Identifiers: | ORCID iD: Maysam F. Abbod https://orcid.org/0000-0002-8515-7933 |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
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FullText.pdf | Copyright © Elsevier. All rights reserved. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/ | 2.3 MB | Adobe PDF | View/Open |
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