A graph-based cardiac arrhythmia classification methodology using one-lead ECG recordings

Simple item page
dc.citation.articleNumber200385en_US
dc.citation.journalTitleIntelligent Systems with Applicationsen_US
dc.citation.volumeNumber22en_US
dc.contributor.authorEPMoghaddam, Dorsaen_US
dc.contributor.authorMuguli, Ananyaen_US
dc.contributor.authorRazavi, Mehdien_US
dc.contributor.authorAazhang, Behnaamen_US
dc.date.accessioned2024-08-02T13:32:06Zen_US
dc.date.available2024-08-02T13:32:06Zen_US
dc.date.issued2024en_US
dc.description.abstractIn this study, we present a novel graph-based methodology for an accurate classification of cardiac arrhythmia diseases using a single-lead electrocardiogram (ECG). The proposed approach employs the visibility graph technique to generate graphs from time signals. Subsequently, informative features are extracted from each graph and then fed into classifiers to match the input ECG signal with the appropriate target arrhythmia class. The six target classes in this study are normal (N), left bundle branch block (LBBB), right bundle branch block (RBBB), premature ventricular contraction (PVC), atrial premature contraction (A), and fusion (F) beats. Three classification models were explored, including graph convolutional neural network (GCN), multi-layer perceptron (MLP), and random forest (RF). ECG recordings from the MIT-BIH arrhythmia database were utilized to train and evaluate these classifiers. The results indicate that the multi-layer perceptron model attains the highest performance, showcasing an average accuracy of 99.02%. Following closely, the random forest achieves a strong performance as well, with an accuracy of 98.94% while providing critical intuitions.en_US
dc.identifier.citationEPMoghaddam, D., Muguli, A., Razavi, M., & Aazhang, B. (2024). A graph-based cardiac arrhythmia classification methodology using one-lead ECG recordings. Intelligent Systems with Applications, 22, 200385. https://doi.org/10.1016/j.iswa.2024.200385en_US
dc.identifier.digital1-s20-S2667305324000607-mainen_US
dc.identifier.doihttps://doi.org/10.1016/j.iswa.2024.200385en_US
dc.identifier.urihttps://hdl.handle.net/1911/117552en_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.rightsExcept where otherwise noted, this work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives (CC BY-NC-ND) license.  Permission to reuse, publish, or reproduce the work beyond the terms of the license or beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.titleA graph-based cardiac arrhythmia classification methodology using one-lead ECG recordingsen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
1-s20-S2667305324000607-main.pdf
Size:
1.95 MB
Format:
Adobe Portable Document Format
Download
Collections
Faculty Publications