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Nonlinear Analysis of Electrocardiography Signals for Atrial Fibrillation
Author
Source
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-4, 4 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-05-13
Country of Publication
Egypt
No. of Pages
4
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
This paper aims to analyze the electrocardiography (ECG) signals for patient with atrial fibrillation (AF) by using bispectrum and extreme learning machine (ELM).
AF is the most common irregular heart beat disease which may cause many cardiac diseases as well.
Bispectral analysis was used to extract the nonlinear information in the ECG signals.
The bispectral features of each ECG episode were determined and fed to the ELM classifier.
The classification accuracy of ELM to distinguish nonterminating, terminating AF, and terminating immediately AF was 96.25%.
In this study, the normal ECG signal was also compared with AF ECG signal due to the nonlinearity which was determined by bispectrum.
The classification result of ELM was 99.15% to distinguish AF ECGs from normal ECGs.
American Psychological Association (APA)
Sezgin, Necmettin. 2013. Nonlinear Analysis of Electrocardiography Signals for Atrial Fibrillation. The Scientific World Journal،Vol. 2013, no. 2013, pp.1-4.
https://search.emarefa.net/detail/BIM-1033005
Modern Language Association (MLA)
Sezgin, Necmettin. Nonlinear Analysis of Electrocardiography Signals for Atrial Fibrillation. The Scientific World Journal No. 2013 (2013), pp.1-4.
https://search.emarefa.net/detail/BIM-1033005
American Medical Association (AMA)
Sezgin, Necmettin. Nonlinear Analysis of Electrocardiography Signals for Atrial Fibrillation. The Scientific World Journal. 2013. Vol. 2013, no. 2013, pp.1-4.
https://search.emarefa.net/detail/BIM-1033005
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1033005