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A Novel ECG Eigenvalue Detection Algorithm Based on Wavelet Transform
Joint Authors
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-05-17
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
This study investigated an electrocardiogram (ECG) eigenvalue automatic analysis and detection method; ECG eigenvalues were used to reverse the myocardial action potential in order to achieve automatic detection and diagnosis of heart disease.
Firstly, the frequency component of the feature signal was extracted based on the wavelet transform, which could be used to locate the signal feature after the energy integral processing.
Secondly, this study established a simultaneous equations model of action potentials of the myocardial membrane, using ECG eigenvalues for regression fitting, in order to accurately obtain the eigenvalue vector of myocardial membrane potential.
The experimental results show that the accuracy of ECG eigenvalue recognition is more than 99.27%, and the accuracy rate of detection of heart disease such as myocardial ischemia and heart failure is more than 86.7%.
American Psychological Association (APA)
Peng, Ziran& Wang, Guojun. 2017. A Novel ECG Eigenvalue Detection Algorithm Based on Wavelet Transform. BioMed Research International،Vol. 2017, no. 2017, pp.1-12.
https://search.emarefa.net/detail/BIM-1137466
Modern Language Association (MLA)
Peng, Ziran& Wang, Guojun. A Novel ECG Eigenvalue Detection Algorithm Based on Wavelet Transform. BioMed Research International No. 2017 (2017), pp.1-12.
https://search.emarefa.net/detail/BIM-1137466
American Medical Association (AMA)
Peng, Ziran& Wang, Guojun. A Novel ECG Eigenvalue Detection Algorithm Based on Wavelet Transform. BioMed Research International. 2017. Vol. 2017, no. 2017, pp.1-12.
https://search.emarefa.net/detail/BIM-1137466
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1137466