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BioMed Research International
Volume 2017 (2017), Article ID 5168346, 12 pages
Research Article

A Novel ECG Eigenvalue Detection Algorithm Based on Wavelet Transform

1School of Information Science and Engineering, Central South University, Changsha, Hunan Province 410083, China
2Hunan Vocational College of Commerce, Changsha, Hunan Province 410205, China
3School of Computer Science and Educational Software, Guangzhou University, Guangzhou, Guangdong Province 510006, China

Correspondence should be addressed to Guojun Wang; nc.ude.usc@gnawjgsc

Received 16 December 2016; Revised 18 February 2017; Accepted 2 April 2017; Published 17 May 2017

Academic Editor: Volker Rasche

Copyright © 2017 Ziran Peng and Guojun Wang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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%.