A Novel ECG Eigenvalue Detection Algorithm Based on Wavelet Transform.

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 w...

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Publicado en:BioMed Research International Vol. 2017; pp. 1 - 13
Autores principales: Peng, Ziran, Wang, Guojun
Formato: equations & formulas research tables/charts tracings Journal Article
Publicado: Wiley-Blackwell 5/17/2017
Acceso en línea:Ver este registro en EBSCOhost
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      dt: 5/17/2017
      vid: 2017
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      pub: Wiley-Blackwell
      place: Malden, Massachusetts
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        10.1155/2017/5168346
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        atl: A Novel ECG Eigenvalue Detection Algorithm Based on Wavelet Transform.
      aug:
        au:
          Peng, Ziran
          Wang, Guojun
        affil: School of Information Science and Engineering, Central South University, Changsha, Hunan Province 410083, China
      sug:
        subj:
          Electrocardiography Evaluation
          Algorithms Utilization
          Heart Diseases Diagnosis
          Human
          Descriptive Statistics
          Data Analysis Software
          Algorithms
          Funding Source
      ab: 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%.
      pubtype: Academic Journal
      doctype:
        equations & formulas
        research
        tables/charts
        tracings
        Journal Article
      ougenre: Article
    language: English
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