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Characterization of Cardiovascular Diseases Using Wavelet Packet Decomposition and Nonlinear Measures of Electrocardiogram Signal

  • Hamido FujitaEmail author
  • Vidya K. Sudarshan
  • Muhammad Adam
  • Shu Lih Oh
  • Jen Hong Tan
  • Yuki Hagiwara
  • Kuang Chua Chua
  • Kok Poo Chua
  • U. Rajendra Acharya
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10350)

Abstract

Cardiovascular diseases (CVDs) remain as the primary causes of disability and mortality worldwide and are predicted to continue rise in the future due to inadequate preventive actions. Electrocardiogram (ECG) signal contains vital clinical information that assists significantly in the diagnosis of CVDs. Assessment of subtle ECG parameters that indicate the presence of CVDs are extremely difficult and requires long hours of manual examination for accurate diagnosis. Hence, automated computer-aided diagnosis systems might help in overcoming these limitations. In this study, a novel algorithm is proposed based on the combination of wavelet packet decomposition (WPD) and nonlinear features. The proposed method achieved classification results of 97.98% accuracy, 99.61% sensitivity and 94.84% specificity with 8 reliefF ranked features. The proposed methodology is highly efficient in helping clinical staff to detect cardiac abnormalities using a single algorithm.

Keywords

Coronary artery disease Myocardial infarction Congestive heart failure Electrocardiogram Entropies Wavelet packet decomposition 

Notes

Acknowledgment

First author appreciates the support given by Japan Society for Promotion of Science (JSPS) KAKENHI Grant Number: 15K00439.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Hamido Fujita
    • 1
    Email author
  • Vidya K. Sudarshan
    • 2
  • Muhammad Adam
    • 2
  • Shu Lih Oh
    • 2
  • Jen Hong Tan
    • 2
  • Yuki Hagiwara
    • 2
  • Kuang Chua Chua
    • 2
  • Kok Poo Chua
    • 2
  • U. Rajendra Acharya
    • 2
    • 3
    • 4
  1. 1.Faculty of Software and Information ScienceIwate Prefectural University (IPU)TakizawaJapan
  2. 2.Department of Electronics and Computer EngineeringNgee Ann PolytechnicSingaporeSingapore
  3. 3.Department of Biomedical Engineering, School of Science and TechnologySIM UniversitySingaporeSingapore
  4. 4.Department of Biomedical Engineering, Faculty of EngineeringUniversity of MalayaKuala LumpurMalaysia

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