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

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Book cover Advances in Artificial Intelligence: From Theory to Practice (IEA/AIE 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,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.

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Acknowledgment

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

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Authors and Affiliations

  1. Faculty of Software and Information Science, Iwate Prefectural University (IPU), Takizawa, Iwate, 020-0693, Japan

    Hamido Fujita

  2. Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore

    Vidya K. Sudarshan, Muhammad Adam, Shu Lih Oh, Jen Hong Tan, Yuki Hagiwara, Kuang Chua Chua, Kok Poo Chua & U. Rajendra Acharya

  3. Department of Biomedical Engineering, School of Science and Technology, SIM University, Singapore, Singapore

    U. Rajendra Acharya

  4. Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia

    U. Rajendra Acharya

Authors
  1. Hamido Fujita
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  2. Vidya K. Sudarshan
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  3. Muhammad Adam
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  4. Shu Lih Oh
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  5. Jen Hong Tan
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  6. Yuki Hagiwara
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  7. Kuang Chua Chua
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  9. U. Rajendra Acharya
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Corresponding author

Correspondence to Hamido Fujita .

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Editors and Affiliations

  1. Artois University, Lens, France

    Salem Benferhat

  2. Artois University, Lens, France

    Karim Tabia

  3. Texas State University, San Marcos, Texas, USA

    Moonis Ali

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Fujita, H. et al. (2017). Characterization of Cardiovascular Diseases Using Wavelet Packet Decomposition and Nonlinear Measures of Electrocardiogram Signal. In: Benferhat, S., Tabia, K., Ali, M. (eds) Advances in Artificial Intelligence: From Theory to Practice. IEA/AIE 2017. Lecture Notes in Computer Science(), vol 10350. Springer, Cham. https://doi.org/10.1007/978-3-319-60042-0_30

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  • DOI: https://doi.org/10.1007/978-3-319-60042-0_30

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