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Coronary artery disease prediction method using linear and nonlinear feature of heart rate variability in three recumbent postures

Author

Listed:
  • Heon Gyu Lee

    (Chungbuk National University)

  • Wuon-Shik Kim

    (Korea Research Institute of Standards and Science)

  • Ki Yong Noh

    (Korea Research Institute of Standards and Science)

  • Jin-Ho Shin

    (Korea Electric Power Research Institute)

  • Unil Yun

    (Chungbuk National University)

  • Keun Ho Ryu

    (Chungbuk National University)

Abstract

In present study, we proposed not only a novel methodology useful in developing the various features of heart rate variability (HRV), but also a suitable prediction model to enhance the reliability of medical examinations and treatments for coronary artery disease. In order to develop the various features of HRV, we analyzed HRV for three recumbent postures. The interaction effects between the recumbent postures and groups of normal people and heart patients were observed based on linear and nonlinear features of HRV. Forty-three control subjects and 64 patients with coronary artery disease participated in this study. In order to extract various features, we tested five classification methods and evaluated performance of classifiers. As a result, SVM and CMAR (gave about 72–88% goodness of accuracy) outperformed the other classifiers.

Suggested Citation

  • Heon Gyu Lee & Wuon-Shik Kim & Ki Yong Noh & Jin-Ho Shin & Unil Yun & Keun Ho Ryu, 2009. "Coronary artery disease prediction method using linear and nonlinear feature of heart rate variability in three recumbent postures," Information Systems Frontiers, Springer, vol. 11(4), pages 419-431, September.
    • Handle: RePEc:spr:infosf:v:11:y:2009:i:4:d:10.1007_s10796-009-9155-2
    DOI: 10.1007/s10796-009-9155-2
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    Cited by:

    1. Tsatsral Amarbayasgalan & Kwang Ho Park & Jong Yun Lee & Keun Ho Ryu, 2019. "Reconstruction error based deep neural networks for coronary heart disease risk prediction," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-17, December.

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