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

Authors:
Heon Gyu Lee;Wuon-Shik Kim;Ki Yong Noh;Jin-Ho Shin;Unil Yun;Keun Ho Ryu
Affiliations:
School of Electrical & Computer Engineering, Chungbuk National University, Cheongju, Korea (361---763);Korea Research Institute of Standards and Science, Daejeon, Korea;Korea Research Institute of Standards and Science, Daejeon, Korea;Korea Electric Power Research Institute, Daejeon, Korea;School of Electrical & Computer Engineering, Chungbuk National University, Cheongju, Korea (361---763);School of Electrical & Computer Engineering, Chungbuk National University, Cheongju, Korea (361---763)
Venue:
Information Systems Frontiers
Year:
2009

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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.