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

Heon Gyu Lee (), Wuon-Shik Kim (), Ki Yong Noh (), Jin-Ho Shin (), Unil Yun () and Keun Ho Ryu ()
Additional contact information
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

Information Systems Frontiers, 2009, vol. 11, issue 4, No 7, 419-431

Abstract: 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.

Keywords: Classification; Linear and nonlinear features; Heart rate variability; Coronary artery disease diagnosis (search for similar items in EconPapers)
Date: 2009
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s10796-009-9155-2 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:infosf:v:11:y:2009:i:4:d:10.1007_s10796-009-9155-2

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10796

DOI: 10.1007/s10796-009-9155-2

Access Statistics for this article

Information Systems Frontiers is currently edited by Ram Ramesh and Raghav Rao

More articles in Information Systems Frontiers from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().