Predicting Coronary Artery Disease from Heart Rate Variability Using Classification and Statistical Analysis

Aizu-Wakamatsu City, Fukushima, Japan October 16-October 19

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http://doi.ieeecomputersociety.org/10.1109/CIT.2007.163

7th IEEE International Conference on ...

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	Heon Gyu Lee, Ki Yong Noh, Hong Kyu Park, Keun Ho Ryu, "Predicting Coronary Artery Disease from Heart Rate Variability Using Classification and Statistical Analysis," Computer and Information Technology, International Conference on, pp. 59-64, 7th IEEE International Conference on Computer and Information Technology (CIT 2007), 2007.

	BibTex	x
	@article{ 10.1109/CIT.2007.163, author = {Heon Gyu Lee and Ki Yong Noh and Hong Kyu Park and Keun Ho Ryu}, title = {Predicting Coronary Artery Disease from Heart Rate Variability Using Classification and Statistical Analysis}, journal ={Computer and Information Technology, International Conference on}, volume = {0}, year = {2007}, isbn = {0-7695-2983-6}, pages = {59-64}, doi = {http://doi.ieeecomputersociety.org/10.1109/CIT.2007.163}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Computer and Information Technology, International Conference on TI - Predicting Coronary Artery Disease from Heart Rate Variability Using Classification and Statistical Analysis SN - 0-7695-2983-6 SP59 EP64 A1 - Heon Gyu Lee, A1 - Ki Yong Noh, A1 - Hong Kyu Park, A1 - Keun Ho Ryu, PY - 2007 VL - 0 JA - Computer and Information Technology, International Conference on ER -

Heon Gyu Lee, Chungbuk National University, Republic of Korea

Ki Yong Noh, Korea Research Institutes of Standards and Science, Republic Korea

Hong Kyu Park, Chungbuk National University, Republic of Korea

Keun Ho Ryu, Chungbuk National University, Republic of Korea

HRV (Heart Rate Variability) is one of the most promising quantitative indications of autonomic activity. In present study, our aim is to develop the multi-pararmetric feature including linear and nonlinear features of HRV. We also propose a suitable prediction model to enhance the reliability of medical examination for cardiovascular disease. This study analyzes the HRV for three recumbent positions. Interaction effect between recumbent positions and groups (Normal, Patient) was observed based on the HRV indices. We have carried out various experiments on linear and nonlinear features of HRV to evaluate classifiers. In our experiments, SVM and Bayesian classifiers outperformed the other classifiers.

Citation:

Heon Gyu Lee, Ki Yong Noh, Hong Kyu Park, Keun Ho Ryu, "Predicting Coronary Artery Disease from Heart Rate Variability Using Classification and Statistical Analysis," cit, pp.59-64, 7th IEEE International Conference on Computer and Information Technology (CIT 2007), 2007

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