Abstract
The main purpose of our study is to propose a novel methodology to develop the multi-parametric feature including linear and nonlinear features of HRV (Heart Rate Variability) diagnosing cardiovascular disease. To develop the multi-parametric feature of HRV, we used the statistical and classification techniques. This study analyzes the linear and the non-linear properties of HRV for three recumbent positions, namely the supine, left lateral and right lateral position. Interaction effect between recumbent positions and groups (normal and patients) was observed based on the HRV indices and the extracted HRV indices used to classify the CAD (Coronary Artery Disease) group from the normal people. We have carried out various experiments on linear and non-linear features of HRV indices to evaluate several classifiers, e.g., Bayesian classifiers, CMAR, C4.5 and SVM. In our experiments, SVM outperformed the other classifiers.
This work was supported by the Korea Research Foundation Grant funded by the Korean Government (The Regional Research Universities Program/Chungbuk BIT Research-Oriented University Consortium).
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Lee, H.G., Noh, K.Y., Ryu, K.H. (2007). Mining Biosignal Data: Coronary Artery Disease Diagnosis Using Linear and Nonlinear Features of HRV. In: Washio, T., et al. Emerging Technologies in Knowledge Discovery and Data Mining. PAKDD 2007. Lecture Notes in Computer Science(), vol 4819. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77018-3_23
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