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.
· 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.
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