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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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In order to diagnose cardiovascular disease, we proposed EP-based(emerging pattern-based) classification technique using multi-parametric diagnosis indexes. We analyzed linear/nonlinear features of HRV for three recumbent postures and extracted four diagnosis indexes from ST-segments to apply the multi-parametric diagnosis indexes. In this paper, classification model using essential emerging patterns for diagnosing disease was applied. This classification technique discovers disease patterns of patient group and these emerging patterns are frequent in patients with cardiovascular disease but are not frequent in the normal group. To evaluate proposed classification algorithm, 120 patients with AP (angina pectrois), 13 patients with ACS(acute coronary syndrome) and 128 normal people data were used. As a result of classification, when multi-parametric indexes were used, the percent accuracy in classifying three groups was turned out to be about 88.3%.