earticle

영어

Power spectral analysis of the heart rate and blood pressure variations has commonly used to provide indices of autonomic cardiovascular modulation, but the effect of changing posture from lying to standing on these variations and the interpretation of their power spectra is still largely in dispute. It was due to the reason that till now no study was made yet that clearly outlines the variations in terms of RR-interval and blood pressure series from lying to standing position. Thus the aim of this paper lies in the application of classifying the subjects based on their RR-intervals, systolic and diastolic blood pressure series, prior to spectral analysis, at two different physical activity related postures. In this paper K-Nearest Neighbor algorithm has been proposed as a classifier for classifying the subjects based on lying and standing postures. Here we also studied the classification accuracy achievable with a KNN classifier using three different methods (i) Euclidean (ii) City block and (iii) Correlation of calculating the nearest distance in order to propose the optimal one. Further an attempt has been made to evaluate each of these methods for five different values of K=1, 3, 5, 7 and 9 in order to propose the best fit value of K for classifying the subjects. After performing the comparative analysis between these three methods of distance metrics and for different values of K, it is found that K=1 is the best choice out of 3, 5, 7 and 9 and Correlation has been emerged as one of the optimal method for computing the nearest distance with highest classification accuracy of 98.60 % with K=1 for lying and 99.95 % with K=1 for standing postures.