| Abstract: | ![]()
A method is proposed for classifying subjects according to their convex, flat, or concave change patterns of 24-hours blood pressure measurements. To obtain such a classification is useful for detecting subjects who show abnormal change patterns and giving them appropriate medical treatments. Therefore, an appropriate statistic is proposed for detecting a systematic change along the time axis, as well as a statistic with its inverse characteristic appropriate for evaluating the noise variation. The method is based on the ratio of those two types of statistics; it is verified to work well on real data, giving a classification of subjects into four types of subgroups: extreme dipper, dipper, nondipper, and inverted dipper. It also suggests that there might be an ultra-extreme dipper subgroup. |
