Abstract

The analysis of 24 hour (24h) ambulatory blood pressure monitoring (ABPM) profiles and their variability has been of interest in literature for considerable time. The development of sophisticated algorithms, which are integrated into mobile sphygmomanometers, allows the performance of 24h ABPM including pulse wave analysis (PWA). The recording involves the measurement of standard ABPM parameters as well as the estimation of central aortic pressures and other systemic cardiovascular parameters at regular time intervals throughout the day. The resulting time series often show a diurnal profile. Therefore, the analysis of these profiles and their variability is of interest. In this context, the analysis of diurnal blood pressure (BP) profiles serves as a model. The methods are adapted to be applicable to the time series independent of the parameter. In this article a selection of mathematical models and indices to quantify this profile and the variability of the time series are presented. The considered fitting models are a square wave fit, a fourier fit and a double logistic fit. The modelling process as well as advantages and disadvantages of each method are given. The results show that the algorithms performing the fits are feasible for the 24h profiles and provide several indices quantifying certain characteristics of the profiles.