COMPUTERISED BLOOD PRESSURE MONITORING IN OUTPATIENT SETTINGS (Computer systems and information technologies, 2025)
The paper presents the measurement of a normal 24-hour heart rate and blood pressure analysis of an anonymous patient. The object of study in this paper is computer processing of outpatient blood pressure monitoring.  The goal is to mathematically model the data as a sum of relatively smooth trends and detrended fluctuations. Tasks: decomposition of the primary series by two independent methods, stability and spectral analysis of the shifted fluctuations using the Wiener-Hinchin theorem, and proving the self-similarity of such fluctuations. The methods used are: singular spectrum analysis, exponential smoothing of the simulation and analysis of autocorrelation functions. The following results are obtained. The dataset is a sum of fairly smooth trends and detrended fluctuations; blood pressure trends have certain nighttime minima; detrended fluctuations are fractional Gaussian noise with a Hurst index of about (0.80 ±  0.016), the energy spectra of detrended fluctuations were found for the first time. Scientific novelty of the results: 1) the measured 24-hour heart rate and blood pressure analyses are decomposed into fairly smooth trends and detrended fluctuations; 2) trends allow for a more reliable assessment of 24-hour, nightly and daily average blood pressure values, which are the main indicators of a series of blood pressure measurements and monitoring; 3) detrended fluctuations contain other valuable diagnostic information, such as short-term blood pressure variability or persistence index. 4) fluctuation analysis provides information about the power spectra of the blood pressure monitoring series and their similarity to the spectra of fractional Gaussian noise; 4) knowledge of short-term changes in blood pressure is the basis for constructing informative repeatability graphs for blood pressure monitoring; 5) detrended fluctuations are identified as fractional Gaussian noise, which is a self-similar stochastic process.
Keywords: mathematical modelling; computing technologies; singular spectrum analysis; fluctuation analysis; stochastic process; computer blood pressure monitoring