In this paper, a multi-fractal analysis on a diastolic blood pressure signal is conducted. The signal is measured in a time span of circa one day through the multifractal detrended fluctuation analysis framework. The analysis is performed on asymptotic timescales where complex regulating mechanisms play a fundamental role in the blood pressure stability. Given a suitable frequency range and after removing non-stationarities, the blood pressure signal shows interesting scaling properties and a pronounced multifractality imputed to long-range correlations. Finally, a binomial multiplicative model is investigated showing how the analyzed signal can be described by a concise multifractal model with only two parameters.

Multifractal characterization and modeling of blood pressure signals / De Santis, Enrico.; Naraei, Parisa.; Martino, Alessio.; Sadeghian, Alireza.; Rizzi, Antonello.. - In: ALGORITHMS. - ISSN 1999-4893. - 15:8(2022), pp. 1-17. [10.3390/a15080259]

Multifractal characterization and modeling of blood pressure signals

De Santis Enrico.
;
Martino Alessio.;Rizzi Antonello.
2022

Abstract

In this paper, a multi-fractal analysis on a diastolic blood pressure signal is conducted. The signal is measured in a time span of circa one day through the multifractal detrended fluctuation analysis framework. The analysis is performed on asymptotic timescales where complex regulating mechanisms play a fundamental role in the blood pressure stability. Given a suitable frequency range and after removing non-stationarities, the blood pressure signal shows interesting scaling properties and a pronounced multifractality imputed to long-range correlations. Finally, a binomial multiplicative model is investigated showing how the analyzed signal can be described by a concise multifractal model with only two parameters.
2022
feature extraction; multifractal analysis; multiplicative models; physiological signals
01 Pubblicazione su rivista::01a Articolo in rivista
Multifractal characterization and modeling of blood pressure signals / De Santis, Enrico.; Naraei, Parisa.; Martino, Alessio.; Sadeghian, Alireza.; Rizzi, Antonello.. - In: ALGORITHMS. - ISSN 1999-4893. - 15:8(2022), pp. 1-17. [10.3390/a15080259]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1657762
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