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

Alessio Martino
;
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.
physiological signals; multifractal analysis; multiplicative models; feature extraction
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: http://hdl.handle.net/11385/219998
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