TY - GEN
T1 - Comparing Multiscale Estimators of the Degree of Multifractality by Detrended Fluctuation Analysis
AU - Castiglioni, Paolo
AU - Faini, Andrea
PY - 2020/7
Y1 - 2020/7
N2 - Some physiological series, like the cardiovascular signals, show multifractal structures that depend on the temporal scale. Thus, their fractal nature is better assessed by a Detrended Fluctuation Analysis (DFA) approach that provides multifractal coefficients scale by scale. Our aim is to compare two estimators of the degree of scale-by-scale multifractality based on the width of the singularity spectrum or on the statistical dispersion of the DFA coefficients. We synthesized 1000 series of white noise (monofractal and monoscale), of autoregressive noise (monofractal and multiscale) and of Cauchy-distributed noise (multifractal and monoscale) comparing the two estimators at scales between 8 and 228 samples. We found that the two estimators provide similar scale-by-scale profiles of multifractality. However, the statistical-dispersion estimator better distinguishes multifractal from monofractal noises at all the scales, thus appearing more suitable than the singularity-spectrum width to describe the fractal structure of physiological time series.
AB - Some physiological series, like the cardiovascular signals, show multifractal structures that depend on the temporal scale. Thus, their fractal nature is better assessed by a Detrended Fluctuation Analysis (DFA) approach that provides multifractal coefficients scale by scale. Our aim is to compare two estimators of the degree of scale-by-scale multifractality based on the width of the singularity spectrum or on the statistical dispersion of the DFA coefficients. We synthesized 1000 series of white noise (monofractal and monoscale), of autoregressive noise (monofractal and multiscale) and of Cauchy-distributed noise (multifractal and monoscale) comparing the two estimators at scales between 8 and 228 samples. We found that the two estimators provide similar scale-by-scale profiles of multifractality. However, the statistical-dispersion estimator better distinguishes multifractal from monofractal noises at all the scales, thus appearing more suitable than the singularity-spectrum width to describe the fractal structure of physiological time series.
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U2 - 10.1109/ESGCO49734.2020.9158193
DO - 10.1109/ESGCO49734.2020.9158193
M3 - Conference contribution
AN - SCOPUS:85091097961
T3 - 2020 11th Conference of the European Study Group on Cardiovascular Oscillations: Computation and Modelling in Physiology: New Challenges and Opportunities, ESGCO 2020
BT - 2020 11th Conference of the European Study Group on Cardiovascular Oscillations
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 11th Conference of the European Study Group on Cardiovascular Oscillations, ESGCO 2020
Y2 - 15 July 2020
ER -