TY - GEN
T1 - Self-similarity in physiological time series
T2 - 8th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2011
AU - Castiglioni, Paolo
PY - 2012
Y1 - 2012
N2 - Most physiological time series have self-similar properties which reflect the functioning of physiological control mechanisms. Self-similarity is usually assessed by detrended fluctuation analysis (DFA) assuming that mono- or bi-fractal models generate the self-similar dynamics. Our group recently proposed a new DFA approach describing self-similarity as a continuous temporal spectrum of coefficients, thus not assuming that "lumped-parameter" fractal models generate the data. This paper reviews the rationale for calculating a spectrum of DFA coefficients and applies this method on datasets of signals whose self-similarity has been extensively studied in the past. The first dataset consists of six electroencephalographic (EEG) derivations collected in a healthy volunteer. The second dataset consists of cardiac intervals and diastolic blood pressures recorded in 60 volunteers at different levels of cardiac sympatho/vagal balance. Results reveal the limits of the traditional "lumped-parameter" approach, and provide information on the role of autonomic outflows in determining cardiovascular self-similarity.
AB - Most physiological time series have self-similar properties which reflect the functioning of physiological control mechanisms. Self-similarity is usually assessed by detrended fluctuation analysis (DFA) assuming that mono- or bi-fractal models generate the self-similar dynamics. Our group recently proposed a new DFA approach describing self-similarity as a continuous temporal spectrum of coefficients, thus not assuming that "lumped-parameter" fractal models generate the data. This paper reviews the rationale for calculating a spectrum of DFA coefficients and applies this method on datasets of signals whose self-similarity has been extensively studied in the past. The first dataset consists of six electroencephalographic (EEG) derivations collected in a healthy volunteer. The second dataset consists of cardiac intervals and diastolic blood pressures recorded in 60 volunteers at different levels of cardiac sympatho/vagal balance. Results reveal the limits of the traditional "lumped-parameter" approach, and provide information on the role of autonomic outflows in determining cardiovascular self-similarity.
KW - Detrended fluctuation analysis
KW - EEG
KW - Fractals
KW - Heart rate variability
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U2 - 10.1007/978-3-642-35686-5_14
DO - 10.1007/978-3-642-35686-5_14
M3 - Conference contribution
AN - SCOPUS:84871553910
SN - 9783642356858
VL - 7548 LNBI
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 164
EP - 175
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Y2 - 30 June 2011 through 2 July 2011
ER -