Application of DFA to Application of DFA to heart rate variability heart rate variability Mariusz Sozański Mariusz Sozański * , Jan Żebrowski , Jan Żebrowski * , Rafał Baranowski , Rafał Baranowski + * Faculty of Physics, Warsaw University of Technology + National Institute of Cardiology, Warsaw
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Application of DFA to heart rate variability Mariusz Sozański *, Jan Żebrowski *, Rafał Baranowski + * Faculty of Physics, Warsaw University of Technology.
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Application of DFA to heart Application of DFA to heart rate variabilityrate variability
Mariusz SozańskiMariusz Sozański**, Jan Żebrowski, Jan Żebrowski**, Rafał Baranowski, Rafał Baranowski++
*Faculty of Physics, Warsaw University of Technology
+National Institute of Cardiology, Warsaw
1. Intro – overview of DFA1. Intro – overview of DFA1. Intro – overview of DFA1. Intro – overview of DFAR
R
1. Intro – overview of DFA1. Intro – overview of DFA
If we observe scaling:If we observe scaling:
• We may conclude that:We may conclude that:– For For =0.5 fluctuations are not self-correlated;=0.5 fluctuations are not self-correlated;– For 0.5<For 0.5<1 long-range correlations exist;1 long-range correlations exist;– For 0<For 0< long-range anticorrelations exist; long-range anticorrelations exist;
– =1=1 corresponds to flicker (corresponds to flicker (11//ff) noise;) noise;
– =1.5 corresponds to Brownian noise;=1.5 corresponds to Brownian noise;
• In other words:In other words:the „smoother” the time series, the bigger the „smoother” the time series, the bigger is obtained. is obtained.
nnF ~)(
1. Intro – overview of the 1. Intro – overview of the methodmethod
22.. Scale-independent Scale-independent DFA DFA
*Goldberger,Peng et al., PNAS 99, supp.1, 2466(2002)
2. Scale-dependent version2. Scale-dependent version