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Application of DFA to heart rate variability Mariusz Sozański *, Jan Żebrowski *, Rafał Baranowski + * Faculty of Physics, Warsaw University of Technology + National Institute of Cardiology, Warsaw

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1. Intro – overview of DFA RR

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1. Intro – overview of DFA

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If we observe scaling: We may conclude that:We may conclude that: –For =0.5 fluctuations are not self-correlated; –For 0.5< 1 long-range correlations exist; –For 0< long-range anticorrelations exist; – =1 corresponds to flicker ( 1 / f ) noise; – =1.5 corresponds to Brownian noise; In other words:In other words: the smoother the time series, the bigger is obtained. 1. Intro – overview of the method

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2. Scale-independent DFA *Goldberger,Peng et al., PNAS 99, supp.1, 2466(2002)

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2. Scale-dependent version * K. Saermark et al., Fractals 8, 4, (2000).

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3. coronary disease scale-independentscale-dependent

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3. cardiomiopathy scale-independentscale-dependent

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3. cardiac infarction scale-independentscale-dependent

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3. cardiac infarction window length=16 RR window length=8 RR

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4. Comparison with SDNN

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THANK YOU FOR YOUR ATTENTION!

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