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

2. 1. Intro – overview of DFA
1. Intro – overview of DFA RR

3. 1. Intro – overview of DFA

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

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

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

7. 3. coronary disease
scale-independent
scale-dependent

8. 3. cardiomiopathy
scale-independent
scale-dependent

9. 3. cardiac infarction
scale-independent
scale-dependent

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

11. 4. Comparison with SDNN

12. THANK YOU FOR YOUR ATTENTION!