Fractal characterization of complexity in temporal physiological signals

By András Eke, Peter Herman, László Kocsis, L. Kozák. Physiological Measurement, 2002

629 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Molecular Biology, Signal Processing

View Paper: https://www.semanticscholar.org/paper/5d1e8d860a9ba896302d405e7758a63d8c3262dd

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Espen Alexander Fürst Ihlen

In Molecular Biology

1. Huibert D. Mansvelder
2. Espen Alexander Fürst Ihlen
3. Wolfram Schultz

In Signal Processing

1. Vadim V. Nikulin
2. Espen Alexander Fürst Ihlen

Citations in the corpus

• Introduction to Multifractal Detrended Fluctuation Analysis in Matlab (2012, methodology): “The rest of this section gives guidelines to the parameter settings MFDFA1 and MFDFA2 :”
• Detrended Fluctuation Analysis: A Scale-Free View on Neuronal Oscillations (2012, background): “Physiological time series may exhibit statistical self-affine properties (Eke et al., 2000, 2002).”
• Fractal analyses for 'short' time series: A re-assessment of classical methods (2006, background): “As claimed by Eke et al. (2002), researchers were not aware before a recent past of the necessity of this dichotomic model.”
• Aperiodic and Hurst EEG exponents across early human brain development: A systematic review (2024, unknown): “…(2) time domain effects occurring due to reduced sampling windows and recording lengths, (3) data self-similarity which must be verified in source data for DFA and (4) conversion assuming DFA scaling exponents ( ⍺ ) are Gaussian ( i.e. > 0.5 or 0.5) and not Brownian (~0.5)(Eke et al., 2002).”