HFD | R Documentation |
Higuchi's fractal dimension
HFD(P, k_max = 10)
P |
A time series (numeric) |
k_max |
Maximum window length in points |
The Higuchi’s fractal dimension is a measure of local complexity and it increases together with the “roughness” of the time series at a single cycle level (thus the term “local”). Higuchi’s fractal dimension values range from 1 to 2, with increasing values correlating to increasingly complex data and Higuchi’s fractal dimension = 1.5 indicating random Gaussian noise (Higuchi, 1988; Anmuth et al., 1994; Kesić & Spasić, 2016) For motor primitives, only the most linear part of the log-log plot should be used, as reported in Santuz, Akay (2020).
A list with elements:
loglog
containing the log-log plot from which the HFD is calculated
Higuchi
containing the Higuchi's fractal dimension of the time series.
Higuchi, T.
Approach to an irregular time series on the basis of the fractal theory.
Phys. D Nonlinear Phenom. 31, 277–283 (1988).
Anmuth C. J., Goldberg G. & Mayer N. H.
Fractal dimension of electromyographic signals recorded with surface electrodes during
isometric contractions is linearly correlated with muscle activation.
Muscle Nerve 17, 953–954 (1994).
Kesić S. & Spasić S. Z.
Application of Higuchi’s fractal dimension from basic to clinical neurophysiology: A review.
Comput Methods Programs Biomed 133, 55–70 (2016).
Santuz, A. & Akay, T. Fractal analysis of muscle activity patterns during locomotion: pitfalls and how to avoid them. J. Neurophysiol. 124, 1083–1091 (2020).
# Measurements of the annual flow of the river Nile at Aswan flow <- datasets::Nile # Calculate HFD fractal_dimension <- HFD(flow)$Higuchi message("Higuchi's fractal dimension: ", round(fractal_dimension, 3)) # Thirty-cycle locomotor primitive from Santuz & Akay (2020) data(primitive) fractal_dimension <- HFD(primitive$signal)$Higuchi message("Higuchi's fractal dimension: ", round(fractal_dimension, 3))
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