Naslov
Data series embedding and scale invariant statistics

Autori
Michieli, Ivan ; Medved Rogina, Branka ; Ristov, Strahil

Izvornik
Human movement science (0167-9457) 29 (2010), 3; 449-463

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
scale invariance ; embedding ; stride interval ; power-law correlation ; principal components ; long range dependence

Sažetak
Data sequences acquired from bio-systems such as human gait data, heart rate interbeat data or DNA sequences exibit complex dynamics that is frequently described by long-memory or power– law decay of autocorrelation function. One way of characterizing that dynamics is trough scale invariant statistics or "fractal like" behavior. For quantifying scale invariant parameters of physiologic signals several methods have been proposed. Among them the most common are detrended fluctuation analysis, sample mean variance analyses, power spectral density analysis, R/S analysis and recently in the realm of the multifractal approach, wavelet analysis. In this paper it is demonstrated that embedding the time series data in the high-dimensional pseudo-phase space reveals scale invariant statistics in the simple fashion. The procedure is applied on different stride interval data sets from human gait measurements time series (Physio-Bank data library). Results show that introduced mapping adequately separates long-memory from random behavior. Smaller gait data sets were analyzed and scale-free trends for limited scale intervals were successfully detected. The method was verified on artificially produced time series with known scaling behavior and with the various content of noise. The possibility of the method to falsely detect long range dependence in the artificially generated short range dependence series was investigated.

Izvorni jezik
Engleski

Znanstvena područja
Biologija, Elektrotehnika, Strojarstvo

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