2005/07/11 by Luciano da Fontoura Costa, Ruth Caldeira de Melo, Costa, Luciano da Fontoura +6
Computer Science · Economics, Econometrics and Finance · Medicine · #Complex Systems and Time Series Analysis #Disordered Systems and Neural Networks (cond-mat.dis-nn) #FOS: Biological sciences #FOS: Physical sciences #Heart Rate Variability and Autonomic Control #Nonlinear Dynamics and Pattern Formation #Quantitative Methods (q-bio.QM) #Tissues and Organs (q-bio.TO)
paper · pdf · doi:10.48550/arxiv.q-bio/0507016
openalex publication_date 2005/07/11 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28
Detrend fluctuation analysis (DFA) has become a choice method for effective analysis of a broad variety of nonstationary signals. We show in the present article that, provided the nonstationary fluctuations occur at a large enough time scale, an alternative approach can be obtained by using the Fourier series of the signal. More specifically, signal reconstructions considering Fourier series with increasing number of higher spectral components are subtracted from the signal, while the dispersion of such a difference is calculated. The slope of the loglog representation of the dispersions in terms of the time scale (reciprocal of the frequency) is calculated and used for the characterization of the signal. The detrend action in this methodology is performed by the early incorporation of the low frequency spectral components in the signal representation. The application of the spectral DFA to the analysis of heart rate variability data has yielded results which are similar to those obtained by traditional DFA. Because of the direct relationship with the spectral content of the analyzed signal, the spectral DFA may be used as a complementary resource for characterization and analysis of some types of nonstationary signals.