Detrended fluctuation analysis of a systolic blood pressure control loop

Analysis of Electroencephalogram Signals in Different Sleep Stages using Detrended Fluctuation Analysis

“…It has subsequently been used in various fields especially in med ical and physiological time series such as cardiac dynamics [16], breathing [17], blood pressure [18], nerve spike intervals [19], hu man gait [20], g lucose levels [21], and EEG t ime series [22].…”

Fractal aspects in O2 enriched combustion

“…In general, the physical interpretation of the problem must consider the behavior of the long-range correlation exponent, i.e., α < 1/2-anti-correlated or anti-persistent signal; α = 1/2-no correlation, white noise, does not have memory; α > 1/2-has persistent long-range correlation; α = 1-noise. For a higher exponent of long-range correlation values (α > 1), the dynamics of the system is non-steady (transitory regime or transient conditions) [9] and closer to a random walk, in summary:…”

“…If the exponent is higher than 0.5, the series is called persistent, and the fluctuation behavior in the past tends to continue with time. Finally, if the exponent α > 1, this value suggests a random walk, and the diffusion phenomenon [9] is established.…”

“…DFA has been used to analyze time series in different areas of scientific knowledge. For example, it shows the change in behavior in the liquid-vapor phase transition [6], the behavior of astrophysical systems [7,8], the dynamics of arterial pressure variations [9], the behavior of a waterway transportation system [10], economic series [11], meteorological factors [12,13], proteins [14], heartbeat fluctuations [15], and sunspots [16], vegetation [17], geomagnetic signals [18], fires [19], among other applications. The main objective of this article is to investigate the self-affinity of the oxygen content in the time series of the formation of soot in confined flames, to evaluate the temporal correlation of the measurements obtained from a combustion process, and to assess the efficiency of this process.…”

“…The diffusion phenomenon in time series occurs when the system shows periods tending to seasonality [9,10] or moments of sudden growth in the mean values of the observed physical magnitudes [6][7][8][9][10]16], such as the passenger demand increase on navigation systems during weekends and vacations [6] and the outbursts observed in x-ray binary systems [7]. As long as a diffuse process occurs, a set of observable elements and magnitudes, such as energy, linear momentum, pressure, number of molecules, or atoms, may change constantly (random character) [9].…”