Interpretation of heart rate variability via detrended fluctuation analysis and αβ filter

Abstract

Detrended fluctuation analysis (DFA), suitable for the analysis of nonstationary time series, has confirmed the existence of persistent long-range correlations in healthy heart rate variability data. In this paper, we present the incorporation of the alphabeta filter to DFA to determine patterns in the power-law behavior that can be found in these correlations. Well-known simulated scenarios and real data involving normal and pathological circumstances were used to evaluate this process. The results presented here suggest the existence of evolving patterns, not always following a uniform power-law behavior, that cannot be described by scaling exponents estimated using a linear procedure over two predefined ranges. Instead, the power law is observed to have a continuous variation with segment length. We also show that the study of these patterns, avoiding initial assumptions about the nature of the data, may confer advantages to DFA by revealing more clearly abnormal physiological conditions detected in congestive heart failure patients related to the existence of dominant characteristic scales.

Multifractal‐Multiscale Analysis of Cardiovascular Signals: A DFA‐Based Characterization of Blood Pressure and Heart‐Rate Complexity by Gender

“…In this way, however, local deviations from the linear trend occurring at specific scales n cannot be detected. The assessment of local deviations may reflect changes in the sympathetic and vagal cardiac control not otherwise visible [7,16], revealing subtle alterations in the overall autonomic regulation of the cardiovascular system [21] and characterizing pathological conditions [6,22]. To evaluate a local slope, that is, as function of n, methods with higher scale resolution are required.…”

Effect of nonlinear filters on detrended fluctuation analysis

“…Recent comparative studies have demonstrated that the DFA method outperforms conventional techniques in accurately quantifying correlation properties over a wide range of scales [6,7,8,9,10]. The DFA method has been widely applied to DNA [4,6,7,11,12,13], cardiac dynamics [14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30], human electroencephalographic (EEG) fluctuations [31], human motor activity [32] and gait [33,34,35,36,37], meteorology [38,39], climate temperature fluctuations [40,41,42,43,44,45], river flow and discharge [46,47], electric signals [48,49,50], stellar x-ray binary systems...…”

Quantifying signals with power-law correlations: A comparative study of detrended fluctuation analysis and detrended moving average techniques

“…There is growing evidence that output signals of many physical [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15], biological [16,17,18,19], physiological [20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35] and economic systems [36,37,38,39,40,41,42,43], where multiple component feedback interactions play a central role, exhibit complex self-similar fluctuations over a broad range of space and/or time scales. These fluctuating signals can be characterized by long-range power-law correlations.…”