Conference Paper
Detrended fluctuation analysis of heart rate by means of symbolic series
Dept ESAII, Univ. Politec. de Catalunya, Barcelona, Spain
Conference: Computers in Cardiology, 2009 Source: OAI
 Citations (12)
 Cited In (0)

Article: Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series.
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ABSTRACT: The healthy heartbeat is traditionally thought to be regulated according to the classical principle of homeostasis whereby physiologic systems operate to reduce variability and achieve an equilibriumlike state [Physiol. Rev. 9, 399431 (1929)]. However, recent studies [Phys. Rev. Lett. 70, 13431346 (1993); Fractals in Biology and Medicine (BirkhauserVerlag, Basel, 1994), pp. 5565] reveal that under normal conditions, beattobeat fluctuations in heart rate display the kind of longrange correlations typically exhibited by dynamical systems far from equilibrium [Phys. Rev. Lett. 59, 381384 (1987)]. In contrast, heart rate time series from patients with severe congestive heart failure show a breakdown of this longrange correlation behavior. We describe a new methoddetrended fluctuation analysis (DFA)for quantifying this correlation property in nonstationary physiological time series. Application of this technique shows evidence for a crossover phenomenon associated with a change in short and longrange scaling exponents. This method may be of use in distinguishing healthy from pathologic data sets based on differences in these scaling properties.Chaos An Interdisciplinary Journal of Nonlinear Science 02/1995; 5(1):827. · 2.19 Impact Factor  [Show abstract] [Hide abstract]
ABSTRACT: We propose an approach for analyzing signals with longrange correlations by decomposing the signal increment series into magnitude and sign series and analyzing their scaling properties. We show that signals with identical longrange correlations can exhibit different time organization for the magnitude and sign. We find that the magnitude series relates to the nonlinear properties of the original time series, while the sign series relates to the linear properties. We apply our approach to the heartbeat interval series and find that the magnitude series is longrange correlated, while the sign series is anticorrelated and that both magnitude and sign series may have clinical applications.Physical Review Letters 03/2001; 86(9):19003. · 7.73 Impact Factor  [Show abstract] [Hide abstract]
ABSTRACT: This report has a much more modest goal: to describe newly developed techniques using "Surrogate Data" to detect nonlinearities and nonstationarities in data. Detecting nonlinearities  or failing to detect them  allows us to know when linear analysis techniques are and are not capturing all of the information in the time series. Detecting nonstationarities allows us to make informed decisions about issues such as whether collecting longer runs of data provides better estimates of physiological variables, or about which are the best analysis techniques that can allow us to track changes in the physiological system without unnecessarily increasing the variance of the estimates. 1.1 Nonlinearity There are many sources of nonlinearity in cardiovascular regulation. One of the earliest to be given a mathematical formulation is the interaction between sympathetic and parasympathetic innervation of the SA node, as described by (Rosenblueth and Simeone, 1934). Other commonplace physiological mechanisms also correspond directly to mathematical nonlinearities: adaptation of the baroreceptors to changes in blood pressure; saturation of receptors; changes in gain of feedback systems with changing baseline levels of blood pressure; reduction in cardiac output at high heart rates. Delays are ubiquitous in physiological systems. Coupled with high gains in feedback loops, delays cause instability. Such instabilities are always associated with nonlinearities; in a linear system instability leads to a physically impossible blowup to infinity. Guyton's textbook introduction (Guyton, 1991) to cardiovascular control is practically a catalogue of nonlinear mechanisms. It is now widely appreciated that nonlinear systems can show irregular oscillations without any random input. This is ...10/1999;
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