Statistical physics and physiology: monofractal and multifractal approaches.
Even under healthy, basal conditions, physiologic systems show erratic fluctuations resembling those found in dynamical systems driven away from a single equilibrium state. Do such "nonequilibrium" fluctuations simply reflect the fact that physiologic systems are being constantly perturbed by external and intrinsic noise? Or, do these fluctuations actually, contain useful, "hidden" information about the underlying nonequilibrium control mechanisms? We report some recent attempts to understand the dynamics of complex physiologic fluctuations by adapting and extending concepts and methods developed very recently in statistical physics. Specifically, we focus on interbeat interval variability as an important quantity to help elucidate possibly non-homeostatic physiologic variability because (i) the heart rate is under direct neuroautonomic control, (ii) interbeat interval variability is readily measured by noninvasive means, and (iii) analysis of these heart rate dynamics may provide important practical diagnostic and prognostic information not obtainable with current approaches. The analytic tools we discuss may be used on a wider range of physiologic signals. We first review recent progress using two analysis methods--detrended fluctuation analysis and wavelets--sufficient for quantifying monofractual structures. We then describe recent work that quantifies multifractal features of interbeat interval series, and the discovery that the multifractal structure of healthy subjects is different than that of diseased subjects.
Stanley
H E
HE
Department of Physics, Boston University, MA 02215, USA. hes@bu.edu
Amaral
L A
LA
Goldberger
A L
AL
Havlin
S
S
Ivanov PCh
Peng
C K
CK
D055592:Biophysical Phenomena; D001703:Biophysics; D016757:Death, Sudden, Cardiac; D017709:Fractals; D006331:Heart Diseases / Q000503:physiopathology; D006333:Heart Failure; D006339:Heart Rate / Q000502:physiology*; D006706:Homeostasis; D006801:Humans; D008955:Models, Cardiovascular; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D013269:Stochastic Processes