title: Fractal, entropic and chaotic approaches to complex physiological time series analysis: a critical appraisal.
abstract: A wide variety of methods based on fractal, entropic or chaotic approaches have been applied to the analysis of complex physiological time series. In this paper, we show that fractal and entropy measures are poor indicators of nonlinearity for gait data and heart rate variability data. In contrast, the noise titration method based on Volterra autoregressive modeling represents the most reliable currently available method for testing nonlinear determinism and chaotic dynamics in the presence of measurement noise and dynamic noise.
authors: Department of Mechanics and Engineering Science of Fudan University, Shanghai 200032, P. R. China.
authors: Cheng
authors: C
authors: Li
authors: Guang-Hong
authors: GH
authors: Ding
authors: Guo-Qiang
authors: GQ
authors: Wu
authors: Chi-Sang
authors: CS
authors: Poon
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D019277:Entropy; D017709:Fractals; D006801:Humans; D015233:Models, Statistical; D008991:Monitoring, Physiologic / Q000295:instrumentation; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D016011:Normal Distribution; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors