Posture as a chaotic system and an application to the Parkinson’s disease

Pascolo, P.; Marini, Alfio; Carniel, Roberto; Barazza, Fausto

doi:10.1016/j.chaos.2004.09.062

Cited by 29 publications

(24 citation statements)

References 6 publications

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“…In [18,19] it was demonstrated that the postural act is indeed chaotic although it was difficult to discriminate between control and Parkinsonian subjects. In fact the difficulty in estimating nonlinear parameters, i.e., LLE and dimension of correlation, did not help to clarify the chaoticity of the system.…”

Section: Introductionmentioning

confidence: 97%

Nonlinear analysis of posturographic data

Ladislao

Fioretti

2007

Med Bio Eng Comput

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The aim of this work is to determine whether postural sway can be well described by nonlinear deterministic modelling. Since the results of nonlinear analysis depend on experimental data processing, emphasis was given to the assessment of a proper methodology to process posturographic data. Centre of Pressure (CoP) anterior-posterior (AP) displacements (stabilogram) were obtained by static posturography tests performed on control subjects. A nonlinear determinism test was applied to investigate the nature of data. A nonlinear filtering method allowed us to estimate properly the parameters of the nonlinear model without altering signal dynamics. The largest Lyapunov exponent (LLE) was estimated to quantify the chaotic behaviour of postural sway. LLE values were found to be positive although close to zero. This suggests that postural sway derives from a process exhibiting weakly chaotic dynamics.

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Section: Introductionmentioning

confidence: 97%

Nonlinear analysis of posturographic data

Ladislao

Fioretti

2007

Med Bio Eng Comput

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“…Hence, in order to identify the stochastic dynamics of postural sway, COP analyses that account for the non-linear and stochastic temporal evolution of postural sway appear appropriate. While it has already been demonstrated that analyses borrowed from the field of dynamical systems can be meaningfully applied to measured COP trajectories (e.g., Newell et al 1993;Yamada 1995;Pascolo et al 2005), we expected that they would also be valuable in evaluating the effects of health status, rehabilitation, and task manipulations in clinical studies (cf. Raymakers et al 2005).…”

Section: Introductionmentioning

confidence: 99%

“…Sample entropy (Richman and Moorman 2000) quantifies the regularity (or predictability) of a time series, while scaling factors like the Hurst exponent [e.g., determined via detrended fluctuation analysis (DFA), Peng et al 1994Peng et al , 1995 quantify the extent to which a recorded COP time series exhibits long-range correlations. To date, these dynamical measures have rarely been used in clinical studies of upright standing (see, e.g., Newell et al 1993;Pascolo et al 2005, for exceptions), in spite of their capacity to provide new insights, as widely demonstrated in, for instance, physiology and the study of ''dynamical diseases'' (e.g., Goldberger et al 1996Goldberger et al , 1997Goldberger et al , 2002Glass 2001;Lipsitz 2002;Kyriazis 2003).…”

Section: Introductionmentioning

confidence: 99%

“…In this context, the nature of postural sway has been characterized both as deterministic chaotic (e.g., Newell et al 1993;Yamada 1995;Pascolo et al 2005) and stochastic (e.g., Collins and De Luca 1993;Newell et al 1997;Delignie`res et al 2003). Chaotic time series appear random and unpredictable but arise from deterministic non-linear processes, whereas stochastic time series are governed by chance alone (e.g., Brownian motion, see below) or by a combination of deterministic and random processes (e.g., biased random walk).…”

Section: Introductionmentioning

confidence: 99%

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Dynamical structure of center-of-pressure trajectories in patients recovering from stroke

et al. 2006

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In a recent study, De Haart et al. (Arch Phys Med Rehabil 85:886-895, 2004) investigated the recovery of balance in stroke patients using traditional analyses of center-of-pressure (COP) trajectories to assess the effects of health status, rehabilitation, and task conditions like standing with eyes open or closed and standing while performing a cognitive dual task. To unravel the underlying control processes, we reanalyzed these data in terms of stochastic dynamics using more advanced analyses. Dimensionality, local stability, regularity, and scaling behavior of COP trajectories were determined and compared with shuffled and phase-randomized surrogate data. The presence of long-range correlations discarded the possibility that the COP trajectories were purely random. Compared to the healthy controls, the COP trajectories of the stroke patients were characterized by increased dimensionality and instability, but greater regularity in the frontal plane. These findings were taken to imply that the stroke patients actively (i.e., cognitively) coped with the stroke-induced impairment of posture, as reflected in the increased regularity and decreased local stability, by recruiting additional control processes (i.e., more degrees of freedom) and/or by tightening the present control structure while releasing non-essential degrees of freedom from postural control. In the course of rehabilitation, dimensionality stayed fairly constant, whereas local stability increased and regularity decreased. The progressively less regular COP trajectories were interpreted to indicate a reduction of cognitive involvement in postural control as recovery from stroke progressed. Consistent with this interpretation, the dual task condition resulted in less regular COP trajectories of greater dimensionality, reflecting a task-related decrease of active, cognitive contributions to postural control. In comparison with conventional posturography, our results show a clear surplus value of dynamical measures in studying postural control.

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“…The complex and unpredictable behavior exhibited by sensorimotor control system, as illustrated in Fig.1, has been examined from different perspectives. Many literatures suggested that this complexity may be instances of deterministic physiological chaos and arises from a low dimensional chaotic system [Newell et al 1993], [Yamada, 1995], [Pascolo et al, 2005], [Roerdink et al 2006], [Donker et al 2007], [Ladislao & Fioretti, 2007] while some others believe that is the blend of deterministic and random processes [Collins & Luca, 1993], [Newell et al 1997], [Duarte & Zatsiorsky, 2000], [Amoud et al, 2007]. *** FIGURE 1 NEAR HERE *** 1 Center of pressure Standing posture is still poorly understood and weakness of postural control mechanism certainly plays a role in balance control during quiet standing.…”

Section: Introductionmentioning

confidence: 99%