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Detrended Fluctuation Analysis and Adaptive Fractal Analysis of Stride Time Data in Parkinson's Disease: Stitching Together Short Gait Trials

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  • Marietta Kirchner
  • Patric Schubert
  • Magnus Liebherr
  • Christian T Haas

Abstract

Variability indicates motor control disturbances and is suitable to identify gait pathologies. It can be quantified by linear parameters (amplitude estimators) and more sophisticated nonlinear methods (structural information). Detrended Fluctuation Analysis (DFA) is one method to measure structural information, e.g., from stride time series. Recently, an improved method, Adaptive Fractal Analysis (AFA), has been proposed. This method has not been applied to gait data before. Fractal scaling methods (FS) require long stride-to-stride data to obtain valid results. However, in clinical studies, it is not usual to measure a large number of strides (e.g., strides). Amongst others, clinical gait analysis is limited due to short walkways, thus, FS seem to be inapplicable. The purpose of the present study was to evaluate FS under clinical conditions. Stride time data of five self-paced walking trials ( strides each) of subjects with PD and a healthy control group (CG) was measured. To generate longer time series, stride time sequences were stitched together. The coefficient of variation (CV), fractal scaling exponents (DFA) and (AFA) were calculated. Two surrogate tests were performed: A) the whole time series was randomly shuffled; B) the single trials were randomly shuffled separately and afterwards stitched together. CV did not discriminate between PD and CG. However, significant differences between PD and CG were found concerning and . Surrogate version B yielded a higher mean squared error and empirical quantiles than version A. Hence, we conclude that the stitching procedure creates an artificial structure resulting in an overestimation of true . The method of stitching together sections of gait seems to be appropriate in order to distinguish between PD and CG with FS. It provides an approach to integrate FS as standard in clinical gait analysis and to overcome limitations such as short walkways.

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  • Marietta Kirchner & Patric Schubert & Magnus Liebherr & Christian T Haas, 2014. "Detrended Fluctuation Analysis and Adaptive Fractal Analysis of Stride Time Data in Parkinson's Disease: Stitching Together Short Gait Trials," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-6, January.
    • Handle: RePEc:plo:pone00:0085787
    DOI: 10.1371/journal.pone.0085787
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    References listed on IDEAS

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    Cited by:

    1. Robert J Ellis & Yee Sien Ng & Shenggao Zhu & Dawn M Tan & Boyd Anderson & Gottfried Schlaug & Ye Wang, 2015. "A Validated Smartphone-Based Assessment of Gait and Gait Variability in Parkinson’s Disease," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-22, October.
    2. He, Hong-di & Wang, Jun-li & Wei, Hai-rui & Ye, Cheng & Ding, Yi, 2016. "Fractal behavior of traffic volume on urban expressway through adaptive fractal analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 443(C), pages 518-525.
    3. Neelakshi, J. & Rosa, Reinaldo R. & Savio, Siomel & Stephany, Stephan & de Meneses, Francisco C. & Kherani, Esfhan Alam & Muralikrishna, P., 2022. "Multifractal characteristics of the low latitude equatorial ionospheric E–F valley region irregularities," Chaos, Solitons & Fractals, Elsevier, vol. 156(C).
    4. Ian D Colley & Roger T Dean, 2019. "Origins of 1/f noise in human music performance from short-range autocorrelations related to rhythmic structures," PLOS ONE, Public Library of Science, vol. 14(5), pages 1-15, May.

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