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Fluctuation and synchronization of gait intervals and gait force profiles distinguish stages of Parkinson's disease

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  • Bartsch, Ronny
  • Plotnik, Meir
  • Kantelhardt, Jan W.
  • Havlin, Shlomo
  • Giladi, Nir
  • Hausdorff, Jeffrey M.

Abstract

We study the effects of Parkinson's disease (PD) on the long-term fluctuation and phase synchronization properties of gait timing (series of interstride intervals) as well as gait force profiles (series characterizing the morphological changes between the steps). We find that the fluctuations in the gait timing are significantly larger for PD patients and early PD patients, who were not treated yet with medication, compared to age-matched healthy controls. Simultaneously, the long-term correlations and the phase synchronization of right and left leg are significantly reduced in both types of PD patients. Surprisingly, long-term correlations of the gait force profiles are relatively weak for treated PD patients and healthy controls, while they are significantly larger for early PD patients. The results support the idea that timing and morphology of recordings obtained from a complex system can contain complementary information.

Suggested Citation

  • Bartsch, Ronny & Plotnik, Meir & Kantelhardt, Jan W. & Havlin, Shlomo & Giladi, Nir & Hausdorff, Jeffrey M., 2007. "Fluctuation and synchronization of gait intervals and gait force profiles distinguish stages of Parkinson's disease," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 383(2), pages 455-465.
    • Handle: RePEc:eee:phsmap:v:383:y:2007:i:2:p:455-465
    DOI: 10.1016/j.physa.2007.04.120
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    Cited by:

    1. Kembro, Jackelyn M. & Flesia, Ana Georgina & Gleiser, Raquel M. & Perillo, María A. & Marin, Raul H., 2013. "Assessment of long-range correlation in animal behavior time series: The temporal pattern of locomotor activity of Japanese quail (Coturnix coturnix) and mosquito larva (Culex quinquefasciatus)," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(24), pages 6400-6413.
    2. Lahmiri, Salim, 2017. "Parkinson’s disease detection based on dysphonia measurements," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 98-105.
    3. Echeverria, Juan C. & Rodriguez, Eduardo & Velasco, Alejandra & Alvarez-Ramirez, Jose, 2010. "Limb dominance changes in walking evolution explored by asymmetric correlations in gait dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(8), pages 1625-1634.
    4. de Oliveira, M. Elias & Menegaldo, L.L. & Lucarelli, P. & Andrade, B.L.B. & Büchler, P., 2011. "On the use of information theory for detecting upper limb motor dysfunction: An application to Parkinson’s disease," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(23), pages 4451-4458.
    5. 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.
    6. Segun Goh & Kyungreem Han & Jehkwang Ryu & Seonjin Kim & MooYoung Choi, 2015. "Failure of Arm Movement Control in Stroke Patients, Characterized by Loss of Complexity," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-17, November.
    7. Mitsuru Yoneyama, 2014. "A study of gait acceleration and synchronisation in healthy adult subjects," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 17(14), pages 1542-1552, October.
    8. Boon Leong Lan & Jacob Hsiao Wen Yeo, 2019. "Comparison of computer-key-hold-time and alternating-finger-tapping tests for early-stage Parkinson’s disease," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-7, June.

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