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Fractal measures of video-recorded trajectories can classify motor subtypes in Parkinson’s Disease

Author

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  • Figueiredo, Thiago C.
  • Vivas, Jamile
  • Peña, Norberto
  • Miranda, José G.V.

Abstract

Parkinson’s Disease is one of the most prevalent neurodegenerative diseases in the world and affects millions of individuals worldwide. The clinical criteria for classification of motor subtypes in Parkinson’s Disease are subjective and may be misleading when symptoms are not clearly identifiable. A video recording protocol was used to measure hand tremor of 14 individuals with Parkinson’s Disease and 7 healthy subjects. A method for motor subtype classification was proposed based on the spectral distribution of the movement and compared with the existing clinical criteria. Box-counting dimension and Hurst Exponent calculated from the trajectories were used as the relevant measures for the statistical tests. The classification based on the power-spectrum is shown to be well suited to separate patients with and without tremor from healthy subjects and could provide clinicians with a tool to aid in the diagnosis of patients in an early stage of the disease.

Suggested Citation

  • Figueiredo, Thiago C. & Vivas, Jamile & Peña, Norberto & Miranda, José G.V., 2016. "Fractal measures of video-recorded trajectories can classify motor subtypes in Parkinson’s Disease," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 12-20.
    • Handle: RePEc:eee:phsmap:v:462:y:2016:i:c:p:12-20
    DOI: 10.1016/j.physa.2016.05.050
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    References listed on IDEAS

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    1. Miranda, José G.V & Andrade, Roberto F.S, 2001. "R/S analysis of pluviometric records: comparison with numerical experiments," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 295(1), pages 38-41.
    2. Costa, Rogério L. & Vasconcelos, G.L., 2003. "Long-range correlations and nonstationarity in the Brazilian stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 329(1), pages 231-248.
    3. R. L. Costa & G. L. Vasconcelos, 2003. "Long-range correlations and nonstationarity in the Brazilian stock market," Papers cond-mat/0302342, arXiv.org.
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    Cited by:

    1. Lahmiri, Salim, 2018. "Generalized Hurst exponent estimates differentiate EEG signals of healthy and epileptic patients," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 378-385.
    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.

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