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On the use of information theory for detecting upper limb motor dysfunction: An application to Parkinson’s disease

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  • de Oliveira, M. Elias
  • Menegaldo, L.L.
  • Lucarelli, P.
  • Andrade, B.L.B.
  • Büchler, P.

Abstract

Parkinson’s disease (PD) is a chronic neurodegenerative disorder characterized by a selective loss of dopaminergic neurons in the substantia nigra, decreased striatal dopamine levels, and consequent extrapyramidal motor dysfunctions. Several potential early diagnostic markers of PD have been proposed. Since they have not been validated in presymptomatic PD, the diagnosis and monitoring of the disease is based on subjective clinical assessment of cognitive and motor symptoms. In this study, we investigated interjoint coordination synergies in the upper limb of healthy and parkinsonian subjects during the performance of unconstrained linear-periodic movements in a horizontal plane using the mutual information (MI). We found that the MI is a sensitive metric in detecting upper limb motor dysfunction, thus suggesting that this method might be applicable to quantitatively evaluating the effects of the antiparkinsonian medication and to monitor the disease progression.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:phsmap:v:390:y:2011:i:23:p:4451-4458
    DOI: 10.1016/j.physa.2011.06.048
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    References listed on IDEAS

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    1. Hausdorff, Jeffrey M & Balash, Y & Giladi, Nir, 2003. "Time series analysis of leg movements during freezing of gait in Parkinson's disease: akinesia, rhyme or reason?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 321(3), pages 565-570.
    2. Yulmetyev, R.M. & Demin, S.A. & Panischev, O. Yu. & Hänggi, Peter & Timashev, S.F. & Vstovsky, G.V., 2006. "Regular and stochastic behavior of Parkinsonian pathological tremor signals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 369(2), pages 655-678.
    3. 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.
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    Cited by:

    1. E. M. S. Ribeiro & G. A. Prataviera, 2014. "Information theoretic approach for accounting classification," Papers 1401.2954, arXiv.org, revised Sep 2014.
    2. Ribeiro, E.M.S. & Prataviera, G.A., 2014. "Information theoretic approach for accounting classification," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 651-660.
    3. Yang, Shuangming & Wei, Xile & Deng, Bin & Liu, Chen & Li, Huiyan & Wang, Jiang, 2018. "Efficient digital implementation of a conductance-based globus pallidus neuron and the dynamics analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 494(C), pages 484-502.
    4. 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.
    5. 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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