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Scaling behavior in measured keystroke time series from patients with Parkinson’s disease

Author

Listed:
  • Ata Madanchi

    (McGill University
    Montreal Institute for Learning Algorithms (MILA))

  • Fatemeh Taghavi-Shahri

    (Ferdowsi University of Mashhad)

  • Seyed Mahmood Taghavi-Shahri

    (School of Public Health, Isfahan University of Medical Sciences)

  • Mohammed Reza Rahimi Tabar

    (Sharif University of Technology
    Institute of Physics and ForWind, Carl von Ossietzky University)

Abstract

Parkinson has remained as one of the most difficult diseases to diagnose, as there are no biomarkers to be measured, and this requires one patient to do neurological and physical examinations. As Parkinson is a progressive disease, accurate detection of its symptoms is a crucial factor for therapeutic reasons. In this study, we perform Multifractal Detrended Fluctuation Analysis (MFDFA) on measured keystroke time series for three different categories of subjects: healthy, early-PD, and De-Novo patients. We have observed different scaling behavior in terms of multifractality of the measured time series, which can be used as a practical tool for diagnosis purposes. Additionally, the source of the multifractality has been studied which shows that in healthy and early-PD subjects, multifractality due to the long-range correlations is stronger than the influence of its probability distribution function (PDF) fatness, while in De-Novo patients, both shape of PDF and long-range correlations are contributing to observed multifractality. Graphical abstract

Suggested Citation

  • Ata Madanchi & Fatemeh Taghavi-Shahri & Seyed Mahmood Taghavi-Shahri & Mohammed Reza Rahimi Tabar, 2020. "Scaling behavior in measured keystroke time series from patients with Parkinson’s disease," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 93(7), pages 1-8, July.
    • Handle: RePEc:spr:eurphb:v:93:y:2020:i:7:d:10.1140_epjb_e2020-100561-4
    DOI: 10.1140/epjb/e2020-100561-4
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    Cited by:

    1. Rahvar, Sepehr & Reihani, Erfan S. & Golestani, Amirhossein N. & Hamounian, Abolfazl & Aghaei, Fatemeh & Sahimi, Muhammad & Manshour, Pouya & Paluš, Milan & Feudel, Ulrike & Freund, Jan A. & Lehnertz,, 2024. "Characterizing time-resolved stochasticity in non-stationary time series," Chaos, Solitons & Fractals, Elsevier, vol. 185(C).

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    Keywords

    Statistical and Nonlinear Physics;

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