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  Recurrence Quantification Analysis at work: Quasi-periodicity based interpretation of gait force profiles for patients with Parkinson disease

Afsar, O., Tirnakli, U., Marwan, N. (2018): Recurrence Quantification Analysis at work: Quasi-periodicity based interpretation of gait force profiles for patients with Parkinson disease. - Scientific Reports, 8, 9102.
https://doi.org/10.1038/s41598-018-27369-2

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Afsar, Ozgur1, Autor              
Tirnakli, U.2, Autor
Marwan, Norbert1, Autor              
Affiliations:
1Potsdam Institute for Climate Impact Research, ou_persistent13              
2External Organizations, ou_persistent22              

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 Zusammenfassung: In this letter, making use of real gait force profiles of healthy and patient groups with Parkinson disease which have different disease severity in terms of Hoehn-Yahr stage, we calculate various heuristic complexity measures of the recurrence quantification analysis (RQA). Using this technique, we are able to evince that entropy, determinism and average diagonal line length (divergence) measures decrease (increases) with increasing disease severity. We also explain these tendencies using a theoretical model (based on the sine-circle map), so that we clearly relate them to decreasing degree of irrationality of the system as a course of gait’s nature. This enables us to interpret the dynamics of normal/pathological gait and is expected to increase further applications of this technique on gait timings, gait force profiles and combinations of them with various physiological signals.

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 Datum: 2018
 Publikationsstatus: Final veröffentlicht
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 Ort, Verlag, Ausgabe: -
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 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.1038/s41598-018-27369-2
PIKDOMAIN: Transdisciplinary Concepts & Methods - Research Domain IV
eDoc: 8175
Organisational keyword: RD4 - Complexity Science
Research topic keyword: Health
Research topic keyword: Nonlinear Dynamics
Model / method: Nonlinear Data Analysis
Working Group: Development of advanced time series analysis techniques
Working Group: Network- and machine-learning-based prediction of extreme events
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Titel: Scientific Reports
Genre der Quelle: Zeitschrift, SCI, Scopus, p3, OA
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Seiten: - Band / Heft: 8 Artikelnummer: 9102 Start- / Endseite: - Identifikator: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journals2_395