Abstract
BackgroundIdentifying patients with early stages of Parkinson's disease (PD) in a home environment is an important area of neurological disorder research, because it is of therapeutic and economic benefits to optimal intervention and management of the disease. New methodThis paper presents a nonlinear dynamics approach, including recurrence plots, recurrence quantification analysis, fuzzy recurrence plots, and scalable recurrence networks for visualization, classification, and characterization of keystroke time series obtained from healthy control (HC) and early-stage PD subjects. ResultsSeveral differentiative properties for characterizing early PD and HC subjects can be obtained from fuzzy recurrence plots (FRPs) and scalable recurrence networks. Comparison with existing methods: cross-validation results obtained from FRP-based texture are highest among other methods. The method of fuzzy recurrence plots outperforms other existing methods for classification of HC and PD subjects. ConclusionsFeatures extracted from the nonlinear dynamics analysis of the keystroke time series are found to be very effective for machine learning and the properties of the scalable recurrence networks have the potential to be utilized as physiologic markers of the disease.
Published Version
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