Non-linear time series analysis of Parkinsonian tremor signals

Abstract

Nonlinear time series analysis of Parkinsonian tremor signals involves exploring the complex dynamics and nonlinear interactions within the tremor signals to gain insights into the underlying physiological processes. By applying advanced analytical techniques, researchers aim to uncover hidden patterns, chaotic behavior, and self-organization within the signals, which can provide valuable information for the diagnosis and monitoring of Parkinson's disease. The vibrational phenomena studied in this work regards the arm and forearm vibration with the purpose to detect and recognize the dynamic properties and correlations of onset of pathological tremor in patients affected by Parkinson disease. Experimental data measured by patients will be analyzed using multiscale recursive analysis methodologies through the TISEAN package (1). Recurrence plot analysis is a method that visually represents the recurrent patterns and dependencies within a time series and helps to identify important features such as deterministic dynamics, periodicity, and phase relationships (2,3).