[4D5] Non-linear time-series analysis of Parkinsonian tremor signals
A Zippo, G Iarriccio and F Pellicano
University of Modena and Reggio Emilia, Italy
Non-linear time-series analysis of Parkinsonian tremor signals involves exploring the complex dynamics and non-linear 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 behaviour and self-organisation 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 of detecting and recognising the dynamic properties and correlations of the onset of pathological tremor in patients affected by Parkinson's disease. Experimental data measured by patients will be analysed using multi-scale recursive analysis methodologies through the TISEAN software package. 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.
The vibrational phenomena studied in this work regards the arm and forearm vibration with the purpose of detecting and recognising the dynamic properties and correlations of the onset of pathological tremor in patients affected by Parkinson's disease. Experimental data measured by patients will be analysed using multi-scale recursive analysis methodologies through the TISEAN software package. 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.
