Data processing for Parkinson's disease: Tremor, speech and gait signal analysis

The Parkinson has been placed on the second position of the most frequent neurodegenerative illnesses list, after Alzheimer, consisting in slowly and progressive neurons damage. Parkinson's disease is associated with motor symptoms, including tremor, postural instability, rigidity, bradykinesia and dysphonia. The time series specific to tremor, speech and gait Parkinson's Disease signals were firstly analyzed, by tools derived from chaotic analysis, such as: correlation dimension, recurrence plot, recurrence quantification analysis or Lyapunov exponent, as well. From the tremor management point of view and based on the results, this paper emphasizes the importance of the non-linear dynamics specific parameters in Parkinson tremor analysis. Currently, there hasn't been underlined any “gold standard” method, by which quantitative and qualitative evaluation of symptoms' gravity for patients with Parkinson are observed. The goal of the research carried out in this way consists in finding a screening test, in order to identify early the Parkinson's disease. By analyzing the tremor, gait and speech signals for patients with Parkinson's disease, this paper brings an approach over the most important information that can be used within a knowledge based system, specific to Parkinson's disease diagnosis.