Discrimination and Characterization of Parkinsonian Rest Tremors by Analyzing Long-Term Correlations and Multifractal Signatures.

Abstract

We analyze 48 signals of rest tremor velocity related to 12 distinct subjects affected by the Parkinson's disease. The subjects belong to two different groups, high- and low-amplitude rest tremors, with four and eight subjects, respectively. Each subject has been tested in four settings given by combining the use of deep brain stimulation and L-DOPA medication. We develop two main feature-based representations of the signals, which are obtained by considering 1) the long-term correlations and multifractal properties, and 2) the power spectra. Our results show that, when medication is used, a qualitative change is observed in the related signals from anticorrelated to long term positively correlated. In addition, the medication effect yields statistically significant differences in both high- and low-amplitude tremor groups. We successively consider three different classification problems, involving the recognition of 1) the use of medication, 2) the use of deep brain stimulation, and 3) the membership to the high- and low-amplitude tremor groups. Classification results show that the best results are obtained with a parsimonious, two-dimensional (2-D) representation encoding the long-term correlations and multifractal properties of the signals. Long-term correlations and multifractal signatures of time series provided an effective tool to analyze Parkinsonian rest tremor signals. The developed 2-D representation is a parsimonious and effective representation for rest tremor signals that could be adopted in clinical settings, even by considering resource-constrained scenarios.