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The Fractal Geometry of the Brain pp 431–443Cite as

On Multiscaling of Parkinsonian Rest Tremor Signals and Their Classification

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Part of the book series: Springer Series in Computational Neuroscience ((NEUROSCI))

Abstract

Self-similar stochastic processes and broad probability distributions are ubiquitous in Nature and in many man-made systems. The brain is a particularly interesting example of (natural) complex system where those features play a pivotal role. In fact, the controversial yet experimentally validated “criticality hypothesis” explaining the functioning of the brain implies the presence of scaling laws for correlations. Recently, we have analyzed a collection of rest tremor velocity signals recorded from patients affected by Parkinson’s disease, with the aim of determining and hence exploiting the presence of scaling laws. Our results show that multiple scaling laws are required in order to describe the dynamics of such signals, stressing the complexity of the underlying generating mechanism. We successively extracted numeric features by using the multifractal detrended fluctuation analysis procedure. We found that such features can be effective for discriminating classes of signals recorded in different experimental conditions. Notably, we show that the use of medication (L-DOPA) can be recognized with high accuracy.

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Authors and Affiliations

  1. Department of Computer Science, Ryerson University, 350 Victoria Street, Toronto, ON, M5B 2K3, Canada

    Lorenzo Livi

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  1. Lorenzo Livi
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Correspondence to Lorenzo Livi .

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  1. Department of Surgery Division of Neurosurgery, University of Toronto St. Michael's Hospital, Toronto, Ontario, Canada

    Antonio Di Ieva

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Livi, L. (2016). On Multiscaling of Parkinsonian Rest Tremor Signals and Their Classification. In: Di Ieva, A. (eds) The Fractal Geometry of the Brain. Springer Series in Computational Neuroscience. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-3995-4_26

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