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      Non-Linear Dynamical Analysis of Resting Tremor for Demand-Driven Deep Brain Stimulation

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          Abstract

          Parkinson’s Disease (PD) is currently the second most common neurodegenerative disease. One of the most characteristic symptoms of PD is resting tremor. Local Field Potentials (LFPs) have been widely studied to investigate deviations from the typical patterns of healthy brain activity. However, the inherent dynamics of the Sub-Thalamic Nucleus (STN) LFPs and their spatiotemporal dynamics have not been well characterized. In this work, we study the non-linear dynamical behaviour of STN-LFPs of Parkinsonian patients using ε -recurrence networks. RNs are a non-linear analysis tool that encodes the geometric information of the underlying system, which can be characterised (for example, using graph theoretical measures) to extract information on the geometric properties of the attractor. Results show that the activity of the STN becomes more non-linear during the tremor episodes and that ε -recurrence network analysis is a suitable method to distinguish the transitions between movement conditions, anticipating the onset of the tremor, with the potential for application in a demand-driven deep brain stimulation system.

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          Independent coordinates for strange attractors from mutual information

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            Detecting strange attractors in turbulence

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              Assortative Mixing in Networks

              M. Newman (2002)
              A network is said to show assortative mixing if the nodes in the network that have many connections tend to be connected to other nodes with many connections. Here we measure mixing patterns in a variety of networks and find that social networks are mostly assortatively mixed, but that technological and biological networks tend to be disassortative. We propose a model of an assortatively mixed network, which we study both analytically and numerically. Within this model we find that networks percolate more easily if they are assortative and that they are also more robust to vertex removal.
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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                31 May 2019
                June 2019
                : 19
                : 11
                : 2507
                Affiliations
                [1 ]Department of Computer Science, Carlos III University of Madrid, 28903 Madrid, Spain
                [2 ]Centre for Biomedical Technology, Technical University of Madrid, 28040 Madrid, Spain; eperdepa@ 123456ull.edu.es
                [3 ]Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, FI-00076 Helsinki, Finland; narayan.subramaniyam@ 123456aalto.fi (N.P.S.); lauri.parkkonen@ 123456aalto.fi (L.P.)
                [4 ]Vice Chancellors Office, Coventry University, Coventry CV1 5FB, UK; k.warwick@ 123456reading.ac.uk
                [5 ]Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford OX1 2JD, UK; tipu.aziz@ 123456nds.ox.ac.uk
                [6 ]Department of Industrial Engineering, Laboratory of Electrical Engineering and Bioengineering, Universidad de La Laguna, 38200 Tenerife, Spain
                Author notes
                [* ]Correspondence: macamara@ 123456pa.uc3m.es ; Tel.: +34-916246260
                [†]

                These authors contributed equally to this work.

                Author information
                https://orcid.org/0000-0002-2728-8724
                https://orcid.org/0000-0003-3722-9960
                https://orcid.org/0000-0002-0130-0801
                https://orcid.org/0000-0001-5965-164X
                Article
                sensors-19-02507
                10.3390/s19112507
                6603524
                31159311
                460cf91e-576d-468d-a84b-25c02e9fdd59
                © 2019 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 15 April 2019
                : 24 May 2019
                Categories
                Article

                Biomedical engineering
                nonlinear dynamics,recurrence networks (rns),support vector machine (svm),deep brain stimulation (dbs),parkinson’s disease (pd),local field potentials (lfps)

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