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

Carmen Camara; Narayan Subramaniyan; Kevin Warwick; Lauri Parkkonen; Tipu Aziz; Ernesto Pereda

doi:10.3390/s19112507

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

Carmen Camara, Narayan Subramaniyan, Kevin Warwick, Lauri Parkkonen, Tipu Aziz, Ernesto Pereda

Vice-Chancellor's Office (VCO)

Research output: Contribution to journal › Article › peer-review

9 Citations (Scopus)

68 Downloads (Pure)

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.

Original language	English
Article number	2507
Number of pages	16
Journal	Sensors
Volume	19
Issue number	11
Early online date	31 May 2019
DOIs	https://doi.org/10.3390/s19112507
Publication status	Published - 31 May 2019

Bibliographical note

© 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/).

Keywords

Deep Brain Stimulation (DBS)
Local Field Potentials (LFPs)
Nonlinear dynamics
Parkinson’s Disease (PD)
Recurrence Networks (RNs)
Support Vector Machine (SVM)

ASJC Scopus subject areas

Analytical Chemistry
Atomic and Molecular Physics, and Optics
Biochemistry
Instrumentation
Electrical and Electronic Engineering

Access to Document

10.3390/s19112507Licence: CC BY

PublishedFinal published version, 1.23 MBLicence: CC BY

Cite this

@article{d7aa016810c443f98009b0f5077525f2,

title = "Non-Linear Dynamical Analysis of Resting Tremor for Demand-Driven Deep Brain Stimulation",

abstract = "Parkinson{\textquoteright}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.",

keywords = "Deep Brain Stimulation (DBS), Local Field Potentials (LFPs), Nonlinear dynamics, Parkinson{\textquoteright}s Disease (PD), Recurrence Networks (RNs), Support Vector Machine (SVM)",

author = "Carmen Camara and Narayan Subramaniyan and Kevin Warwick and Lauri Parkkonen and Tipu Aziz and Ernesto Pereda",

note = "{\textcopyright} 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/).",

year = "2019",

month = may,

day = "31",

doi = "10.3390/s19112507",

language = "English",

volume = "19",

journal = "Sensors",

issn = "1424-3210",

publisher = "MDPI",

number = "11",

}

TY - JOUR

T1 - Non-Linear Dynamical Analysis of Resting Tremor for Demand-Driven Deep Brain Stimulation

AU - Camara, Carmen

AU - Subramaniyan, Narayan

AU - Warwick, Kevin

AU - Parkkonen, Lauri

AU - Aziz, Tipu

AU - Pereda, Ernesto

N1 - © 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/).

PY - 2019/5/31

Y1 - 2019/5/31

N2 - 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.

AB - 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.

KW - Deep Brain Stimulation (DBS)

KW - Local Field Potentials (LFPs)

KW - Nonlinear dynamics

KW - Parkinson’s Disease (PD)

KW - Recurrence Networks (RNs)

KW - Support Vector Machine (SVM)

UR - http://www.scopus.com/inward/record.url?scp=85067186586&partnerID=8YFLogxK

U2 - 10.3390/s19112507

DO - 10.3390/s19112507

M3 - Article

SN - 1424-3210

VL - 19

JO - Sensors

JF - Sensors

IS - 11

M1 - 2507

ER -

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

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this