RT Journal Article
T1 Non-linear dynamics analysis of resting tremor for demand-driven deep brain stimulation
A1 Cámara Núñez, María Carmen
A1 Subramaniyam, Narayan P.
A1 Warwick, Kevin
A1 Parkkonen, Lauri
A1 Aziz, Tipu
A1 Pereda, Ernesto
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 epsilon-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 epsilon-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.
PB MDPI
SN 1424-8220
YR 2019
FD 2019-05-31
LK https://hdl.handle.net/10016/28543
UL https://hdl.handle.net/10016/28543
LA eng
NO This article belongs to the Special Issue Signal Processing for Intelligent Sensor Systems
DS e-Archivo
RD 1 sept. 2024