Non linear electrical charge balancing device: A dynamical analysis approach

2021 ◽  
Vol 36 ◽  
pp. 102425
Author(s):  
Jamila Lakbir ◽  
Omar Bouattane ◽  
Ahmed Rebbani
Keyword(s):  
Electrical Charge ◽  
Dynamical Analysis ◽  
Analysis Approach ◽  
Non Linear ◽  
Charge Balancing

scholarly journals Non-linear dynamical analysis of the Blazhko effect with the Kepler space telescope: the case of V783 Cyg

2014 ◽  
Vol 445 (3) ◽  
pp. 2810-2817 ◽  
Author(s):  
E. Plachy ◽  
J. M. Benkő ◽  
Z. Kolláth ◽  
L. Molnár ◽  
R. Szabó
Keyword(s):  
Dynamical Analysis ◽  
Space Telescope ◽  
Non Linear ◽  
Blazhko Effect ◽  
Kepler Space Telescope

scholarly journals Non-Linear Dynamical Analysis of EEG Time Series Distinguishes Patients with Parkinson’s Disease from Healthy Individuals

2013 ◽  
Vol 4 ◽  
Author(s):  
Claudia Lainscsek ◽  
Manuel E. Hernandez ◽  
Jonathan Weyhenmeyer ◽  
Terrence J. Sejnowski ◽  
Howard Poizner
Keyword(s):  
Parkinson’S Disease ◽  
Time Series ◽  
Parkinson's Disease ◽  
Dynamical Analysis ◽  
Healthy Individuals ◽  
Non Linear

Organization of the narrative components in autobiographical speech of anorexic adolescents: A statistical and non-linear dynamical analysis

2008 ◽  
Vol 26 (2) ◽  
pp. 295-308 ◽  
Author(s):  
Karyn Doba ◽  
Jean-Louis Nandrino ◽  
Annick Lesne ◽  
Christine Humez ◽  
Laurent Pezard
Keyword(s):  
Dynamical Analysis ◽  
Non Linear

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

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2507 ◽  
Author(s):  
Carmen Camara ◽  
Narayan P. Subramaniyam ◽  
Kevin Warwick ◽  
Lauri Parkkonen ◽  
Tipu Aziz ◽  
...  
Keyword(s):  
Deep Brain Stimulation ◽  
Brain Stimulation ◽  
Brain Activity ◽  
Dynamical Behaviour ◽  
Dynamical Analysis ◽  
Analysis Tool ◽  
Resting Tremor ◽  
Non Linear ◽  
Stimulation System ◽  
Deep Brain

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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