1,203 research outputs found
Regular and stochastic behavior of Parkinsonian pathological tremor signals
Regular and stochastic behavior in the time series of Parkinsonian
pathological tremor velocity is studied on the basis of the statistical theory
of discrete non-Markov stochastic processes and flicker-noise spectroscopy. We
have developed a new method of analyzing and diagnosing Parkinson's disease
(PD) by taking into consideration discreteness, fluctuations, long- and
short-range correlations, regular and stochastic behavior, Markov and
non-Markov effects and dynamic alternation of relaxation modes in the initial
time signals. The spectrum of the statistical non-Markovity parameter reflects
Markovity and non-Markovity in the initial time series of tremor. The
relaxation and kinetic parameters used in the method allow us to estimate the
relaxation scales of diverse scenarios of the time signals produced by the
patient in various dynamic states. The local time behavior of the initial time
correlation function and the first point of the non-Markovity parameter give
detailed information about the variation of pathological tremor in the local
regions of the time series. The obtained results can be used to find the most
effective method of reducing or suppressing pathological tremor in each
individual case of a PD patient. Generally, the method allows one to assess the
efficacy of the medical treatment for a group of PD patients.Comment: 39 pages, 10 figures, 1 table Physica A, in pres
Computational Intelligence in Electromyography Analysis
Electromyography (EMG) is a technique for evaluating and recording the electrical activity produced by skeletal muscles. EMG may be used clinically for the diagnosis of neuromuscular problems and for assessing biomechanical and motor control deficits and other functional disorders. Furthermore, it can be used as a control signal for interfacing with orthotic and/or prosthetic devices or other rehabilitation assists. This book presents an updated overview of signal processing applications and recent developments in EMG from a number of diverse aspects and various applications in clinical and experimental research. It will provide readers with a detailed introduction to EMG signal processing techniques and applications, while presenting several new results and explanation of existing algorithms. This book is organized into 18 chapters, covering the current theoretical and practical approaches of EMG research
A chaotically driven model climate: extreme events and snapshot attractors
In a low-order chaotic global atmospheric circulation model the effects of deterministic chaotic driving are investigated. As a result of driving, peak-over-threshold type extreme events, e. g. cyclonic activity in the model, become more extreme, with increased frequency of recurrence. When the characteristic time of the driving is comparable to that of the undriven system, a resonance effect with amplified variance shows up. For very fast driving we find a reduced enhancement of variance, which is also the case with white noise driving. Snapshot attractors and their natural measures are determined as a function of time, and a resonance effects is also identified. The extreme value statistics of group maxima is found to follow a Weibull distribution
Stochastic Effects in Physical Systems
A tutorial review is given of some developments and applications of
stochastic processes from the point of view of the practicioner physicist. The
index is the following: 1.- Introduction 2.- Stochastic Processes 3.- Transient
Stochastic Dynamics 4.- Noise in Dynamical Systems 5.- Noise Effects in
Spatially Extended Systems 6.- Fluctuations, Phase Transitions and
Noise-Induced Transitions.Comment: 93 pages, 36 figures, LaTeX. To appear in Instabilities and
Nonequilibrium Structures VI, E. Tirapegui and W. Zeller,eds. Kluwer Academi
Predictability: a way to characterize Complexity
Different aspects of the predictability problem in dynamical systems are
reviewed. The deep relation among Lyapunov exponents, Kolmogorov-Sinai entropy,
Shannon entropy and algorithmic complexity is discussed. In particular, we
emphasize how a characterization of the unpredictability of a system gives a
measure of its complexity. Adopting this point of view, we review some
developments in the characterization of the predictability of systems showing
different kind of complexity: from low-dimensional systems to high-dimensional
ones with spatio-temporal chaos and to fully developed turbulence. A special
attention is devoted to finite-time and finite-resolution effects on
predictability, which can be accounted with suitable generalization of the
standard indicators. The problems involved in systems with intrinsic randomness
is discussed, with emphasis on the important problems of distinguishing chaos
from noise and of modeling the system. The characterization of irregular
behavior in systems with discrete phase space is also considered.Comment: 142 Latex pgs. 41 included eps figures, submitted to Physics Reports.
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