This overview summarizes findings obtained from analyzing electroencephalographic
(EEG) recordings from epilepsy patients with methods from the theory
of nonlinear dynamical systems. The last two decades have shown that
nonlinear time series analysis techniques allow an improved characterization
of epileptic brain states and help to gain deeper insights into the
spatial and temporal dynamics of the epileptic process. Nonlinear
EEG analyses can help to improve the evaluation of patients prior
to neurosurgery, and with an unequivocal identification of precursors
of seizures, they can be of great value in the development of seizure
warning and prevention techniques.
%0 Journal Article
%1 Lehnertz2008
%A Lehnertz, Klaus
%D 2008
%J J Biol Phys.
%K , EEG Epilepsy Nonlinear Presurgical Seizure analysis evaluation prediction
%P 253–266
%T Epilepsy and Nonlinear Dynamics
%V 34
%X This overview summarizes findings obtained from analyzing electroencephalographic
(EEG) recordings from epilepsy patients with methods from the theory
of nonlinear dynamical systems. The last two decades have shown that
nonlinear time series analysis techniques allow an improved characterization
of epileptic brain states and help to gain deeper insights into the
spatial and temporal dynamics of the epileptic process. Nonlinear
EEG analyses can help to improve the evaluation of patients prior
to neurosurgery, and with an unequivocal identification of precursors
of seizures, they can be of great value in the development of seizure
warning and prevention techniques.
@article{Lehnertz2008,
abstract = {This overview summarizes findings obtained from analyzing electroencephalographic
(EEG) recordings from epilepsy patients with methods from the theory
of nonlinear dynamical systems. The last two decades have shown that
nonlinear time series analysis techniques allow an improved characterization
of epileptic brain states and help to gain deeper insights into the
spatial and temporal dynamics of the epileptic process. Nonlinear
EEG analyses can help to improve the evaluation of patients prior
to neurosurgery, and with an unequivocal identification of precursors
of seizures, they can be of great value in the development of seizure
warning and prevention techniques.},
added-at = {2012-01-27T14:10:42.000+0100},
author = {Lehnertz, Klaus},
biburl = {https://www.bibsonomy.org/bibtex/21fb6038c5b172059c4ef626e23e7d369/muhe},
file = {Epilepsy and Nonlinear Dynamics.pdf:2008\\Epilepsy and Nonlinear Dynamics.pdf:PDF},
interhash = {d6b0d1d4f943762acd61b662d93ab482},
intrahash = {1fb6038c5b172059c4ef626e23e7d369},
journal = {J Biol Phys.},
keywords = {, EEG Epilepsy Nonlinear Presurgical Seizure analysis evaluation prediction},
owner = {Mu},
pages = {253–266},
pdf = {English\没什么用的\Epilepsy and Nonlinear Dynamics.pdf},
timestamp = {2012-01-27T14:10:59.000+0100},
title = {Epilepsy and Nonlinear Dynamics},
volume = 34,
year = 2008
}