EEG analysis with nonlinear excitable media.
The detection of patterns embedded within a complex, nonstationary, and noisy background activity is a crucial and important task in EEG analysis. The authors present a biologically inspired, analog approach to EEG analysis that is conceptually different from a variety of statistical approaches curr...
| Publicado en: | Journal of Clinical Neurophysiology Vol. 22; no. 5; pp. 314 - 330 |
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| Autores principales: | , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Lippincott Williams & Wilkins
Oct2005
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=105950363&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105950363 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 07360258 8CL jtl: Journal of Clinical Neurophysiology issn: 07360258 maglogo: N pubinfo: dt: Oct2005 vid: 22 iid: 5 pid: 5086 pub: Lippincott Williams & Wilkins place: Baltimore, Maryland artinfo: ui: 105950363 105950363 2009622416 NLM16357636 105950363 ppf: 314 ppct: 16 formats: tig: atl: EEG analysis with nonlinear excitable media. aug: au: Chernihovskyi A Mormann F Müller M Elger CE Baier G Lehnertz K sug: subj: Chaos Theory Electroencephalography Models, Theoretical Seizures Diagnosis Animals Neural Networks (Computer) Neurons Physiology Predictive Value of Tests Seizures Physiopathology Animal Studies ab: The detection of patterns embedded within a complex, nonstationary, and noisy background activity is a crucial and important task in EEG analysis. The authors present a biologically inspired, analog approach to EEG analysis that is conceptually different from a variety of statistical approaches currently used. A nonlinear, excitable, spatially extended medium that is composed of diffusively coupled model neurons is considered. When EEG recordings are applied as local perturbations to such an excitable neural tissue, the induced transient changes in the dynamics of the perturbed system can be regarded as an instantaneous characterization of transient processes in the brain reflected by the EEG, e.g., in the form of a sequence of correlated dynamical events (patterns). Nonlinear excitable media can be implemented in form of an array of locally coupled integrated analog nonlinear electrical circuits called cellular neural networks, which represent a next evolutionary step in the parallel analog computer architecture. Using cellular neural networks, the authors show that the concept of signal-induced pattern generation allows an almost instantaneous and unsupervised detection of seizure onsets in EEG recordings. In addition, they show that a cellular neural network can be trained in a supervised way to approximate the degree of synchronization in EEG recordings. The resulting pattern-recognition device may be suitable for the prediction of epileptic seizures. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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