Literature DB >> 12849542

Prediction of epileptic seizures.

Brian Litt1, Javier Echauz.   

Abstract

For almost 40 years, neuroscientists thought that epileptic seizures began abruptly, just a few seconds before clinical attacks. There is now mounting evidence that seizures develop minutes to hours before clinical onset. This change in thinking is based on quantitative studies of long digital intracranial electroencephalographic (EEG) recordings from patients being evaluated for epilepsy surgery. Evidence that seizures can be predicted is spread over diverse sources in medical, engineering, and patent publications. Techniques used to forecast seizures include frequency-based methods, statistical analysis of EEG signals, non-linear dynamics (chaos), and intelligent engineered systems. Advances in seizure prediction promise to give rise to implantable devices able to warn of impending seizures and to trigger therapy to prevent clinical epileptic attacks. Treatments such as electrical stimulation or focal drug infusion could be given on demand and might eliminate side-effects in some patients taking antiepileptic drugs long term. Whether closed-loop seizure-prediction and treatment devices will have the profound clinical effect of their cardiological predecessors will depend on our ability to perfect these techniques. Their clinical efficacy must be validated in large-scale, prospective, controlled trials.

Entities:  

Mesh:

Year:  2002        PMID: 12849542     DOI: 10.1016/s1474-4422(02)00003-0

Source DB:  PubMed          Journal:  Lancet Neurol        ISSN: 1474-4422            Impact factor:   44.182


  56 in total

1.  Epileptic seizures from abnormal networks: why some seizures defy predictability.

Authors:  William S Anderson; Feraz Azhar; Pawel Kudela; Gregory K Bergey; Piotr J Franaszczuk
Journal:  Epilepsy Res       Date:  2011-12-12       Impact factor: 3.045

Review 2.  Seizure prediction and its applications.

Authors:  Leon D Iasemidis
Journal:  Neurosurg Clin N Am       Date:  2011-10       Impact factor: 2.509

Review 3.  Novel surgical treatments for epilepsy.

Authors:  Guy M McKhann
Journal:  Curr Neurol Neurosci Rep       Date:  2004-07       Impact factor: 5.081

Review 4.  Optogenetic tools for modulating and probing the epileptic network.

Authors:  Mingrui Zhao; Rose Alleva; Hongtao Ma; Andy G S Daniel; Theodore H Schwartz
Journal:  Epilepsy Res       Date:  2015-06-21       Impact factor: 3.045

5.  Controlling bursting in cortical cultures with closed-loop multi-electrode stimulation.

Authors:  Daniel A Wagenaar; Radhika Madhavan; Jerome Pine; Steve M Potter
Journal:  J Neurosci       Date:  2005-01-19       Impact factor: 6.167

6.  Studies of stimulus parameters for seizure disruption using neural network simulations.

Authors:  William S Anderson; Pawel Kudela; Jounhong Cho; Gregory K Bergey; Piotr J Franaszczuk
Journal:  Biol Cybern       Date:  2007-07-07       Impact factor: 2.086

7.  Epileptic spike recognition in electroencephalogram using deterministic finite automata.

Authors:  Anup Kumar Keshri; Rakesh Kumar Sinha; Rajesh Hatwal; Barda Nand Das
Journal:  J Med Syst       Date:  2009-06       Impact factor: 4.460

Review 8.  Toward new paradigms of seizure detection.

Authors:  Devin K Binder; Sheryl R Haut
Journal:  Epilepsy Behav       Date:  2012-12-12       Impact factor: 2.937

9.  Phase-dependent stimulation effects on bursting activity in a neural network cortical simulation.

Authors:  William S Anderson; Pawel Kudela; Seth Weinberg; Gregory K Bergey; Piotr J Franaszczuk
Journal:  Epilepsy Res       Date:  2009-01-29       Impact factor: 3.045

10.  The statistics of a practical seizure warning system.

Authors:  David E Snyder; Javier Echauz; David B Grimes; Brian Litt
Journal:  J Neural Eng       Date:  2008-09-30       Impact factor: 5.379

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