Literature DB >> 17008335

Seizure prediction: the long and winding road.

Florian Mormann1, Ralph G Andrzejak, Christian E Elger, Klaus Lehnertz.   

Abstract

The sudden and apparently unpredictable nature of seizures is one of the most disabling aspects of the disease epilepsy. A method capable of predicting the occurrence of seizures from the electroencephalogram (EEG) of epilepsy patients would open new therapeutic possibilities. Since the 1970s investigations on the predictability of seizures have advanced from preliminary descriptions of seizure precursors to controlled studies applying prediction algorithms to continuous multi-day EEG recordings. While most of the studies published in the 1990s and around the turn of the millennium yielded rather promising results, more recent evaluations could not reproduce these optimistic findings, thus raising a debate about the validity and reliability of previous investigations. In this review, we will critically discuss the literature on seizure prediction and address some of the problems and pitfalls involved in the designing and testing of seizure-prediction algorithms. We will give an account of the current state of this research field, point towards possible future developments and propose methodological guidelines for future studies on seizure prediction.

Entities:  

Mesh:

Year:  2006        PMID: 17008335     DOI: 10.1093/brain/awl241

Source DB:  PubMed          Journal:  Brain        ISSN: 0006-8950            Impact factor:   13.501


  172 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

2.  Seizure prediction: methods.

Authors:  Paul R Carney; Stephen Myers; James D Geyer
Journal:  Epilepsy Behav       Date:  2011-12       Impact factor: 2.937

Review 3.  Seizure prediction and its applications.

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

4.  Discriminating preictal and interictal states in patients with temporal lobe epilepsy using wavelet analysis of intracerebral EEG.

Authors:  Kais Gadhoumi; Jean-Marc Lina; Jean Gotman
Journal:  Clin Neurophysiol       Date:  2012-04-03       Impact factor: 3.708

5.  A phase-synchronization and random-matrix based approach to multichannel time-series analysis with application to epilepsy.

Authors:  Ivan Osorio; Ying-Cheng Lai
Journal:  Chaos       Date:  2011-09       Impact factor: 3.642

6.  Marching Towards a Seizure: Spatio-Temporal Evolution of Preictal Activity.

Authors:  Archana Proddutur; Viji Santhakumar
Journal:  Epilepsy Curr       Date:  2015 Sep-Oct       Impact factor: 7.500

7.  State-dependent precursors of seizures in correlation-based functional networks of electrocorticograms of patients with temporal lobe epilepsy.

Authors:  Hirokazu Takahashi; Shuhei Takahashi; Ryohei Kanzaki; Kensuke Kawai
Journal:  Neurol Sci       Date:  2012-01-21       Impact factor: 3.307

8.  Early seizure detection in rats based on vagus nerve activity.

Authors:  Kristian R Harreby; Cristian Sevcencu; Johannes J Struijk
Journal:  Med Biol Eng Comput       Date:  2010-10-02       Impact factor: 2.602

9.  Evaluation of Brain Network Properties in Patients with MRI-Negative Temporal Lobe Epilepsy: An MEG Study.

Authors:  Yuejun Li; Haitao Zhu; Qiqi Chen; Lu Yang; Xincai Bao; Fangqing Chen; Haiyan Ma; Honghao Xu; Lei Luo; Rui Zhang
Journal:  Brain Topogr       Date:  2021-06-26       Impact factor: 3.020

10.  Estimating short-run and long-run interaction mechanisms in interictal state.

Authors:  Ata Ozkaya; Mehmet Korürek
Journal:  J Comput Neurosci       Date:  2009-11-10       Impact factor: 1.621

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