Literature DB >> 30131521

Seizure prediction - ready for a new era.

Levin Kuhlmann1,2,3, Klaus Lehnertz4,5, Mark P Richardson6, Björn Schelter7, Hitten P Zaveri8.   

Abstract

Epilepsy is a common disorder characterized by recurrent seizures. An overwhelming majority of people with epilepsy regard the unpredictability of seizures as a major issue. More than 30 years of international effort have been devoted to the prediction of seizures, aiming to remove the burden of unpredictability and to couple novel, time-specific treatment to seizure prediction technology. A highly influential review published in 2007 concluded that insufficient evidence indicated that seizures could be predicted. Since then, several advances have been made, including successful prospective seizure prediction using intracranial EEG in a small number of people in a trial of a real-time seizure prediction device. In this Review, we examine advances in the field, including EEG databases, seizure prediction competitions, the prospective trial mentioned and advances in our understanding of the mechanisms of seizures. We argue that these advances, together with statistical evaluations, set the stage for a resurgence in efforts towards the development of seizure prediction methodologies. We propose new avenues of investigation involving a synergy between mechanisms, models, data, devices and algorithms and refine the existing guidelines for the development of seizure prediction technology to instigate development of a solution that removes the burden of the unpredictability of seizures.

Entities:  

Mesh:

Year:  2018        PMID: 30131521     DOI: 10.1038/s41582-018-0055-2

Source DB:  PubMed          Journal:  Nat Rev Neurol        ISSN: 1759-4758            Impact factor:   42.937


  51 in total

1.  Elevation in plasma tRNA fragments precede seizures in human epilepsy.

Authors:  Marion C Hogg; Rana Raoof; Hany El Naggar; Naser Monsefi; Norman Delanty; Donncha F O'Brien; Sebastian Bauer; Felix Rosenow; David C Henshall; Jochen Hm Prehn
Journal:  J Clin Invest       Date:  2019-04-30       Impact factor: 14.808

2.  Forecasting seizure risk in adults with focal epilepsy: a development and validation study.

Authors:  Timothée Proix; Wilson Truccolo; Marc G Leguia; Thomas K Tcheng; David King-Stephens; Vikram R Rao; Maxime O Baud
Journal:  Lancet Neurol       Date:  2020-12-17       Impact factor: 44.182

3.  Novel Seizure Biomarkers in Continuous Electrocardiograms from Pediatric Epilepsy Patients.

Authors:  Fiona Cheung; Phillip L Pearl; Catherine Stamoulis
Journal:  Annu Int Conf IEEE Eng Med Biol Soc       Date:  2021-11

4.  Data-driven separation and estimation of atrial dynamics in very high-dimensional electrocardiograms from epilepsy patients.

Authors:  Catherine Stamoulis; Jack Connoly; Frank H Duffy
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2019-07

5.  Heterogeneity of Preictal Dynamics in Human Epileptic Seizures.

Authors:  Anup DAS; Sydney S Cash; Terrence J Sejnowski
Journal:  IEEE Access       Date:  2020-03-16       Impact factor: 3.367

Review 6.  [Mobile seizure monitoring in epilepsy patients].

Authors:  A Schulze-Bonhage; S Böttcher; M Glasstetter; N Epitashvili; E Bruno; M Richardson; K V Laerhoven; M Dümpelmann
Journal:  Nervenarzt       Date:  2019-12       Impact factor: 1.214

7.  Prediction of Seizure Recurrence. A Note of Caution.

Authors:  William J Bosl; Alan Leviton; Tobias Loddenkemper
Journal:  Front Neurol       Date:  2021-05-13       Impact factor: 4.003

8.  Directed Connectivity Analysis of the Neuro-Cardio- and Respiratory Systems Reveals Novel Biomarkers of Susceptibility to SUDEP.

Authors:  T Noah Hutson; Farnaz Rezaei; Nicole M Gautier; Jagadeeswaran Indumathy; Edward Glasscock; Leonidas Iasemidis
Journal:  IEEE Open J Eng Med Biol       Date:  2020-11-06

Review 9.  Cycles in epilepsy.

Authors:  Philippa J Karoly; Vikram R Rao; Maxime O Baud; Nicholas M Gregg; Gregory A Worrell; Christophe Bernard; Mark J Cook
Journal:  Nat Rev Neurol       Date:  2021-03-15       Impact factor: 42.937

10.  Artificial neural network trained on smartphone behavior can trace epileptiform activity in epilepsy.

Authors:  Robert B Duckrow; Enea Ceolini; Hitten P Zaveri; Cornell Brooks; Arko Ghosh
Journal:  iScience       Date:  2021-05-13
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