Seizure prediction — ready for a new era

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.

Key points

  • One clinical trial has shown that prospective seizure prediction in humans is possible.

  • Databases of EEG data provide a standard reference for comparison of seizure prediction algorithms and for hypothesis generation.

  • Competitions provide a platform for identification of the best seizure prediction algorithms.

  • The network theory of epilepsy, multimodal recording techniques, long-term monitoring and computational modelling are providing new approaches to seizure prediction.

  • The field is ready for a large-scale clinical trial of seizure prediction.

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Fig. 1: The evolution of seizure prediction.
Fig. 2: Seizure prediction systems.
Fig. 3: Evaluation of seizure prediction algorithms.

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Acknowledgements

The authors acknowledge colleagues in the international seizure prediction group for valuable discussions. L.K. acknowledges funding support from the National Health and Medical Research Council (APP1130468) and the James S. McDonnell Foundation (220020419) and acknowledges the contribution of Dean R. Freestone at the University of Melbourne, Australia, to the creation of Fig. 3.

Review criteria

We searched PubMed, Web of Science, Google Scholar and IEEExplore with the terms “seizure prediction”, “seizure anticipation”, “seizure forecasting” and “preictal” for human and animal studies published between 1 January 2006 and 30 June 2018. We did not restrict publications by language. We also manually searched the proceedings of seizure prediction workshops and the reference lists of papers identified and extracted relevant papers from our records.

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All authors contributed equally to the conception of the Review, reviewing of the literature, writing and editing. L.K. designed the figures and tables with feedback from K.L., M.R., B.S. and H.Z.

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Correspondence to Klaus Lehnertz.

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Related links

American Epilepsy Society Seizure Prediction Challenge: www.kaggle.com/c/seizure-prediction

EPILEPSIAE: www.epilepsiae.eu

Epilepsy Ecosystem: www.epilepsyecosystem.org

IEEG.org: www.ieeg.org

Melbourne University AES/MathWorks/NIH Seizure Prediction: https://www.kaggle.com/c/melbourne-university-seizure-prediction

RADAR-CNS: https://www.radar-cns.org

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Kuhlmann, L., Lehnertz, K., Richardson, M.P. et al. Seizure prediction — ready for a new era. Nat Rev Neurol 14, 618–630 (2018). https://doi.org/10.1038/s41582-018-0055-2

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