The DOI matches its Crossref record; 223 of the 223 references it cites were found in a registry or an index.
Seizure prediction – ready for a new era
Open accessAbstract
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.
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Metadata
- Authors
- Kuhlmann, L., Lehnertz, K., Richardson, M. P., Schelter, B., & Zaveri, H. P.
- Affiliations
- The University of Melbourne (AU)
- University of Bonn (DE)
- King's College London (GB)
- University of Aberdeen (GB)
- Yale University (US)
- Corresponding author
- Lehnertz, Klaus
- Venue
- Nature Reviews Neurology
- Published
- 2018-08-21
- Volume
- 14
- Issue
- 10
- Pages
- 618-630
- ISSN
- 1759-4758 (ISSN-L)
- 1759-4766
- OpenAlex
- W2888598199
- Cited by
- 450
- References
- 223
- Citations by year
- 2026: 39 · 2025: 53 · 2024: 70 · 2023: 64 · 2022: 61 · 2021: 73 · 2020: 53 · 2019: 32 · 2018: 4
- FWCI
- 6.16
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Reviews Neurology (publishedVersion)
- PubMed (publishedVersion)
- Aberdeen University Research Archive (Aberdeen University) (submittedVersion)
- Research Portal (King's College London) (submittedVersion)
- Swinburne figshare (Swinburne University of Technology) (submittedVersion)
- Funders
- James S. McDonnell Foundation (220020419)
- Medical Research Council
- National Health and Medical Research Council (1130468)
- Topics
- Health Sciences › Medicine › Clinical Neurology › Epilepsy research and treatment
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Computer Science › Artificial Intelligence › Seismology and Earthquake Studies
- Keywords
- seizure prediction, epilepsy, intracranial EEG, EEG database, seizure mechanisms, seizure burden
- MeSH terms
- Electroencephalography, Epilepsy, Humans, Seizures, Databases, Factual, Monitoring, Ambulatory
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