Cited by 450Epilepsy research and treatmentEEG and Brain-Computer InterfacesSeismology and Earthquake Studies

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Seizure prediction – ready for a new era

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Kuhlmann, Levin, Lehnertz, Klaus, Richardson, Mark P., Schelter, Björn, Zaveri, Hitten P.·2018·Nature Reviews Neurology

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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.

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Complex brain networks: graph theoretical analysis of structural and functional systems
Bullmore, E., & Sporns, O. (2009) · Nature Reviews Neuroscience · doi:10.1038/nrn2575 · Cited by 11,820
Electrical stimulation of the anterior nucleus of thalamus for treatment of refractory epilepsy
Fisher, R. S., Salanova, V., Witt, T. C., Worth, R. M., Henry, T. R., Gross, R. E., Oommen, K. J., Osorio, I., Nazzaro, J. M., Labar, D., et al. (2010) · Epilepsia · doi:10.1111/j.1528-1167.2010.02536.x · Cited by 1,930
Modern network science of neurological disorders
Stam, C. J. (2014) · Nature reviews. Neuroscience · doi:10.1038/nrn3801 · Cited by 1,327
Drug-Resistant Epilepsy
Kwan, P., Schachter, S. C., & Brodie, M. J. (2011) · New England Journal of Medicine · doi:10.1056/nejmra1004418 · Cited by 1,113
Seizure prediction: the long and winding road
Mormann, F., Andrzejak, R. G., Elger, C. E., & Lehnertz, K. (2006) · Brain · doi:10.1093/brain/awl241 · Cited by 1,112
Self-citation
The descriptive epidemiology of epilepsy—A review
Banerjee, P. N., Filippi, D., & Hauser, W. A. (2009) · Epilepsy Research · doi:10.1016/j.eplepsyres.2009.03.003 · Cited by 927
Neural Networks in Human Epilepsy: Evidence of and Implications for Treatment
Spencer, S. S. (2002) · Epilepsia · doi:10.1046/j.1528-1157.2002.26901.x · Cited by 917
Extracellular hippocampal glutamate and spontaneous seizure in the conscious human brain
During, M. J., & Spencer, D. D. (1993) · The Lancet · doi:10.1016/0140-6736(93)90754-5 · Cited by 909
Prediction of seizure likelihood with a long-term, implanted seizure advisory system in patients with drug-resistant epilepsy: a first-in-man study
Cook, M. J., O'Brien, T. J., Berkovic, S. F., Murphy, M. A., Morokoff, A. P., Fabinyi, G., D'Souza, W. J., Yerra, R., Archer, J. S., Litewka, L., et al. (2013) · The Lancet Neurology · doi:10.1016/s1474-4422(13)70075-9 · Cited by 881
Dynamical principles in neuroscience
Rabinovich, M. I., Varona, P., Selverston, A. I., & Abarbanel, H. D. I. (2006) · Reviews of Modern Physics · doi:10.1103/revmodphys.78.1213 · Cited by 839

Show all 223 references

Research works printed but not linked: 5

Osorio, I., Zaveri, H. P., Frei, M. G. & Arthurs, S. Epilepsy: The Intersection of Neurosciences, Biology, Mathematics, Engineering, and Physics (CRC Press, 2011).
Tetzlaff, R., Elger, C. E. & Lehnertz, K. Recent Advances in Predicting and Preventing Epileptic Seizures (World Scientific, 2013).
Soltesz, I. & Staley, K. Computational Neuroscience in Epilepsy Vol. 6 (Academic Press, 2008).
Freestone, D. et al. in Recent advances in predicting and preventing epileptic seizures (eds Tetzlaff, R., Elger, C. E. & Lehnertz, K.) 63–82 (World Scientific, 2013).
Ansmann, G., Lehnertz, K. & Feudel, U. Self-induced pattern switching on complex networks of excitable units. Phys. Rev. X 6, 011030 (2016).

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Whitlock, J. H., Soelter, T. M., Williams, A. S., Hardigan, A. A., & Lasseigne, B. N. (2021) · Human Cell · doi:10.1007/s13577-021-00624-x · Cited by 16
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Parameter estimation in a whole-brain network model of epilepsy: Comparison of parallel global optimization solvers
Penas, D. R., Hashemi, M., Jirsa, V., & Banga, J. R. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011642 · Cited by 15
Comparison of Frontal-Temporal Channels in Epilepsy Seizure Prediction Based on EEMD-ReliefF and DNN
Romney, A., & Manian, V. B. (2020) · Computers · doi:10.3390/computers9040078 · Cited by 15
A Wearable ForeSeiz Headband for Forecasting Real-Time Epileptic Seizures
Prathaban, B. P., Баласубраманиан, Р., & Kalpana, R. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3120307 · Cited by 15
Semisupervised Seizure Prediction in Scalp EEG Using Consistency Regularization
Deng, L., Liu, A., Wu, L., Li, C., Qian, R., Ward, R. K., & Chen, X. (2022) · Journal of Healthcare Engineering · doi:10.1155/2022/1573076 · Cited by 15
Correlation lags give early warning signals of approaching bifurcations
Tirabassi, G., & Masoller, C. (2021) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2021.111720 · Cited by 14
Predictive Models for Health Deterioration: Understanding Disease Pathways for Personalized Medicine
Eskofier, B. M., & Klucken, J. (2023) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-110220-030247 · Cited by 15
On temporal scale-free non-periodic stimulation and its mechanisms as an infinite improbability drive of the brain’s functional connectogram
Cota, V. R., Cançado, S. A. V., & Moraes, M. F. D. (2023) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2023.1173597 · Cited by 14
SCALO: An Accelerator-Rich Distributed System for Scalable Brain-Computer Interfacing
Sriram, K., Pothukuchi, R. P., Gerasimiuk, M., Ugur, M., Ye, O., Manohar, R., Khandelwal, A., & Bhattacharjee, A. (2023) · Proceedings - International Symposium on Computer Architecture/Proceedings · doi:10.1145/3579371.3589107 · Cited by 14
Concept-drifts adaptation for machine learning EEG epilepsy seizure prediction
Pontes, E. D., Pinto, M. F., Lopes, F., & Teixeira, C. (2024) · Scientific Reports · doi:10.1038/s41598-024-57744-1 · Cited by 14
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Chen, H., Shiao, H.-T., & Cherkassky, V. (2021) · Brain Sciences · doi:10.3390/brainsci11121554 · Cited by 14

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
ORCID: Kuhlmann, Levin
Lehnertz, Klaus
Richardson, Mark P.
Zaveri, Hitten P.
Venue: Nature Reviews Neurology
Publisher: Springer Science and Business Media LLC
Published: 2018-08-21
Volume: 14
Issue: 10
Pages: 618-630
ISSN: 1759-4758 (ISSN-L)
1759-4766
DOI: 10.1038/s41582-018-0055-2
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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Seizure prediction – ready for a new era

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