The DOI matches its Crossref record; 76 of the 76 references it cites were found in a registry or an index.
A forward-looking review of seizure prediction
Abstract
Purpose of review: Seizure prediction has made important advances over the last decade, with the recent demonstration that prospective seizure prediction is possible, though there remain significant obstacles to broader application. In this review, we will describe insights gained from long-term trials, with the aim of identifying research goals for the next decade. Recent findings: Unexpected results from these studies, including strong and highly individual relationships between spikes and seizures, diurnal patterns of seizure activity, and the coexistence of different seizure populations within individual patients exhibiting distinctive dynamics, have caused us to re-evaluate many prior assumptions in seizure prediction studies and suggest alternative strategies that could be employed in the search for algorithms providing greater clinical utility. Advances in analytical approaches, particularly deep-learning techniques, harbour great promise and in combination with less-invasive systems with sufficiently power-efficient computational capacity will bring broader clinical application within reach. Summary: We conclude the review with an exercise in wishful thinking, which asks what the ideal seizure prediction dataset would look like and how these data should be manipulated to maximize benefits for patients. The motivation for structuring the review in this way is to create a forward-looking, optimistic critique of the existing methodologies.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Ambulatory seizure monitoring using wearables is now within reach1–7”
From Data quality evaluation in wearable monitoring (2022) · cites this paper as [4]
Citations
Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Early-warning signals for critical transitions
Scheffer, M., Bascompte, J., Brock, W. A., Brovkin, V., Carpenter, S. R., Dakos, V., Held, H., van Nes, E. H., Rietkerk, M., & Sugihara, G. (2009) · Nature · doi:10.1038/nature08227 · Cited by 4,854
A million spiking-neuron integrated circuit with a scalable communication network and interface
Merolla, P. A., Arthur, J. V., Alvarez-Icaza, R., Cassidy, A. S., Sawada, J., Akopyan, F., Jackson, B. L., Imam, N., Chen, G., Nakamura, Y., et al. (2014) · Science · doi:10.1126/science.1254642 · Cited by 4,270
Fisher, R. S., Boas, W. V. E., Blume, W. T., Elger, C. E., Genton, P., Lee, P., & Engel, J. (2005) · Epilepsia · doi:10.1111/j.0013-9580.2005.66104.x · Cited by 3,678
BCI2000: A General-Purpose Brain-Computer Interface (BCI) System
Schalk, G., McFarland, D. J., Hinterberger, T., Birbaumer, N., & Wolpaw, J. R. (2004) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2004.827072 · Cited by 3,129
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
Similar network activity from disparate circuit parameters
Prinz, A. A., Bucher, D., & Marder, E. (2004) · Nature Neuroscience · doi:10.1038/nn1352 · Cited by 1,091
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
Self-citationCortical cellular phenomena in experimental epilepsy: Interictal manifestations
Matsumoto, H., & Marsan, C. A. (1964) · Experimental Neurology · doi:10.1016/0014-4886(64)90025-1 · Cited by 840
On the nature of seizure dynamics
Jirsa, V., Stacey, W. C., Quilichini, P., Ivanov, A., & Bernard, C. (2014) · Brain · doi:10.1093/brain/awu133 · Cited by 760
Flagged first, then by citations
Cited by
Sorted by citations
Seizure prediction – ready for a new era
Kuhlmann, L., Lehnertz, K., Richardson, M. P., Schelter, B., & Zaveri, H. P. (2018) · Nature Reviews Neurology · doi:10.1038/s41582-018-0055-2 · Cited by 450
Machine learning and wearable devices of the future
Beniczky, S., Karoly, P., Nurse, E., Ryvlin, P., & Cook, M. (2021) · Epilepsia · doi:10.1111/epi.16555 · Cited by 155
Self-citationData quality evaluation in wearable monitoring
Böttcher, S., Vieluf, S., Bruno, E., Joseph, B., Epitashvili, N., Biondi, A., Zabler, N., Glasstetter, M., Dümpelmann, M., Van Laerhoven, K., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-25949-x · Cited by 91
Metadata
- Authors
- Freestone, D. R., Karoly, P. J., & Cook, M. J.
- Affiliations
- The University of Melbourne (AU)
- Corresponding author
- Freestone, Dean R.
- Karoly, Philippa J.
- Venue
- Current Opinion in Neurology
- Published
- 2017-04
- Volume
- 30
- Issue
- 2
- Pages
- 167-173
- ISSN
- 1350-7540 (ISSN-L)
- 1473-6551
- OpenAlex
- W2578325050
- Cited by
- 169
- References
- 76
- Citations by year
- 2026: 6 · 2025: 8 · 2024: 24 · 2023: 21 · 2022: 25 · 2021: 25 · 2020: 18 · 2019: 20 · 2018: 16 · 2017: 6
- FWCI
- 2.49
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Current Opinion in Neurology (publishedVersion)
- PubMed (publishedVersion)
- 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 › Machine Learning in Healthcare
- Keywords
- seizure prediction, epilepsy, epileptic spikes, deep learning, prospective prediction, long-term experiment
- MeSH terms
- Algorithms, Animals, Electroencephalography, Humans, Seizures, Databases, Factual
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
