The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Seizure prediction: the long and winding road
Abstract
The sudden and apparently unpredictable nature of seizures is one of the most disabling aspects of the disease epilepsy. A method capable of predicting the occurrence of seizures from the electroencephalogram (EEG) of epilepsy patients would open new therapeutic possibilities. Since the 1970s investigations on the predictability of seizures have advanced from preliminary descriptions of seizure precursors to controlled studies applying prediction algorithms to continuous multi-day EEG recordings. While most of the studies published in the 1990s and around the turn of the millennium yielded rather promising results, more recent evaluations could not reproduce these optimistic findings, thus raising a debate about the validity and reliability of previous investigations. In this review, we will critically discuss the literature on seizure prediction and address some of the problems and pitfalls involved in the designing and testing of seizure-prediction algorithms. We will give an account of the current state of this research field, point towards possible future developments and propose methodological guidelines for future studies on seizure prediction.
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Cites
Detecting strange attractors in turbulence
Takens, F. (1981) · Lecture Notes in Mathematics · doi:10.1007/bfb0091924 · Cited by 10,459
Cited by
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
Tsiouris, Κ. Μ., Pezoulas, V. C., Zervakis, M., Konitsiotis, S., Koutsouris, D. D., & Fotiadis, D. I. (2018) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2018.05.019 · Cited by 632
Multi-day rhythms modulate seizure risk in epilepsy
Baud, M. O., Kleen, J. K., Mirro, E. A., Andrechak, J. C., King-Stephens, D., Chang, E. F., & Rao, V. R. (2018) · Nature Communications · doi:10.1038/s41467-017-02577-y · Cited by 532
Automatic Seizure Detection Based on Time-Frequency Analysis and Artificial Neural Networks
Tzallas, A. T., Tsipouras, M. G., & Fotiadis, D. I. (2007) · Computational Intelligence and Neuroscience · doi:10.1155/2007/80510 · Cited by 473
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
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Metadata
- Authors
- Mormann, F., Andrzejak, R. G., Elger, C. E., & Lehnertz, K.
- Venue
- Brain
- Publisher
- Oxford University Press (OUP)
- Published
- 2007-02-01
- Volume
- 130
- Issue
- 2
- Pages
- 314-333
- ISSN
- 0006-8950 (ISSN-L)
- 1460-2156
- OpenAlex
- W2088222765
- Cited by
- 1,109
- References
- 1
- FWCI
- 16.74
- Type
- journal article
- Language
- en
- Open access
- closed
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- Mormann, F., Andrzejak, R. G., Elger, C. E., & Lehnertz, K. (2007). Seizure prediction: the long and winding road. Brain. https://doi.org/10.1093/brain/awl241
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