Seizure prediction: the long and winding road.
TLDR
A 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, and point towards possible future developments and propose methodological guidelines for future studies on seizure predictions.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.read more
Citations
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Journal ArticleDOI
Early-warning signals for critical transitions
Marten Scheffer,Jordi Bascompte,William A. Brock,Victor Brovkin,Stephen R. Carpenter,Vasilis Dakos,Hermann Held,Egbert H. van Nes,Max Rietkerk,George Sugihara +9 more
TL;DR: Work in different scientific fields is now suggesting the existence of generic early-warning signals that may indicate for a wide class of systems if a critical threshold is approaching.
Journal ArticleDOI
Prediction of seizure likelihood with a long-term, implanted seizure advisory system in patients with drug-resistant epilepsy: a first-in-man study.
Mark J. Cook,Mark J. Cook,Terence J. O'Brien,Terence J. O'Brien,Samuel F. Berkovic,Michael Murphy,Michael Murphy,Andrew P. Morokoff,Andrew P. Morokoff,Gavin Fabinyi,Wendyl D'Souza,Wendyl D'Souza,Raju Yerra,John S. Archer,L. Litewka,Sean Hosking,Paul A. Lightfoot,Vanessa Ruedebusch,W. Douglas Sheffield,David Snyder,Kent W. Leyde,David M. Himes +21 more
TL;DR: This study showed that intracranial electroencephalographic monitoring is feasible in ambulatory patients with drug-resistant epilepsy and accurate definition of preictal electrical activity might improve understanding of seizure generation and eventually lead to new management strategies.
Journal ArticleDOI
Epileptic Seizure Detection in EEGs Using Time–Frequency Analysis
TL;DR: The suitability of the time-frequency ( t-f) analysis to classify EEG segments for epileptic seizures, and several methods for t- f analysis of EEGs are compared.
Journal ArticleDOI
Single-neuron dynamics in human focal epilepsy
Wilson Truccolo,Wilson Truccolo,Wilson Truccolo,Jacob A. Donoghue,Leigh R. Hochberg,Leigh R. Hochberg,Leigh R. Hochberg,Emad N. Eskandar,Joseph R. Madsen,William S. Anderson,Emery N. Brown,Emery N. Brown,Eric Halgren,Sydney S. Cash +13 more
TL;DR: In this paper, the spike train patterns of single neurons during seizures in human epilepsy patients were analyzed and it was found that spiking activity during seizure initiation was highly heterogeneous in small cortical patches and across the network.
Journal ArticleDOI
Automatic seizure detection based on time-frequency analysis and artificial neural networks
TL;DR: A method of analysis of EEG signals, which is based on time-frequency analysis, which provides the final classification of the EEG segments concerning the existence of seizures or not.
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