Forecasting seizures in dogs with naturally occurring epilepsy.
J. Jeffry Howbert,Edward E. Patterson,S. Matt Stead,Ben Brinkmann,Vincent M. Vasoli,Daniel Crepeau,Charles H. Vite,Beverly K. Sturges,Vanessa Ruedebusch,Jaideep Mavoori,Kent W. Leyde,W. Douglas Sheffield,Brian Litt,Gregory A. Worrell +13 more
TLDR
It is demonstrated that seizures in canine epilepsy are not randomly occurring events, and the feasibility of long-term seizure forecasting using iEEG monitoring is highlighted.Abstract:
Seizure forecasting has the potential to create new therapeutic strategies for epilepsy, such as providing patient warnings and delivering preemptive therapy. Progress on seizure forecasting, however, has been hindered by lack of sufficient data to rigorously evaluate the hypothesis that seizures are preceded by physiological changes, and are not simply random events. We investigated seizure forecasting in three dogs with naturally occurring focal epilepsy implanted with a device recording continuous intracranial EEG (iEEG). The iEEG spectral power in six frequency bands: delta (0.1–4 Hz), theta (4–8 Hz), alpha (8–12 Hz), beta (12–30 Hz), low-gamma (30–70 Hz), and high-gamma (70–180 Hz), were used as features. Logistic regression classifiers were trained to discriminate labeled pre-ictal and inter-ictal data segments using combinations of the band spectral power features. Performance was assessed on separate test data sets via 10-fold cross-validation. A total of 125 spontaneous seizures were detected in continuous iEEG recordings spanning 6.5 to 15 months from 3 dogs. When considering all seizures, the seizure forecasting algorithm performed significantly better than a Poisson-model chance predictor constrained to have the same time in warning for all 3 dogs over a range of total warning times. Seizure clusters were observed in all 3 dogs, and when the effect of seizure clusters was decreased by considering the subset of seizures separated by at least 4 hours, the forecasting performance remained better than chance for a subset of algorithm parameters. These results demonstrate that seizures in canine epilepsy are not randomly occurring events, and highlight the feasibility of long-term seizure forecasting using iEEG monitoring.read more
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Crowdsourcing reproducible seizure forecasting in human and canine epilepsy
Benjamin H. Brinkmann,Joost B. Wagenaar,Drew Abbot,Phillip Adkins,Simone C. Bosshard,Min Chen,Quang M. Tieng,Jialune He,F. J. Muñoz-Almaraz,Paloma Botella-Rocamora,Juan Pardo,Francisco Zamora-Martínez,Michael Hills,Wei Wu,Iryna Korshunova,William Cukierski,Charles H. Vite,Edward E. Patterson,Brian Litt,Gregory A. Worrell +19 more
TL;DR: An online, open-access seizure forecasting competition using intracranial EEG recordings from canines with naturally occurring epilepsy and human patients undergoing presurgical monitoring wins, with the winning algorithms forecast seizures at rates significantly greater than chance.
References
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Journal ArticleDOI
Early Identification of Refractory Epilepsy
TL;DR: Patients who have many seizures before therapy or who have an inadequate response to initial treatment with antiepileptic drugs are likely to have refractory epilepsy.
Book
Epilepsy : a comprehensive textbook
TL;DR: The neurobiology of epilepsy: neuronal excitability experimental modes of epilepsy and the autonomic nervous system comorbidity neuroendocrinology and the delivery of health care and socioeconomic issues.
Journal ArticleDOI
Seizure prediction: the long and winding road.
TL;DR: 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.
Journal ArticleDOI
Responsive cortical stimulation for the treatment of medically intractable partial epilepsy
TL;DR: This study provides Class I evidence that responsive cortical stimulation is effective in significantly reducing seizure frequency for 12 weeks in adults who have failed 2 or more antiepileptic medication trials, 3 or more seizures per month, and 1 or 2 seizure foci.
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.
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