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PLoS One. 2014 Jan 8;9(1):e81920. doi: 10.1371/journal.pone.0081920. eCollection 2014.

Forecasting seizures in dogs with naturally occurring epilepsy.

Author information

1
NeuroVista Corp., Seattle, Washington, United States of America.
2
Veterinary Medical Center, University of Minnesota, St. Paul, Minnesota, United States of America.
3
Mayo Systems Electrophysiology Laboratory, Mayo Clinic, Rochester, Minnesota, United States of America.
4
School of Veterinary Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
5
Veterinary School, University of California Davis, Davis, California, United States of America.
6
Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.

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.

PMID:
24416133
PMCID:
PMC3885383
DOI:
10.1371/journal.pone.0081920
[Indexed for MEDLINE]
Free PMC Article
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