Forecasting seizures in dogs with naturally occurring epilepsy

J. Jeffry Howbert, Edward E. Patterson, S. Matt Stead, Ben Brinkmann, Vincent Vasoli, Daniel Crepeau, Charles H. Vite, Beverly Sturges, Vanessa Ruedebusch, Jaideep Mavoori, Kent Leyde, W. Douglas Sheffield, Brian Litt, Gregory A. Worrell

Research output: Contribution to journalArticlepeer-review

87 Scopus citations

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.

Original languageEnglish (US)
Article numbere81920
JournalPloS one
Volume9
Issue number1
DOIs
StatePublished - Jan 8 2014

Bibliographical note

Funding Information:
Drs. Worrell, Litt, Vite, and Patterson have received research funding from NeuroVista Inc. for portions of this project. Drs. Worrell and Litt have served as paid consultants for NeuroVista. NeuroVista Inc. participated in the study design, analysis, decision to publish, and preparation of the manuscript. Drs. Howbert, Sheffield, Leyde, and Mavoori served as employees of NeuroVista during the period of the research activity. Drs. Worrell, Litt, and Mr. Leyde hold patents pertaining to seizure forecasting devices. Full details of the 45 patents are available upon request. The remaining authors have no additional conflicts of interest. There are no further patents, products in development or marketed products to declare. This does not alter the authors' adherence to all the PLOS ONE policies on sharing data and materials.

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