Objective: This paper describes a data-analytic modeling approach for the prediction of epileptic seizures from intracranial electroencephalogram (iEEG) recording of brain activity. Even though it is widely accepted that statistical characteristics of iEEG signal change prior to seizures, robust seizure prediction remains a challenging problem due to subject-specific nature of data-analytic modeling. Methods: Our work emphasizes the understanding of clinical considerations important for iEEG-based seizure prediction, and proper translation of these clinical considerations into data-analytic modeling assumptions. Several design choices during preprocessing and postprocessing are considered and investigated for their effect on seizure prediction accuracy. Results: Our empirical results show that the proposed support vector machine-based seizure prediction system can achieve robust prediction of preictal and interictal iEEG segments from dogs with epilepsy. The sensitivity is about 90-100%, and the false-positive rate is about 0-0.3 times per day. The results also suggest that good prediction is subject specific (dog or human), in agreement with earlier studies. Conclusion : Good prediction performance is possible only if the training data contain sufficiently many seizure episodes, i.e., at least 5-7 seizures. Significance: The proposed system uses subject-specific modeling and unbalanced training data. This system also utilizes three different time scales during training and testing stages.
|Original language||English (US)|
|Number of pages||12|
|Journal||IEEE Transactions on Biomedical Engineering|
|State||Published - May 2017|
Bibliographical noteFunding Information:
This research was supported by the National Institutes of Health under Grant UH2-NS095495 and Grant R01-NS92882. Data collection was supported by NeuroVista Inc.
© 1964-2012 IEEE.
- Data-analytic modeling
- feature representation
- intracranial electroencephalogram (iEEG)
- seizure prediction
- subject-specific modeling
- support vector machine (SVM)
- unbalanced classification