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.
OBJECTIVE: This paper describes a data-analytic modeling approach for the prediction of epilepticseizures 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.
Authors: B Litt; R Esteller; J Echauz; M D'Alessandro; R Shor; T Henry; P Pennell; C Epstein; R Bakay; M Dichter; G Vachtsevanos Journal: Neuron Date: 2001-04 Impact factor: 17.173
Authors: 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 Journal: PLoS One Date: 2014-01-08 Impact factor: 3.240
Authors: Benjamin H Brinkmann; Edward E Patterson; Charles Vite; Vincent M Vasoli; Daniel Crepeau; Matt Stead; J Jeffry Howbert; Vladimir Cherkassky; Joost B Wagenaar; Brian Litt; Gregory A Worrell Journal: PLoS One Date: 2015-08-04 Impact factor: 3.240
Authors: Benjamin H Brinkmann; Joost 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-Martinez; Michael Hills; Wei Wu; Iryna Korshunova; Will Cukierski; Charles Vite; Edward E Patterson; Brian Litt; Gregory A Worrell Journal: Brain Date: 2016-03-31 Impact factor: 15.255