@inproceedings{f5d61f14961f49b8a9889f77e7660627,
title = "Reliable seizure prediction from EEG data",
abstract = "There is a growing interest in data-analytic modeling for prediction and/or detection of epileptic seizures from EEG recording of brain activity [1-10]. Even though there is clear evidence that many patients have changes in EEG signal prior to seizures, development of robust seizure prediction methods remains elusive [1]. We argue that the main issue for development of effective EEG-based predictive models is an apparent disconnect between clinical considerations and dataanalytic modeling assumptions. We present an SVM-based system for seizure prediction, where design choices and performance metrics are clearly related to clinical objectives and constraints. This system achieves very accurate prediction of preictal and interictal EEG segments in dogs with naturally occurring epilepsy. However, our empirical results suggest that good prediction performance may be possible only if the training data set has sufficiently many preictal segments, i.e. at least 6-7 seizure episodes.",
keywords = "SVM classification, epilepsy, iEEG, patient-specific modeling, predictive data analytics, seizure prediction, unbalanced data",
author = "Cherkassky, {Vladimir S} and Brandon Veber and Jieun Lee and Shiao, {Han Tai} and Ned Patterson and Worrell, {Gregory A.} and Brinkmann, {Benjamin H.}",
year = "2015",
month = sep,
day = "28",
doi = "10.1109/IJCNN.2015.7280327",
language = "English (US)",
series = "Proceedings of the International Joint Conference on Neural Networks",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2015 International Joint Conference on Neural Networks, IJCNN 2015",
note = "International Joint Conference on Neural Networks, IJCNN 2015 ; Conference date: 12-07-2015 Through 17-07-2015",
}