Predictive modeling of EEG time series for evaluating surgery targets in epilepsy patients.

ABSTRACT: During the last 20 years, predictive modeling in epilepsy research has largely been concerned with the prediction of seizure events, whereas the inference of effective brain targets for resective surgery has received surprisingly little attention. In this exploratory pilot study, we describe a distributional clustering framework for the modeling of multivariate time series and use it to predict the effects of brain surgery in epilepsy patients. By analyzing the intracranial EEG, we demonstrate how patients who became seizure free after surgery are clearly distinguished from those who did not. More specifically, for 5 out of 7 patients who obtained seizure freedom (= Engel class I) our method predicts the specific collection of brain areas that got actually resected during surgery to yield

SUBMITTER: Steimer A 

PROVIDER: S-EPMC6866829 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature