A rule-based seizure prediction method for focal neocortical epilepsy.

ABSTRACT:

Objective

In the present study, we have developed a novel patient-specific rule-based seizure prediction system for focal neocortical epilepsy.

Methods

Five univariate measures including correlation dimension, correlation entropy, noise level, Lempel-Ziv complexity, and largest Lyapunov exponent as well as one bivariate measure, nonlinear interdependence, were extracted from non-overlapping 10-s segments of intracranial electroencephalogram (iEEG) data recorded using electrodes implanted deep in the brain and/or placed on the cortical surface. The spatio-temporal information was then integrated by using rules established based on patient-specific changes observed in the period prior to a seizure sample for each patient. The system was tested on 316 h of iEEG data containing

SUBMITTER: Aarabi A 

PROVIDER: S-EPMC3361618 | biostudies-literature | 2012 Jun

REPOSITORIES: biostudies-literature