title: Fuzzy rule-based seizure prediction based on correlation dimension changes in intracranial EEG.
abstract: In this paper, we present a method for epileptic seizure prediction from intracranial EEG recordings. We applied correlation dimension, a nonlinear dynamics based univariate characteristic measure for extracting features from EEG segments. Finally, we designed a fuzzy rule-based system for seizure prediction. The system is primarily designed based on expert's knowledge and reasoning. A spatial-temporal filtering method was used in accordance with the fuzzy rule-based inference system for issuing forecasting alarms. The system was evaluated on EEG data from 10 patients having 15 seizures.
authors.affiliation: BRAIN Team at the Biomedical Signal Processing Laboratory, Department of Electrical Engineering, University of North Dakota, Grand Forks, ND 58202, USA. ahmed.rabbi@und.edu
authors.forename: Ahmed F
authors.initials: AF
authors.lastname: Rabbi
authors.forename: Ardalan
authors.initials: A
authors.lastname: Aarabi
authors.forename: Reza
authors.initials: R
authors.lastname: Fazel-Rezai
mesh_terms: D000465:Algorithms; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D017143:Fuzzy Logic; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012640:Seizures / Q000175:diagnosis*; D012680:Sensitivity and Specificity