Fuzzy rule-based seizure prediction based on correlation dimension changes in intracranial EEG

Ahmed F Rabbi; Ardalan Aarabi; Reza Fazel-Rezai

doi:10.1109/IEMBS.2010.5627247

Fuzzy rule-based seizure prediction based on correlation dimension changes in intracranial EEG

Annu Int Conf IEEE Eng Med Biol Soc. 2010:2010:3301-4. doi: 10.1109/IEMBS.2010.5627247.

Authors

Ahmed F Rabbi¹, Ardalan Aarabi, Reza Fazel-Rezai

Affiliation

¹ 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

PMID: 21096618
DOI: 10.1109/IEMBS.2010.5627247

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.

Publication types

Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Algorithms*
Diagnosis, Computer-Assisted / methods*
Electroencephalography / methods*
Fuzzy Logic*
Humans
Pattern Recognition, Automated / methods*
Reproducibility of Results
Seizures / diagnosis*
Sensitivity and Specificity