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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Rabbi, A.F. Azinfar, L. Fazel-Rezai, R. |
| Copyright Year | 2013 |
| Description | Author affiliation: Electr. Eng. Dept., Univ. of North Dakota, Grand Forks, ND, USA (Rabbi, A.F.; Azinfar, L.; Fazel-Rezai, R.) |
| Abstract | In this study, we present a neuro-fuzzy approach of seizure prediction from invasive Electroencephalogram (EEG) by applying adaptive neuro-fuzzy inference system (ANFIS). Three nonlinear seizure predictive features were extracted from a patient's data obtained from the European Epilepsy Database, one of the most comprehensive EEG database for epilepsy research. A total of 36 hours of recordings including 7 seizures was used for analysis. The nonlinear features used in this study were similarity index, phase synchronization, and nonlinear interdependence. We designed an ANFIS classifier constructed based on these features as input. Fuzzy if-then rules were generated by the ANFIS classifier using the complex relationship of feature space provided during training. The membership function optimization was conducted based on a hybrid learning algorithm. The proposed method achieved highest sensitivity of 80% with false prediction rate as low as 0.46 per hour. |
| Starting Page | 2100 |
| Ending Page | 2103 |
| File Size | 304430 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781457702167 |
| ISSN | 1557170X |
| DOI | 10.1109/EMBC.2013.6609947 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-07-03 |
| Publisher Place | Japan |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Electroencephalography Feature extraction Epilepsy Training Prediction algorithms Databases Synchronization |
| Content Type | Text |
| Resource Type | Article |
| Subject | Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition |
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