par Hocepied, Gatien 
;Legros, Benjamin ;Van Bogaert, Patrick ;Grenez, Francis ;Nonclercq, Antoine
Référence Computers in biology and medicine, 43, 11, page (1773-1782)
Publication Publié, 2013
;Legros, Benjamin ;Van Bogaert, Patrick ;Grenez, Francis ;Nonclercq, Antoine Référence Computers in biology and medicine, 43, 11, page (1773-1782)
Publication Publié, 2013
Article révisé par les pairs
| Résumé : | Physiologically based models are attractive for seizure detection, as their parameters can be explicitly related to neurological mechanisms. We propose an early seizure detection algorithm based on parameter identification of a neural mass model. The occurrence of a seizure is detected by analysing the time shift of key model parameters. The algorithm was evaluated against the manual scoring of a human expert on intracranial EEG samples from 16 patients suffering from different types of epilepsy. Results suggest that the algorithm is best suited for patients suffering from temporal lobe epilepsy (sensitivity was 95.0%±10.0% and false positive rate was 0.20±0.22 per hour). © 2013 Elsevier Ltd. |
