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. 2013 Nov;43(11):1773-82.
doi: 10.1016/j.compbiomed.2013.08.022. Epub 2013 Sep 2.

Early detection of epileptic seizures based on parameter identification of neural mass model

Gatien Hocepied  1 , Benjamin Legros, Patrick Van Bogaert, Francis Grenez, Antoine Nonclercq
Affiliations

Early detection of epileptic seizures based on parameter identification of neural mass model

Gatien Hocepied et al. Comput Biol Med. 2013 Nov.

Abstract

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).

Keywords: Brain modelling; Epilepsy; Nonlinear systems; Nonstationarity; Seizure detection.

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