Linear time-varying model characterizes invasive EEG signals generated from complex epileptic networks.
Annu Int Conf IEEE Eng Med Biol Soc
; 2017: 2802-2805, 2017 Jul.
Article
em En
| MEDLINE
| ID: mdl-29060480
ABSTRACT
Electrocorticography (ECoG) and stereotactic electroencephalography (SEEG) are popular tools for studying neural mechanisms governing behavior and neural disorders, such as epilepsy. In particular, clinicians are interested in identifying brain regions that start seizures, i.e., the epileptogenic zone (EZ) from such invasive recordings. Currently, they visually inspect signals from each electrode to locate abnormal activity, and are not informed by predictive models that can characterize such recordings and potentially increase accuracy in localizing the EZ. In this paper, we test whether a simple linear time varying (LTV) model is sufficient to characterize both ECoG and SEEG activity. Specifically, we construct linear time invariant models in consecutive time windows before, during and after seizure events creating an LTV model from data collected in one ECoG and one SEEG patient. We find that these LTV models accurately reconstruct both ECoG and SEEG time series measured suggesting that these LTV models can be used for EZ localization.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Eletroencefalografia
/
Epilepsia
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
Annu Int Conf IEEE Eng Med Biol Soc
Ano de publicação:
2017
Tipo de documento:
Article