Epileptic seizure suppression: A computational approach for identification and control using real data.

ABSTRACT: Epilepsy affects millions of people worldwide every year and remains an open subject for research. Current development on this field has focused on obtaining computational models to better understand its triggering mechanisms, attain realistic descriptions and study seizure suppression. Controllers have been successfully applied to mitigate epileptiform activity in dynamic models written in state-space notation, whose applicability is, however, restricted to signatures that are accurately described by them. Alternatively, autoregressive modeling (AR), a typical data-driven tool related to system identification (SI), can be directly applied to signals to generate more realistic models, and since it is inherently convertible into state-space representation, it can thus be used for the artifi

SUBMITTER: Brogin JAF 

PROVIDER: S-EPMC10901337 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature