Graph-generative neural network for EEG-based epileptic seizure detection via discovery of dynamic brain functional connectivity.

ABSTRACT: Dynamic complexity in brain functional connectivity has hindered the effective use of signal processing or machine learning methods to diagnose neurological disorders such as epilepsy. This paper proposed a new graph-generative neural network (GGN) model for the dynamic discovery of brain functional connectivity via deep analysis of scalp electroencephalogram (EEG) signals recorded from various regions of a patient's scalp. Brain functional connectivity graphs are generated for the extraction of spatial-temporal resolution of various onset epilepsy seizure patterns. Our supervised GGN model was substantiated by seizure detection and classification experiments. We train the GGN model using a clinically proven dataset of over 3047 epileptic seizure cases. The GGN model achieved a 91% accurac

SUBMITTER: Li Z 

PROVIDER: S-EPMC9643358 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature