Expert-Level Intracranial Electroencephalogram Ictal Pattern Detection by a Deep Learning Neural Network.

ABSTRACT: Background: Decision-making in epilepsy surgery is strongly connected to the interpretation of the intracranial EEG (iEEG). Although deep learning approaches have demonstrated efficiency in processing extracranial EEG, few studies have addressed iEEG seizure detection, in part due to the small number of seizures per patient typically available from intracranial investigations. This study aims to evaluate the efficiency of deep learning methodology in detecting iEEG seizures using a large dataset of ictal patterns collected from epilepsy patients implanted with a responsive neurostimulation system (RNS). Methods: Five thousand two hundred and twenty-six ictal events were collected from 22 patients implanted with RNS. A convolutional neural network (CNN) architecture was create

SUBMITTER: Constantino AC 

PROVIDER: S-EPMC8126697 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature