Localizing seizure onset zones in surgical epilepsy with neurostimulation deep learning.

ABSTRACT:

Objective

In drug-resistant temporal lobe epilepsy, automated tools for seizure onset zone (SOZ) localization that use brief interictal recordings could supplement presurgical evaluations and improve care. Thus, the authors sought to localize SOZs by training a multichannel convolutional neural network on stereoelectroencephalography (SEEG) cortico-cortical evoked potentials.

Methods

The authors performed single-pulse electrical stimulation in 10 drug-resistant temporal lobe epilepsy patients implanted with SEEG. Using 500,000 unique poststimulation SEEG epochs, the authors trained a multichannel 1-dimensional convolutional neural network to determine whether an SOZ had been stimulated.

Results

SOZs were classified with mean sensitivity of 78.1% and specificity of 74.

SUBMITTER: Johnson GW 

PROVIDER: S-EPMC10619627 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature