Unsupervised Learning of Spatiotemporal Interictal Discharges in Focal Epilepsy.

ABSTRACT:

Background

Interictal epileptiform discharges are an important biomarker for localization of focal epilepsy, especially in patients who undergo chronic intracranial monitoring. Manual detection of these pathophysiological events is cumbersome, but is still superior to current rule-based approaches in most automated algorithms.

Objective

To develop an unsupervised machine-learning algorithm for the improved, automated detection and localization of interictal epileptiform discharges based on spatiotemporal pattern recognition.

Methods

We decomposed 24 h of intracranial electroencephalography signals into basis functions and activation vectors using non-negative matrix factorization (NNMF). Thresholding the activation vector and the basis function of interest detected in

SUBMITTER: Baud MO 

PROVIDER: S-EPMC6454796 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature