Detection of mesial temporal lobe epileptiform discharges on intracranial electrodes using deep learning.

ABSTRACT:

Objective

Develop a high-performing algorithm to detect mesial temporal lobe (mTL) epileptiform discharges on intracranial electrode recordings.

Methods

An epileptologist annotated 13,959 epileptiform discharges from a dataset of intracranial EEG recordings from 46 epilepsy patients. Using this dataset, we trained a convolutional neural network (CNN) to recognize mTL epileptiform discharges from a single intracranial bipolar channel. The CNN outputs from multiple bipolar channel inputs were averaged to generate the final detector output. Algorithm performance was estimated using a nested 5-fold cross-validation.

Results

On the receiver-operating characteristic curve, our algorithm achieved an area under the curve (AUC) of 0.996 and a partial AUC (for specificity > 0.9

SUBMITTER: Abou Jaoude M 

PROVIDER: S-EPMC6879011 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature