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) of 0.981. AUC on a precision-recall curve was 0.807. A sensitivity of 84% was attained at a false positive rate of 1 per minute. 35.9% of the false positive detections corresponded to epileptiform discharges that were missed during expert annotation.

CONCLUSIONS:

Using deep learning, we developed a high-performing, patient non-specific algorithm for detection of mTL epileptiform discharges on intracranial electrodes.

SIGNIFICANCE:

Our algorithm has many potential applications for understanding the impact of mTL epileptiform discharges in epilepsy and on cognition, and for developing therapies to specifically reduce mTL epileptiform activity.

• Authors: Maurice Abou Jaoude, Jin Jing, Haoqi Sun, Claire S Jacobs, Kyle R Pellerin, M Brandon Westover, Sydney S Cash, Alice D Lam
• Journal: Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology (Clin Neurophysiol) Vol. 131 Issue 1 Pg. 133-141 (01 2020) ISSN: 1872-8952 [Electronic] Netherlands
• PMID: 31760212 (Publication Type: Journal Article, Research Support, N.I.H., Extramural, Research Support, Non-U.S. Gov't)
• Copyright: Copyright © 2019 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Topics

• Adult
• Algorithms
• Area Under Curve
• Artifacts
• Datasets as Topic
• Deep Learning
• Electrocorticography (instrumentation, methods, standards)
• Electrodes, Implanted
• Epilepsy, Temporal Lobe (diagnosis, physiopathology)
• Female
• Foramen Ovale (physiopathology)
• Humans
• Male
• ROC Curve
• Reference Standards
• Sensitivity and Specificity
• Temporal Lobe (physiopathology)