Localizing epileptogenic regions using high-frequency oscillations and machine learning.

Authors

• Shennan A Weiss
• Zachary Waldman
• Federico Raimondo
• Diego Slezak
• Mustafa Donmez
• Gregory Worrell
• Anatol Bragin
• Jerome Engel
• Richard Staba
• Michael Sperling

Publication/Presentation Date

4-1-2019

Abstract

Pathological high frequency oscillations (HFOs) are putative neurophysiological biomarkers of epileptogenic brain tissue. Utilizing HFOs for epilepsy surgery planning offers the promise of improved seizure outcomes for patients with medically refractory epilepsy. This review discusses possible machine learning strategies that can be applied to HFO biomarkers to better identify epileptogenic regions. We discuss the role of HFO rate, and utilizing features such as explicit HFO properties (spectral content, duration, and power) and phase-amplitude coupling for distinguishing pathological HFO (pHFO) events from physiological HFO events. In addition, the review highlights the importance of neuroanatomical localization in machine learning strategies.

Volume

13

Issue

5

First Page

409

Last Page

418

ISSN

1752-0371

Published In/Presented At

Weiss, S. A., Waldman, Z., Raimondo, F., Slezak, D., Donmez, M., Worrell, G., Bragin, A., Engel, J., Staba, R., & Sperling, M. (2019). Localizing epileptogenic regions using high-frequency oscillations and machine learning. Biomarkers in medicine, 13(5), 409–418. https://doi.org/10.2217/bmm-2018-0335

Disciplines

Medicine and Health Sciences

PubMedID

31044598

Department(s)

Department of Medicine

Document Type

Article