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

HFO; artificial intelligence; epilepsy; epilepsy surgery; epileptiform spike; fast ripple; high-frequency oscillation; machine learning; phase-amplitude coupling; ripple; seizure; wavelet

Abstract :

[en] 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.

Disciplines :

Neurology

Author, co-author :

Weiss, Shennan A.

Waldman, Zachary

Raimondo, Federico ; Université de Liège - ULiège > Consciousness-Coma Science Group

Slezak, Diego

Donmez, Mustafa

Worrell, Gregory

Bragin, Anatol

Engel, Jerome

Staba, Richard

Sperling, Michael

Language :

English

Title :

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

Publication date :

2019

Journal title :

Biomarkers in Medicine

ISSN :

1752-0363

eISSN :

1752-0371

Publisher :

Future Medicine, London, United Kingdom

Volume :

Issue :

Pages :

409-418

Peer reviewed :

Peer Reviewed verified by ORBi

Available on ORBi :

since 17 January 2020

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