info:eu-repo/semantics/article
Localizing epileptogenic regions using high-frequency oscillations and machine learning
Fecha
2019-04Registro en:
Weiss, Shennan A.; Waldman, Zachary; Raimondo, Federico; Fernandez Slezak, Diego; Donmez, Mustafa; et al.; Localizing epileptogenic regions using high-frequency oscillations and machine learning; Future Medicine; Biomarkers In Medicine; 13; 5; 4-2019; 409-418
1752-0363
CONICET Digital
CONICET
Autor
Weiss, Shennan A.
Waldman, Zachary
Raimondo, Federico
Fernandez Slezak, Diego
Donmez, Mustafa
Worrell, Gregory
Bragin, Anatol
Engel, Jerome
Staba, Richard
Sperling, Michael
Resumen
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.