ICC 25427
INSTITUTO DE INVESTIGACION EN CIENCIAS DE LA COMPUTACION
Unidad Ejecutora - UE
artículos
Título:
Localizing epileptogenic regions using high-frequency oscillations and machine learning
Autor/es:
RAIMONDO, FEDERICO; WORRELL, GREGORY; STABA, RICHARD; WALDMAN, ZACHARY; DONMEZ, MUSTAFA; ENGEL, JEROME; WEISS, SHENNAN A; SLEZAK, DIEGO; BRAGIN, ANATOL; SPERLING, MICHAEL
Revista:
BIOMARKERS IN MEDICINE
Editorial:
FUTURE MEDICINE LTD
Referencias:
Año: 2019 vol. 13 p. 409 - 418
ISSN:
1752-0363
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.
