Localizing epileptogenic regions using high-frequency oscillations and machine learning

dc.creatorWeiss, Shennan A.
dc.creatorWaldman, Zachary
dc.creatorRaimondo, Federico
dc.creatorFernandez Slezak, Diego
dc.creatorDonmez, Mustafa
dc.creatorWorrell, Gregory
dc.creatorBragin, Anatol
dc.creatorEngel, Jerome
dc.creatorStaba, Richard
dc.creatorSperling, Michael
dc.date.accessioned2020-12-23T16:52:13Z
dc.date.accessioned2022-10-15T14:37:38Z
dc.date.available2020-12-23T16:52:13Z
dc.date.available2022-10-15T14:37:38Z
dc.date.created2020-12-23T16:52:13Z
dc.date.issued2019-04
dc.identifierWeiss, 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
dc.identifier1752-0363
dc.identifierhttp://hdl.handle.net/11336/121142
dc.identifierCONICET Digital
dc.identifierCONICET
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/4397731
dc.description.abstractPathological 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.
dc.languageeng
dc.publisherFuture Medicine
dc.relationinfo:eu-repo/semantics/altIdentifier/url/https://www.futuremedicine.com/doi/10.2217/bmm-2018-0335
dc.relationinfo:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.2217/bmm-2018-0335
dc.rightshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.subjectARTIFICIAL INTELLIGENCE
dc.subjectEPILEPSY
dc.subjectEPILEPSY SURGERY
dc.subjectEPILEPTIFORM SPIKE
dc.subjectFAST RIPPLE
dc.subjectHFO
dc.subjectHIGH-FREQUENCY OSCILLATION
dc.subjectMACHINE LEARNING
dc.subjectPHASE-AMPLITUDE COUPLING
dc.subjectRIPPLE
dc.subjectSEIZURE
dc.subjectWAVELET
dc.titleLocalizing epileptogenic regions using high-frequency oscillations and machine learning
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:ar-repo/semantics/artículo
dc.typeinfo:eu-repo/semantics/publishedVersion


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