Predicting surgical outcome in drug-resistant epilepsy by combining interictal biomarkers within a machine learning framework

Abstract Delineating the epileptogenic zone (EZ) is essential for achieving seizure freedom in drug-resistant epilepsy (DRE). Conventionally, seizure onset derived from ictal intracranial EEG (iEEG) approximates the EZ, but acquiring ictal data can be challenging. Interictal iEEG abnormalities offer...

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Autori principali:	Hmayag Partamian, Saeed Jahromi, M. Scott Perry, Daniel Hansen, Madhan Bosemani, Jason Wang, Eleonora Tamilia, Joseph R. Madsen, Jeffrey Bolton, Scellig S. D. Stone, Phillip L. Pearl, Christos Papadelis
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	Nature Portfolio 2026-03-01
Serie:	Scientific Reports
Soggetti:	Interictal epilepsy biomarkers Epilepsy surgery Combined interictal features Supervised machine learning
Accesso online:	https://doi.org/10.1038/s41598-026-45177-x
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