title: [Identification of highly specific electroencephalographic patterns in patients with neuroepithelial tumors of the III ventricle by algorithmic methods].
abstract: An algorithm was suggested for identifying highly specific electroencephalographic (EEG) patterns in neurooncologic patients. The algorithm provides selection of patients with their further classification into main and control groups based on the already existing database of EEG indicators; requests to it; generation of mono-indicator candidates for EEG-patterns on the basis of a 4-dipole table for selecting and verifying sensitive and specific EEG patterns and outlining the best ones. Our material included 368 patients with basal-diencephalic tumors. Algorithmic methods revealed new EEG patterns in patients with different anatomical and topographical variants of neuroepithelial tumors in the III ventricle. We think it reasonable to use the revealed syndromes to improve diagnosis and identify pathophysiological basis of clinical syndromes.
authors.forename: I A
authors.initials: IA
authors.lastname: Voronina
authors.forename: V G
authors.initials: VG
authors.lastname: Voronov
authors.forename: G A
authors.initials: GA
authors.lastname: Shchekut'ev
mesh_terms: D000293:Adolescent; D000328:Adult; D000465:Algorithms; D002551:Cerebral Ventricle Neoplasms / Q000175:diagnosis / Q000503:physiopathology*; D016208:Databases, Factual; D003937:Diagnosis, Differential; D004569:Electroencephalography; D005260:Female; D006801:Humans; D008297:Male; D015201:Meta-Analysis as Topic; D008875:Middle Aged; D018302:Neoplasms, Neuroepithelial / Q000175:diagnosis / Q000503:physiopathology*; D012189:Retrospective Studies; D020542:Third Ventricle / Q000503:physiopathology*