title: Automated EEG detection algorithms and clinical semiology in epilepsy: importance of correlations.
abstract: With advances in technological innovation, electroencephalography has remained the gold standard for classification and localization of epileptic seizures. Like other diagnostic modalities, technological advances have opened new avenues for assessment of data, and hold great promise to improve interpretive capabilities. However, proper overall interpretation and application of electroencephalographic findings relies on valid correlations of associated clinical semiology. This article addresses interpretation of clinical signs and symptoms in the context of the diagnostic predictive value of electroencephalographic, clinical, and electrographic definitions of seizures, and upcoming challenges of interpreting intracranial high-frequency electroencephalographic data. This article is part of a Supplemental Special Issue entitled The Future of Automated Seizure Detection and Prediction.
authors.affiliation: Department of Neurology, Washington University, St. Louis, MO 63110, USA. hogan@wustl.edu
authors.forename: R Edward
authors.initials: RE
authors.lastname: Hogan
mesh_terms: D000465:Algorithms; D004569:Electroencephalography / Q000379:methods*; D001330:Electronic Data Processing / Q000379:methods*; D004827:Epilepsy / Q000145:classification / Q000175:diagnosis* / Q000503:physiopathology*; D006801:Humans; D011237:Predictive Value of Tests