Publikation

Automatic classification of epilepsy types using ontology-based and genetics-based machine learning

Yohannes Kassahun, Roberta Perrone, Elena De Momi, Elmar Berghöfer, Laura Tassi, Maria Paola Canevini, Roberto Spreafico, Giancarlo Ferrigno, Frank Kirchner

In: Artificial Intelligence in Medicine (AIM) 61 Seiten 79-88 Elsevier 3/2014.

Abstrakt

Objectives. In the presurgical analysis for drug-resistant focal epilepsies, the definition of the epileptogenic zone, which is the cortical area where ictal discharges originate, is usually carried out by using clinical, electrophysiological and neuroimaging data analysis. Clinical evaluation is based on the visual detection of symptoms during epileptic seizures. This work aims at developing a fully automatic classifier of epileptic types and their localization using ictal symptoms and machine learning methods. Methods. We present the results achieved by using two machine learning methods. The first is an ontology-based classification that can directly incorporate human knowledge, while the second is a genetics-based data mining algorithm that learns or extracts the domain knowledge from medical data in implicit form.

Projekte

Weitere Links

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence