title: Automatic classification of epilepsy types using ontology-based and genetics-based machine learning.
abstract: OBJECTIVE
  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.
  
  
  RESULTS
  The developed methods are tested on a clinical dataset of 129 patients. The performance of the methods is measured against the performance of seven clinicians, whose level of expertise is high/very high, in classifying two epilepsy types: temporal lobe epilepsy and extra-temporal lobe epilepsy. When comparing the performance of the algorithms with that of a single clinician, who is one of the seven clinicians, the algorithms show a slightly better performance than the clinician on three test sets generated randomly from 99 patients out of the 129 patients. The accuracy obtained for the two methods and the clinician is as follows: first test set 65.6% and 75% for the methods and 56.3% for the clinician, second test set 66.7% and 76.2% for the methods and 61.9% for the clinician, and third test set 77.8% for the methods and the clinician. When compared with the performance of the whole population of clinicians on the rest 30 patients out of the 129 patients, where the patients were selected by the clinicians themselves, the mean accuracy of the methods (60%) is slightly worse than the mean accuracy of the clinicians (61.6%). Results show that the methods perform at the level of experienced clinicians, when both the methods and the clinicians use the same information.
  
  
  CONCLUSIONS
  Our results demonstrate that the developed methods form important ingredients for realizing a fully automatic classification of epilepsy types and can contribute to the definition of signs that are most important for the classification.
authors.affiliation: Fachbereich 3 - Mathematics and Computer Science, University of Bremen, Robert-Hooke-Str. 5, D-28359 Bremen, Germany. Electronic address: kassahun@informatik.uni-bremen.de.
authors.forename: Yohannes
authors.initials: Y
authors.lastname: Kassahun
authors.affiliation: Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy. Electronic address: roberta.perrone@polimi.it.
authors.forename: Roberta
authors.initials: R
authors.lastname: Perrone
authors.affiliation: Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy.
authors.forename: Elena
authors.initials: E
authors.lastname: De Momi
authors.affiliation: German Research Center for Artificial Intelligence (DFKI), Robotics Innovation Center, Robert-Hooke-Str. 5, D-28359 Bremen, Germany.
authors.forename: Elmar
authors.initials: E
authors.lastname: Berghöfer
authors.affiliation: Centro Chirurgia Epilessia "Claudio Munari", Dipartimento di Neuroscienze, Ospedale Niguarda Cà Granda, Piazza Ospedale Maggiore 3, 20162 Milano, Italy.
authors.forename: Laura
authors.initials: L
authors.lastname: Tassi
authors.affiliation: Epilepsy Center, San Paolo Hospital, Via A. Di Rudini 8, 20142 Milan, Italy.
authors.forename: Maria Paola
authors.initials: MP
authors.lastname: Canevini
authors.affiliation: Department of Experimental Neurophysiology and Epileptology, Istituto Nazionale Neurologico 'C. Besta', Via Celoria 11, 20133 Milano, Italy.
authors.forename: Roberto
authors.initials: R
authors.lastname: Spreafico
authors.affiliation: Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy.
authors.forename: Giancarlo
authors.initials: G
authors.lastname: Ferrigno
authors.affiliation: Fachbereich 3 - Mathematics and Computer Science, University of Bremen, Robert-Hooke-Str. 5, D-28359 Bremen, Germany; German Research Center for Artificial Intelligence (DFKI), Robotics Innovation Center, Robert-Hooke-Str. 5, D-28359 Bremen, Germany.
authors.forename: Frank
authors.initials: F
authors.lastname: Kirchner
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D057225:Data Mining / Q000379:methods*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D003937:Diagnosis, Differential; D004569:Electroencephalography; D004828:Epilepsies, Partial / Q000175:diagnosis* / Q000235:genetics*; D004833:Epilepsy, Temporal Lobe / Q000175:diagnosis / Q000235:genetics; D006801:Humans
keywords: Data mining (knowledge discovery) from medical data; Epileptogenic zone identification; Genetics-based classification; Ontology-based classification