Yohannes Kassahun1, Roberta Perrone2, Elena De Momi3, Elmar Berghöfer4, Laura Tassi5, Maria Paola Canevini6, Roberto Spreafico7, Giancarlo Ferrigno3, Frank Kirchner8. 1. Fachbereich 3 - Mathematics and Computer Science, University of Bremen, Robert-Hooke-Str. 5, D-28359 Bremen, Germany. Electronic address: kassahun@informatik.uni-bremen.de. 2. Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy. Electronic address: roberta.perrone@polimi.it. 3. Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy. 4. German Research Center for Artificial Intelligence (DFKI), Robotics Innovation Center, Robert-Hooke-Str. 5, D-28359 Bremen, Germany. 5. Centro Chirurgia Epilessia "Claudio Munari", Dipartimento di Neuroscienze, Ospedale Niguarda Cà Granda, Piazza Ospedale Maggiore 3, 20162 Milano, Italy. 6. Epilepsy Center, San Paolo Hospital, Via A. Di Rudini 8, 20142 Milan, Italy. 7. Department of Experimental Neurophysiology and Epileptology, Istituto Nazionale Neurologico 'C. Besta', Via Celoria 11, 20133 Milano, Italy. 8. 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.
Abstract
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. 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. CONCLUSION: 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.
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 epilepticseizures. 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. CONCLUSION: 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.
Keywords:
Data mining (knowledge discovery) from medical data; Epileptogenic zone identification; Genetics-based classification; Ontology-based classification
Authors: Omar Ibrahim; Heidi G Sutherland; Rodney A Lea; Fatima Nasrallah; Neven Maksemous; Robert A Smith; Larisa M Haupt; Lyn R Griffiths Journal: J Mol Med (Berl) Date: 2021-11-19 Impact factor: 4.599
Authors: David Lewis-Smith; Peter D Galer; Ganna Balagura; Hugh Kearney; Shiva Ganesan; Mahgenn Cosico; Margaret O'Brien; Priya Vaidiswaran; Roland Krause; Colin A Ellis; Rhys H Thomas; Peter N Robinson; Ingo Helbig Journal: Epilepsia Date: 2021-05-05 Impact factor: 6.740