Target-vs-One and Target-vs-All Classification of Epilepsy using deep learning technique

dc.contributor.authorAmin, Adnan
dc.contributor.authorAl-Obeidat, Feras
dc.contributor.authorAlgeelani, Nasir Ahmed
dc.contributor.authorShudaiber, Ahmed
dc.contributor.authorMoreira, Fernando
dc.date.accessioned2024-05-20T15:12:49Z
dc.date.available2024-05-20T15:12:49Z
dc.date.issued2024-05-13
dc.description.abstractWith the pervasive generation of medical data, there is a need for the worldwide medical and health care sector to find appropriate computational intelligence techniques for various medical conditions such as epilepsy seizures (ES). ES is a brain disorder that affects people of all ages, is a chronic, non-communicable disease, and can occur for no apparent reason owing to a genetic defect at any time. The unpredictable nature of ES poses a significant threat to human life where we have a target variable with five labels of seizure, namely pre-seizure, seizure and seizure-free, are classified using real clinical data. In order to accurately classify seizure activity (e.g., the target label) without extensive feature engineering or selection, we employ a deep learning classifier as the study’s baseline classifier. Deep learning is a branch of artificial intelligence and currently the most successful computational intelligence technique for diagnosing ES in health informatics. This paper deals with a real-life application of epilepsy classification using computational techniques namely, Target-vs-One and Target-vs-All using deep learning approach. It is investigated that the baseline classifier on Target-vs-One strategy achieved the highest f1-score and accuracy about 0.9815 and 0.9818, respectively, as compared to the performance of baseline classifier on Target-vs-All strategy (e.g., achieved 0.94 of f1-score and 0.98 of accuracy).
dc.identifier.citationAmin, A., Al-Obeidat, F., Algeelani, N. A., Shudaiber, A., & Moreira, F. (2024). Target-vs-One and Target-vs-All Classification of Epilepsy using deep learning technique. In Á. Rocha, H. Adeli, G. Dzemyda, Moreira, F., & A. Poniszewska-Marańda (Eds.), Good Practices and New Perspectives in Information Systems and Technologies. WorldCIST 2024, volume 2, (Part of: Lecture Notes in Networks and Systems, vol. 986, pp. 85-94). Springer. https://doi.org/10.1007/978-3-031-60218-4_9. Repositório Institucional UPT. https://hdl.handle.net/11328/5639
dc.identifier.isbn978-3-031-60218-4
dc.identifier.isbn978-3-031-60217-7
dc.identifier.urihttps://hdl.handle.net/11328/5639
dc.language.isoeng
dc.publisherSpringer
dc.relation.hasversionhttps://doi.org/10.1007/978-3-031-60218-4_9
dc.rightsrestricted access
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectDeep learning
dc.subjectOne-vs-One
dc.subjectOne-vs-All
dc.subjectEpilepsy
dc.subjectClassification
dc.subject.fosCiências Naturais - Ciências da Computação e da Informação
dc.titleTarget-vs-One and Target-vs-All Classification of Epilepsy using deep learning technique
dc.typeconference paper
dspace.entity.typePublication
oaire.citation.endPage94
oaire.citation.startPage85
oaire.citation.titleGood Practices and New Perspectives in Information Systems and Technologies. WorldCIST 2024
oaire.citation.volume2
person.affiliation.nameUniversidade Portucalense
person.familyNameMoreira
person.givenNameFernando
person.identifier.ciencia-id7B1C-3A29-9861
person.identifier.orcid0000-0002-0816-1445
person.identifier.ridP-9673-2016
person.identifier.scopus-author-id8649758400
relation.isAuthorOfPublicationbad3408c-ee33-431e-b9a6-cb778048975e
relation.isAuthorOfPublication.latestForDiscoverybad3408c-ee33-431e-b9a6-cb778048975e

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