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dc.contributor.authorShoeibi, Afshin
dc.contributor.authorGhassemi, Navid
dc.contributor.authorKhodatars, Marjane
dc.contributor.authorMoridian, Parisa
dc.contributor.authorAlizadehsani, Roohallah
dc.contributor.authorZare, Assef
dc.contributor.authorKhosravi, Abbas
dc.contributor.authorSubasi, Abdulhamit
dc.contributor.authorAcharya, U Rajendra
dc.date.accessioned2023-03-15T13:09:48Z
dc.date.available2023-03-15T13:09:48Z
dc.date.issued2022-03-01
dc.identifier.citationAfshin Shoeibi, Navid Ghassemi, Marjane Khodatars, Parisa Moridian, Roohallah Alizadehsani, Assef Zare, Abbas Khosravi, Abdulhamit Subasi, U. Rajendra Acharya, Juan M. Gorriz, Detection of epileptic seizures on EEG signals using ANFIS classifier, autoencoders and fuzzy entropies, Biomedical Signal Processing and Control, Volume 73, 2022, 103417, ISSN 1746-8094, https://doi.org/10.1016/j.bspc.2021.103417. (https://www.sciencedirect.com/science/article/pii/S1746809421010144)en_US
dc.identifier.issn1746-8094en_US
dc.identifier.doihttps://doi.org/10.1016/j.bspc.2021.103417en_US
dc.identifier.urihttp://hdl.handle.net/20.500.14131/677
dc.description.abstractpileptic seizures are one of the most crucial neurological disorders, and their early diagnosis will help the clinicians to provide accurate treatment for the patients. The electroencephalogram (EEG) signals are widely used for epileptic seizures detection, which provides specialists with substantial information about the functioning of the brain. In this paper, a novel diagnostic procedure using fuzzy theory and deep learning techniques is introduced. The proposed method is evaluated on the Bonn University dataset with six classification combinations and also on the Freiburg dataset. The tunable-Q wavelet transform (TQWT) is employed to decompose the EEG signals into different sub-bands. In the feature extraction step, 13 different fuzzy entropies are calculated from different sub-bands of TQWT, and their computational complexities are calculated to help researchers choose the best set for various tasks. In the following, an autoencoder (AE) with six layers is employed for dimensionality reduction. Finally, the standard adaptive neuro-fuzzy inference system (ANFIS), and also its variants with grasshopper optimization algorithm (ANFIS-GOA), particle swarm optimization (ANFIS-PSO), and breeding swarm optimization (ANFIS-BS) methods are used for classification. Using our proposed method, ANFIS-BS method has obtained an accuracy of 99.74% in classifying into two classes and an accuracy of 99.46% in ternary classification on the Bonn dataset and 99.28% on the Freiburg dataset, reaching state-of-the-art performances on both of them.en_US
dc.publisherElsevieren_US
dc.subjectEpileptic Seizuresen_US
dc.subjectDiagnosisen_US
dc.subjectEEGen_US
dc.subjectTQWTen_US
dc.subjectFuzzy entropiesen_US
dc.subjectAEen_US
dc.subjectANFIS-BSen_US
dc.titleDetection of epileptic seizures on EEG signals using ANFIS classifier, autoencoders and fuzzy entropiesen_US
dc.source.journalBiomedical Signal Processing and Controlen_US
dc.source.volume73en_US
dc.contributor.researcherExternal Collaborationen_US
dc.contributor.labArtificial Intelligence & Cyber Security Laben_US
dc.subject.KSAHEALTHen_US
dc.source.indexScopusen_US
dc.source.indexWoSen_US
dc.contributor.departmentComputer Scienceen_US
dc.contributor.firstauthorAfshin, Shoeibi


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