• Login
    View Item 
    •   Home
    • Computer Science
    • Faculty Research and Publications
    • Articles
    • View Item
    •   Home
    • Computer Science
    • Faculty Research and Publications
    • Articles
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of Effat University RepositoryCommunitiesPublication DateAuthorsTitlesSubjectsPublisherJournalTypeDepartmentThis CollectionPublication DateAuthorsTitlesSubjectsPublisherJournalTypeDepartmentProfilesView

    My Account

    Login

    Statistics

    Display statistics

    Detection of epileptic seizures on EEG signals using ANFIS classifier, autoencoders and fuzzy entropies

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Author
    Shoeibi, Afshin
    Ghassemi, Navid
    Khodatars, Marjane
    Moridian, Parisa
    Alizadehsani, Roohallah
    Zare, Assef
    Khosravi, Abbas
    Subasi, Abdulhamit cc
    Acharya, U Rajendra
    Subject
    Epileptic Seizures
    Diagnosis
    EEG
    TQWT
    Fuzzy entropies
    AE
    ANFIS-BS
    Date
    2022-03-01
    
    Metadata
    Show full item record
    Abstract
    pileptic 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.
    Department
    Computer Science
    Publisher
    Elsevier
    Journal title
    Biomedical Signal Processing and Control
    DOI
    https://doi.org/10.1016/j.bspc.2021.103417
    ae974a485f413a2113503eed53cd6c53
    https://doi.org/10.1016/j.bspc.2021.103417
    Scopus Count
    Collections
    Articles

    entitlement

     
    DSpace software (copyright © 2002 - 2023)  DuraSpace
    Quick Guide | Contact Us
    Open Repository is a service operated by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.