Evaluation of eeg connectivity network measures based features in schizophrenia classification

Vasiliki Bougou, Iosif Mporas, Pascal Schirmer, Todor Ganchev

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    In this paper an architecture for the classification of Schizophrenia using EEG-based brain connectivity is proposed. Functional and effective networks were constructed from the EEG using a variety of connectivity measures and with graph theory metrics complex network features were extracted. Several classification algorithms were used for the evaluation of the architecture. Promising results were observed when using connectivity measures that also capture directionality properties of the network. The best classification accuracy was 82.36% and was achieved by Random Forest classifier with Direct Transfer Function as a connectivity measure.
    Original languageEnglish
    Title of host publicationProceedings of the International Conference on "Biomedical Innovations and Applications", BIA 2019
    EditorsValentina Markova, Todor Ganchev
    PublisherInstitute of Electrical and Electronics Engineers (IEEE)
    ISBN (Electronic)9781728147543
    DOIs
    Publication statusPublished - Nov 2019
    Event2019 International Conference on Biomedical Innovations and Applications, BIA 2019 - Varna, Bulgaria
    Duration: 8 Nov 20199 Nov 2019

    Publication series

    NameProceedings of the International Conference on "Biomedical Innovations and Applications", BIA 2019

    Conference

    Conference2019 International Conference on Biomedical Innovations and Applications, BIA 2019
    Country/TerritoryBulgaria
    CityVarna
    Period8/11/199/11/19

    Keywords

    • classification
    • connectivity
    • EEG
    • schizophrenia

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