University of Hertfordshire

By the same authors

Evaluation of eeg connectivity network measures based features in schizophrenia classification

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

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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 Inc.
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
CountryBulgaria
CityVarna
Period8/11/199/11/19

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.

ID: 20230002