Abstract
An accurate identification of schizophrenia is important for its early diagnosis and treatment. Due to the complexity and inherent heterogeneity of this disorder, an accurate marker has not been established yet. In this paper is proposed a new methodology for the detection of schizophrenia using EEG microstate analysis and graph theory. The proposed method, called Microstate Graphs, allows modeling and interpreting each microstate as a complex network, which permits to identify the effect of schizophrenia on some characteristics of the built networks. In an experiment carried out in a public dataset, the proposed method was useful to identify schizophrenic patients with an accuracy of (\(91.67 \pm 2.06 \))% using an MLP trained with metrics extracted from microstate networks: assortativity, small-worldness, and local efficiency, indicating that the proposed method is promising.
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Acknowledgements
The authors would like to thank the National Mental Health Center of the Russian Academy of Medical Sciences for providing the database, and the Postgraduate Program in Electrical Engineering—UFES. This research received financial support from Fundação de Amparo à Pesquisa do Espírito Santo (FAPES), number 598/2018.
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Alves, L.M., Côco, K.F., de Souza, M.L., Ciarelli, P.M. (2022). Microstate Graphs: A Node-Link Approach to Identify Patients with Schizophrenia. In: Bastos-Filho, T.F., de Oliveira Caldeira, E.M., Frizera-Neto, A. (eds) XXVII Brazilian Congress on Biomedical Engineering. CBEB 2020. IFMBE Proceedings, vol 83. Springer, Cham. https://doi.org/10.1007/978-3-030-70601-2_245
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