k-Means clustering by using the calculated Z-scores from QEEG data of children with dyslexia

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dc.authorid0000-0001-8382-8417
dc.contributor.authorEroğlu, Güneten_US
dc.contributor.authorArman, Fehimen_US
dc.date.accessioned2022-05-22T11:48:00Z
dc.date.available2022-05-22T11:48:00Z
dc.date.issued2023
dc.departmentIşık Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.departmentIşık University, Faculty of Engineering, Department of Computer Engineeringen_US
dc.description.abstractLearning the subtype of dyslexia may help shorten the rehabilitation process and focus more on the relevant special education or diet for children with dyslexia. For this purpose, the resting-state eyes-open 2-min QEEG measurement data were collected from 112 children with dyslexia (84 male, 28 female) between 7 and 11 years old for 96 sessions per subject on average. The z-scores are calculated for each band power and each channel, and outliers are eliminated afterward. Using the k-Means clustering method, three different clusters are identified. Cluster 1 (19% of the cases) has positive z-scores for theta, alpha, beta-1, beta-2, and gamma-band powers in all channels. Cluster 2 (76% of the cases) has negative z-scores for theta, alpha, beta-1, beta-2, and gamma-band powers in all channels. Cluster 3 (5% of the cases) has positive z-scores for theta, alpha, beta-1, beta-2, and gamma-band powers at AF3, F3, FC5, and T7 channels and mostly negative z-scores for other channels. In Cluster 3, there is temporal disruption which is a typical description of dyslexia. In Cluster 1, there is a general brain inflammation as both slow and fast waves are detected in the same channels. In Cluster 2, there is a brain maturation delay and a mild inflammation. After Auto Train Brain training, most of the cases resemble more of Cluster 2, which may mean that inflammation is reduced and brain maturation delay comes up to the surface which might be the result of inflammation. Moreover, Cluster 2 center values at the posterior parts of the brain shift toward the mean values at these channels after 60 sessions. It means, Auto Train Brain training improves the posterior parts of the brain for children with dyslexia, which were the most relevant regions to be strengthened for dyslexia.en_US
dc.description.versionPublisher's Versionen_US
dc.identifier.citationEroğlu, G. & Arman, F. (2022). k-Means clustering by using the calculated Z-scores from QEEG data of children with dyslexia. Applied Neuropsychology: Child, 12(3), 214-220. doi:10.1080/21622965.2022.2074298en_US
dc.identifier.doi10.1080/21622965.2022.2074298
dc.identifier.endpage220
dc.identifier.issn2162-2965
dc.identifier.issn2162-2973
dc.identifier.issue3
dc.identifier.pmid35575241
dc.identifier.scopus2-s2.0-85130469645
dc.identifier.scopusqualityQ2
dc.identifier.startpage214
dc.identifier.urihttps://hdl.handle.net/11729/4348
dc.identifier.urihttp://dx.doi.org/10.1080/21622965.2022.2074298
dc.identifier.volume12
dc.identifier.wosWOS:000795733400001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.indekslendigikaynakScience Citation Index Expanded (SCI-EXPANDED)en_US
dc.institutionauthorEroğlu, Güneten_US
dc.institutionauthorid0000-0001-8382-8417
dc.language.isoenen_US
dc.peerreviewedYesen_US
dc.publicationstatusPublisheden_US
dc.publisherTaylor & Francisen_US
dc.relation.journalApplied Neuropsychology: Childen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAuto train brainen_US
dc.subjectClusteringen_US
dc.subjectDevelopmental dyslexiaen_US
dc.subjectDevelopmental dyslexİaen_US
dc.subjectEEGen_US
dc.subjectAttentionen_US
dc.subjectDyslexiaen_US
dc.subjectReading disabilityen_US
dc.subjectLiteracyen_US
dc.titlek-Means clustering by using the calculated Z-scores from QEEG data of children with dyslexiaen_US
dc.typeArticleen_US
dspace.entity.typePublication

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