Effective channels in classification and functional connectivity pattern of prefrontal cortex by functional near infrared spectroscopy signals

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dc.authoridAkin, Ata/0000-0002-1773-0857|maghooli, keivan/0000-0003-0980-0154
dc.authorwosidAkin, Ata/AAF-2494-2019
dc.contributor.authorEinalou, Zahra
dc.contributor.authorMaghooli, Keivan
dc.contributor.authorSetarehdan, Seyed Kamaledin
dc.contributor.authorAkin, Ata
dc.date.accessioned2024-07-18T20:42:44Z
dc.date.available2024-07-18T20:42:44Z
dc.date.issued2016
dc.departmentİstanbul Bilgi Üniversitesien_US
dc.description.abstractIn this paper, we apply support vector machine (SVM) based classification of functional near-infrared spectroscopy (fNIRS) which is non-invasive monitoring of human brain function by measuring the changes in the concentration of oxyhemoglobin and deoxyhemoglobin. Data collected from 11 healthy volunteers and 16 schizophrenia subjects. Signals were first preprocessed and decomposed by using discrete wavelet transform DWT to eliminate systemic physiological interference. A preliminary analysis based on Genetic Algorithm (GA) favored eight channels of the reconstructed fNIRS signals for further analysis. Energy in these 8 reconstructed signals was computed and used for classification of signals. SVM based classifier was employed to diagnosis schizophrenia. The results show the promising classification accuracy of nearly 84% in detection of schizophrenia from healthy subjects. The major finding of this study is that selected channels were able to identify differences in functional connectivity patterns of prefrontal cortex (PFC) elicited by Stroop task. (C) 2015 Elsevier GmbH. All rights reserved.en_US
dc.identifier.doi10.1016/j.ijleo.2015.12.090
dc.identifier.endpage3275en_US
dc.identifier.issn0030-4026
dc.identifier.issue6en_US
dc.identifier.scopus2-s2.0-84955607047en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage3271en_US
dc.identifier.urihttps://doi.org/10.1016/j.ijleo.2015.12.090
dc.identifier.urihttps://hdl.handle.net/11411/7394
dc.identifier.volume127en_US
dc.identifier.wosWOS:000369456500018en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Gmbhen_US
dc.relation.ispartofOptiken_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectSupport Vector Machineen_US
dc.subjectGenetic Algorithmen_US
dc.subjectWaveleten_US
dc.subjectStroop Tasken_US
dc.subjectPartial Correlationen_US
dc.subjectSchizophreniaen_US
dc.subjectOxygenationen_US
dc.subjectActivationen_US
dc.subjectNetworksen_US
dc.titleEffective channels in classification and functional connectivity pattern of prefrontal cortex by functional near infrared spectroscopy signalsen_US
dc.typeArticleen_US

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