Investigation of the Effects of age and Gender Differences on Brain Activity Analysing Complexity Measures of Sleep EEG Signals by Machine Learning

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dc.authorscopusid57220957672
dc.authorscopusid57220963017
dc.authorscopusid36083412800
dc.contributor.authorAkkaya, E.
dc.contributor.authorKarabudak, R.
dc.contributor.authorTosun, P. D.
dc.date.accessioned2021-12-01T18:38:49Z
dc.date.available2021-12-01T18:38:49Z
dc.date.issued2020
dc.department[Belirlenecek]en_US
dc.description2020 Innovations in Intelligent Systems and Applications Conference, ASYU 2020 -- 15 October 2020 through 17 October 2020 -- -- 165305en_US
dc.description.abstractMachine learning applications are used in the health sector, and as a result, many easy and applicable methods have been developed for analysis, interpretation, diagnosis and treatment. With the use of these applications in the sleep area, progress is expected in the diagnosis and treatment of sleep disorders, which is a major problem for millions of people. In order to achieve this goal, understanding the effects of age and gender factors that significantly affect brain activity during sleep, is important for all studies to be conducted. In this study, Lempel-Ziv complexity measures calculated from all-night sleep electroencephalogram signals were used in age and gender classification. Artificial Neural Networks (ANN) model has been used as the basic classifier in the studies. For ANN classifier, firstly, a network model was coded using MATLAB, and then a network model was designed using MATLAB nntool application for performance comparison. With the Classification Learner application, other classifier models were trained and their performances were examined, Decision Trees and Ensemble Classifiers showing the most successful results were included in the study. At the end of the study, age classification with 54.5% accuracy and general gender classification with 66.7% accuracy were achieved with the ANN model created using the MATLAB. © 2020 IEEE.en_US
dc.identifier.doi10.1109/ASYU50717.2020.9259838
dc.identifier.isbn9781728191362
dc.identifier.scopus2-s2.0-85097962560en_US
dc.identifier.urihttps://doi.org/10.1109/ASYU50717.2020.9259838
dc.identifier.urihttps://hdl.handle.net/20.500.12684/9858
dc.indekslendigikaynakScopusen_US
dc.language.isotren_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofProceedings - 2020 Innovations in Intelligent Systems and Applications Conference, ASYU 2020en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial Neural Networksen_US
dc.subjectBrain Activityen_US
dc.subjectElectroencephalogramen_US
dc.subjectLempel-Ziv Complexityen_US
dc.subjectMachine Learningen_US
dc.subjectSleepen_US
dc.titleInvestigation of the Effects of age and Gender Differences on Brain Activity Analysing Complexity Measures of Sleep EEG Signals by Machine Learningen_US
dc.typeConference Objecten_US

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