Effect of some power spectral density estimation methods on automatic sleep stage scoring using artificial neural networks

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dc.contributor.authorYucelbas C.
dc.contributor.authorOzsen S.
dc.contributor.authorGunes S.
dc.contributor.authorYosunkaya S.
dc.date.accessioned2020-03-26T18:48:12Z
dc.date.available2020-03-26T18:48:12Z
dc.date.issued2013
dc.departmentSelçuk Üniversitesien_US
dc.descriptionIADIS International Conference Intelligent Systems and Agents 2013, ISA 2013, IADIS European Conference on Data Mining 2013, ECDM 2013, Part of the IADIS Multi Conference on Computer Science and Information Systems 2013, MCCSIS 2013 -- 22 July 2013 through 24 July 2013 -- Prague -- 100179en_US
dc.description.abstractSleep staging has an important role in diagnosing sleep disorders. It is usually done by a sleep expert through examining sleep Electroencephalogram (EEG), Electrooculogram (EOG), Electromyogram (EMG) signals of the patients and determining the stages of sleep in different time sections named as epochs. Manual sleep staging is preferred among the sleep experts but because it is rather tiring and time consuming task, automatic sleep stage scoring systems get popularity. In this study, we obtained EEG, EMG and EOG signals of four healthy people at sleep laboratory of Meram Medicine Faculty of Necmettin Erbakan University to use them in sleep staging and extracted 20 different features by using some power spectral density estimation methods which are: Fast Fourier Transform (FFT), Welch and Autoregressive (AR). We evaluated the effects of these methods on sleep staging through using ANN classifier. Comparison between these methods was done on each individual whose data were utilized separately from others. According to the results, mean of test classification accuracies for all of subjects were obtained as 74.14%, 71,57 and 70.34% with use of FFT, Welch and AR, respectively. © 2013 IADIS.en_US
dc.identifier.endpage50en_US
dc.identifier.isbn9.78973E+12
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage43en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12395/30136
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.relation.ispartofProceedings of the IADIS International Conference Intelligent Systems and Agents 2013, ISA 2013, Proceedings of the IADIS European Conference on Data Mining 2013, ECDM 2013en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectArtificial neural networksen_US
dc.subjectAutomatic sleep stageen_US
dc.subjectEEGen_US
dc.subjectPSDen_US
dc.titleEffect of some power spectral density estimation methods on automatic sleep stage scoring using artificial neural networksen_US
dc.typeConference Objecten_US

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