Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/3772
Title: Classification of Sleep Stages Using PSG Recording Signals
Authors: Koca, Yasin
Özşen, Seral
Göğüş, Fatma Zehra
Tezel, Gülay
Küççüktürk, Serkan
Vatansev, Hülya
Keywords: PSG
Sleep Stages
EEG
EOG
EMG
Bagged Trees PSG
Uyku Evreleme
EEG
EOG
EMG
Torbalı Ağaçlar
Abstract: Automatic sleep staging is aimed within the scope of this paper. Sleep staging is a study by a sleep specialist. Since this process takes quite a long time and sleep is a method based on the knowledge and experience, it is inevitable for each person to show different results. For this, an automatic sleep staging method has been introduced. In the study, EEG (Electroencephalogram), EOG (Electrooculogram), EMG (Electromyogram) data recorded by PSG (Polysomnography) device for seven patients in Necmettin Erbakan University sleep laboratory were used. 81 different features were taken from the data in time and frequency environment. Also, PCA (Principal component analysis) and SFS (Sequential forward selection) feature selection methods were used. The classification success of the sleep phases in different machine learning methods was measured by using the received features. Linear D. (Linear Discriminant Analysis), Cubic SVM (Support vector machine), Weighted kNN (k nearest neighbor), Bagged Trees, ANN (Artificial neural network) were used as classifiers. System success was achieved with a 5 fold cross-validation method. Accuracy rates obtained were respectively 55.6%, 65.8%, 67%, 72.1%, and 69.1%.
URI: https://doi.org/10.31590/ejosat.804709
https://search.trdizin.gov.tr/yayin/detay/1135937
https://hdl.handle.net/20.500.13091/3772
ISSN: 2148-2683
Appears in Collections:TR Dizin İndeksli Yayınlar Koleksiyonu / TR Dizin Indexed Publications Collections

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