TY  - JOUR
TI  - Classification of Sleep Stages Using PSG Recording Signals
AB  - 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%.
AU  - TEZEL, Gülay
AU  - GÖĞÜŞ, Fatma Zehra
AU  - KÜÇÇÜKTÜRK, Serkan
AU  - Koca, Yasin
AU  - ÖZŞEN, SERAL
AU  - vatansev, hülya
DO  - 10.31590/ejosat.804709
PY  - 2020
JO  - Avrupa Bilim ve Teknoloji Dergisi
VL  - 0
IS  - Ejosat Özel Sayı 2020 (ICCEES)
SN  - 2148-2683
SP  - 315
EP  - 321
DB  - TRDizin
UR  - http://search/yayin/detay/1135937
ER  -