Yucelbas C.Yucelbas S.Ozsen S.Tezel G.Yosunkaya S.2020-03-262020-03-2620179.78151E+12https://dx.doi.org/10.1109/SIU.2017.7960708https://hdl.handle.net/20.500.12395/3570725th Signal Processing and Communications Applications Conference, SIU 2017 -- 15 May 2017 through 18 May 2017 -- 128703Sleep scoring is performed by examining the recorded electroencephalogram (EEG) and some other signals recorded by a polysomnography (PSG) device. This process is considered more reliable as it is done manually by experts. However, due to the fact that experts may also be mistaken, it has led to an increase in the importance given to automatic sleep staging studies. Many methods have been tested on the signals in order to increase the success of these systems. In this study, an automatic sleep staging system was implemented using the Hilbert-Huang transformation method which is a new time-frequency analysis type. In the study, EEG signals from 5 subjects were used in the sleep laboratory. In the 5-class (Alpha, Beta, Theta, Delta and Spindle bands) applications, the highest classification success was 84.75% as a result of sequential feature selection method. © 2017 IEEE.tr10.1109/SIU.2017.7960708info:eu-repo/semantics/closedAccessANNEEGHilbert-Huang transformSleep stagingConference ObjectN/A