Ozsen S.Dursun M.Yosunkaya S.2020-03-262020-03-2620159.78147E+12https://dx.doi.org/10.1109/SIU.2015.7129904https://hdl.handle.net/20.500.12395/327652015 23rd Signal Processing and Communications Applications Conference, SIU 2015 -- 16 May 2015 through 19 May 2015 -- 113052Sleep spindle is a very determinant factor for detection of Non-REM2 stage in sleep staging studies. When it is considered that about half of the sleep consists of Non-REM2 stage, the importance of automatic sleep spindle detection stands out. In this study, three different spectral analysis method- FFT, Welch and AR have been used to estimate the frequency spectrum of sleep EEG signal and feature extraction from this spectrum has been realized. Obtained features have been used in ANN to classify EEG epochs as epochs with spindle and epochs without spindle. It has been observed that least classification error was obtained with FFT as 15.16%. © 2015 IEEE.tr10.1109/SIU.2015.7129904info:eu-repo/semantics/closedAccessANNFftSleep spindle classificationWelchYule-ARConference Object636639N/A