Comparison of some spectral analysis methods in detection of sleep spindles using YSA

Author

Ozsen, Seral ; Dursun, Mehmet ; Yosunkaya, Sebnem

Author_Institution

Elektrik-Elektron. Muhendisligi Bolumu, Selcuk Univ., Konya, Turkey

fYear

2015

fDate

16-19 May 2015

Firstpage

636

Lastpage

639

Abstract

Sleep 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%.

Keywords

electroencephalography; fast Fourier transforms; feature extraction; medical signal processing; neural nets; ANN; EEG signal; FFT; YSA; automatic sleep spindle detection; feature extraction; least classification error; non-REM2; sleep spindles detection; spectral analysis methods; Artificial neural networks; Brain modeling; Electroencephalography; Electromyography; Electrooculography; Sleep; Spectral analysis; ANN; Sleep spindle classification; Yule-AR; fft; welch;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing and Communications Applications Conference (SIU), 2015 23th

Conference_Location

Malatya

Type

conf

DOI

10.1109/SIU.2015.7129904

Filename

7129904