Sleep spindles detection using short time Fourier transform and neural networks

Author

Gorur, Dilan ; Halici, Ugur ; Aydin, Hamdullah ; Ongun, Guclu ; Ozgen, Fuat ; Leblebicioglu, Kemal

Author_Institution

Dept. of Electr. & Electron. Eng., Middle East Tech. Univ., Ankara, Turkey

Volume

2

fYear

2002

fDate

6/24/1905 12:00:00 AM

Firstpage

1631

Lastpage

1636

Abstract

Sleep spindles are 2 hallmark of the stage 2 sleep. Their distribution over the non-REM sleep is clinically important. In this paper, a method that detects the sleep spindles in sleep EEG is proposed. Short time Fourier transform is used for feature extraction. Both multilayer perceptron and Support Vector Machine are utilized in detection of the spindles in sleep EEG for comparison. The classification performance of MLP is found to be 88.7% and that of SVM as 95.4%. It should be noted that there might be differences also in visual scoring by experts, so the results obtained are quite satisfactory

Keywords

Fourier transforms; electroencephalography; feature extraction; learning automata; medical image processing; multilayer perceptrons; sleep; classification performance; feature extraction; multilayer perceptron; neural networks; non-REM sleep; short time Fourier transform; sleep EEG; sleep spindles detection; stage 2 sleep; support vector machine; visual scoring; Artificial neural networks; Brain; Electroencephalography; Electromyography; Electrooculography; Fourier transforms; Neural networks; Sleep; Support vector machine classification; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on

Conference_Location

Honolulu, HI

ISSN

1098-7576

Print_ISBN

0-7803-7278-6

Type

conf

DOI

10.1109/IJCNN.2002.1007762

Filename

1007762