Sleep spindles detection using short time Fourier transform and neural networks

Date

2002-01-01

Author

Gorur, Dilan
Halıcı, Uğur
Aydin, Hamdullah
Ongun, Guclu
Ozgen, Fuat
Leblebicioglu, Kemal

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Sleep spindles are a 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.

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https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=0036082854&origin=inward
https://hdl.handle.net/11511/87697

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Sleep spindles detecton using short time fourier transform and neural networks Gorur, Dilan; Halıcı, Uğur; Aydın, Hamdullah; Ongun, Güçlü; Özgen, Fuat; Leblebicioğlu, Mehmet Kemal (2002-05-17) Sleep spindles are a 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 d...
Sleep spindles detection using autoregressive modeling GORUR, DILAN; Halıcı, Uğur; AYDIN, HAMDULLAH; GUCLU, ONGUN; ÖZGEN, FUAT; Leblebicioğlu, Mehmet Kemal (2003-06-29) Being one of the well-defined and functional rhythmic activities observed in sleep EEG, sleep spindles are significant for brain research. Visual detection of sleep spindles is very time consuming and subjective. In this study, automated spindle detection by using AR modeling for feature extraction is proposed. Multilayer Perceptron (MLP) and Support Vector Machine (SVM) are used as classifiers for comparison. Performances were found as 93.6% for the MLP and 94.4% for the SVM classifiers.
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Citation Formats

D. Gorur, U. Halıcı, H. Aydin, G. Ongun, F. Ozgen, and K. Leblebicioglu, “Sleep spindles detection using short time Fourier transform and neural networks,” 2002, Accessed: 00, 2021. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=0036082854&origin=inward.