Sleep spindles detection using autoregressive modeling

Date

2003-06-29

Author

GORUR, DILAN
Halıcı, Uğur
AYDIN, HAMDULLAH
GUCLU, ONGUN
ÖZGEN, FUAT
Leblebicioğlu, Mehmet Kemal

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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.

URI

https://hdl.handle.net/11511/39762

DOI

https://doi.org/10.1.1.323.8013

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Department of Electrical and Electronics Engineering, Conference / Seminar

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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...
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Citation Formats

D. GORUR, U. Halıcı, H. AYDIN, O. GUCLU, F. ÖZGEN, and M. K. Leblebicioğlu, “Sleep spindles detection using autoregressive modeling,” 2003, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/39762.