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Sleep spindles detecton using short time fourier transform and neural networks
Date
2002-05-17
Author
Gorur, Dilan
Halıcı, Uğur
Aydın, Hamdullah
Ongun, Güçlü
Özgen, Fuat
Leblebicioğlu, Mehmet Kemal
Metadata
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This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
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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.
Subject Keywords
Sleep spindles
,
Short time Fourier transform
,
Multi layer perceptron
,
Support vector machine
URI
https://hdl.handle.net/11511/33408
DOI
https://doi.org/10.1109/ijcnn.2002.1007762
Collections
Department of Electrical and Electronics Engineering, Conference / Seminar