Sleep spindles detection using autoregressive modeling

Date

2003-06-29

Author

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

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

1
views

0
downloads

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

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar