Sleep spindles detection using autoregressive modeling

2003-06-29
GORUR, DILAN
Halıcı, Uğur
AYDIN, HAMDULLAH
GUCLU, ONGUN
ÖZGEN, FUAT
Leblebicioğlu, Mehmet Kemal
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.
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.