%0 Journal Article
	%A J. Costa and  M. Ortigueira and  A. Batista and  T. Paiva
	%D 2012
	%J International Journal of Biomedical and Biological Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 68, 2012
	%T An Automatic Sleep Spindle Detector based on WT, STFT and WMSD
	%U https://publications.waset.org/pdf/2170
	%V 68
	%X Sleep spindles are the most interesting hallmark of
stage 2 sleep EEG. Their accurate identification in a
polysomnographic signal is essential for sleep professionals to help
them mark Stage 2 sleep. Sleep Spindles are also promising objective
indicators for neurodegenerative disorders. Visual spindle scoring
however is a tedious workload. In this paper three different
approaches are used for the automatic detection of sleep spindles:
Short Time Fourier Transform, Wavelet Transform and Wave
Morphology for Spindle Detection. In order to improve the results, a
combination of the three detectors is presented and comparison with
human expert scorers is performed. The best performance is obtained
with a combination of the three algorithms which resulted in a
sensitivity and specificity of 94% when compared to human expert
	%P 397 - 400