@article{(Open Science Index):https://publications.waset.org/pdf/2170,
	  title     = {An Automatic Sleep Spindle Detector based on WT, STFT and WMSD},
	  author    = {J. Costa and  M. Ortigueira and  A. Batista and  T. Paiva},
	  country	= {},
	  institution	= {},
	  abstract     = {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
scorers.},
	    journal   = {International Journal of Biomedical and Biological Engineering},
	  volume    = {6},
	  number    = {8},
	  year      = {2012},
	  pages     = {397 - 400},
	  ee        = {https://publications.waset.org/pdf/2170},
	  url   	= {https://publications.waset.org/vol/68},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 68, 2012},
	}