Excellence in Research and Innovation for Humanity
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@article{(International Science Index):http://waset.org/publications/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   = {},
  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
    journal   = {International Journal of Medical, Health, Biomedical, Bioengineering and Pharmaceutical Engineering},  volume    = {6},
  number    = {8},
  year      = {2012},
  pages     = {397 - 400},
  ee        = {http://waset.org/publications/2170},
  url       = {http://waset.org/Publications?p=68},
  bibsource = {http://waset.org/Publications},
  issn      = {PISSN:2010-376X, EISSN:2010-3778},
  publisher = {World Academy of Science, Engineering and Technology},
  index     = {International Science Index 68, 2012},