%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 scorers. %P 397 - 400