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||Proceedings Of World Academy Of Science, Engineering And Technology|
|Publication status||Published - 1 Jan 2012|
Batista, A. M. G., Ortigueira, M. D., & DEE Group Author (2012). An Automatic Sleep Spindle Detector based on WT, STFT and WMSD. Proceedings Of World Academy Of Science, Engineering And Technology, 68(NA), 2154-2157.