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. Visual spindle scoring however is a tedious workload. In this paper two different approaches are used for the automatic detection of sleep spindles: Short Time Fourier Transform and Automatic Visual Scoring. The results obtained using both methods are compared with human expert scorers.
|Title of host publication||IFIP Advances in Information and Communication Technology|
|Publication status||Published - 1 Jan 2012|
|Event||3rd IFIP/SOCOLNET Doctoral Conference on Computing, Electrical and Industrial Systems - |
Duration: 1 Jan 2012 → …
|Conference||3rd IFIP/SOCOLNET Doctoral Conference on Computing, Electrical and Industrial Systems|
|Period||1/01/12 → …|