Sleep spindles are a hallmark of stage 2 sleep and are promising indicators of neurodegenerative disorders such as schizophrenia and dementia. In this paper two sleep spindle detectors are presented. The first is based on the Short Time Fourier Transform (STFT), the second is a novel algorithm and is based in the wave morphology of sleep spindles. Finally, a combination of the previous is proposed in a novel mixed algorithm. Performance results are presented applying the algorithms to a signal scored by two human experts. It is showed in that the combination of two algorithms, which separately provided seasonable results (around 91% sensibility), improves when they are mixed using the approach proposed (93%sensibility).
|Journal||International Journal Of Bioelectromagnetism|
|Publication status||Published - 1 Jan 2012|