Automatic Sleep Spindle Detection and Genetic Influence Estimation Using Continuous Wavelet Transform.

ABSTRACT: Mounting evidence for the role of sleep spindles in neuroplasticity has led to an increased interest in these non-rapid eye movement (NREM) sleep oscillations. It has been hypothesized that fast and slow spindles might play a different role in memory processing. Here, we present a new sleep spindle detection algorithm utilizing a continuous wavelet transform (CWT) and individual adjustment of slow and fast spindle frequency ranges. Eighteen nap recordings of ten subjects were used for algorithm validation. Our method was compared with both a human scorer and a commercially available SIESTA spindle detector. For the validation set, mean agreement between our detector and human scorer measured during sleep stage 2 using kappa coefficient was 0.45, whereas mean agreement between our detector

SUBMITTER: Adamczyk M 

PROVIDER: S-EPMC4652604 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature