Automatic sleep spindle detection and genetic influence estimation using continuous wavelet transform

Abstract

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Mounting evidence for the role of sleep spindles for neuroplasticity led to an increased interest in these 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 and individual adjustment of slow and fast spindle frequency ranges. 18 nap recordings of 10 subjects were used for algorithm validation. Our method was compared with human scorer and 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 and SIESTA algorithm was 0.62. Our algorithm was also applied to sleep-related memory consolidation data previously analyzed with SIESTA detector and confirmed previous findings of significant correlation between spindle density and declarative memory consolidation. Then, we applied our method to a study in monozygotic (MZ) and dizygotic (DZ) twins examining the heritability of slow and fast sleep spindle parameters. Our analysis revealed strong genetic influence of all slow spindle parameters, weaker genetic effect on fast spindles and no effects on fast spindle density and number during stage 2 sleep.

Keywords

• Twins
• EEG
• heritability
• automatic detection
• sleep spindle