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 and SIESTA algorithm was 0.62. Our algorithm was also applied to sleep-related memory consolidation data previously analyzed with a SIESTA detector and confirmed previous findings of significant correlation between spindle density and declarative memory consolidation. We then applied our method to a study in monozygotic (MZ) and dizygotic (DZ) twins, examining the genetic component of slow and fast sleep spindle parameters. Our analysis revealed strong genetic influence on variance of all slow spindle parameters, weaker genetic effect on fast spindles, and no effects on fast spindle density and number during stage 2 sleep.
Highlights
Sleep spindles are one of the hallmarks in electroencephalographic (EEG) signal during non-rapid eye movement (NREM) sleep
The experimental protocol was approved by the Ethics Committee of the Ludwigs Maximilian University, Faculty of Medicine, Munich and written informed consent was obtained from the participants
Results show that a similar amount of detected spindles between our algorithm and SIESTA detector resulted in the highest possible combination of sensitivity and precision
Summary
Sleep spindles are one of the hallmarks in electroencephalographic (EEG) signal during non-rapid eye movement (NREM) sleep. They are characterized as bursts of rhythmical activity in the 10–16 Hz frequency range, with waxing and waning shapes lasting usually from 0.5–2.5 s. Low-resolution electromagnetic tomography (LORETA) demonstrated a distributed slow spindle source in the prefrontal cortex and a fast spindle source in the precuneus (Anderer et al, 2001). Both spindle types are generated via thalamic-cortical loops (Astori et al, 2013). The average slow spindle peak is 11.5 Hz and fast spindle peak is 13 Hz, with large inter-subject variation (Werth et al, 1997)
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