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

Contains fulltext : 151960.pdf (publisher's version ) (Open Access)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

A comparison of two sleep spindle detection methods based on all night averages:individually adjusted vs. fixed frequencies

Sleep spindles are frequently studied for their relationship with state and trait cognitive variables, and they are thought to play an important role in sleep-related memory consolidation. Due to their frequent occurrence in NREM sleep, the detection of sleep spindles is only feasible using automatic algorithms, of which a large number is available. We compared subject averages of the spindle parameters computed by a fixed frequency (11-13 Hz for slow spindles, 13-15 Hz for fast spindles) automatic detection algorithm and the individual adjustment method (IAM), which uses individual frequency bands for sleep spindle detection. Fast spindle duration and amplitude are strongly correlated in the two algorithms, but there is little overlap in fast spindle density and slow spindle parameters in general. The agreement between fixed and manually determined sleep spindle frequencies is limited, especially in case of slow spindles. This is the most likely reason for the poor agreement between the two detection methods in case of slow spindle parameters. Our results suggest that while various algorithms may reliably detect fast spindles, a more sophisticated algorithm primed to individual spindle frequencies is necessary for the detection of slow spindles as well as individual variations in the number of spindles in general

Sleep-spindle detection: crowdsourcing and evaluating performance of experts, non-experts and automated methods

Sleep spindles are discrete, intermittent patterns of brain activity observed in human electroencephalographic data. Increasingly, these oscillations are of biological and clinical interest because of their role in development, learning and neurological disorders. We used an Internet interface to crowdsource spindle identification by human experts and non-experts, and we compared their performance with that of automated detection algorithms in data from middle- to older-aged subjects from the general population. We also refined methods for forming group consensus and evaluating the performance of event detectors in physiological data such as electroencephalographic recordings from polysomnography. Compared to the expert group consensus gold standard, the highest performance was by individual experts and the non-expert group consensus, followed by automated spindle detectors. This analysis showed that crowdsourcing the scoring of sleep data is an efficient method to collect large data sets, even for difficult tasks such as spindle identification. Further refinements to spindle detection algorithms are needed for middle- to older-aged subjects

Nicotine increases sleep spindle activity

Studies have shown that both nicotine and sleep spindles are associated with enhanced memorisation. Further, a few recent studies have shown how cholinergic input through nicotinic and muscarinic receptors can trigger or modulate sleep processes in general, and sleep spindles in particular. To better understand the interaction between nicotine and sleep spindles, we compared in a single blind randomised study the characteristics of sleep spindles in 10 healthy participants recorded for 2 nights, one with a nicotine patch and one with a sham patch. We investigated differences in sleep spindle duration, amplitude, intra-spindle oscillation frequency and density (i.e. spindles per min). We found that under nicotine, spindles are more numerous (average increase: 0.057 spindles per min; 95% confidence interval: [0.025-0.089]; p = .0004), have higher amplitude (average amplification: 0.260 μV; confidence interval: [0.119-0.402]; p = .0032) and last longer (average lengthening: 0.025 s; confidence interval: [0.017-0.032]; p = 2.7e-11). These results suggest that nicotine can increase spindle activity by acting on nicotinic acetylcholine receptors, and offer an attractive hypothesis for common mechanisms that may support memorisation improvements previously reported to be associated with nicotine and sleep spindles