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

Abstract

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 (FixF) (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.

Investigation of sleep spindle activity and morphology as predictors of neurocognitive functioning in medicated patients with schizophrenia

“…We detected and subtyped spindles using fixed‐frequency cut‐offs, which is an area of inconsistencies as it has been suggested that there are pronounced inter‐subject differences in the frequency profiles of fast and slow spindles (Bodizs, Kormendi, Rigo, & Lazar, ; Ujma et al., ). Despite inter‐subject variation in oscillation frequency and frequency boundary for subtyping spindles, we chose in this study to detect spindles using an automatic detector that is rather simple and has been used and validated previously (Christensen et al., ; Latreille et al., ; Martin et al., ; Schabus et al., ).…”

Memory in 3‐month‐old infants benefits from a short nap

“…Sleep spindles were detected with the Individual Adjustment Method(Bodizs, Kormendi, Rigo, & Lazar, 2009;Ujma et al, 2015), which uses individual frequency bands and amplitude criteria for sleep spindle detection. Sleep stages were scored on the basis of the EEG according to standard criteria and artefacts were manually removed using 4-s epochs.…”

“…Sleep stages were scored on the basis of the EEG according to standard criteria and artefacts were manually removed using 4-s epochs. Sleep spindles were detected with the Individual Adjustment Method(Bodizs, Kormendi, Rigo, & Lazar, 2009;Ujma et al, 2015), which uses individual frequency bands and amplitude criteria for sleep spindle detection. We performed spectral analysis for all chan-nels (fast Fourier transformation, Hanning window, 20-s epochs) with 0.25 Hz frequency bins between 1 and 48 Hz.…”

Understanding the interplay of sleep and aging: Methodological challenges

“…Based on individually identified peaks in the NREM sleep power spectrum at topographical locations where slow and fast spindles are expected (cf. Supplementary Figure 2), individually adjusted spindle frequency ranges can be definedeven if they might not match conventionally prescribed bands exactly (Adamczyk et al, 2015;Bódizs et al, 2009;Cox et al, 2017;Doppelmayr et al, 1998;Grandy et al, 2013;Klimesch, 1999;Ujma et al, 2015). Comparisons between spindle detection algorithms that use commonly predefined frequency ranges and those that adjust their frequency range definitions within individuals, provide a strikingly poor agreement with conventional (fixed frequency) algorithms.…”

“…Standard criteria to describe these age-related alterations in sleep physiology are only rarely tested and validated within heterogeneous samples that include older adults (but see, e.g., Ujma et al, 2015Ujma et al, , 2019Warby et al, 2014). Therefore, in the following, we aim to demonstrate the possible benefits and limitations of applying established indicators of sleep physiology in the context of aging 4 research.…”