Bódizs R, Körmendi J, Rigó P, Lázár AS : The individual adjustment method of sleep spindle analysis: Methodological improvements and roots in the fingerprint paradigm. J. Neurosci. Methods 178(1) 205-213. (2009) (original) (raw)
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Journal of Neuroscience Methods, 2009
Evidence supports the robustness and stability of individual differences in non-rapid eye movement (NREM) sleep electroencephalogram (EEG) spectra with a special emphasis on the 9-16 Hz range corresponding to sleep spindle activity. These differences cast doubt on the universal validity of sleep spindle analysis methods based on strict amplitude and frequency criteria or a set of templates of natural spindles. We aim to improve sleep spindle analysis by the individual adjustments of frequency and amplitude criteria, the use of a minimum set of a priori knowledge, and by clear dissections of slow-and fast sleep spindles as well as to transcend the concept of visual inspection as being the ultimate test of the method's validity. We defined spindles as those segments of the NREM sleep EEG which contribute to the two peak regions within the 9-16 Hz EEG spectra. These segments behaved as slow-and fast sleep spindles in terms of topography and sleep cycle effects, while age correlated negatively with the occurrence of fast type events only. Automatic detections covered 92.9% of visual spindle detections (A&VD). More than half of the automatic detections (58.41%) were exclusively automatic detections (EADs). The spectra of EAD correlated significantly and positively with the spectra of A&VD as well as with the average (AVG) spectra. However, both EAD and A&VD had higher individual-specific spindle spectra than AVG had. Results suggest that the individual adjustment method (IAM) detects EEG segments possessing the individual-specific spindle spectra with higher sensitivity than visual scoring does.
Assessing EEG sleep spindle propagation. Part 1: Theory and proposed methodology
Journal of neuroscience methods, 2013
BACKGROUND: A convergence of studies has revealed sleep spindles to be associated with sleep-related cognitive processing and even with fundamental waking state capacities such as intelligence. However, some spindle characteristics, such as propagation direction and delay, may play a decisive role but are only infrequently investigated because of technical difficulties. NEW METHOD: A new methodology for assessing sleep spindle propagation over the human scalp using noninvasive electroencephalography (EEG) is described. This approach is based on the alignment of time-frequency representations of spindle activity across recording channels. RESULTS: This first of a two-part series concentrates on framing theoretical considerations related to EEG spindle propagation and on detailing the methodology. A short example application is provided that illustrates the repeatability of results obtained with the new propagation measure in a sample of 32 night recordings. A more comprehensive experimental investigation is presented in part two of the series. COMPARISON WITH EXISTING METHOD(S): Compared to existing methods, this approach is particularly well adapted for studying the propagation of sleep spindles because it estimates time delays rather than phase synchrony and it computes propagation properties for every individual spindle with windows adjusted to the specific spindle duration. CONCLUSIONS: The proposed methodology is effective in tracking the propagation of spindles across the scalp and may thus help in elucidating the temporal aspects of sleep spindle dynamics, as well as other transient EEG and MEG events. A software implementation (the Spyndle Python package) is provided as open source software.
Journal of Neural Engineering, 2018
Objective. EEG spindles, narrow-band oscillatory signal bursts, are widely-studied biomarkers of subject state and neurological function. Most existing methods for spindle detection select algorithm parameters by optimizing agreement with expert labels. We propose a new framework for selecting algorithm parameters based on stability of spindle properties and elucidate the dependence of these properties on parameter selection for several algorithms. Approach. To demonstrate this approach we developed a new algorithm (Spindler) that decomposes the signal using matching pursuit with Gabor atoms and computes the spindles for each point in a fine grid of parameter values. After computing characteristic surfaces as a function of parameters, Spindler selects algorithm parameters based on the stability of characteristic surface geometry. Main results. Spindler performs well relative to several common supervised and unsupervised EEG sleep spindle detection methods. Spindler is available as an open-source MATLAB toolbox (https://github.com/VisLab/EEG-Spindles). In addition to Spindler, the toolbox provides implementations of several other spindle detection algorithms as well as standardized methods for matching ground truth to predictions and a framework for understanding algorithm parameter surfaces. Significance. This work demonstrates that parameter selection based on physical constraints rather than labelled data can provide effective, fully-automated, unsupervised spindle detection. This work also exposes the dangers of applying cross-validation without considering the dependence of spindle properties on parameters. Parameters selected to optimize one performance metric or matching method are not optimized for others. Furthermore, elucidation of the stability of predicted indicators with respect to algorithm parameter selection is critical to practical application of these algorithms.
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.
Assessing EEG sleep spindle propagation. Part 2: Experimental characterization
Journal of neuroscience methods, 2013
BACKGROUND:This communication is the second of a two-part series that describes and tests a new methodology for assessing the propagation of EEG sleep spindles. Whereas the first part describes the methodology in detail, this part proposes a thorough evaluation of the approach by applying it to a sample of laboratory sleep recordings. NEW METHOD:The tested methodology is based on the alignment of time-frequency representations of spindle activity across recording channels and is used for assessing sleep spindle propagation over the human scalp using noninvasive EEG. RESULTS: Spindle propagation displays features that suggest wave displacements of global synaptic potential fields. Propagation patterns that are coherent (as opposed to random), laterally symmetrical, and highly repeatable within and between subjects were observed. Propagation was slower from posterior to anterior and from central to lateral brain regions than in the opposite directions. Propagation speeds varied between 2.3 and 7.0m/s were obtained. A distinct grouping of propagation properties was noted for a small cluster of frontal electrodes. No propagation between distantly separated scalp locations was observed. The values of spindle characteristics such as average frequency, RMS amplitude, frequency slope, and duration, depend largely on propagation direction but are only mildly correlated with propagation delay. COMPARISON WITH EXISTING METHOD(S): Results obtained are in line with many results published in the literature and offer new measures for describing sleep spindle behavior. CONCLUSIONS: Propagation properties provide new information about sleep spindle behaviors and thus allow more precise automated assessments of spindle-related functions.
European Journal of Neuroscience, 2006
Stage 2 sleep spindles have been previously viewed as useful markers for the development and integrity of the CNS and were more currently linked to ‘offline re‐processing’ of implicit as well as explicit memory traces. Additionally, it had been discussed if spindles might be related to a more general learning or cognitive ability. In the present multicentre study we examined the relationship of automatically detected slow (< 13 Hz) and fast (> 13 Hz) stage 2 sleep spindles with: (i) the Raven's Advanced Progressive Matrices (testing ‘general cognitive ability’); as well as (ii) the Wechsler Memory scale‐revised (evaluating memory in various subdomains). Forty‐eight healthy subjects slept three times (separated by 1 week) for a whole night in a sleep laboratory with complete polysomnographic montage. Whereas the first night only served adaptation and screening purposes, the two remaining nights were preceded either by an implicit mirror‐tracing or an explicit word‐pair asso...
Characteristics of human EEG sleep spindles assessed by Gabor transform
Physica A: Statistical …, 2003
The aim of this study is to show an application of the Gabor transform on the detection and characterization of human sleep EEG spindles in a sample of 10 healthy young adults, trying to identify the most useful parameters that can be used for the automatic detection and characterization of such events.