Abstract
We present a complete framework for time-frequency parametrization of EEG transients, based upon matching pursuit (MP) decomposition, applied to the detection of sleep spindles. Ranges of spindles duration (>0.5 s) and frequency (11–16 Hz) are taken directly from their standard definitions. Minimal amplitude is computed from the distribution of the root mean square (RMS) amplitude of the signal within the frequency band of sleep spindles. Detection algorithm depends on the choice of just one free parameter, which is a percentile of this distribution. Performance of detection is assessed on the first cohort/second subset of the Montreal Archive of Sleep Studies (MASS-C1/SS2). Cross-validation performed on the 19 available overnight recordings returned the optimal percentile of the RMS distribution close to 97 in most cases, and the following overall performance measures: sensitivity 0.63 ± 0.06, positive predictive value 0.47 ± 0.08, and Matthews coefficient of correlation 0.51 ± 0.04. These concordances are similar to the results achieved on this database by other automatic methods. Proposed detailed parametrization of sleep spindles within a universal framework, encompassing also other EEG transients, opens new possibilities of high resolution investigation of their relations and detailed characteristics. MP decomposition, selection of relevant structures, and simple creation of EEG profiles used previously for assessment of brain activity of patients in disorders of consciousness are implemented in a freely available software package Svarog (Signal Viewer, Analyzer and Recorder On GPL) with user-friendly, mouse-driven interface for review and analysis of EEG. Svarog can be downloaded from http://braintech.pl/svarog.
Highlights
Sleep spindles are defined in Rechtschaffen and Kales (1968); Ibert et al (2007) as a train of distinct waves with frequency 11–16 Hz with a duration ≥ 0.5 s Detection of these structures by human experts, trained in visual analysis of EEG, constitutes a gold standard.Spindles in Svarogthe inter-expert agreement in scoring sleep spindles is limited
The inter-expert agreement in scoring sleep spindles is limited. This drawback undermines the idea of repeatability of experiments, which lies at the foundations of hard sciences: the same study of sleep spindles on the same dataset may yield different results, because of differences in the visual selections done by human experts
Performance of Sleep Spindles Detection in Individual Cases As described in Section 2.4, the minimal amplitude of candidate waveform is a free parameter in the proposed detector of sleep spindles
Summary
Sleep spindles are defined in Rechtschaffen and Kales (1968); Ibert et al (2007) as a train of distinct waves with frequency 11–16 Hz (most commonly 12–14 Hz) with a duration ≥ 0.5 s Detection of these structures by human experts, trained in visual analysis of EEG, constitutes a gold standard. The inter-expert agreement in scoring sleep spindles is limited. This drawback undermines the idea of repeatability of experiments, which lies at the foundations of hard sciences: the same study of sleep spindles on the same dataset may yield different results, because of differences in the visual selections done by human experts. Explosion of the applications of computerized signal processing methods resulted in a multitude of automatic detection algorithms. Signal from the previous step is subjected to amplitude thresholding in the time domain
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