Sleep spindle and K-complex detection using tunable Q-factor wavelet transform and morphological component analysis

Abstract

A novel framework for joint detection of sleep spindles and K-complex events, two hallmarks of sleep stage S2, is proposed. Sleep electroencephalography (EEG) signals are split into oscillatory (spindles) and transient (K-complex) components. This decomposition is conveniently achieved by applying morphological component analysis (MCA) to a sparse representation of EEG segments obtained by the recently introduced discrete tunable Q-factor wavelet transform (TQWT). Tuning the Q-factor provides a convenient and elegant tool to naturally decompose the signal into an oscillatory and a transient component. The actual detection step relies on thresholding (i) the transient component to reveal K-complexes and (ii) the time-frequency representation of the oscillatory component to identify sleep spindles. Optimal thresholds are derived from ROC-like curves (sensitivity vs. FDR) on training sets and the performance of the method is assessed on test data sets. We assessed the performance of our method using full-night sleep EEG data we collected from 14 participants. In comparison to visual scoring (Expert 1), the proposed method detected spindles with a sensitivity of 83.18% and false discovery rate (FDR) of 39%, while K-complexes were detected with a sensitivity of 81.57% and an FDR of 29.54%. Similar performances were obtained when using a second expert as benchmark. In addition, when the TQWT and MCA steps were excluded from the pipeline the detection sensitivities dropped down to 70% for spindles and to 76.97% for K-complexes, while the FDR rose up to 43.62 and 49.09%, respectively. Finally, we also evaluated the performance of the proposed method on a set of publicly available sleep EEG recordings. Overall, the results we obtained suggest that the TQWT-MCA method may be a valuable alternative to existing spindle and K-complex detection methods. Paths for improvements and further validations with large-scale standard open-access benchmarking data sets are discussed.

Increased Evoked Potentials to Arousing Auditory Stimuli during Sleep: Implication for the Understanding of Dream Recall

“…KCs were semi-automatically detected using an open-access validated method (Lajnef et al, 2015, 2017) which is based on a combination of the tunable Q-factor wavelet transform and a morphological component analysis. This approach requires an initial calibration step where a small subset of the data is visually scored for KCs and then used to derive an optimal threshold.…”

The case for using digital EEG analysis in clinical sleep medicine

“…Accordingly, such potentially useful information is not captured. A number of digital methods for identifying and characterizing spindles (Ferrarelli et al 2007;Martin et al 2013;Wamsley et al 2012;Mölle et al 2002;Bódizs et al 2009;Wendt et al 2012;Devuyst et al 2010;Lajnef et al 2015b;Ray et al 2015) and K complexes (Bremer et al 1970;Krohne et al 2014;Richard and Lengelle 1998;Bankman et al 1992;Parekh et al 2015) have been proposed and used in research studies but none has been adapted to clinical studies. Although there is agreement on the visual appearance of these events (Berry et al 2012), there is no agreement on how to define them in quantitative terms.…”

An automatic analysis of sleep stage 2 based on the Fujimori method

“…Meanwhile in computer‐based automatic scoring, methods in which the electroencephalogram for a certain period of time is subjected to frequency conversion and then examined are common. Examples thereof include methods using band pass filters, methods using short‐time Fourier transform, methods using rapid Fourier transform, and methods using wavelet transform . These analysis methods are based on mathematical knowledge and while they are effective for examining the physiological aspects of electroencephalograms, they cannot be used to analyze chronological changes because the analysis area is subdivided.…”