An automatic sleep spindle detector based on wavelets and the teager energy operator

Abstract

Sleep spindles are one of the most important short-lasting rhythmic events occurring in the EEG during Non-Rapid Eye Movement sleep. Their accurate identification in a polysomnographic signal is essential for sleep professionals to help them mark Stage 2 sleep. Visual spindle scoring however is a tedious workload, as there are often a thousand spindles in an all-night recording. In this paper a novel approach for the automatic detection of sleep spindles based upon the Teager Energy Operator and wavelet packets has been presented. The Teager operator was found to accurately enhance periodic activity in epochs of the EEG containing spindles. The wavelet packet transform proved effective in accurately locating spindles in the time-frequency domain. The autocorrelation function of the resultant Teager signal and the wavelet packet energy ratio were used to identify epochs with spindles. These two features were integrated into a spindle detection algorithm which achieved an accuracy of 93.7%.

A review of signals used in sleep analysis

“…The features are almost always based on frequency domain parameters such as an autoregressive (AR) model (Roberts & Tarassenko 1992), Fourier or bispectral analysis (Wang et al 2009), or wavelet approaches (Ahmed et al 2009). Occasionally, time domain features are used instead, or as well, such as entropy (Jiayi et al 2007).…”

Learning machines and sleeping brains: Automatic sleep stage classification using decision-tree multi-class support vector machines

“…It is possible that higher performances could be achieved by exploring the discriminative power of further sleep specific neuronal phenomena: Quantifying the presence of K-complex waves (Colrain, 2005;Loomis et al, 1938), sleep spindles (Andrillon et al, 2011;Contreras and Steriade, 1996), bursts of high-frequency gamma oscillations (Ayoub et al, 2012;Dalal et al, 2010;Le Van Quyen et al, 2010;Valderrama et al, 2012;Worrell et al, 2012), monofractal and multifractal properties of the human sleep EEG (Weiss et al, 2009(Weiss et al, , 2011Zorick and Mandelkern, 2013) and including them in the proposed DSVM method could potentially lead to an even better classification. The detection of some of these phenomena might be enhanced by recent methodological developments (Ahmed et al, 2009;Babadi et al, 2012;Chaibi et al, 2012Chaibi et al, , 2013Chaibi et al, , 2014Jaleel et al, 2014;Nonclercq et al, 2013;O'Reilly and Nielsen, 2014a,b;Warby et al, 2014;Worrell et al, 2012). Furthermore, features such as cross-frequency interactions, longrange coupling among distant electrodes and long-range temporal correlations may also provide efficient novel markers for distinct sleep stages.…”

Sleep Stage Classification Using EEG Signal Analysis: A Comprehensive Survey and New Investigation

“…The dataset used in this study is publicly available from the Sleep-EDF database (expanded) on the Physionet website (https://physionet.org/physiobank/database/sleep-edfx/) and has been widely used in the literature [2,7,11,12,16,18,24,33,36,38,48,49,57,87,91,105,106,108,109,114]. The database is a collection of 61 PSGs obtained from 1987-2002 including the older Sleep EDF database recordings prior to 1991.…”