Multifractal detrended fluctuation analysis of human EEG: preliminary investigation and comparison with the wavelet transform modulus maxima technique

Abstract

Recently, many lines of investigation in neuroscience and statistical physics have converged to raise the hypothesis that the underlying pattern of neuronal activation which results in electroencephalography (EEG)

Generalized Information Equilibrium Approaches to EEG Sleep Stage Discrimination

“…Thus, there is an overall richness of the IE description of differences in sleep and consciousness states that may well suit it to be used as a general tool to study states of altered cortical function. Indeed, the apparent discriminative power of IE (Tables 1 – 4 ) for sleep staging compares favorably with many other descriptions of computer-based analytic techniques for EEG, including fractals [ 6 ], multifractals [ 7 , 9 , 29 , 30 ], and Tsallis entropy [ 4 ], not to mention automatic feature extraction from spectral analysis (reviewed in [ 31 ]).…”

“…An additional dataset of n = 13 subjects of waking EEG, n = 10 subjects of REM sleep EEG, and n = 8 subjects of sleep stage 1 EEG (1 minute each, nonoverlapping with the larger 8 min EEG dataset) was also generated from the larger dataset. The exact dataset used has previously been described in a prior unrelated study [ 9 ]. EEG segments chosen for further analysis were selected on the basis of the absence of movement artifacts and disordered breathing, which limited the amount of suitable tracings.…”

“…No demographic and limited clinical information was available from the dataset. Digitized 250 Hz EEG recordings on a 10–20 international system were used with a single EEG lead for each subject, which differed among subjects; no information was provided about reference electrode placement [ 9 ]. Use of the dataset for this study was approved by the VA West Los Angeles IRB.…”

“…Given that the cortical neuronal dynamics largely responsible for the summed local field potentials that comprise EEG are characterized by scale-free avalanches consistent with a system at a critical state that is well described by power-law dynamics, many attempts have been made to analyze EEG using methods derived from fractal and other nonlinear theories, with some degree of success [ 4 – 9 ]. Another avenue of physical understanding of cortical avalanche dynamics would be via statistical physics and thermodynamics; however, the relatively large magnitude changes in scalp-recorded voltages in EEG clearly could not be characteristic of a system in thermodynamic equilibrium [ 10 ].…”

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).…”