Nonlinear dynamical analysis of sleep electroencephalography using fractal and entropy approaches

Ma, Yan; Shi, Wenbin; Peng, Chung-Kang; Yang, Albert C.

doi:10.1016/j.smrv.2017.01.003

Cited by 138 publications

(93 citation statements)

References 80 publications

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“…The aberrant neural structural or functional connectivity that indicates the deficit of information communication may play a role in mental illness, for example, in schizophrenia (Friston and Frith, 1995;Sokunbi et al, 2014). Such a perspective has inspired a number of studies in various neuropsychiatric disorders, such as sleep (Lo et al, 2002;Ma et al, 2017), mood disorders (Boettger et al, 2008;Voss et al, 2006), attention-deficit hyperactivity disorder (ADHD) (Ghassemi et al, 2012;Sokunbi et al, 2013), and autism spectrum disorder (Geschwind and Levitt, 2007;Wass, 2011).…”

Section: Introductionmentioning

confidence: 99%

Alteration of Power Law Scaling of Spontaneous Brain Activity in Schizophrenia

Lee

Huang

Lin³

et al. 2020

Preprint

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Nonlinear dynamical analysis has been used to quantify the complexity of brain signal at temporal scales. Power law scaling is a well-validated method in physics that has been used to describe the complex nature of a system across different time scales. In this research, we investigated the change of power-law characteristics in a large-scale resting-state fMRI data of schizophrenia (N = 200) and healthy participants (N = 200) derived from Taiwan Aging and Mental Illness cohort. Fourier transform was used to determine the power spectral density (PSD) of resting-state fMRI signal. We estimated the power law scaling of PSD of resting-state fMRI signal by determining the slope of the regression line fitting to the log-log plot of PSD. The power law scaling represents the dynamical properties of resting-state fMRI signal ranging from noisy oscillation (e.g., white noise) to complex fluctuations (e.g., slope approaches -1).Linear regression model was used to assess the statistical difference in power law scaling between schizophrenia and healthy participants. The significant differences in power law scaling were found in six brain regions. Schizophrenia patients has significantly more positive power law scaling (i.e., frequency components become more homogenous) at four brain regions: left precuneus, left medial dorsal nucleus, right inferior frontal gyrus, and right middle temporal gyrus, compared with healthy participants. Additionally, schizophrenia exhibited less positive power law scaling (i.e., frequency components are more dominant at lower frequency range) in bilateral putamen. Significant correlations of power law scaling with the severity of 3 psychosis were found in these identified brain areas in schizophrenia. These findings suggest that schizophrenia has abnormal brain signal complexity toward random patterns, which is linked to psychotic symptoms. The power law scaling analysis may serve as a novel functional brain imaging marker for evaluating patients with mental illness.

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Section: Introductionmentioning

confidence: 99%

Alteration of Power Law Scaling of Spontaneous Brain Activity in Schizophrenia

Lee

Huang

Lin³

et al. 2020

Preprint

Self Cite

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“…In the context of brain oscillations typically frequency-resolved information is exploited by means of spectral methods such as Fast Fourier. More recently however, a stronger focus on irregular dynamics of brain signals gave rise to entropy-based features (for a review see [13]). Entropy quantifies the extent of irregularity in the EEG time signal, where repeating, predictable signal yield low entropy, while irregular, unpredictable signal yields high entropy.…”

Section: Introductionmentioning

confidence: 99%

On the development of sleep states in the first weeks of life

Wielek

Giudice

Lang

et al. 2019

Preprint

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21Human newborns spend up to 18 hours sleeping. The organization of their sleep differs 52 life in human newborns. These changes were limited to "quiet" NREM and "active" REM sleep. Also 53 our classifier data demonstrated that we can classify well above chance and similar to human scorers 54 using multi-scale permutation entropy (and just 6 EEG and 5 physiological channels). 55 56 57 58 59 60 61 62 63 4 64 101 entropy, are typically more robust against common EEG artifacts as compared to spectral measures 102 [17] which is especially important when analyzing the data of newborns. 103 Mounting evidence suggests that fluctuations in EEG entropy reflect both transient, state-like 104 changes in human brain activity (e.g. wake and sleep states), as well as slower and more long lasting 105 dynamics across a day or even brain maturation. Sleep studies investigating healthy adults report an 106 overall trend of entropy decrease from WAKE, across transitional (N1) and light (N2) to deep (N3) 107 sleep, with a relative increase during REM sleep [18-20]. A similar pattern was reported in newborns 108 with higher entropy levels in active/REM sleep as compared to quite/NREM sleep [21]. Diurnal 109 changes in EEG entropy between daytime and night time periods -although diminished in size in 110 relation to healthy individuals -were also found in patients following a severe brain-injury [22]. 111 Across the development from childhood to adulthood a permanent increase of EEG entropy has 112 been observed [23]. Also in adults (19-74 years) an age-related increase of EEG entropy was 113 reported [24]. Interestingly, during the first weeks of infancy, this pattern is much less consistent, 114 and may even be accompanied by transient declines in EEG entropy. For instance, Zhang and 115 colleagues [25] reported EEG entropy during sleep to increase across the first month of baby's life. 126 the previously extracted entropy measures in a machine learning approach. This approach could 127 ultimately complement or even replace visual staging and thus make the sleep scoring in newborns 128 more objective and replicable. Last but not least, we tried cross-session classification such that we 129 could assess whether our algorithm can generalize between age groups and reveal the similarity or 130 "dissimilarity" in sleep organization this early in life. 131 132 133 134 135 7 136

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“…Electroencephalographic (EEG) signals recorded from the scalp are extensively used in the medical practice to analyze brain activity for the diagnosis, the management and the investigation of neurological problems such as, but not limited to, epilepsy [1,2], neurodegenerative diseases [3,4] and sleep disorders [5,6]. Another important application for scalp EEG can be found in Brain-Computer Interface (BCI), which has made significant advances in neurorehabilitation and assistive technologies, while also targeting the improvement of quality of life for disabled people [7].…”

Section: Introductionmentioning

confidence: 99%

Low Complexity Automatic Stationary Wavelet Transform for Elimination of Eye Blinks from EEG

Shahbakhti

Maugeon²,

Beiramvand

et al. 2019

Brain Sciences

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The electroencephalogram signal (EEG) often suffers from various artifacts and noises that have physiological and non-physiological origins. Among these artifacts, eye blink, due to its amplitude is considered to have the most influence on EEG analysis. In this paper, a low complexity approach based on Stationary Wavelet Transform (SWT) and skewness is proposed to remove eye blink artifacts from EEG signals. The proposed method is compared against Automatic Wavelet Independent Components Analysis (AWICA) and Enhanced AWICA. Normalized Root Mean Square Error (NRMSE), Peak Signal-to-Noise Ratio (PSNR), and correlation coefficient ( ρ ) between filtered and pure EEG signals are utilized to quantify artifact removal performance. The proposed approach shows smaller NRMSE, larger PSNR, and larger correlation coefficient values compared to the other methods. Furthermore, the speed of execution of the proposed method is considerably faster than other methods, which makes it more suitable for real-time processing.

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Nonlinear dynamical analysis of sleep electroencephalography using fractal and entropy approaches

Cited by 138 publications

References 80 publications

Alteration of Power Law Scaling of Spontaneous Brain Activity in Schizophrenia

Alteration of Power Law Scaling of Spontaneous Brain Activity in Schizophrenia

On the development of sleep states in the first weeks of life

Low Complexity Automatic Stationary Wavelet Transform for Elimination of Eye Blinks from EEG

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