One-point fluctuation analysis of IceCube neutrino events outlines a significant unassociated isotropic component and constrains the Galactic contribution

In this work, the scaling behavior of the sleep process is evaluated by using detrended fluctuation analysis based on wavelets. The analysis is carried out from arrivals of short and recurrent cortical events called A-phases, which in turn build up the Cyclic Alternating Pattern phenomenon, and are classified in three types: A1, A2 and A3. In this study, 61 sleep recordings corresponding to healthy, nocturnal frontal lobe epilepsy patients and sleep-state misperception subjects, were analyzed. From the A-phase annotations, the onsets were extracted and a binary sequence with one second resolution was generated. An item in the sequence has a value of one if an A-phase onset occurs in the corresponding window, and a value of zero otherwise. In addition, we consider other different temporal resolutions from 2[Formula: see text]s to 256[Formula: see text]s. Furthermore, the same analysis was carried out for sequences obtained from the different types of A-phases and their combinations. The results of the numerical analysis showed a relationship between the time resolutions and the scaling exponents; specifically, for higher time resolutions a white noise behavior is observed, whereas for lower time resolutions a behavior towards to [Formula: see text]-noise is exhibited. Statistical differences among groups were observed by applying various wavelet functions from the Daubechies family and choosing the appropriate sequence of A-phase onsets. This scaling analysis allows the characterization of the free-scale dynamic of the sleep process that is specific for each sleep condition. The scaling exponent could be useful as a diagnosis parameter in clinics when sleep macrostructure does not offer enough information.

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Effects of white Gaussian noise on dynamic balance in healthy young adults

Scientific Reports ◽

10.1038/s41598-021-84706-8 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Ziyou Zhou ◽

Can Wu ◽

Zhen Hu ◽

Yujuan Chai ◽

Kai Chen ◽

...

Keyword(s):

Young Adults ◽

Gaussian Noise ◽

Center Of Pressure ◽

Dynamic Balance ◽

White Gaussian Noise ◽

Fluctuation Analysis ◽

Balance Performance ◽

Short Term ◽

Crossover Point ◽

Short Time

AbstractIt has been known that short-time auditory stimulation can contribute to the improvement of the balancing ability of the human body. The present study aims to explore the effects of white Gaussian noise (WGN) of different intensities and frequencies on dynamic balance performance in healthy young adults. A total of 20 healthy young participants were asked to stand at a dynamic balance force platform, which swung along the x-axis with an amplitude of ± 4° and frequency of 1 Hz. Their center of pressure (COP) trajectories were recorded when they were stimulated by WGN of different intensities (block 1) and different frequencies (block 2). A traditional method and detrended fluctuation analysis (DFA) were used for data preprocessing. The authors found that only with 75–85 dB WGN, the COP parameters improved. WGN frequency did not affect the dynamic balance performance of all the participants. The DFA results indicated stimulation with 75 dB WGN enhanced the short-term index and reduced the crossover point. Stimulation with 500 Hz and 2500 Hz WGN significantly enhanced the short-term index. These results suggest that 75 dB WGN and 500 Hz and 2500 Hz WGN improved the participants’ dynamic balance performance. The results of this study indicate that a certain intensity of WGN is indispensable to achieve a remarkable improvement in dynamic balance. The DFA results suggest that WGN only affected the short-term persistence, indicating the potential of WGN being considered as an adjuvant therapy in low-speed rehabilitation training.

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Detrended Fluctuation Analysis of the Aggregated Traffic Dynamics

2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus) ◽

10.1109/elconrus51938.2021.9396696 ◽

2021 ◽

Author(s):

Nikita S. Pyko

Keyword(s):

Detrended Fluctuation Analysis ◽

Fluctuation Analysis ◽

Traffic Dynamics ◽

Detrended Fluctuation

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Multifractal detrended fluctuation analysis of temperature in Spain (1960–2019)

Physica A Statistical Mechanics and its Applications ◽

10.1016/j.physa.2021.126118 ◽

2021 ◽

pp. 126118

Author(s):

Javier Gómez-Gómez ◽

Rafael Carmona-Cabezas ◽

Ana B. Ariza-Villaverde ◽

Eduardo Gutiérrez de Ravé ◽

Francisco José Jiménez-Hornero

Keyword(s):

Detrended Fluctuation Analysis ◽

Fluctuation Analysis ◽

Multifractal Detrended Fluctuation Analysis ◽

Detrended Fluctuation

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EEG microstate in obstructive sleep apnea patients

Scientific Reports ◽

10.1038/s41598-021-95749-2 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Xin Xiong ◽

Yuyan Ren ◽

Shenghan Gao ◽

Jianhua Luo ◽

Jiangli Liao ◽

...

Keyword(s):

Obstructive Sleep Apnea ◽

Sleep Apnea ◽

Correlation Analysis ◽

Transition Probability ◽

Transient State ◽

Sample Entropy ◽

Sleep Stages ◽

Fluctuation Analysis ◽

Alpha Power ◽

Obstructive Sleep

AbstractObstructive sleep apnea (OSA) is a common sleep respiratory disease. Previous studies have found that the wakefulness electroencephalogram (EEG) of OSA patients has changed, such as increased EEG power. However, whether the microstates reflecting the transient state of the brain is abnormal is unclear during obstructive hypopnea (OH). We investigated the microstates of sleep EEG in 100 OSA patients. Then correlation analysis was carried out between microstate parameters and EEG markers of sleep disturbance, such as power spectrum, sample entropy and detrended fluctuation analysis (DFA). OSA_OH patients showed that the microstate C increased presence and the microstate D decreased presence compared to OSA_withoutOH patients and controls. The fifth microstate E appeared during N1-OH, but the probability of other microstates transferring to microstate E was small. According to the correlation analysis, OSA_OH patients in N1-OH showed that the microstate D was positively correlated with delta power, and negatively correlated with beta and alpha power; the transition probability of the microstate B → C and E → C was positively correlated with alpha power. In other sleep stages, the microstate parameters were not correlated with power, sample entropy and FDA. We might interpret that the abnormal transition of brain active areas of OSA patients in N1-OH stage leads to abnormal microstates, which might be related to the change of alpha activity in the cortex.

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