Generalized Hurst exponent estimates differentiate EEG signals of healthy and epileptic patients

2018 ◽  
Vol 490 ◽  
pp. 378-385 ◽  
Author(s):  
Salim Lahmiri
Keyword(s):  
Hurst Exponent ◽  
Eeg Signals ◽  
Epileptic Patients ◽  
Generalized Hurst Exponent

scholarly journals Wavelet Analysis Applied on EEG Signals for Identification of Preictal States in Epileptic Patients

2020 ◽  
Vol 4 (3) ◽  
pp. 1730-1747
Author(s):  
Jade Barbosa Kill ◽  
Patrick Marques Ciarelli ◽  
Klaus Fabian Côco ◽  
Mariane Lima Souza
Keyword(s):  
Wavelet Analysis ◽  
Eeg Signals ◽  
Epileptic Patients

MULTIFRACTIONAL PROPERTY ANALYSIS OF HUMAN SLEEP EEG SIGNALS

2012 ◽  
Vol 22 (04) ◽  
pp. 1250080 ◽  
Author(s):  
HU SHENG ◽  
YANGQUAN CHEN ◽  
TIANSHUANG QIU
Keyword(s):  
Hurst Exponent ◽  
Dynamic Properties ◽  
Dynamic Assessment ◽  
Sleep Eeg ◽  
Dynamical Analysis ◽  
Sleep Stages ◽  
Hölder Exponent ◽  
Eeg Signals ◽  
Analysis Techniques ◽  
Human Sleep

Electroencephalogram (EEG), the measures and records of the electrical activity of the brain, exhibits evidently nonlinear, nonstationary, chaotic and complex dynamic properties. Based on these properties, many nonlinear dynamical analysis techniques have emerged, and much valuable information has been extracted from complex EEG signals using these nonlinear analysis techniques. Among these techniques, the Hurst exponent estimation was widely used to characterize the fractional or scaling property of the EEG signals. However, the constant Hurst exponent H cannot capture the detailed information of dynamic EEG signals. In this research, the multifractional property of the normal human sleep EEG signals is investigated and characterized using local Hölder exponent H(t). The comparison of the analysis results for human sleep EEG signals in different stages using constant Hurst exponent H and the local Hölder exponent H(t) are summarized with tables and figures in the paper. The results of the analysis show that local Hölder exponent provides a novel and valid tool for dynamic assessment of brain activities in different sleep stages.

scholarly journals Log-Periodic Power Law and Generalized Hurst Exponent Analysis in Estimating an Asset Bubble Bursting Time

e-Finanse ◽  
2016 ◽  
Vol 12 (3) ◽  
pp. 49-58 ◽  
Author(s):  
Marcin Wątorek ◽  
Bartosz Stawiarski
Keyword(s):  
Power Law ◽  
Hurst Exponent ◽  
Financial Time Series ◽  
Power Law Model ◽  
Bubble Behavior ◽  
Bubble Bursting ◽  
Financial Time ◽  
Asset Bubble ◽  
Generalized Hurst Exponent ◽  
The Moment

Abstract We closely examine and compare two promising techniques helpful in estimating the moment an asset bubble bursts. Namely, the Log-Periodic Power Law model and Generalized Hurst Exponent approaches are considered. Sequential LPPL fitting to empirical financial time series exhibiting evident bubble behavior is presented. Estimating the critical crash-time works satisfactorily well also in the case of GHE, when substantial „decorrelation“ prior to the event is visible. An extensive simulation study carried out on empirical data: stock indices and commodities, confirms very good performance of the two approaches.

MULTISCALE AND FRACTAL ANALYSIS OF SILICON CONTENT TIME SERIES OBSERVED IN BLAST FURNACE HOT METAL USING HURST EXPONENT CHAIN

Fractals ◽  
2015 ◽  
Vol 23 (04) ◽  
pp. 1550036 ◽  
Author(s):  
SHIHUA LUO ◽  
FAN GUO ◽  
DEJIAN LAI ◽  
FANG YAN ◽  
FEILAI TANG
Keyword(s):  
Time Series ◽  
Blast Furnace ◽  
Negative Correlation ◽  
Hurst Exponent ◽  
Silicon Content ◽  
Coarse Grained ◽  
Fluctuation Analysis ◽  
Hot Metal ◽  
Iron And Steel ◽  
Generalized Hurst Exponent

Hurst exponent is an important measure of nonlinearity of dynamical time series. In this paper, using rescaled-range ([Formula: see text]/[Formula: see text]) analysis, multi-fractal detrended fluctuation analysis (MF-DFA) methods, the multiscale Hurst exponent (MHE) and the multiscale generalized Hurst exponent (MGHE) of coarse-grained silicon content ([Si]) time series in blast furnace (BF) hot metal were calculated. First, we collected these [Si] time series from No. 1 BF of Nanchang Iron and Steel Co. and No. 10 BF of Xinyu Iron and Steel Co. in Jiangxi Province, China. Then, we analyzed and compared the estimated Hurst exponents and the generalized Hurst exponent of these observed time series with some simulated time series. Our results show that the observed time series from these BFs have negative correlation with the Hurst exponent less than 0.5, the generalized Hurst exponent [Formula: see text] is a nonlinear function of [Formula: see text], and such negative correlation and local various structure persist in their moving averages of the observed time series up to lag 5 or 10.

scholarly journals Spectral characteristics of high latitude raw 40 MHz cosmic noise signals

2015 ◽  
Vol 2 (4) ◽  
pp. 969-987
Author(s):  
C. M. Hall
Keyword(s):  
Brownian Motion ◽  
Fractional Brownian Motion ◽  
Hurst Exponent ◽  
Spectral Characteristics ◽  
Noise Signal ◽  
Density Profiles ◽  
Generalized Hurst Exponent ◽  
Electron Density Profiles ◽  
Cosmic Noise ◽  
Non Gaussian

Abstract. Cosmic noise at 40 MHz is measured at Ny-Ålesund (79° N, 12° E) using a relative ionospheric opacity meter ("riometer"). A riometer is normally used to determine the degree to which cosmic noise is absorbed by the intervening ionosphere, giving an indication of ionization of the atmosphere at altitudes lower than generally monitored by other instruments. The usual course is to determine a "quiet-day" variation, this representing the galactic noise signal itself in the absence of absorption; the current signal is then subtracted from this to arrive at absorption expressed in dB. By a variety of means and assumptions, it is thereafter possible to estimate electron density profiles in the very lowest reaches of the ionosphere. Here however, the entire signal, i.e. including the cosmic noise itself will be examined and spectral characteristics identified. It will be seen that distinct spectral subranges are evident which can, in turn be identified with non-Gaussian processes characterized by generalized Hurst exponents, α. Considering all periods greater than 1 h, α ≈ 1.24 – an indication of fractional Brownian motion, whereas for periods greater than 1 day α ≈ 0.9 – approximately pink noise and just in the domain of fractional Gaussian noise. The results are compared with other physical processes suggesting that absorption of cosmic noise is characterized by a generalized Hurst exponent ≈ 1.24 and thus non-persistent fractional Brownian motion, whereas generation of cosmic noise is characterized by a generalized Hurst exponent ≈ 1.

scholarly journals INFORMATION AND MEMORY-BASED ANALYSIS FOR DECODING OF THE HUMAN LEARNING BETWEEN NORMAL AND VIRTUAL REALITY (VR) CONDITIONS

Fractals ◽  
2021 ◽  
Vol 29 (03) ◽  
pp. 2150163
Author(s):  
HAMIDREZA NAMAZI ◽  
MOHAMMAD HOSSEIN BABINI ◽  
KAMIL KUCA ◽  
ONDREJ KREJCAR
Keyword(s):  
Virtual Reality ◽  
Shannon Entropy ◽  
Mathematical Analysis ◽  
Strong Correlation ◽  
Hurst Exponent ◽  
Human Learning ◽  
Learning Ability ◽  
Eeg Signals ◽  
2D And 3D ◽  
Electroencephalogram Eeg

In this paper, we investigated the learning ability of students in normal versus virtual reality (VR) watching of videos by mathematical analysis of electroencephalogram (EEG) signals. We played six videos in the 2D and 3D modes for nine subjects and calculated the Shannon entropy of recorded EEG signals to investigate how much their embedded information changes between these modes. We also calculated the Hurst exponent of EEG signals to compare the changes in the memory of signals. The analysis results showed that watching the videos in a VR condition causes greater information and memory in EEG signals. A strong correlation was obtained between the increment of information and memory of EEG signals. These increments also have been verified based on the answers that subjects gave to the questions about the content of videos. Therefore, we can say that when subjects watch a video in a VR condition, more information is transferred to their brains that cause increments in their memory.

scholarly journals Data Mining Based Approach for Evaluation of EEG Signals for Epilepsy Detection

2020 ◽  
Vol 17 ◽  
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Physiological Signals ◽  
Alternative Formulation ◽  
Eeg Signal ◽  
Detection Problem ◽  
Eeg Signals ◽  
Epileptic Patients ◽  
To Come

The objective of this proposed research is to come up with a general methodology for classification of time series events, and to apply that methodology to the analysis of physiological signals recorded from epileptic patients for seizure analysis depending on EEG signal. In contrast to previous works, this research considered an alternative formulation of seizure analysis as a detection problem. This approach offers a good treatment of seizure detection

Emotional States Analyze from Scaling Properties of EEG Signals Using Hurst Exponent for Stroke and Normal Groups

2019 ◽  
pp. 526-534 ◽  
Author(s):  
Choong Wen Yean ◽  
Wan Khairunizam ◽  
Mohammad Iqbal Omar ◽  
Murugappan Murugappan ◽  
Zunaidi Ibrahim ◽  
...  
Keyword(s):  
Hurst Exponent ◽  
Emotional States ◽  
Eeg Signals ◽  
Scaling Properties
Sign in / Sign up

Export Citation Format

Share Document