Effect of Ethanol on Human Sleep EEG Using Correlation Dimension Analysis

2002 ◽  
Vol 46 (2) ◽  
pp. 104-110 ◽  
Author(s):  
Toshio Kobayashi ◽  
Shigeki Madokoro ◽  
Yuji Wada ◽  
Kiwamu Misaki ◽  
Hiroki Nakagawa
Keyword(s):  
Correlation Dimension ◽  
Sleep Eeg ◽  
Human Sleep ◽  
Dimension Analysis

scholarly journals Developmental Changes in the Human Sleep EEG During Early Adolescence

SLEEP ◽  
2010 ◽  
Vol 33 (6) ◽  
pp. 801-809 ◽  
Author(s):  
Leila Tarokh ◽  
Mary A. Carskadon
Keyword(s):  
Early Adolescence ◽  
Sleep Eeg ◽  
Developmental Changes ◽  
Human Sleep

Exposure to pulsed high-frequency electromagnetic field during waking affects human sleep EEG

Neuroreport ◽  
2000 ◽  
Vol 11 (15) ◽  
pp. 3321-3325 ◽  
Author(s):  
Reto Huber ◽  
Thomas Graf ◽  
Kimberly A. Cote ◽  
Lutz Wittmann ◽  
Eva Gallmann ◽  
...  
Keyword(s):  
Electromagnetic Field ◽  
High Frequency ◽  
Sleep Eeg ◽  
Human Sleep ◽  
High Frequency Electromagnetic Field

Correlation dimension analysis of the artificial circulation

2002 ◽  
Vol 5 (1) ◽  
pp. 0001-0005
Author(s):  
T. Yambe ◽  
S. Nanka ◽  
S. Naganuma ◽  
S. Kobayashi ◽  
S. Nitta ◽  
...  
Keyword(s):  
Correlation Dimension ◽  
Artificial Circulation ◽  
Dimension Analysis

Signal processing techniques applied to human sleep EEG signals—A review

2014 ◽  
Vol 10 ◽  
pp. 21-33 ◽  
Author(s):  
Shayan Motamedi-Fakhr ◽  
Mohamed Moshrefi-Torbati ◽  
Martyn Hill ◽  
Catherine M. Hill ◽  
Paul R. White
Keyword(s):  
Signal Processing ◽  
Sleep Eeg ◽  
Eeg Signals ◽  
Human Sleep ◽  
Processing Techniques ◽  
Signal Processing Techniques

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.

Human sleep EEG following the 5-HT1A antagonist pindolol: possible disinhibition of raphe neuron activity

1997 ◽  
Vol 759 (1) ◽  
pp. 84-91 ◽  
Author(s):  
Erich Seifritz ◽  
Stephen M Stahl ◽  
J.Christian Gillin
Keyword(s):  
Sleep Eeg ◽  
Neuron Activity ◽  
Human Sleep ◽  
Raphe Neuron

Detecting Chaotic Behavior in Agricultural Exchange-traded Funds

2018 ◽  
Vol 3 (1) ◽  
Author(s):  
Jo-Hui Chen ◽  
Batsukh Tushigmaa ◽  
Yu-Fang Huang
Keyword(s):  
Correlation Dimension ◽  
Chaotic Behavior ◽  
Conditional Heteroskedasticity ◽  
Exchange Traded Funds ◽  
Range Analysis ◽  
Random Data ◽  
Standardized Residuals ◽  
Dimension Analysis ◽  
Rescaled Range ◽  
Rescaled Range Analysis

<p>This study investigates the chaos effect of agricultural exchange-traded funds (ETFs) using Brock, Dechert, and Scheinkman test, rescaled range analysis, and correlation dimension analysis. The standardized residuals from generalized autoregressive conditional heteroskedasticity models are fitted into eight ETFs and examined in each case for evidence of chaotic behavior. This study also examines whether or not the ETFs are consistent with the chaos effect based on the underlying random data with trend-reinforcing series. Research results outline the financial insights for the agricultural ETF field of investment forecasting to eliminate trading emotions, while pursuing considerable profitable experience for investors.</p>

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