scholarly journals Effect of Signal-Dependent Noise on Phase Synchrony Pattern

2018 ◽  
Vol 1 (1) ◽  
Author(s):  
Kalina A. Misiolek ◽  
Leonid L. Rubchinsky ◽  
Robert M. Worth
Keyword(s):  
Experimental Data ◽  
Time Series ◽  
Multiplicative Noise ◽  
Specific Pattern ◽  
Phase Synchrony ◽  
Neuronal Circuits ◽  
Eeg Signals ◽  
Potential Impact ◽  
Synchronization Pattern ◽  
Noise Impacts

Background: Intermittent phase synchrony is a phenomenon that occurs at subthreshold levels of oscillator coupling, where two oscillators appear to be synchronized at some times and desynchronized at others. Here, periods of “synchrony” are defined by a certain amount of statistically significant correlation between the time series of the oscillators.1 While general synchrony is observable in any number of settings (e.g. coupled pendula), intermittent synchrony has been detected in EEG readings from specific pairs of electrodes from patients with schizophrenia and Parkinson’s Disease.1 However, the extent to which EEG noise impacts synchrony pattern within the Rubchinsky et. al. model has not yet been studied.1  Methods: Using non-experimental data in MATLAB, we propose to run a series of trials to study the effect of signal-dependent multiplicative noise on the patterns of phase synchrony between oscillators. In the first condition, we simulate two completely synchronized signals, add signal-dependent noise to one, and observe the resulting changes to the synchronization pattern. In the second condition, we begin with two completely desynchronized signals.  Potential Impact: In studies of intermittent phase synchrony, it has been suggested that this pattern is the result of neuronal circuits which, as the EEG signals synchronize, fire more strongly and as a result become less responsive to outside input. This interpretation has the power to explain some of the symptoms experienced by patients. Thus, the specific pattern of synchronized and de-synchronized episodes is potentially highly significant. Our study is a necessary first step to understanding if the existing model and interpretations are accurate.  References: Rubchinsky, L. L., Ahn, S., & Park, C. (2014). Dynamics of desynchronized episodes in intermittent synchronization. Frontiers in Physics, 2. doi:10.3389/fphy.2014.00038 

Correlation in the heart rate data

1998 ◽  
Vol 2 ◽  
pp. 141-148
Author(s):  
J. Ulbikas ◽  
A. Čenys ◽  
D. Žemaitytė ◽  
G. Varoneckas
Keyword(s):  
Nonlinear Dynamics ◽  
Experimental Data ◽  
Heart Rate ◽  
Time Series ◽  
Cardiovascular Function ◽  
Statistical Properties ◽  
Rate Data ◽  
Heart Rate Data ◽  
Dynamical Nature ◽  
Analysis Of Time Series

Variety of methods of nonlinear dynamics have been used for possibility of an analysis of time series in experimental physiology. Dynamical nature of experimental data was checked using specific methods. Statistical properties of the heart rate have been investigated. Correlation between of cardiovascular function and statistical properties of both, heart rate and stroke volume, have been analyzed. Possibility to use a data from correlations in heart rate for monitoring of cardiovascular function was discussed.

HIGHER ORDER COMPLEXITY OF TIME SERIES

2004 ◽  
Vol 14 (08) ◽  
pp. 2979-2990 ◽  
Author(s):  
FANJI GU ◽  
ENHUA SHEN ◽  
XIN MENG ◽  
YANG CAO ◽  
ZHIJIE CAI
Keyword(s):  
Time Series ◽  
Mental Arithmetic ◽  
Higher Order ◽  
Second Order ◽  
Stationary Time Series ◽  
Eeg Signals ◽  
Eyes Closed ◽  
Logistic Mapping ◽  
Original Time ◽  
Different Order

A concept of higher order complexity is proposed in this letter. If a randomness-finding complexity [Rapp & Schmah, 2000] is taken as the complexity measure, the first-order complexity is suggested to be a measure of randomness of the original time series, while the second-order complexity is a measure of its degree of nonstationarity. A different order is associated with each different aspect of complexity. Using logistic mapping repeatedly, some quasi-stationary time series were constructed, the nonstationarity degree of which could be expected theoretically. The estimation of the second-order complexity of these time series shows that the second-order complexities do reflect the degree of nonstationarity and thus can be considered as its indicator. It is also shown that the second-order complexities of the EEG signals from subjects doing mental arithmetic are significantly higher than those from subjects in deep sleep or resting with eyes closed.

Information-based analysis of the coupling between brain and heart reactions to olfactory stimulation

2021 ◽  
pp. 1-11
Author(s):  
Najmeh Pakniyat ◽  
Mohammad Hossein Babini ◽  
Vladimir V. Kulish ◽  
Hamidreza Namazi
Keyword(s):  
Time Series ◽  
Biomedical Science ◽  
Strongly Correlated ◽  
Eeg Signals ◽  
Ecg Signals ◽  
Olfactory Stimuli ◽  
Electrocardiogram Ecg ◽  
First Time ◽  
Electroencephalogram Eeg ◽  
The Brain

BACKGROUND: Analysis of the heart activity is one of the important areas of research in biomedical science and engineering. For this purpose, scientists analyze the activity of the heart in various conditions. Since the brain controls the heart’s activity, a relationship should exist among their activities. OBJECTIVE: In this research, for the first time the coupling between heart and brain activities was analyzed by information-based analysis. METHODS: Considering Shannon entropy as the indicator of the information of a system, we recorded electroencephalogram (EEG) and electrocardiogram (ECG) signals of 13 participants (7 M, 6 F, 18–22 years old) in different external stimulations (using pineapple, banana, vanilla, and lemon flavors as olfactory stimuli) and evaluated how the information of EEG signals and R-R time series (as heart rate variability (HRV)) are linked. RESULTS: The results indicate that the changes in the information of the R-R time series and EEG signals are strongly correlated (ρ=-0.9566). CONCLUSION: We conclude that heart and brain activities are related.

Software Tool for Assessment of Complexity and Variability in Physiological Signals of Respiration

2011 ◽  
Vol 1 (2) ◽  
pp. 28-53
Author(s):  
Ireneusz Jablonski ◽  
Kamil Subzda ◽  
Janusz Mroczka
Keyword(s):  
Complex System ◽  
Experimental Data ◽  
Time Series ◽  
Data Analysis ◽  
Input Signal ◽  
Software Tool ◽  
Point Of View ◽  
Software Implementation ◽  
Physiological Signals ◽  
Event Time

In this paper, the authors examine software implementation and the initial preprocessing of data and tools during the assessment of the complexity and variability of long physiological time-series. The algorithms presented advance a bigger Matlab library devoted to complex system and data analysis. Commercial software is unavailable for many of these functions and is generally unsuitable for use with multi-gigabyte datasets. Reliable inter-event time extraction from input signal is an important step for the presented considerations. Knowing the distribution of the inter-event time distances, it is possible to calculate exponents due to power-law scaling. From a methodology point of view, simulations and considerations with experimental data supported each stage of the work presented. In this paper, initial calibration of the procedures with accessible data confirmed assessments made during earlier studies, which raise objectivity of measurements planned in the future.

scholarly journals About the parabolic relation existing between the skewness and the kurtosis in time series of experimental data

2009 ◽  
Vol 79 (4) ◽  
pp. 045006 ◽  
Author(s):  
F Sattin ◽  
M Agostini ◽  
R Cavazzana ◽  
G Serianni ◽  
P Scarin ◽  
...  
Keyword(s):  
Experimental Data ◽  
Time Series

scholarly journals Evaluating Noise Sensitivity on the Time Series Determination of Lyapunov Exponents Applied to the Nonlinear Pendulum

2003 ◽  
Vol 10 (1) ◽  
pp. 37-50 ◽  
Author(s):  
L.F.P. Franca ◽  
M.A. Savi
Keyword(s):  
Experimental Data ◽  
Mathematical Model ◽  
Time Series ◽  
Lyapunov Exponents ◽  
Random Noise ◽  
Noise Sensitivity ◽  
Correct Identification ◽  
Chaotic Motions ◽  
Nonlinear Pendulum

This contribution presents an investigation on noise sensitivity of some of the most disseminated techniques employed to estimate Lyapunov exponents from time series. Since noise contamination is unavoidable in cases of data acquisition, it is important to recognize techniques that could be employed for a correct identification of chaos. State space reconstruction and the determination of Lyapunov exponents are carried out to investigate the response of a nonlinear pendulum. Signals are generated by numerical integration of the mathematical model, selecting a single variable of the system as a time series. In order to simulate experimental data sets, a random noise is introduced in the signal. Basically, the analyses of periodic and chaotic motions are carried out. Results obtained from mathematical model are compared with the one obtained from time series analysis, evaluating noise sensitivity. This procedure allows the identification of the best techniques to be employed in the analysis of experimental data.

Non-Linear Time Series Analysis of the Infuence of Rolling Motion on Natural Circulation System

2010 ◽  
Author(s):  
Wenchao Zhang ◽  
Sichao Tan ◽  
Puzhen Gao
Keyword(s):  
Experimental Data ◽  
Time Series ◽  
Time Series Analysis ◽  
Two Phase Flow ◽  
Flow Instability ◽  
Natural Circulation ◽  
Rolling Motion ◽  
Phase Flow ◽  
Two Phase ◽  
Series Analysis

Two-phase natural circulation flow instability under rolling motion condition was studied experimentally and theoretically. Experimental data were analyzed with nonlinear time series analysis methods. The embedding dimension, correlation dimension and K2 entropy were determined based on phase space reconstruction theory and G-P method. The maximal Lyapunov exponent was calculated according to the methods of small data sets. The nonlinear features of the two phase flow instability under rolling motion were analyzed with the results of geometric invariants coupling with the experimental data. The results indicated that rolling motion strengthened the nonlinear characteristics of two phase flow instability. Some typical nonlinear phenomena such as period-doubling bifurcations and chaotic oscillations were found in different cases.

Research on Attention EEG Based on the Multiscale Jensen-Shannon Divergence

2014 ◽  
Vol 529 ◽  
pp. 675-678
Author(s):  
Zheng Xia Zhang ◽  
Si Qiu Xu ◽  
Er Ning Zhou ◽  
Xiao Lin Huang ◽  
Jun Wang
Keyword(s):  
Time Series ◽  
Brain Function ◽  
Complexity Analysis ◽  
Function Parameter ◽  
Function Evaluation ◽  
Analysis Method ◽  
Eeg Signals ◽  
Eyes Closed ◽  
Statistical Complexity ◽  
Jensen Shannon Divergence

The article adopted the multiscale Jensen-Shannon Divergence analysis method for EEG complexity analysis. Then the study found that this method can distinguish between three different status (Eyes closed, count, in a daze) acquisition of EEG time series. It showed that three different states of EEG time series have significant differences. In each state of the three different states (Eyes closed, count, in a daze), we aimed at comparing and analyzing the statistical complexity of EEG time series itself and the statistical complexity of EEG time series shuffled data. It was found that there are large amounts of nonlinear time series in the EEG signals. This method is also fully proved that the multiscale JSD algorithm can be used to analyze attention EEG signals. The multiscale Jensen-Shannon Divergence statistical complexity can be used as a measure of brain function parameter, which can be applied to the auxiliary clinical brain function evaluation in the future.

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