Comparative study of nonlinear properties of EEG signals of a normal person and an epileptic patient

Background: Investigation of the functioning of the brain in living systems has been a major effort amongst scientists and medical practitioners. Amongst the various disorder of the brain, epilepsy has drawn the most attention because this disorder can affect the quality of life of a person. In this paper we have reinvestigated the EEGs for normal and epileptic patients using surrogate analysis, probability distribution function and Hurst exponent. Results: Using random shuffled surrogate analysis, we have obtained some of the nonlinear features that was obtained by Andrzejak \textit{et al.} [Phys Rev E 2001, 64:061907], for the epileptic patients during seizure. Probability distribution function shows that the activity of an epileptic brain is nongaussian in nature. Hurst exponent has been shown to be useful to characterize a normal and an epileptic brain and it shows that the epileptic brain is long term anticorrelated whereas, the normal brain is more or less stochastic. Among all the techniques, used here, Hurst exponent is found very useful for characterization different cases. Conclusions: In this article, differences in characteristics for normal subjects with eyes open and closed, epileptic subjects during seizure and seizure free intervals have been shown mainly using Hurst exponent. The H shows that the brain activity of a normal man is uncorrelated in nature whereas, epileptic brain activity shows long range anticorrelation.Comment: Keywords:EEG, epilepsy, Correlation dimension, Surrogate analysis, Hurst exponent. 9 page

Effects of plasma turbulence on the nonlinear evolution of magnetic island in tokamak

Magnetic islands (MIs), resulting from a magnetic field reconnection, are ubiquitous structures in magnetized plasmas. In tokamak plasmas, recent researches suggested that the interaction between an MI and ambient turbulence can be important for the nonlinear MI evolution, but a lack of detailed experimental observations and analyses has prevented further understanding. Here, we provide comprehensive observations such as turbulence spreading into an MI and turbulence enhancement at the reconnection site, elucidating intricate effects of plasma turbulence on the nonlinear MI evolution

Investigation of obstructive sleep apnea using nonlinear mode interations in nonstationary snore signals

Acoustic studies on snoring sounds have recently drawn attention as a potential alternative to polysomnography in the diagnosis of obstructive sleep apnea (OSA). This paper investigates the feasibility of using nonlinear coupling between frequency modes in snore signals via wavelet bicoherence (WBC) analysis for screening of OSA. Two novel markers (PF1 and PSF), which are frequency modes with high nonlinear coupling strength in their respective WBC spectrum, are proposed to differentiate between apneic and benign snores in same- or both-gender snorers. Snoring sounds were recorded from 40 subjects (30 apneic and 10 benign) by a hanging microphone, and subsequently preprocessed within a wavelet transform domain. Forty inspiratory snores (30 as training and 10 as test data) from each subject were examined. Results demonstrate that nonlinear mode interactions in apneic snores are less self-coupled and usually occupy higher and wider frequency ranges than that of benign snores. PF1 and PSF are indicative of apneic and benign snores (p < 0.0001), with optimal thresholds of PF1 = 285 Hz and PSF = 492 Hz (for both genders combined), as well as sensitivity and specificity values between 85.0 and 90.7%, respectively, outperforming the conventional diagnostic indicator (spectral peak frequency, PF = 243-275 Hz, sensitivity = 77.7-79.7%, specificity = 72.0-78.0%, p < 0.0001). Relationships between apnea-hypopnea index and the proposed markers could likely take the functional form of exponential or power. Perspectives on nonlinear dynamics analysis of snore signals are promising for further research and development of a reliable and inexpensive diagnostic tool for OSA

Radial variation of heat transport in L-mode JET discharges

In this paper, we analyze heat transport in the JET tokamak using data from its high resolution ECE diagnostic and analyses based on the transfer entropy (TE). The analysis reveals that heat transport is not smooth and continuous, but is characterized by 'trapping regions' separated by `minor transport barriers'. Meat may 'jump over' these barriers and when the heating power is raised, this 'jumping' behavior becomes more prominent. To check that our results are relevant for global heat transport, we deduced an effective diffusion coefficient from the TE results. Both its value and overall radial variation are consistent with heat diffusivities reported in literature. The detailed radial structure of the effective diffusion coefficient was shown to be linked to the mentioned minor transport barriers

Radial variation of heat transport in L-mode JET discharges

In this paper, we analyze heat transport in the JET tokamak using data from its high resolution ECE diagnostic and analyses based on the transfer entropy (TE). The analysis reveals that heat transport is not smooth and continuous, but is characterized by 'trapping regions' separated by `minor transport barriers'. Meat may 'jump over' these barriers and when the heating power is raised, this 'jumping' behavior becomes more prominent. To check that our results are relevant for global heat transport, we deduced an effective diffusion coefficient from the TE results. Both its value and overall radial variation are consistent with heat diffusivities reported in literature. The detailed radial structure of the effective diffusion coefficient was shown to be linked to the mentioned minor transport barriers