Approximate Entropy of Brain Network in the Study of Hemispheric Differences

Human brain, a dynamic complex system, can be studied with different approaches, including linear and nonlinear ones. One of the nonlinear approaches widely used in electroencephalographic (EEG) analyses is the entropy, the measurement of disorder in a system. The present study investigates brain networks applying approximate entropy (ApEn) measure for assessing the hemispheric EEG differences; reproducibility and stability of ApEn data across separate recording sessions were evaluated. Twenty healthy adult volunteers were submitted to eyes-closed resting EEG recordings, for 80 recordings. Significant differences in the occipital region, with higher values of entropy in the left hemisphere than in the right one, show that the hemispheres become active with different intensities according to the performed function. Besides, the present methodology proved to be reproducible and stable, when carried out on relatively brief EEG epochs but also at a 1-week distance in a group of 36 subjects. Nonlinear approaches represent an interesting probe to study the dynamics of brain networks. ApEn technique might provide more insight into the pathophysiological processes underlying age-related brain disconnection as well as for monitoring the impact of pharmacological and rehabilitation treatments.     

基于小波变换的混沌信号相空间重构研究

应用小波变换和非线性动力学方法研究了混沌信号在相空间中的行为，指出混沌时间序 列的小波变换实质上是在重构的相空间中，混沌吸引子向小波滤波器向量所张的空间中的投 影，与Packard等人提出的相空间重构方法本质上是一致的.实验结果表明，混沌信号经过 小波变换后，吸引子轨迹与原有轨迹具有相似的结构，同时，系统的关联维数、Kolmogorov 熵等非线性不变量仍然得到保留.这些结果表明，利用小波变换研究混沌信号是有效的. 关键词： 小波变换 相空间重构 混沌信号 脑电信号     

Electroencephalogram (EEG) time series classification: Applications in epilepsy

Epilepsy is among the most common brain disorders. Approximately 25–30% of epilepsy patients remain unresponsive to anti-epileptic drug treatment, which is the standard therapy for epilepsy. In this study, we apply optimization-based data mining techniques to classify the brain's normal and epilepsy activity using intracranial electroencephalogram (EEG), which is a tool for evaluating the physiological state of the brain. A statistical cross validation and support vector machines were implemented to classify the brain's normal and abnormal activities. The results of this study indicate that it may be possible to design and develop efficient seizure warning algorithms for diagnostic and therapeutic purposes. Research was partially supported by the Rutgers Research Council grant-202018, the NSF grants DBI-980821, CCF-0546574, IIS-0611998, and NIH grant R01-NS-39687-01A1.     

Wavelet Jensen–Shannon divergence as a tool for studying the dynamics of frequency band components in EEG epileptic seizures

We develop a quantitative method of analysis of EEG records. The method is based on the wavelet analysis of the record and on the capability of the Jensen–Shannon divergence (JSD) to identify dynamical changes in a time series. The JSD is a measure of distance between probability distributions. Therefore for its evaluation it is necessary to define a (time dependent) probability distribution along the record. We define this probability distribution from the wavelet decomposition of the associated time series. The wavelet JSD provides information about dynamical changes in the scales and can be considered a complementary methodology reported earlier [O.A. Rosso, S. Blanco, A. Rabinowicz, Signal Processing 86 (2003) 1275; O.A. Rosso, S. Blanco, J. Yordanova, V. Kolev, A. Figliola, M. Schürmann, E. Ba?ar, J. Neurosci. Methods 105 (2001) 65; O.A. Rosso, M.T. Martin, A. Figliola, K. Keller, A. Plastino, J. Neurosci. Methods 153 (2006) 163]. In the present study we have demonstrated it by analyzing EEG signal of tonic–clonic epileptic seizures applying the JSD method. The display of the JSD curves enables easy comparison of frequency band component dynamics. This would, in turn, promise easy and successful comparison of the EEG records from various scalp locations of the brain.     

EFFECT OF SIMULATED WEIGHTLESSNESS ON THE RESPONSE CHARACTERISTICS OF HUMAN BRAIN

In order to systematically investigate the effects of simulated weightlessness on thefunction state of human brain, 15° head-down tilt (HDT) was used to simulate weightless-ness, and the response changes of event-related EEG power spectra, medium-frequencysynchronous potentials and slow-waves were examined in the present study. It was foundthat HDT had characteristic effects on the above EEG responses, suggesting that the ef-fects mainly occurred in the brain's regulatory system, therefore, resulting in changes ofthe brain function state.     

EEG Fractal Analysis Reflects Brain Impairment after Stroke

Stroke is the commonest cause of disability. Novel treatments require an improved understanding of the underlying mechanisms of recovery. Fractal approaches have demonstrated that a single metric can describe the complexity of seemingly random fluctuations of physiological signals. We hypothesize that fractal algorithms applied to electroencephalographic (EEG) signals may track brain impairment after stroke. Sixteen stroke survivors were studied in the hyperacute (<48 h) and in the acute phase (∼1 week after stroke), and 35 stroke survivors during the early subacute phase (from 8 days to 32 days and after ∼2 months after stroke): We compared resting-state EEG fractal changes using fractal measures (i.e., Higuchi Index, Tortuosity) with 11 healthy controls. Both Higuchi index and Tortuosity values were significantly lower after a stroke throughout the acute and early subacute stage compared to healthy subjects, reflecting a brain activity which is significantly less complex. These indices may be promising metrics to track behavioral changes in the very early stage after stroke. Our findings might contribute to the neurorehabilitation quest in identifying reliable biomarkers for a better tailoring of rehabilitation pathways.     

Weak solutions of the forward problem in EEG for different conductivity values

The potential distribution on the scalp produced by current sources in the brain can be measured by an EEG recorder. The relationship between these sources and the scalp potential distribution may be described by a well-known mathematical model where some simplifications are usually introduced. The head is modeled as a multicompartment nested set and the conductivity of the different tissues is approximated by a positive piecewise constant function. This simplified model is used to solve the forward problem (FP), i.e., to calculate the scalp potential for a current source configuration. In this work, we prove that the weak solutions of the FP are continuous with respect to the conductivity values, that is, the difference between the scalp potentials is small if the conductivity values are closed enough. We present numerical examples that illustrates this property.     

一种基于三阶Volterra滤波器的混沌时间序列自适应预测方法

基于Takens的相空间延迟坐标重构,研究了用于混沌信号预测的三阶Volterra滤波器的一种乘积耦合近似实现结构,并应用于典型的低维混沌时间序列和具有高维混沌特性的EEG信号的预测.数值研究表明:这种滤波器结构对于低维混沌时间序列的预测精度可以比二阶Volterra滤波器提高103倍,而且能够较好地对一些具有高维混沌特性的EEG信号进行预测 关键词： 混沌 非线性自适应预测 三阶Volterra滤波器 electroencephalography信号     

Proton magnetic resonance spectroscopic images and MRI volumetric studies for lateralization of temporal lobe epilepsy

We obtained 2D magnetic resonance (MR) spectroscopic images (MRSI) and MRI volumetric measurements (MRIV) of amygdala and hippocampus in 30 consecutive patients with temporal lobe epilepsy (TLE) being evaluated for surgical treatment. Both MRSI and MRIV lateralization showed good agreement with the current gold standard of clinical-EEG lateralization. Each exam separately correctly lateralized 25 out of 30 patients with no false lateralization. Combining both exams, lateralization could be achieved in 28 out of 30 patients. The two patients with no significant asymmetry had bitemporal EEG abnormalities, and bilateral damage on both MRIV and MRSI. There was a good correlation between the magnitude of the MRSI and MRIV asymmetry (Pearson COEFFICIENT = 0.83; p < .0001). Both MRSI and MRIV were normal in our patients with seizures originating outside the temporal lobes. Both MRIV and MRSI can lateralize TLE in 83% of patients. Combination of the two modalities allows lateralization in 93% of patients. Patients who cannot be lateralized generally have symmetrical bitemporal abnormalities; they are not incorrectly lateralized. The structural and chemical pathologic abnormalities seen in TLE seem to be associated with the seizure focus, and may be as, or even more, reliable than a few recorded seizures in predicting the side from which most seizures originate.