A. Capurro

Tsallis entropy and cortical dynamics: the analysis of EEG signals

We undertake the study of EEG-signals by recourse to a wavelet based multiresolution analysis as adapted to an information-measure-scenario. Different information measures are employed. It is shown that non-extensive ones seem to be of particular usefulness.

Human brain dynamics: the analysis of EEG signals with Tsallis information measure

We undertake the study of human EEG-signals by recourse to a wavelet based multiresolution analysis as adapted to an Information-Measure-Scenario. Different information measures are employed. It is shown that non-extensive ones seem to be of particular usefulness. Their use opens up perspectives of building up automatic detection devices. Conjectures concerning general characteristics of focal epilepsy are formulated on the basis of a Tsallis-type of analysis.

Nonlinear structures in electroencephalogram signals

We apply a nonlinear prediction algorithm to investigate the presence of nonlinear structure in electroencephalogram (EEG) recordings. The EEG signal could be modeled as a realization of a nonlinear model plus a residual noise (uncorrelated Gaussian noise). Using linear and nonlinear models we analyze the statistical nature of these residual noises in the case of epileptic patients and normal subjects. We found that the residual noise presents Gaussian distribution for epileptic patients if a nonlinear model is used whereas in the case of normal subjects the residual noise will exhibit a Gaussian distribution only if a linear model (autoregressive) is used. These results provide another evidence of the nonlinear character of the epileptic seizure recordings, while the normal EEG seems to be better described as linearly correlated noise.

Model for the heart beat-to-beat time series during meditation

We present a model for the respiratory modulation of the heart beat-to-beat interval series. The model consists of a pacemaker, that simulates the membrane potential of the sinoatrial node, modulated by a periodic input signal plus correlated noise that simulates the respiratory input. The model was used to assess the waveshape of the respiratory signals needed to reproduce in the phase space the trajectory of experimental heart beat-to-beat interval data. The data sets were recorded during meditation practices of the Chi and Kundalini Yoga techniques. Our study indicates that in the first case the respiratory signal has the shape of a smoothed square wave, and in the second case it has the shape of a smoothed triangular wave.

NEURAL NETWORKS THAT LEARN HOW TO DETECT EPILEPTIC SPIKES

Abstract We advance a method based on a neural network environment to detect epileptic spikes in EEG. The concomitant training process of the pertinent feed-forward neural networks involves information theoretic tools. The resulting technique can be used to predict the one-step-forward behavior of the EEG time series. An optimal number of neurons is determined that allows for the maximum correlation between EEG signals and the neural network output. Our approach is illustrated with reference to EEG time series of the interictal activity from focal epileptic patients.

Constructing the Average Natural History of HIV-1 Infection

Many aspects of the natural course of the HIV‐1 infection remains unclear, despite important efforts towards understanding its long‐term dynamics. Using a scaling approach that places progression markers (viral load, CD4+, CD8+) of many individuals on a single average natural course of disease progression, we introduce the concept of inter‐individual scaling and time scaling. Our quantitative assessment of the natural course of HIV‐1 infection indicates that the dynamics of the evolution for the individual that developed AIDS (opportunistic infections) is different from that of the individual that did not develop AIDS. This means that the rate of progression is not relevant for the infection evolution.