Determining embedding dimension for phase-space reconstruction using a geometrical construction

Discontinuous and nondifferentiable functions and dimension increase induced by filtering chaotic data

We show that one can use recently introduced statistics for continuity and differentiability to show the effect of filters of infinite extent in time on a chaotic time series. The statistics point to a discontinuous or nondifferentiable function between the unfiltered attractor and the filtered attractor as the origin of attractor dimension increase when the filtering is severe. The density of discontinuities as a function of resolution follows a scaling relation. We present direct visualization of this effect in the filtered Henon attractor where the origin of dimension increase becomes obvious.

Detection of seizures from small samples using nonlinear dynamic system theory

The electroencephalogram (EEG), like many other biological phenomena, is quite likely governed by nonlinear dynamics. Certain characteristics of the underlying dynamics have recently been quantified by computing the correlation dimensions (D2) of EEG time series data. In this paper, D2 of the unbiased autocovariance function of the scalp EEG data was used to detect electrographic seizure activity. Digital EEG data were acquired at a sampling rate of 200 Hz per channel and organized in continuous frames (duration 2.56 s, 512 data points). To increase the reliability of D2 computations with short duration data, raw EEG data were initially simplified using unbiased autocovariance analysis to highlight the periodic activity that is present during seizures. The D2 computation was then performed from the unbiased autocovariance function of each channel using the Grassberger-Procaccia method with Theiler's box-assisted correlation algorithm. Even with short duration data, this preprocessing proved to be computationally robust and displayed no significant sensitivity to implementation details such as the choices of embedding dimension and box size. The system successfully identified various types of seizures in clinical studies.

Oscillatory, stochastic and chaotic growth rate fluctuations in permittistatically controlled yeast cultures

We describe a continuous culture system related to the turbidostat, but using a feedback system based on biomass estimation from the dielectric permittivity of the cell suspension rather than its optical density. It is shown that this system provides an excellent method of maintaining a constant biomass level within a fermentor. The computer-controlled system was able to effect the essentially continuous registration of growth rate by monitoring the rate of medium addition via the time-dependent activity of the pump. At some biomass setpoints for aerobically grown cultures of baker's yeast substantial time-dependent fluctuations in the growth rate of the culture were thereby observed. At some biomass setpoints, however, or under anaerobic conditions, or when using a non-Crabtree yeast, the growth rate was constant, indicating that the fluctuations were inherent to the biological system and not simply a property of the fermentor and control system. A variety of time series analyses (Fourier transformations, Hurst and Lyapunov exponents, the determination of embedding dimension, and non-linear time series predictions based on the methodology of Sugihara and May) were used to demonstrate, for the first time, that as well as stochastic and periodic components these fluctuations exhibited deterministic chaos. 'Trivial predictors' were unable to give accurate predictions of the growth rate in these cultures. The growth rate fluctuations were studied further by means of offline measurements of changes in percentage viability, bud count, and in the external ethanol and glucose concentrations; these data and other evidence suggested that the growth rate fluctuations were closely linked to the primary respiro-fermentative metabolism of this organism. The identification of chaotic growth rates in cell cultures suggests that there may be novel methods for controlling the growth of such cultures.

Dimensional analysis of resting human EEG. II: Surrogate-data testing indicates nonlinearity but not low-dimensional chaos

Surrogate-data testing has recently been proposed as one way to detect the presence of nonlinearity and low-dimensional chaos in experimental time series. Such testing involves estimating correlation dimension for both the original data and surrogate data from which nonlinearity has been removed. We applied such testing to the same resting, eyes-closed, and eyes-open electroencephalogram (EEG) data set that was originally analyzed using dimension estimation applied only to the original data (Pritchard & Duke, 1992). Two kinds of surrogate-data sets had higher estimated dimension and poorer saturation. This indicates that the normal resting human EEG is nonlinear and therefore not a linear-stochastic system. Because nearly complete saturation at some loci was not differently affected by the surrogate-data procedures, our results also indicate that the normal resting human EEG is high dimensional and does not represent low-dimensional chaos.

Measuring the similarity between trajectories using clustering techniques

A clustering method has been developed to group signals that display similar dynamic behavior. The procedure involves using the method of time delay embedding to construct a trajectory in state space from a time series. Certain features that characterize the geometry of the trajectory have been defined. These features were subjected to a series of statistical tests to determine their usefulness in a hierarchical clustering analysis. The latter is aimed at finding groups of similar trajectories. The trajectory-based clustering algorithm has been applied to simulated data, which included both stochastic data generated by a linear AR model, and nonlinear data generated by a Duffing oscillator. The results show that the algorithm works reliably in both cases.

Operation Everest II: an indication of deterministic chaos in human heart rate variability at simulated extreme altitude

It has been shown that fluctuation of human heartbeat intervals (heart rate variability, HRV) reflects variations in autonomic nervous system activity. We studied HRV at simulated altitudes of over 6000 m from Holter electrocardiograms recorded during the Operation Everest II study (Houston et al. 1987). Stationary, approximately 30-min segments of HRV data from six subjects at sea level and over 6000 m were supplied to (1) spectral analysis to evaluate sympathetic and parasympathetic nervous system (SNS, PNS) activity, (2) the analysis of Poincaré section of the phase space trajectory reconstructed on a delayed coordinate system to evaluate whether there was fluctuation with deterministic dynamics, (3) the estimation of the correlation dimension to evaluate a static property of putative attractors, and (4) the analysis of nonlinear predictability of HRV time series which could reflect a dynamic property of the attractor. Unlike HRV at sea level, the recordings at over 6000 m showed a strong periodicity (period of about 20 s) with small cycle-to-cycle perturbation. When this perturbation was expressed on a Poincaré section, it seemed to be likely that the perturbation itself obeyed a deterministic law. The correlation dimensions of these recordings showed low dimensional values (3.5 +/- 0.4, mean +/- SD), whereas those of the isospectral surrogates showed significantly (P < 0.05) higher values (5.3 +/- 0.5) with embedding dimensions of 5.6 +/- 0.9.(ABSTRACT TRUNCATED AT 250 WORDS)