50 research outputs found
Comparison of models and lattice-gas simulations for Liesegang patterns
For more than a century Liesegang patterns -- self-organized, quasi-periodic
structures occurring in diffusion-limited chemical reactions with two
components -- have been attracting scientists. The pattern formation can be
described by four basic empirical laws. In addition to many experiments,
several models have been devised to understand the formation of the bands and
rings. Here we review the most important models and complement them with
detailed three-dimensional lattice-gas simulations. We show how the mean-field
predictions can be reconciled with experimental data by a redefinition of the
distances suggested by our lattice-gas simulations.Comment: 21 pages, 9 figures, accepted for publication in EPJ Special Topic
Modulations of Heart Rate, ECG, and Cardio-Respiratory Coupling Observed in Polysomnography
The cardiac component of cardio-respiratory polysomnography is covered by ECG and heart rate recordings. However their evaluation is often underrepresented in summarizing reports. As complements to EEG, EOG, and EMG, these signals provide diagnostic information for autonomic nervous activity during sleep. This review presents major methodological developments in sleep research regarding heart rate, ECG and cardio-respiratory couplings in a chronological (historical) sequence. It presents physiological and pathophysiological insights related to sleep medicine obtained by new technical developments. Recorded nocturnal ECG facilitates conventional heart rate variability analysis, studies of cyclical variations of heart rate, and analysis of ECG waveform. In healthy adults, the autonomous nervous system is regulated in totally different ways during wakefulness, slow-wave sleep, and REM sleep. Analysis of beat-to-beat heart-rate variations with statistical methods enables us to estimate sleep stages based on the differences in autonomic nervous system regulation. Furthermore, up to some degree, it is possible to track transitions from wakefulness to sleep by analysis of heart-rate variations. ECG and heart rate analysis allow assessment of selected sleep disorders as well. Sleep disordered breathing can be detected reliably by studying cyclical variation of heart rate combined with respiration-modulated changes in ECG morphology (amplitude of R wave and T wave)
Bivariate phase-rectified signal averaging
Phase-Rectified Signal Averaging (PRSA) was shown to be a powerful tool for
the study of quasi-periodic oscillations and nonlinear effects in
non-stationary signals. Here we present a bivariate PRSA technique for the
study of the inter-relationship between two simultaneous data recordings. Its
performance is compared with traditional cross-correlation analysis, which,
however, does not work well for non-stationary data and cannot distinguish the
coupling directions in complex nonlinear situations. We show that bivariate
PRSA allows the analysis of events in one signal at times where the other
signal is in a certain phase or state; it is stable in the presence of noise
and impassible to non-stationarities.Comment: 19 pages, 6 figures, revised version submitted to Physica
Multifractal detrended fluctuation analysis of nonstationary time series
We develop a method for the multifractal characterization of nonstationary
time series, which is based on a generalization of the detrended fluctuation
analysis (DFA). We relate our multifractal DFA method to the standard partition
function-based multifractal formalism, and prove that both approaches are
equivalent for stationary signals with compact support. By analyzing several
examples we show that the new method can reliably determine the multifractal
scaling behavior of time series. By comparing the multifractal DFA results for
original series to those for shuffled series we can distinguish multifractality
due to long-range correlations from multifractality due to a broad probability
density function. We also compare our results with the wavelet transform
modulus maxima (WTMM) method, and show that the results are equivalent.Comment: 14 pages (RevTex) with 10 figures (eps
Detecting Long-range Correlations with Detrended Fluctuation Analysis
We examine the Detrended Fluctuation Analysis (DFA), which is a
well-established method for the detection of long-range correlations in time
series. We show that deviations from scaling that appear at small time scales
become stronger in higher orders of DFA, and suggest a modified DFA method to
remove them. The improvement is necessary especially for short records that are
affected by non-stationarities. Furthermore, we describe how crossovers in the
correlation behavior can be detected reliably and determined quantitatively and
show how several types of trends in the data affect the different orders of
DFA.Comment: 10 pages, including 8 figure