52 research outputs found
Effect of nonlinear filters on detrended fluctuation analysis
We investigate how various linear and nonlinear transformations affect the
scaling properties of a signal, using the detrended fluctuation analysis (DFA).
Specifically, we study the effect of three types of transforms: linear,
nonlinear polynomial and logarithmic filters. We compare the scaling properties
of signals before and after the transform. We find that linear filters do not
change the correlation properties, while the effect of nonlinear polynomial and
logarithmic filters strongly depends on (a) the strength of correlations in the
original signal, (b) the power of the polynomial filter and (c) the offset in
the logarithmic filter. We further investigate the correlation properties of
three analytic functions: exponential, logarithmic, and power-law. While these
three functions have in general different correlation properties, we find that
there is a broad range of variable values, common for all three functions,
where they exhibit identical scaling behavior. We further note that the scaling
behavior of a class of other functions can be reduced to these three typical
cases. We systematically test the performance of the DFA method in accurately
estimating long-range power-law correlations in the output signals for
different parameter values in the three types of filters, and the three
analytic functions we consider.Comment: 12 pages, 7 figure
Regeneration of Stochastic Processes: An Inverse Method
We propose a novel inverse method that utilizes a set of data to construct a
simple equation that governs the stochastic process for which the data have
been measured, hence enabling us to reconstruct the stochastic process. As an
example, we analyze the stochasticity in the beat-to-beat fluctuations in the
heart rates of healthy subjects as well as those with congestive heart failure.
The inverse method provides a novel technique for distinguishing the two
classes of subjects in terms of a drift and a diffusion coefficients which
behave completely differently for the two classes of subjects, hence
potentially providing a novel diagnostic tool for distinguishing healthy
subjects from those with congestive heart failure, even at the early stages of
the disease development.Comment: 5 pages, two columns, 7 figs. to appear, The European Physical
Journal B (2006
Bayesian Blocks, A New Method to Analyze Structure in Photon Counting Data
I describe a new time-domain algorithm for detecting localized structures
(bursts), revealing pulse shapes, and generally characterizing intensity
variations. The input is raw counting data, in any of three forms: time-tagged
photon events (TTE), binned counts, or time-to-spill (TTS) data. The output is
the most likely segmentation of the observation into time intervals during
which the photon arrival rate is perceptibly constant -- i.e. has a fixed
intensity without statistically significant variations. Since the analysis is
based on Bayesian statistics, I call the resulting structures Bayesian Blocks.
Unlike most, this method does not stipulate time bins -- instead the data
themselves determine a piecewise constant representation. Therefore the
analysis procedure itself does not impose a lower limit to the time scale on
which variability can be detected. Locations, amplitudes, and rise and decay
times of pulses within a time series can be estimated, independent of any
pulse-shape model -- but only if they do not overlap too much, as deconvolution
is not incorporated. The Bayesian Blocks method is demonstrated by analyzing
pulse structure in BATSE -ray data. The MatLab scripts and sample data
can be found on the WWW at: http://george.arc.nasa.gov/~scargle/papers.htmlComment: 42 pages, 2 figures; revision correcting mathematical errors;
clarifications; removed Cyg X-1 sectio
Generalization of entropy based divergence measures for symbolic sequence analysis
Entropy based measures have been frequently used in symbolic sequence analysis. A symmetrized and smoothed form of Kullback-Leibler divergence or relative entropy, the Jensen-Shannon divergence (JSD), is of particular interest because of its sharing properties with families of other divergence measures and its interpretability in different domains including statistical physics, information theory and mathematical statistics. The uniqueness and versatility of this measure arise because of a number of attributes including generalization to any number of probability distributions and association of weights to the distributions. Furthermore, its entropic formulation allows its generalization in different statistical frameworks, such as, non-extensive Tsallis statistics and higher order Markovian statistics. We revisit these generalizations and propose a new generalization of JSD in the integrated Tsallis and Markovian statistical framework. We show that this generalization can be interpreted in terms of mutual information. We also investigate the performance of different JSD generalizations in deconstructing chimeric DNA sequences assembled from bacterial genomes including that of E. coli, S. enterica typhi, Y. pestis and H. influenzae. Our results show that the JSD generalizations bring in more pronounced improvements when the sequences being compared are from phylogenetically proximal organisms, which are often difficult to distinguish because of their compositional similarity. While small but noticeable improvements were observed with the Tsallis statistical JSD generalization, relatively large improvements were observed with the Markovian generalization. In contrast, the proposed Tsallis-Markovian generalization yielded more pronounced improvements relative to the Tsallis and Markovian generalizations, specifically when the sequences being compared arose from phylogenetically proximal organisms.publishedVersionFil: Ré, Miguel Ángel. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación en Informática para la Ingeniería. Departamento de Ciencias Básicas; Argentina.Fil: Ré, Miguel Ángel. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina.Fil: Azad, Rajeev K. University of North Texas. College of Science. Department of Biological Sciences; Estados Unidos de América.Fil: Azad, Rajeev K. University of North Texas. College of Science. Department of Mathematics; Estados Unidos de América.Ciencias de la Información y Bioinformática (desarrollo de hardware va en 2.2 "Ingeniería Eléctrica, Electrónica y de Información" y los aspectos sociales van en 5.8 "Comunicación y Medios"
Hierarchical structure of cascade of primary and secondary periodicities in Fourier power spectrum of alphoid higher order repeats
<p>Abstract</p> <p>Background</p> <p>Identification of approximate tandem repeats is an important task of broad significance and still remains a challenging problem of computational genomics. Often there is no single best approach to periodicity detection and a combination of different methods may improve the prediction accuracy. Discrete Fourier transform (DFT) has been extensively used to study primary periodicities in DNA sequences. Here we investigate the application of DFT method to identify and study alphoid higher order repeats.</p> <p>Results</p> <p>We used method based on DFT with mapping of symbolic into numerical sequence to identify and study alphoid higher order repeats (HOR). For HORs the power spectrum shows equidistant frequency pattern, with characteristic two-level hierarchical organization as signature of HOR. Our case study was the 16 mer HOR tandem in AC017075.8 from human chromosome 7. Very long array of equidistant peaks at multiple frequencies (more than a thousand higher harmonics) is based on fundamental frequency of 16 mer HOR. Pronounced subset of equidistant peaks is based on multiples of the fundamental HOR frequency (multiplication factor <it>n </it>for <it>n</it>mer) and higher harmonics. In general, <it>n</it>mer HOR-pattern contains equidistant secondary periodicity peaks, having a pronounced subset of equidistant primary periodicity peaks. This hierarchical pattern as signature for HOR detection is robust with respect to monomer insertions and deletions, random sequence insertions etc. For a monomeric alphoid sequence only primary periodicity peaks are present. The 1/<it>f</it><sup><it>β </it></sup>– noise and periodicity three pattern are missing from power spectra in alphoid regions, in accordance with expectations.</p> <p>Conclusion</p> <p>DFT provides a robust detection method for higher order periodicity. Easily recognizable HOR power spectrum is characterized by hierarchical two-level equidistant pattern: higher harmonics of the fundamental HOR-frequency (secondary periodicity) and a subset of pronounced peaks corresponding to constituent monomers (primary periodicity). The number of lower frequency peaks (secondary periodicity) below the frequency of the first primary periodicity peak reveals the size of <it>n</it>mer HOR, i.e., the number <it>n </it>of monomers contained in consensus HOR.</p
Multifractal and entropy analysis of resting-state electroencephalography reveals spatial organization in local dynamic functional connectivity
Functional connectivity of the brain fluctuates even in resting-state condition. It has been reported recently that fluctuations of global functional network topology and those of individual connections between brain regions expressed multifractal scaling. To expand on these findings, in this study we investigated if multifractality was indeed an inherent property of dynamic functional connectivity (DFC) on the regional level as well. Furthermore, we explored if local DFC showed region-specific differences in its multifractal and entropy-related features. DFC analyses were performed on 62-channel, resting-state electroencephalography recordings of twelve young, healthy subjects. Surrogate data testing verified the true multifractal nature of regional DFC that could be attributed to the presumed nonlinear nature of the underlying processes. Moreover, we found a characteristic spatial distribution of local connectivity dynamics, in that frontal and occipital regions showed stronger long-range correlation and higher degree of multifractality, whereas the highest values of entropy were found over the central and temporal regions. The revealed topology reflected well the underlying resting-state network organization of the brain. The presented results and the proposed analysis framework could improve our understanding on how resting-state brain activity is spatio-temporally organized and may provide potential biomarkers for future clinical research
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