Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Stochastic Processes”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,081 records · Page 60Linked to original sources
PubMed2004

Detrended fluctuation analysis: a suitable method for studying fetal heart rate variability?

We evaluate the suitability of an enhanced detrended fluctuation analysis for studying fetal heart rate series involving imperfect quality of information. Our results indicate that to explore persistent long-range correlations, or fractality, the collection requirements of the data can be relaxed by allowing the possibility of using averaged fetal heart rate series. In addition, it also appears feasible to employ, without producing major alterations in the long-range scaling behaviour, fragmented fetal heart rate series involving up to 50% of random missing values, or up to 50 min of consecutive missing samples in recordings of approximately equal to 8 h length. These are crucial advantages to overcome the often variable quality of fetal data. Consequently, these findings may open the possibility of obtaining information concerning the development of neural processes from fetal heart rate series, despite their non-stationary and fragmented nature.

Algorithms↗
PubMed2003

Wavelets in bioinformatics and computational biology: state of art and perspectives.

MOTIVATION: At a recent meeting, the wavelet transform was depicted as a small child kicking back at its father, the Fourier transform. Wavelets are more efficient and faster than Fourier methods in capturing the essence of data. Nowadays there is a growing interest in using wavelets in the analysis of biological sequences and molecular biology-related signals. RESULTS: This review is intended to summarize the potential of state of the art wavelets, and in particular wavelet statistical methodology, in different areas of molecular biology: genome sequence, protein structure and microarray data analysis. I conclude by discussing the use of wavelets in modeling biological structures.

Algorithms↗
PubMed1995

Dendritic spines form 'collars' in hippocampal granule cells.

A quantitative study of the distribution of dendritic spines was carried out in three orders of dendritic branches of granule cells from the dentate gyrus of the rat hippocampus. Golgi-stained preparations (7-19 neurones in each of seven rats) were analysed using computerized microscopy. Identification of spines and quantification of stem-spine geometry was performed using a segmentation algorithm and a line skeleton transformation of dendritic images. Analysis of data using the statistics of point processes revealed that, in all three branch orders, the distribution of visible spines along dendrites was not evenly random, but included dense clusters of spines surrounding the dendritic stem (spine 'collars'). Three-dimensional reconstructions from serial ultrathin sections have confirmed the presence of such spine groups. We speculate the spine collars represent a functional element in which associative synaptic plasticity is fostered by the proximity of individual synapses.

Animals↗
PubMed1997

Deviations from uniform power law scaling in nonstationary time series.

A classic problem in physics is the analysis of highly nonstationary time series that typically exhibit long-range correlations. Here we test the hypothesis that the scaling properties of the dynamics of healthy physiological systems are more stable than those of pathological systems by studying beat-to-beat fluctuations in the human heart rate. We develop techniques based on the Fano factor and Allan factor functions, as well as on detrended fluctuation analysis, for quantifying deviations from uniform power-law scaling in nonstationary time series. By analyzing extremely long data sets of up to N = 10(5) beats for 11 healthy subjects, we find that the fluctuations in the heart rate scale approximately uniformly over several temporal orders of magnitude. By contrast, we find that in data sets of comparable length for 14 subjects with heart disease, the fluctuations grow erratically, indicating a loss of scaling stability.

Adult↗
PubMed2000

Rate processes in a delayed, stochastically driven, and overdamped system

A Fokker-Planck formulation of systems described by stochastic delay differential equations has been recently proposed. A separation of time scales approximation allowing this Fokker-Planck equation to be simplified in the case of multistable systems is hereby introduced, and applied to a system consisting of a particle coupled to a delayed quartic potential. In that approximation, population numbers in each well obey a phenomenological rate law. The corresponding transition rate is expressed in terms of the noise variance and the steady-state probability density. The same type of expression is also obtained for the mean first passage time from a given point to another one. The steady-state probability density appearing in these formulas is determined both from simulations and from a small delay expansion. The results support the validity of the separation of time scales approximation. However, the results obtained using a numerically determined steady-state probability are more accurate than those obtained using the small delay expansion, thereby stressing the high sensitivity of the transition rate and mean first passage time to the shape of the steady-state probability density. Simulation results also indicate that the transition rate and the mean first passage time both follow Arrhenius' law when the noise variance is small, even if the delay is large. Finally, deterministic unbounded solutions are found to coexist with the bounded ones. In the presence of noise, the transition rate from bounded to unbounded solutions increases with the delay.

Journal Article↗
PubMed2003

Experimental evidence of "vibrational resonance" in an optical system.

The experimental evidence and characterization of "vibrational resonance" in a bistable vertical cavity laser are reported. The system is driven by two periodic forcings, with frequencies differing by several orders and studied in the case of both symmetrical and asymmetrical quasipotentials. The phenomenon shows up in the dynamics of the polarized laser emission as a resonance in the low-frequency response and signal-to-noise ratio, depending on the amplitude of an applied high-frequency modulation. The possibility to use the phenomenon for low-level detection is experimentally demonstrated.

Models, Theoretical↗
PubMed1994

Statistical analysis of topographic maps of short-latency somatosensory evoked potentials in normal and parkinsonian subjects.

This work had the following objectives: i) to integrate temporal analysis (N30 peak) with power-spectrum topographic mapping of short-latency somatosensory evoked potentials (SEP's) recorded in parkinsonian and normal control subjects; and ii) to analyze with a new statistical approach the between-group topographical differences in both the time and frequency domains. The principal aim was to better determine the topography of the scalp frontal areas where the amplitude of the N30 wave was previously found to be significantly reduced in parkinsonians. The statistical procedure was based on the combined use of descriptive data analysis (DDA) and multivariate analysis. In the context of DDA, an improved version of significance probability mapping (SPM) was used by which it is possible to evaluate homo- and nonhomoscedastic data with parametric tests. The statistical evaluation of between-group differences was performed with the multivariate Hotelling's T2 test and the associated post hoc test. With this statistical procedure, it was possible to determine that the between-group statistical differences in both the temporal and power spectrum distributions were localized only in midline and contiguous contralateral frontal areas of the scalp.

Brain Mapping↗
PubMed1997

Spectral decomposition in multichannel recordings based on multivariate parametric identification.

A method of spectral decomposition in multichannel recordings is proposed, which represents the results of multivariate (MV) parametric identification in terms of classification and quantification of different oscillating mechanisms. For this purpose, a class of MV dynamic adjustment (MDA) models in which a MV autoregressive (MAR) network of causal interactions is fed by uncorrelated autoregressive (AR) processes is defined. Poles relevant to the MAR network closed-loop interactions (cl-poles) and poles relevant to each AR input are disentangled and accordingly classified. The autospectrum of each channel can be divided into partial spectra each relevant to an input. Each partial spectrum is affected by the cl-poles and by the poles of the corresponding input; consequently, it is decomposed into the relevant components by means of the residual method. Therefore, different oscillating mechanisms, even at similar frequencies, are classified by different poles and quantified by the corresponding components. The structure of MDA models is quite flexible and can be adapted to various sets of available signals and a priori hypotheses about the existing interactions; a graphical layout is proposed that emphasizes the oscillation sources and the corresponding closed-loop interactions. Application examples relevant to cardiovascular variability are briefly illustrated.

Algorithms↗
PubMed1998

Using evoked EMG as a synthetic force sensor of isometric electrically stimulated muscle.

A method for the estimation of the force generated by electrically stimulated muscle during isometric contraction is developed here. It is based upon measurements of the evoked electromyogram (EMG) [EEMG] signal. Muscle stimulation is provided to the quadriceps muscle of a paralyzed human subject using percutaneous intramuscular electrodes, and EEMG signals are collected using surface electrodes. Through the use of novel signal acquisition and processing techniques, as well as a mathematical model that reflects both the excitation and activation phenomena involved in isometric muscle force generation, accurate prediction of stimulated muscle forces is obtained for large time horizons. This approach yields synthetic muscle force estimates for both unfatigued and fatigued states of the stimulated muscle. In addition, a method is developed that accomplishes automatic recalibration of the model to account for day-to-day changes in pickup electrode mounting as well as other factors contributing to EEMG gain variations. It is demonstrated that the use of the measured EEMG as the input to a predictive model of muscle torque generation is superior to the use of the electrical stimulation signal as the model input. This is because the measured EEMG signal captures all of the neural excitation, whereas stimulation-to-torque models only reflect that portion of the neural excitation that results directly from stimulation. The time-varying properties of the excitation process cannot be captured by existing stimulation-to-torque models, but they are tracked by the EEMG-to-torque models that are developed here. This work represents a promising approach to the real-time estimation of stimulated muscle force in functional neuromuscular stimulation applications.

Artifacts↗
PubMed1998

Adaptive AR modeling of nonstationary time series by means of Kalman filtering.

An adaptive on-line procedure is presented for autoregressive (AR) modeling of nonstationary multivariate time series by means of Kalman filtering. The parameters of the estimated time-varying model can be used to calculate instantaneous measures of linear dependence. The usefulness of the procedures in the analysis of physiological signals is discussed in two examples: First, in the analysis of respiratory movement, heart rate fluctuation, and blood pressure, and second, in the analysis of multichannel electroencephalogram (EEG) signals. It was shown for the first time that in intact animals the transition from a normoxic to a hypoxic state requires tremendous short-term readjustment of the autonomic cardiac-respiratory control. An application with experimental EEG data supported observations that the development of coherences among cell assemblies of the brain is a basic element of associative learning or conditioning.

Adaptation, Physiological↗
PubMed1998

Influence of joint angle on the calibration and performance of EMG amplitude estimators.

Multiple-channel electromyogram (EMG) amplitude estimators incorporating temporal whitening filters and/or spatial uncorrelation filters contain a characterization of the EMG waveform (specifically, auto- and cross-correlation information) which may vary with joint angle. This paper reports on an experimental study which investigated the influence of joint angle on these EMG amplitude estimators. It was found that little or no relative improvement in estimator performance resulted from altering either temporal whitening or spatial uncorrelation filters as a function of joint angle. Also, the absolute performance level of these estimators did not vary with joint angle.

Adult↗
PubMed2002

A phenomenology model of normal sinus rhythm in healthy humans.

The fractal component in the daytime healthy heartbeat interval data is studied from the perspective of cascade in fluid turbulence. Based on the electrophysiology of the heart muscle cell, a bounded random cascade model is assumed and the scaling property of the model is derived. In application, a "cascade decomposition" is proposed to extract the model parameter based on the experimental data. Simulation results show that healthy heart rate variability (HRV) can be well captured by the multiplicative process and imply the significance of sympatho-vagal interaction in the fractal component of long-term healthy HRV.

Computer Simulation↗
PubMed2002

Blind deconvolution of ultrasound sequences using nonparametric local polynomial estimates of the pulse.

The problem of reconstructing the reflectivity of a biological tissue is examined by means of blind deconvolution of the echo ultrasound signals. It is shown that the quality of the reconstruction procedure can be significantly improved when initially the ultrasonic pulse is accurately estimated. A new approach to the estimation of the ultrasound pulse echo sequences is proposed, using local polynomial approximation, which is closely related to the wavelet transform theory. This approach can be viewed as a modification of homomorphic deconvolution, by using bases different from the Fourier basis of the space of square-integrable functions L2. The bases used here are the orthogonal compactly supported wavelet bases. It is shown that the locality of the estimate can be extremely useful in number of cases of practical interest, resulting in estimates with smaller root-mean squared (rms) errors, as compared with estimates employing the Fourier basis. This approach is applied to ultrasound signals, for estimation of the ultrasound pulse log-spectrum from the log-spectrum of radio-frequency (RF) sequences. It is shown, conceptually and experimentally, that the proposed approach can provide robust and rapidly computed estimates of the ultrasound pulses from the RF-sequences, as obtained in the process of tissue scanning. The pulse phase was recovered using the minimum-phase assumption, which was found to hold for the transducers in use. The obtained pulse estimates are used for the deconvolution of the RF-sequences, which result in stable estimates of the tissue reflectivity function, fairly independent of the properties of the imaging system. Simulated data, data obtained from several phantoms and from in vitro experiments have been processed and the results seem to be quite promising.

Animals↗
PubMed2002

Detection of transient-evoked otoacoustic emissions and the design of time windows.

A new approach to the design of time windows is presented for detection of transient-evoked otoacoustic emissions (TEOAE). The windows are designed with reference to a minimum mean square error criterion involving the correlation properties of the ensemble of responses. Latency information is introduced in the detection process by windowing at different scales that result from wavelet decomposition. The significance of both subject- and population-specific time windows is investigated. The detection performance is evaluated on a health screen database consisting of 4989 records. The results show that the present approach to windowing yields a significantly better performance in separating normal-hearing subjects from hearing-impaired subjects when compared to detection based on unwindowed signals. With time windowing, the specificity increased with almost 15% at a fixed sensitivity of 90%.

Acoustic Stimulation↗
PubMed2002

Higher order statistics and neural network for tremor recognition.

This paper is concerned with the tremor characterization for the purpose of recognition. Three different types of tremor are considered in this paper: the parkinsonian, essential, and physiological. It has been proven that standard second-order statistical description of tremor is not sufficient to distinguish between these three types. Higher order polyspectra based on third- and fourth-order cumulants have been proposed as the additional characterization of the tremor time series. The set of 30 quantities based on the polyspectra has been proposed and investigated as the features for the recognition of tremor. The neural network of the multilayer perceptron structure has been used as a classifier. The results of numerical experiments have proven high efficiency of the proposed approach. The average error of recognition of three types of tremor did not exceed 3%.

Computer Simulation↗
PubMed2002

An advanced detrending method with application to HRV analysis.

An advanced, simple to use, detrending method to be used before heart rate variability analysis (HRV) is presented. The method is based on smoothness priors approach and operates like a time-varying finite-impulse response high-pass filter. The effect of the detrending on time- and frequency-domain analysis of HRV is studied.

Algorithms↗