SEARCH · Search PubMed

Results for “Wavelet Analysis”

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.

SourceResearch connectionFrom yearThrough year

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

At least 613 records · Page 34Linked to original sources

PubMed2001

Algorithms to analyze ventricular fibrillation signals.

Prediction of the success of defibrillation to avoid myocardial injury and performance feedback during CPR requires algorithms to analyze ventricular fibrillation signals. This report reviews investigations on different parameters of ventricular fibrillation electrocardiographic signals, including amplitude, frequency, bispectral analysis, amplitude spectrum area, wavelets, nonlinear dynamics, N(alpha) histograms, and combinations of several of these parameters. To date, no satisfactory methods have been found that cope with CPR artifacts and show adequate predictive power of successful defibrillation. The usual limitations of the studies are the small number of subjects, which precludes separation into training and test data. Because many investigations are animal studies of untreated short ventricular fibrillation, the results may be different for prolonged ventricular fibrillation in humans. The universality of threshold values has to be examined, and promising new parameters have to be monitored over longer time periods and analyzed for the effects of chest compressions, ventilation, and concomitant vasopressor therapy.

Algorithms↗

PubMed2006

Muscle fibre recruitment can respond to the mechanics of the muscle contraction.

This study investigates the motor unit recruitment patterns between and within muscles of the triceps surae during cycling on a stationary ergometer at a range of pedal speeds and resistances. Muscle activity was measured from the soleus (SOL), medial gastrocnemius (MG) and lateral gastrocnemius (LG) using surface electromyography (EMG) and quantified using wavelet and principal component analysis. Muscle fascicle strain rates were quantified using ultrasonography, and the muscle-tendon unit lengths were calculated from the segmental kinematics. The EMG intensities showed that the body uses the SOL relatively more for the higher-force, lower-velocity contractions than the MG and LG. The EMG spectra showed a shift to higher frequencies at faster muscle fascicle strain rates for MG: these shifts were independent of the level of muscle activity, the locomotor load and the muscle fascicle strain. These results indicated that a selective recruitment of the faster motor units occurred within the MG muscle in response to the increasing muscle fascicle strain rates. This preferential recruitment of the faster fibres for the faster tasks indicates that in some circumstances motor unit recruitment during locomotion can match the contractile properties of the muscle fibres to the mechanical demands of the contraction.

Adult↗

PubMed2005

Self-affine fractal distributions of the bulk density, elastic moduli, and seismic wave velocities of rock.

The scale dependence of the bulk density rho and seismic wave velocities V(p) and V(s) and the possibility of the existence of long-range correlations in such properties in field-scale porous media (FSPM) are investigated. We analyze measured data for rho, V(p), and V(s) for nine FSPM, using the maximum entropy method and the wavelet decomposition technique. The analysis indicates the existence of long-range correlations in the data, characterized by self-affine fractal distributions that follow the statistics of the fractional Brownian motion. Therefore, the elastic moduli of the FSPM should also be fractally distributed and contain long-range correlations; our analysis confirms this. The implications of the results for modeling elastic moduli of porous rock, fracture propagation in FSPM, and the interpretation of seismic wave recordings of FSPM are discussed, and the possible deviations from the classical analysis of such phenomena are pointed out.

Journal Article↗

PubMed2003

A software package for the decomposition of long-term multichannel EMG signals using wavelet coefficients.

This paper presents a method to decompose multichannel long-term intramuscular electromyogram (EMG) signals. In contrast to existing decomposition methods which only support short registration periods or single-channel recordings of signals of constant muscle effort, the decomposition software EMG-LODEC (ElectroMyoGram LOng-term DEComposition) is especially designed for multichannel long-term recordings of signals of slight muscle movements. A wavelet-based, hierarchical cluster analysis algorithm estimates the number of classes [motor units (MUs)], distinguishes single MUAPs from superpositions, and sets up the shape of the template for each class. Using three channels and a weighted averaging method to track action potential (AP) shape changes improve the analysis. In the last step, nonclassified segments, i.e., segments containing superimposed APs, are decomposed into their units using class-mean signals. Based on experiments on simulated and long-term recorded EMG signals, our software is capable of providing reliable decompositions with satisfying accuracy. EMG-LODEC is suitable for the study of MU discharge patterns and recruitment order in healthy subjects and patients during long-term measurements.

Action Potentials↗

PubMed2002

On the frequency dependence of the otoacoustic emission latency in hypoacoustic and normal ears.

Experimental measurements of the otoacoustic emission (OAE) latency of adult subjects have been obtained, as a function of frequency, by means of wavelet time-frequency analysis based on the iterative application of filter banks. The results are in agreement with previous OAE latency measurements by Tognola et al. [Hear. Res. 106, 112-122 (1997)], as regards both the latency values and the frequency dependence, and seem to be incompatible with the steep 1/f law that is predicted by scale-invariant full cochlear models. The latency-frequency relationship has been best fitted to a linear function of the cochlear physical distance, using the Greenwood map, and to an exponential function of the cochlear distance, for comparison with derived band ABR latency measurements. Two sets of ears [94 audiometrically normal and 42 impaired with high-frequency (f > 3 kHz) hearing loss] have been separately analyzed. Significantly larger average latencies were found in the impaired ears in the mid-frequency range. Theoretical implications of these findings on the transmission of the traveling wave are discussed.

Adult↗

PubMed2005

Moving average fields, macro-scale response measures, and homogenizing micro-scale variation.

There are two critical issues when deriving a macro-scale prediction model starting from a more complete, underlying model. The first is the precise relationship of the fields predicted by the more complete model and the fields predicted by the macro-scale model. The second is the manner of solving a closure problem that is invariably encountered in all such derivations. The understanding that moving averages of the fields predicted by the more complete model are the fields predicted by the macro-scale model is challenged on the grounds that accomplishing a moving average does not eliminate micro-scale variation, it only appears to do so in one representation of the moving average field. The solution of a closure problem by assumption is challenged on the grounds that the most common assumptions are demonstrably invalid, even while leading to prediction models that can provide reasonable estimates of the macro-scale response in some scenarios. In presenting the challenges, it is further shown how a multiresolution analysis by an orthogonal wavelet system provides a framework for both precisely defining macro-scale response fields, i.e., fields from which all micro-scale variation has been eliminated, and presenting a formally exact solution for a precisely described closure problem.

Journal Article↗

PubMed2001

[Study of EEG processing system and EEG wavelet transform].

In this paper are reported our studies on the time and frequency characteristics of wavelet transform, the EEG signals processing through wavelet transform, the EEG analysis and reconstruction results. The related EEG processing system design and implementation are given in detail. This system may be applied to EEG feedback research and EEG monitoring.

Computers↗

PubMed2001

Can recent innovations in harmonic analysis 'explain' key findings in natural image statistics?

Recently, applied mathematicians have been pursuing the goal of sparse coding of certain mathematical models of images with edges. They have found by mathematical analysis that, instead of wavelets and Fourier methods, sparse coding leads towards new systems: ridgelets and curvelets. These new systems have elements distributed across a range of scales and locations, but also orientations. In fact they have highly direction-specific elements and exhibit increasing numbers of distinct directions as we go to successively finer scales. Meanwhile, researchers in natural scene statistics (NSS) have been attempting to find sparse codes for natural images. The new systems they have found by computational optimization have elements distributed across a range of scales and locations, but also orientations. The new systems are certainly unlike wavelet and Gabor systems, on the one hand because of the multi-orientation and on the other hand because of the multi-scale nature. There is a certain degree of visual resemblance between the findings in the two fields, which suggests the hypothesis that certain important findings in the NSS literature might possibly be explained by the slogan: edges are the dominant features in images, and curvelets are the right tool for representing edges. We consider here certain empirical consequences of this hypothesis, looking at key findings of the NSS literature and conducting studies of curvelet and ridgelet transforms on synthetic and real images, to see if the results are consistent with predictions from this slogan. Our first experiment measures the nonGaussianity of Fourier, wavelet, ridgelet and curvelet coefficients over a database of synthetic and photographic images. Empirically the curvelet coefficients exhibit noticeably higher kurtosis than wavelet, ridgelet, or Fourier coefficients. This is consistent with the hypothesis. Our second experiment studies the inter-scale correlation of wavelet coefficient energies at the same location. We describe a simple experiment showing that presence of edges explains these correlations. We also develop a crude nonlinear 'partial correlation' by considering the correlation between wavelet parents and children after a few curvelet coefficients are removed. When we kill the few biggest coefficients of the curvelet transform, much of the correlation between wavelet subbands disappears--consistent with the hypothesis. We suggest implications for future discussions about NSS.

Algorithms↗

PubMed2004

Decreased fractal correlation in diurnal physical activity in chronic fatigue syndrome.

OBJECTIVES: Our objectives were to study the temporal correlation of physical activity time series in patients with chronic fatigue syndrome (CFS) during normal daily life and to examine if it could identify the altered physical activity in these patients. METHODS: Fractal scaling exponents of diurnal and nocturnal physical activity time series in 10 CFS patients and 6 healthy control subjects (CON) were calculated by the detrended fluctuation analysis (DFA) and the wavelet transform modulus maxima (WTMM) method. We hypothesized that, due to their illness- and/or fatigue-induced resting episodes, altered physical activity patterns in CFS patients might be observed at the interruption of activity bursts. Thus, we further developed a new method, the wavelet transform negative modulus maxima (WTNMM) method, which could evaluate the temporal correlation at the interruption of activities. We compared the fractal scaling exponents for CFS and CON by each method. RESULTS: Both for CFS and CON, we found the fractal time structures in their diurnal physical activity records for at least up to 35 minutes. No group difference was found in nocturnal activities. The WTNMM method revealed that, in diurnal activities, CFS patients had significantly (p < 0.01) smaller fractal scaling exponent (0.87 +/- 0.03) compared to controls (1.01 +/- 0.03). Such a difference was identified neither by the DFA nor WTMM method. CONCLUSIONS: CFS patients had more abrupt interruptions of voluntary physical activity during diurnal periods in normal daily life, probed by the decreased correlation in the negative modulus maxima of the wavelet-transformed activity data, possibly due to their exaggerated fatigue.

Chronic Disease↗

PubMed2006

Learning by operant conditioning as a nonlinear self-organized process.

Responses of individuals have been rewarded in fixed ratio operant conditioning experiments throughout the last 5 years. The long numerical series of performance rates so obtained have been analyzed by using Fourier spectra, obtaining 1/f noise, and wavelet techniques for the analysis of local variability. These techniques reveal that the evolution of data in each individual is nonlinear but it fluctuates showing self-organized patterns in successive time scales, which leads to a long term logarithmic growth. Based on these observations a model has been developed. It can simulate with a great accuracy the rate of response exhibited by our subjects in all time scales.

Conditioning, Operant↗

PubMed2004

[Using wavelet transform for information extraction from remote sensing FTIR spectra].

How to use wavelet transform technology to extract information from remote sensing FTIR spectra, which were weak or always interfered by others, was described in the present paper. The Mexican hat function was used as a wavelet function to continuously transform the signals of pure chloroform, pure acetone and their mixture. The results indicated that the small scales were the guarantee of the accuracy of corresponding position between maximum module of wavelet transform coefficients and break points of peaks. However, only one scale could not determine the position of break point because of the effect of noise in small scales. On the contrary, maximum module of wavelet transform coefficients was relatively stable in large scales when noise was smoothed. But smoothness always brought deviation of orientation. Therefore, multi-scales should be combined to observe the break points of signals when using wavelet transform technology. All in all, the break points of signals could be determined accurately and stably by the wavelet transform technology and useful information was extracted. The signals were smoothed and magnified at the same time. According to the analysis of maximum module of wavelet transform coefficients and their orientation in different scales, some spectra, such as mixed and non-strongly-overlapped remote sensing FTIR spectra, could be recognized magnificently.

English Abstract↗

PubMed1999

Time-frequency analysis of single-sweep event-related potentials by means of fast wavelet transform.

A time-frequency decomposition was applied to the event-related potentials (ERPs) elicited in an auditory oddball condition to assess differences in cognitive information processing. Analysis in the time domain has revealed that cognitive processes are reflected by various ERP components such as N1, P2, N2, P300, and late positive complex. However, the heterogeneous nature of these components has been strongly emphasized due to simultaneously occurring processes. The wavelet transform (WT), which decomposes the signal onto the time-frequency plane, allows the time-dependent and frequency-related information in ERPs to be captured and precisely measured. A four-octave quadratic B-spline wavelet transform was applied to single-sweep ERPs recorded in an auditory oddball paradigm. Frequency components in delta, theta, and alpha ranges reflected specific aspects of cognitive information processing. Furthermore, the temporal position of these components was related to specific cognitive processes.

Adolescent↗

PubMed2001

Non-invasive testing of acquired long QT syndrome: evidence for multiple arrhythmogenic substrates.

BACKGROUND: Although well-defined clinically and electrocardiographically, Acquired Long QT Syndrome (LQTS) remains elusive from a pathophysiologic point of view. An increasingly accepted hypothesis is that it represents an attenuated form of Congenital Long QT Syndrome. To test this hypothesis further, we investigated patients with Acquired LQTS, using various investigations that are known to give information in patients with Congenital LQTS. METHODS: All the investigations were performed in patients with a history of Acquired Long QT Syndrome, defined by marked transient QT lengthening (QT>600 ms) and/or torsades de pointes. Measurement of the QT interval dispersion, the interlead difference for the QT interval on a 12-lead ECG, was performed in 18 patients and compared with 18 controls, matched for age and sex. To assess sympathetic myocardial innervation, I-123 Meta-iodobenzylguanidine (I-123-MIBG) scintigraphy was performed in 12 patients, together with Thallium scintigraphy, to rule out abnormal myocardial perfusion. Time-frequency analysis of a high-resolution ECG using a wavelet technique, was made for nine patients and compared with 38 healthy controls. Finally, genetic studies were performed prospectively in 16 consecutive patients, to look for HERG, KCNE1, KCNE2 and KCNQ1 mutations. The functional profile of a mutated HERG protein was performed using the patch-clamp technique. RESULTS: Compared with the control group, a significant increase in QT dispersion was observed in the patients with a history of Acquired LQTS (55+/-15 vs. 33+/-9 ms, P<0.001). In another group of patients with Acquired LQTS, 123 I-MIBG tomoscintigraphy demonstrated a decrease in the sympathetic myocardial innervation. Time--frequency analysis using wavelet transform, demonstrated an abnormal frequency content within the QRS complexes, in the patients with Acquired LQTS, similar to that found in Congenital LQTS patients. Molecular screening in 16 consecutive patients, identified one patient with a missense mutation on HERG, one of the LQTS genes. Expression of the mutated HERG protein led to altered K(+) channel function. CONCLUSION: Our results suggest that Acquired and Congenital Long QT Syndromes have some common features. They allow the mechanism of the clinical heterogeneity, found in both syndromes, to be understood. Further multi-facet approaches are needed to decipher the complex interplay between the main determinants of these arrhythmogenic diseases.

Aged↗

PubMed2006

A practical guide to time-frequency analysis in the study of human motor behavior: the contribution of wavelet transform.

The authors present a practical guide for studying nonstationary data on human motor behavior in a time-frequency representation. They explain the limits of classical methods founded exclusively on the time or frequency basis and then answer those limits with the windowed Fourier transform and the wavelet transform (WT) methods, both of which are founded on time-frequency bases. The authors stress an interest in the WT method because it permits access to the whole complexity of a signal (in terms of time, frequency, amplitude, and phase). They then show that the WT method is well suited for the analysis of the interaction between two signals, particularly in human movement studies. Finally, to demonstrate its practical applications, the authors apply the method to real data.

Humans↗

PubMed2000

Evaluation of low back muscle surface EMG signals using wavelets.

OBJECTIVE: To compare the ability of observers to correctly detect the reaction time of erector spinae response to unexpected load by inspecting nonprocessed electromyographic signals versus inspection of wavelet transformed electromyographic signals and versus automatic detection on the same wavelet transformed signals. BACKGROUND: Traditionally, electromyographic signal analysis is performed using Fourier transform based methods. However, muscle response to transients such as unexpected load, have limitations when using these methods of electromyographic processing. DESIGN: A comparison was made of the three methods using the same signals attained during sudden loading of the trunk. METHODS: 11 chronic low back pain patients and eleven normal subjects were investigated in sudden loading. Surface electromyographic signals were obtained from the erector spine muscle at L3. The ability of observers to detect reaction time of erector spinae muscle responses of nonprocessed electromyographic signals versus inspection of wavelet transformed electromyographic signals versus an automatic peak detection program was determined. RESULTS: The results have shown that the spine muscle reaction time was easier and more accurately determined in the wavelet domain rather than in its original signal representation. CONCLUSION: Wavelet transform methods improved the analysis of electromyographic signals in the time domain by facilitating the determination of the time of muscle activity. RELEVANCE: Wavelet transform could be a valuable tool for electromyographic analysis in resolving the psychophysical problem of perception involved in the analysis of nonprocessed signals. In clinical environments, where the speed and the accuracy of the analysis of electromyographic signal is critical, the wavelet based signal processing could be very important.

Adult↗

PubMed2004

[Experimental study on brain-computer interface based on visual evoked potentials].

Brain-computer interface is a novel EEG-based communication and control system between human brain and computer or some other electric device, which has found important applications in many fields such as rehabilitation engineering. Study has been done in brain-computer interface using visual evoked potentials(VEP). Multiple stimulation patterns were produced on the computer screen through programming. Several flickering blocks were adopted for representing a number of possible selections, and when the subject was fixing his(her) eyes on an object on the screen, the very object could be distinguished by the analysis of the VEPs. Surface electrodes were placed at Oz of the inion and Cz to collect VEP. The wavelet filter and averaging method was used to extract VEP signal. Off-line experimental data analysis indicated that the proposed method may be valuable for developing real brain-computer interface with relatively high accuracy and speed, and the information transfer rates could be higher than 30 bit/min when 12 selections were on the screen.

Brain↗

PubMed2004

Investigating the wavelet coherence phase of the BOLD signal.

PURPOSE: To investigate the temporal behavior of the blood oxygenation-level dependent (BOLD) signal applying the wavelet coherence phase to functional magnetic resonance imaging (fMRI) data. MATERIALS AND METHODS: The wavelet coherence phase was computed for a group of four subjects using three functional runs of different visual stimulation lengths. In order to consider the variability of the wavelet coherence phase, a correlation analysis was performed between the variance of the phase shift and the associated activation strength. In activated brain regions, correlation was performed between the mean wavelet coherence phase and the spectral density phase shift. RESULTS: The scalograms of the wavelet coherence phase show the temporal variability of the phase shift between fMRI time series. For brain regions with weakly stationary behavior, the mean wavelet coherence phase supports the results obtained by spectral analyses. CONCLUSION: The wavelet coherence phase provides a description of the temporal behavior of the BOLD signal even for the nonstationary case. In particular, temporal changes of the phase shift can be investigated. This makes the wavelet coherence phase more suitable for the investigation of BOLD dynamics than an average phase lag obtained by correlation or spectral methods.

Adult↗

PubMedDate not supplied

A comparison of analytical methods for the study of fractional Brownian motion.

Fractional Brownian motion (FBM) provides a useful model for many physical phenomena demonstrating long-term dependencies and l/f-type spectral behavior. In this model, only one parameter is necessary to describe the complexity of the data, H, the Hurst exponent. FBM is a nonstationary random function not well suited to traditional power spectral analysis however. In this paper we discuss alternative methods for the analysis of FBM, in the context of real-time biomedical signal processing. Regression-based methods utilizing the power spectral density (PSD), the discrete wavelet transform (DWT), and dispersive analysis (DA) are compared for estimation accuracy and precision on synthesized FBM datasets. The performance of a maximum likelihood estimator for H, theoretically the best possible estimator, are presented for reference. Of the regression-based methods, it is found that the estimates provided by the DWT method have better accuracy and precision for H > 0.5, but become biased for low values of H. The DA method is most accurate for H < 0.5 for a 256-point data window size. The PSD method was biased for both H < 0.5 and H > 0.5.

Bias↗