SEARCH · PubMed Health
Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
In this paper, the abilities of factor analysis and wavelet analysis for reducing noise of the absorption spectra are compared. Also, the different joint ways of these two techniques are used to reduce the noise of the same spectra. Some preliminary results are obtained.
Wavelet transform and multiresolution decomposition are described. Examples of the application of orthogonal wavelet transform to acoustic evoked potentials and otoacoustic emissions (OEA) are given and basic features of wavelet packets and wavelet network methods are characterized. An approach that enables the identification of local signal structures--a generalization of wavelet transform called Matching Pursuit--is presented. In the framework of this method the signal is decomposed into time-frequency 'atoms', which offers a possibility of determination of an 'instantaneous frequency' with the accuracy close to the theoretical limit. The method is illustrated by application to OAE signals. The advantages and limitations of the methods presented are discussed.
It was proposed that the human visual system analyzes images into square wavelets. To test this view, comparisons were made between the perceived similarity-dissimilarity of alphabet letters and the wavelet analyses of those same letters. For the proposal to be considered tenable, the coefficients of the wavelet analysis of similar letters must be similar, and the coefficients of the wavelet analysis of dissimilar letters must be dissimilar. From a selection of 12 letters, four pairs of letters had been reported by Van der Heijden, Mathas, and Van den Roovaart as very similar, and four other pairs of letters dissimilar. Each of the 12 letters was separately depicted in 8 x 8 matrices, and the signal represented by each of the matrices was analyzed into square wavelets using a new and original procedure which yielded a single set of coefficients for each matrix. Correlations between sets of coefficients were high (r ranged from .88 to .58) for those letter pairs judged high in similarity; correlations were low (r ranged from -.02 to .29) for those letter pairs judged low in similarity. When the correlations between the coefficients of wavelets of all eight-letter pairs were compared with the judged similarity-dissimilarity of all eight-letter pairs, the linear agreement was statistically significant. Agreement was found between the neurophysiological mapping of receptive fields of visual cortical neurons and the vectors or the pattern of pluses and minuses which characterized the wavelet analysis. Furthermore, regeneration of the visual image, or the pattern of neural activity representing the image, could be described by a tree-like flow of information among visual cortical neurons which received response data from visual receptive fields, the response data being wavelet coefficients. Results indicate the analysis accurately produces reliable transformations of visual patterns and may be a process used by the visual system.
A wavelet-based signal processing technique was employed to improve an implantable blood perfusion monitoring system. Data was acquired from both in vitro and in vivo sources: a perfusion model and the proximal jejunum of an adult pig. Results showed that wavelet analysis could isolate perfusion signals from raw, periodic, in vitro data as well as fast Fourier transform (FFT) methods. However, for the quasi-periodic in vivo data segments, wavelet analysis provided more consistent results than the FFT analysis for data segments of 50, 10, and 5 s in length. Wavelet analysis has thus been shown to require less data points for quasi-periodic data than FFT analysis making it a good choice for an indwelling perfusion monitor where power consumption and reaction time are paramount.
Orthonormal wavelet transformation (OWT) is a computationally efficient technique for quantifying underlying frequencies in nonstationary and gap-infested time series, such as eddy-covariance-measured net ecosystem exchange of CO2 (NEE). We employed OWT to analyze the frequency characteristics of synchronously measured and modeled NEE at adjacent pine (PP) and hardwood (HW) ecosystems. Wavelet cospectral analysis showed that NEE at PP was more correlated to light and vapor pressure deficit at the daily time scale, and NEE at HW was more correlated to leaf area index (LAI) and temperature, especially soil temperature, at seasonal time scales. Models were required to disentangle the impacts of environmental drivers on the components of NEE, ecosystem carbon assimilation (Ac) and ecosystem respiration (RE). Sensitivity analyses revealed that using air temperature rather than soil temperature in RE models improved the modeled wavelet spectral frequency response on time scales longer than 1 day at both ecosystems. Including LAI improved RE model fit on seasonal time scales at HW, and incorporating parameter variability improved the RE model response at annual time scales at both ecosystems. Resolving variability in canopy conductance, rather than leaf-internal CO2, was more important for modeling Ac at both ecosystems. The PP ecosystem was more sensitive to hydrologic variables that regulate canopy conductance: vapor pressure deficit on weekly time scales and soil moisture on seasonal to interannual time scales. The HW ecosystem was sensitive to water limitation on weekly time scales. A combination of intrinsic drought sensitivity and non-conservative water use at PP was the basis for this response. At both ecosystems, incorporating variability in LAI was required for an accurate spectral representation of modeled NEE. However, nonlinearities imposed by canopy light attenuation were of little importance to spectral fit. The OWT revealed similarities and differences in the scale-wise control of NEE by vegetation with implications for model simplification and improvement.
A temporal wavelet analysis method is proposed for velocity, displacement, and three-dimensional surface-profile measurement of a continuously deforming object by use of the shadow moiré technique. A grating is placed close to a deforming object, and its shadow is observed through the grating. The moiré fringe patterns, generated by the interference of the grating lines and their shadows, are captured by a high-speed CCD camera with a telecentric gauging lens. Instantaneous frequency of gray-value variation is evaluated point by point with the continuous wavelet transform. From the instantaneous frequency of each point on the object, the velocity, displacement, and high-quality surface profile at different instants can be retrieved. In this application, two specimens are tested to demonstrate the validity of the proposed method: One is a small coin with a rigid body motion, and the other is a simply supported beam subjected to a central point load. The results are compared with those obtained from temporal Fourier-transform and mechanical stylus methods.
Basing on Wavelet analysis, a new method was introduced and found effective for recognizing of S-T segment. It resolved successfully the technological difficulty of measurement.
On the basis of a systems theoretical approach it was hypothesized that event-related potentials (ERPs) are superpositions of stimulus-evoked and time-locked EEG rhythms reflecting resonance properties of the brain (Başar, 1980). This approach led to frequency analysis of ERPs as a way of analyzing evoked rhythms. The present article outlines the basic features of ERP frequency analysis in comparison to ERP wavelet analysis, a recently introduced method of time-frequency analysis. Both methods were used in an investigation of the functional correlates of evoked rhythms where auditory and visual ERPs were recorded from the cat brain. Intracranial electrodes were located in the primary auditory cortex and in the primary visual cortex thus permitting "cross-modality" experiments. Responses to adequate stimulation (e.g., visual ERP recorded from the visual cortex) were characterized by high amplitude alpha (8-16 Hz) responses which were not observed for inadequate stimulation. This result is interpreted as a hint at a special role of alpha responses in primary sensory processing. The results of frequency analysis and of wavelet analysis were quite similar, with possible advantages of wavelet methods for single-trial analysis. The results of frequency analysis as performed earlier were thus confirmed by wavelet analysis. This supports the view that ERP frequency components correspond to evoked rhythms with a distinct biological significance.
A wavelet transform of the DNA "walk" constructed from a genomic sequence offers a direct visualization of short and long-range patterns in nucleotide sequences. We study sequences that encode diverse biological functions, taken from a variety of genomes. Pattern irregularities in the transform are frequently associated with sequences of biological interest. Exonic regions, for example, visualize differently under wavelet analysis than introns, and ribosomal RNA regions display distinct universal signatures. DNA walk wavelet analysis can provide a sensitive and rapid assessment of the putative biological significance of genomic DNA.
OBJECTIVES: This study examined the feasibility of using a wavelet analysis of radiofrequency (RF) intravascular ultrasound (IVUS) signals in detecting lipid-laden plaque. BACKGROUND: Wavelet analysis is a new mathematical model for assessing local changes in a geometrical profile of time-series signals. METHODS: Radiofrequency IVUS signals of 85 arbitrarily selected vectors were acquired from 27 formalin-fixed noncalcified atherosclerotic plaques from human necropsy with a digitizer at 500 MHz with 8-bit resolution by use of a 40-MHz IVUS catheter. Wavelet analysis of these RF signals was performed using a Daubechies-2 wavelet to obtain a color-coded map of the correlation coefficient with the wavelet reconstructed over the x-y plane of the wavelet scale and the distance from the IVUS catheter. The plaque segment was then examined histologically after being stained with Masson's trichrome stain. This technique also was applied in vivo in 29 human coronary plaque segments. These segments were excised subsequently by directional coronary atherectomy and processed for histologic analysis. RESULTS: In the in vitro study, histologic examination revealed lipid-laden segments in 29 vectors. When performing a wavelet analysis with the Daubechies-2 wavelet, the color-coded mapping revealed a different pattern in lipid-laden plaques compared with other types of plaque. Using this wavelet analysis, lipid-laden plaque could be detected with a sensitivity of 83% (24 of 29) and a specificity of 82% (46 of 56). In the in vivo study, fatty plaque could be detected with a sensitivity of 81% (13 of 16) and a specificity of 85% (11 of 13) with this method. CONCLUSIONS: Wavelet analysis of RF IVUS signals enabled in vitro as well as in vivo detection of lipid-laden plaque. This method may be useful in assessing plaque vulnerability in patients with coronary artery disease.
Tracking spectral changes of rapidly varying signals is a demanding task. In this study, we explore on Monte Carlo-simulated glutamate-activated AMPA patch and synaptic currents whether a wavelet analysis offers such a possibility. Unlike Fourier methods that determine only the frequency content of a signal, the wavelet analysis determines both the frequency and the time. This is owing to the nature of the basis functions, which are infinite for Fourier transforms (sines and cosines are infinite), but are finite for wavelet analysis (wavelets are localized waves). In agreement with previous reports, the frequency of the stationary patch current fluctuations is higher for larger currents, whereas the mean-variance plots are parabolic. The spectra of the current fluctuations and mean-variance plots are close to the theoretically predicted values. The median frequency of the synaptic and nonstationary patch currents is, however, time dependent, though at the peak of synaptic currents, the median frequency is insensitive to the number of glutamate molecules released. Such time dependence demonstrates that the "composite spectra" of the current fluctuations gathered over the whole duration of synaptic currents cannot be used to assess the mean open time or effective mean open time of AMPA channels. The current (patch or synaptic) versus median frequency plots show hysteresis. The median frequency is thus not a simple reflection of the overall receptor saturation levels and is greater during the rise phase for the same saturation level. The hysteresis is due to the higher occupancy of the doubly bound state during the rise phase and not due to the spatial spread of the saturation disk, which remains remarkably constant. Albeit time dependent, the variance of the synaptic and nonstationary patch currents can be accurately determined. Nevertheless the evaluation of the number of AMPA channels and their single current from the mean-variance plots of patch or synaptic currents is not highly accurate owing to the varying number of the activatable AMPA channels caused by desensitization. The spatial nonuniformity of open, bound, and desensitized AMPA channels, and the time dependence and spatial nonuniformity of the glutamate concentration in the synaptic cleft, further reduce the accuracy of estimates of the number of AMPA channels from synaptic currents. In conclusion, wavelet analysis of nonstationary fluctuations of patch and synaptic currents expands our ability to determine accurately the variance and frequency of current fluctuations, demonstrates the limits of applicability of techniques currently used to evaluate the single channel current and number of AMPA channels, and offers new insights into the mechanisms involved in the generation of unitary quantal events at excitatory central synapses.
Multiunit electrophysiological activity recorded by gross electrodes from the olfactory nerve was analyzed by wavelet decomposition, a relatively new method of signal processing. The analysis was run on data from the unstimulated olfactory system as well as on data evoked in response to six different odorant stimuli. Like Fourier analysis, wavelet analysis provides a spectral decomposition of the signal. Unlike Fourier, wavelet analysis also locates the dominant spectral features in time. The output of a wavelet analysis can be further processed to enhance selected features. The increased amplitude of the nerve response evoked by stimulation was the most obvious feature, but efforts to learn from it were unproductive. The temporal pattern of receptor cell activity was much more yielding. The analysis resolved the nerve activity into three classes of events based on duration. On wavelet maps these classes of events separate out into three shifting and overlapping but distinct bands, one of which was interpreted as being associated with individual receptor cell firings and the other two as short and somewhat longer duration bursts of activity that was attributed to the synchronized firing of a group of receptor cells. This interpretation is supported by experiments in which waveforms simulating action potentials and bursts of action potentials are added to recorded data. Stimulation of the olfactory system with odorant molecules evokes a significant increase in the number of short duration bursts, and an amplitude increase that can be related to the number of receptor cells responding. Changes in the patterns of wavelet events can be associated with synchrony of cell firing, reset times for bursts of firing, and possibly other physiological dynamics. A number of differences in activity patterns with different odorants were observed, but without sufficient repeatability to allow reliable discrimination among them. While this study is clearly preliminary in that regard, it shows the potential of the wavelet method for contributing to the understanding of olfaction.
A wavelet analysis approach to contrast enhancement in mammography is presented. The approach consists of application of non-linear enhancement of multiscale gradient magnitudes utilising edge location information. The performance of wavelet analysis is evaluated by means of a breast phantom. Preliminary results indicate the value of the approach in contrast enhancement and edge preservation for both simulated tumours and microcalcifications.
This paper presents a nontechnical, conceptually oriented introduction to wavelet analysis and its application to neuroelectric waveforms such as the EEG and event related potentials (ERP). Wavelet analysis refers to a growing class of signal processing techniques and transforms that use wavelets and wavelet packets to decompose and manipulate time-varying, nonstationary signals. Neuroelectric waveforms fall into this category of signals because they typically have frequency content that varies as a function of time and recording site. Wavelet techniques can optimize the analysis of such signals by providing excellent joint time-frequency resolution. The ability of wavelet analysis to accurately resolve neuroelectric waveforms into specific time and frequency components leads to several analysis applications. Some of these applications are time-varying filtering for denoising single trial ERPs, EEG spike and spindle detection, ERP component separation and measurement, hearing-threshold estimation via auditory brainstem evoked response measurements, isolation of specific EEG and ERP rhythms, scale-specific topographic analysis, and dense-sensor array data compression. The present tutorial describes the basic concepts of wavelet analysis that underlie these and other applications. In addition, the application of a recently developed method of custom designing Meyer wavelets to match the waveshapes of particular neuroelectric waveforms is illustrated. Matched wavelets are physiologically sensible pattern analyzers for EEG and ERP waveforms and their superior performance is illustrated with real data examples.
UNLABELLED: The spectrum of the embolic heart sounds (EHS) detected by precordial Doppler ultrasound has been previously characterized, but only on small volumes of venous air embolism (VAE). We sought to determine whether real-time wavelet analysis is useful in analyzing the signals of EHS and whether the embolic power of the EHS for larger volumes of air is proportionate to the volume of VAE that has been reported for small volumes of VAE. A series of small air boli (0.01, 0.02, 0.05, 0.07, 0.1, 0.15, 0.2, 0.3, 0.4, and 0.8 mL), followed by continuous infusion of larger volumes of air (0.8, 1.6, 2.4, 4.8, and 9.6 mL), was injected into the external jugular vein through a central catheter in seven pentobarbital-anesthetized dogs. We measured the spectrum of the Doppler heart sound (DHS) in a real-time manner by using wavelet analysis at different scales. Wavelet analysis at scale = 1 yielded satisfactory results in distinguishing abnormal EHS from normal DHS with high sensitivity (100%) and good positive predictive value (100%) compared with the conventional method, which requires an anesthesiologist to listen to the audio DHS signals in a real-time manner. There was a linear relationship (y = 1.08x + 7.89, r = 0.75, P < 0.001) between the cumulative embolic power of the EHS and the air volume introduced in the form of either bolus or continuous infusion. The 95% confidence intervals for slope and intercept were 0.89-1.27 and 7.65-8.13, respectively. Our results suggest that wavelet analysis is effective as a real-time monitor and that it is possible to distinguish larger volumes of air emboli based on previous injections of small volumes of air. IMPLICATIONS: The real-time wavelet analysis of the heart sound detected by precordial Doppler ultrasound may be useful in estimating larger volumes of air emboli based on previous injections of small volumes of air in anesthetized dogs.
The wavelet transform is a powerful technique in signal processing and image analysis and it is shown here that wavelet analysis of low-resolution electron-density maps has the potential to increase their resolution. Like Fourier analysis, wavelet analysis expresses the image (electron density) in terms of a set of orthogonal functions. In the case of the Fourier transform, these functions are sines and cosines and each one contributes to the whole of the image. In contrast, the wavelet functions (simply called wavelets) can be quite localized and may only contribute to a small part of the image. This gives control over the amount of detail added to the map as the resolution increases. The mathematical details are outlined and an algorithm which achieves a resolution increase from 10 to 7 A using a knowledge of the wavelet-coefficient histograms, electron-density histogram and the observed structure amplitudes is described. These histograms are calculated from the electron density of known structures, but it seems likely that the histograms can be predicted, just as electron-density histograms are at high resolution. The results show that the wavelet coefficients contain the information necessary to increase the resolution of electron-density maps.
The Wavelet analysis of the time series of the number of insults per day and the variations of heliogeophysical indices was carried out. The patterns of Wavelet coefficients for both processes show a complex nonstationary behavior and the presence of periodic and acyclic constituents on different scales. During heliogeophysipal disturbances, the energy contribution to the processes increases more than threefold compared with the mean value on scales from three to five days.
Wavelet analysis provides a fruitful alternative to standard techniques for the detection of fractionated potentials in signal averaged high-resolution (SA-HR) ECGs. In this study, an attempt is made to optimize the discrimination of post infarction patients prone to ventricular tachycardia (VT), using wavelet analysis. Optimization is based on the choice of the ECG leads or lead combinations to be analyzed, and on the analyzing wavelet to be computed. A set of 40 post-infarction patients (20 patients with VT and 20 patients without any arrhythmia) is analyzed. Individual leads and lead combinations of the SA-HR ECGs are processed using a multiparametric algorithm, based on coherent detection of aligned local maxima of the wavelet transform. Seven basic wavelets are tested: the Morlet's wavelet, and the six first derivatives of a Gaussian function. The first derivative of a Gaussian function provides poor results, and is discarded. All other wavelets prove to perform equivalent classification. A vector magnitude computed from the wavelet transforms of the three SA-HR ECGs achieves better results than individual leads. An optimized risk stratification algorithm leads to 90% sensitivity and 100% specificity in the 40 patients learning set.