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PubMed1995

Time-varying pulmonary arterial input impedance via wavelet decomposition.

Wavelet decomposition is proposed as a novel approach for determining pulmonary arterial input impedance throughout the breathing cycle. The canine pulmonary arterial input impedance was evaluated throughout the ventilatory cycle at 5, 10, and 15 cmH2O of positive end-expiratory pressure. The impedance spectrum was obtained by Fourier transformation of wavelets generated by decomposing the pulmonary arterial pressure and flow waveforms. With wavelet decomposition, each heart beat is viewed individually as a transient pulse rather than as an interval within a continuous function of pressure and flow. The advantage of using this approach is the ability to obtain stable estimates of input impedance spectra with high-frequency resolution over the entire frequency range with only a limited data set of pressure and flow decomposed to wavelets as short as singular extrapolated cardiac cycles. This method was used to define the changes of input impedance that occur during the ventilatory cycle. Results show that the impedance spectrum undergoes notable changes during the breathing cycle and demonstrate the utility of the proposed method.

Animals↗
PubMed2006

Mechanomyographic and electromyographic responses to eccentric muscle contractions.

Little is known regarding the modulation of torque during eccentric muscle actions. Mechanomyographic (MMG) and electromyographic (EMG) signals have been used to examine motor control strategies. The purpose of this study was to examine the MMG and EMG amplitude and frequency in relation to torque during eccentric muscle contractions. Eight women performed eccentric leg extension muscle contractions at 10-100% of peak torque (PT). A piezoelectric crystal contact sensor and bipolar surface electrodes were placed on the vastus medialis to detect the MMG and EMG signals. Polynomial regression analyses indicated that EMG amplitude (r(2)=0.994) and MMG wavelet center frequency (CF) (r(2)=0.846) increased linearly to 100% eccentric PT, whereas there were no significant relationships for EMG wavelet CF or MMG amplitude and eccentric torque. These results suggested that eccentric torque is primarily modulated through changes in motor unit firing rate.

Adult↗
PubMed2003

Short-term postural reflexes in diabetic patients with autonomic dysfunction.

AIMS/HYPOTHESIS: Assessment of autonomic dysfunction provides prognostic data in diabetic patients. Clinical tests are limited to heart rate variability and blood pressure measurements. We investigated whether a detailed analysis of postural reflexes of patients during short-term head-up tilting and standing erect (both for 3 min) would provide useful clinical data. METHODS: We studied 14 control subjects and 56 patients with Type 2 diabetes, stratified into four matched groups: uncomplicated, micro-albuminuria, macro-albuminuria and autonomic neuropathy, who were tilted 55 degrees three times and were standing erect. Non-invasive finger plethysmography blood pressure measurements, using the Portapres, and impedance cardiac output measurements, using the RheoCardioMonitor, were continuously recorded throughout the study. Wavelets of the response to tilting and standing were drawn. The wavelets for the three tilts were combined to improve definition. The degree of autonomic dysfunction was quantified by calculating the baroreflex sensitivity index from the heart-rate and blood-pressure variability data. RESULTS: Baroreflex sensitivity data confirmed that autonomic dysfunction increased as diabetic complications worsened (p<0.001). Both standing and tilting resulted in an initial decrease in blood pressure followed by recovery, an increase in heart rate which was more pronounced with standing and a decrease in stroke volume. Characteristic and incremental changes in these responses were noted as the complications of diabetes worsened (p<0.001). These were a greater decrease in blood pressure with loss of the recovery phase, a reduced heart-rate response and paradoxically an increase in stroke volume. CONCLUSION/INTERPRETATION: Non-invasive blood-pressure and stroke-volume measurements recorded during short-term tilting and standing promises to be useful in assessing diabetic autonomic dysfunction.

Adult↗
PubMed2005

Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients.

This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for classification of electroencephalogram (EEG) signals. Decision making was performed in two stages: feature extraction using the wavelet transform (WT) and the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. Five types of EEG signals were used as input patterns of the five ANFIS classifiers. To improve diagnostic accuracy, the sixth ANFIS classifier (combining ANFIS) was trained using the outputs of the five ANFIS classifiers as input data. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the saliency of features on classification of the EEG signals were obtained through analysis of the ANFIS. The performance of the ANFIS model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS model has potential in classifying the EEG signals.

Artificial Intelligence↗
PubMed2002

Complexity measures of event related potential surface Laplacian data calculated using the wavelet packet transform.

We describe a method to obtain estimates of EEG signal complexity using the well-established wavelet packet transform with best basis selection. In particular, we use the two-dimensional wavelet packet transform to obtain estimates of the complexity of two-dimensional images. This allows us to calculate complexity estimates of high-resolution brain potential maps generated from 61 scalp electrode Visual Oddball paradigm, grand-mean data. A significant reduction in the complexity of the surface Laplacian time-slices is observed during and after the Visual Potential 300 (P3) event for the target case, possibly as a result of increased spatial synchrony associated with visual-related tasks. We also present the results of a statistical analysis of the largest principal component of the time-varying complexity curves, for control, high-risk, and alcoholic groups of male subjects. Parametric and non-parametric analyses show differences in the complexity data which are significant between the control group and the alcoholic and high-risk groups.

Algorithms↗
PubMed2006

A wavelet-based two-stage near-lossless coder.

In this paper, we present a two-stage near-lossless compression scheme. It belongs to the class of "lossy plus residual coding" and consists of a wavelet-based lossy layer followed by arithmetic coding of the quantized residual to guarantee a given L(infinity) error bound in the pixel domain. We focus on the selection of the optimum bit rate for the lossy layer to achieve the minimum total bit rate. Unlike other similar lossy plus lossless approaches using a wavelet-based lossy layer, the proposed method does not require iteration of decoding and inverse discrete wavelet transform in succession to locate the optimum bit rate. We propose a simple method to estimate the optimal bit rate, with a theoretical justification based on the critical rate argument from the rate-distortion theory and the independence of the residual error.

Algorithms↗
PubMed1991

Filtering noise from images with wavelet transforms.

A new method of filtering MR images is presented that uses wavelet transforms instead of Fourier transforms. The new filtering method does not reduce the sharpness of edges. However, the new method does eliminate any small structures that are similar in size to the noise eliminated. There are many possible extensions of the filter.

Artifacts↗
PubMed2006

Bayesian fMRI data analysis with sparse spatial basis function priors.

In previous work we have described a spatially regularised General Linear Model (GLM) for the analysis of brain functional Magnetic Resonance Imaging (fMRI) data where Posterior Probability Maps (PPMs) are used to characterise regionally specific effects. The spatial regularisation is defined over regression coefficients via a Laplacian kernel matrix and embodies prior knowledge that evoked responses are spatially contiguous and locally homogeneous. In this paper we propose to finesse this Bayesian framework by specifying spatial priors using Sparse Spatial Basis Functions (SSBFs). These are defined via a hierarchical probabilistic model which, when inverted, automatically selects an appropriate subset of basis functions. The method includes non-linear wavelet shrinkage as a special case. As compared to Laplacian spatial priors, SSBFs allow for spatial variations in signal smoothness, are more computationally efficient and are robust to heteroscedastic noise. Results are shown on synthetic data and on data from an event-related fMRI experiment.

Algorithms↗
PubMed2002

Strain contouring with Gabor filters: filter bank design.

A novel fringe processing method is proposed to segment whole-field strain distributions from interferometric deformation patterns by use of Gabor filters. This novel strategy is specifically proposed for strain measurement with a Gabor filter used as a set of wavelets. To increase computational speed as well as for selection of contour intervals, judicious design of the filter bank, based on the fringe pattern and the requirements of the user, is crucial in this methodology. A filter design strategy is developed and, based on the proposed filter design scheme, properly designed filter banks are generated and applied for strain contouring in low-strain and strain concentration regions. This scheme allows one to measure engineering strains within regions of interest and hence provides the design engineer great flexibility of monitoring, testing, or analysis.

Journal Article↗
PubMedDate not supplied

Homomorphic wavelet thresholding technique for denoising medical ultrasound images.

A novel homomorphic wavelet thresholding technique for reducing speckle noise in medical ultrasound images is presented. First, we show that the speckle wavelet coefficients in the logarithmically transformed ultrasound images are best described by the Nakagami family of distributions. By exploiting this speckle model and the Laplacian signal prior, a closed form, data-driven, and spatially adaptive threshold is derived in the Bayesian framework. The spatial adaptivity allows the additional information of the image (such as identification of homogeneous or heterogeneous regions) to be incorporated into the algorithm. Further, the threshold has been extended to the redundant wavelet representation, which yields better results than the decimated wavelet transform. Experimental results demonstrate the improved performance of the proposed method over other well-known speckle reduction filters. The application of the proposed method to a realistic US test image shows that the new technique, named HomoGenThresh, outperforms the best wavelet-based denoising method reported in [1] by more than 1.6 dB, Lee filter by 3.6 dB, Kaun filter by 3.1 dB and band-adaptive soft thresholding [2] by 2.1 dB at an input signal-to-noise ratio (SNR) of 13.6 dB.

Algorithms↗
PubMed2000

Linking 600-Hz "spikelike" EEG/MEG wavelets ("sigma-bursts") to cellular substrates: concepts and caveats.

Somatosensory evoked human EEG and magnetoencephalographic (MEG) responses comprise a brief burst of low-amplitude, high-frequency (approximately 600 Hz) spikelike wavelets ("sigma-bursts") superimposed on the primary cortical response (e.g., the N20 to electrical median nerve stimulation). The recent surge of interest in these macroscopic sigma-burst responses is energized by the prospect of monitoring noninvasively, highly synchronized and rapidly repeating population spikes generated in the human thalamic and cortical somatosensory system. Thus, analyses of spike-related sigma-bursts could uniquely complement conventional low-frequency EEG/MEG, reflecting mass excitatory and inhibitory postsynaptic potentials that potentially also incorporate subthreshold activities of undetermined functional relevance. Recent studies using spatiotemporal source analysis of multichannel recordings identified regional burst sources subcortically (near-thalamic) as well as cortically. At the primary somatosensory cortex, sigma-burst generators showed the well-established homuncular somatotopic ordering. Functionally, the 600-Hz burst appears to comprise multiple subcomponents with differential sensitivity to stimulus rate, intensity, sleep-wake cycle, tactile interference, subject age, and certain movement disorders. A plenitude of cellular candidates contributing to burst generation at different levels can already now be envisaged, including cuneothalamic and thalamocortical relay cells, as well as cortical bursting pyramidal cells and fast-spiking inhibitory interneurons. Although cellular burst coding might serve to relay information with high efficiency, concepts to link macroscopic sigma-bursts and cellular substrates call for additional study.

Brain Stem↗
PubMed2006

Feature selection using Haar wavelet power spectrum.

BACKGROUND: Feature selection is an approach to overcome the 'curse of dimensionality' in complex researches like disease classification using microarrays. Statistical methods are utilized more in this domain. Most of them do not fit for a wide range of datasets. The transform oriented signal processing domains are not probed much when other fields like image and video processing utilize them well. Wavelets, one of such techniques, have the potential to be utilized in feature selection method. The aim of this paper is to assess the capability of Haar wavelet power spectrum in the problem of clustering and gene selection based on expression data in the context of disease classification and to propose a method based on Haar wavelet power spectrum. RESULTS: Haar wavelet power spectra of genes were analysed and it was observed to be different in different diagnostic categories. This difference in trend and magnitude of the spectrum may be utilized in gene selection. Most of the genes selected by earlier complex methods were selected by the very simple present method. Each earlier works proved only few genes are quite enough to approach the classification problem 1. Hence the present method may be tried in conjunction with other classification methods. The technique was applied without removing the noise in data to validate the robustness of the method against the noise or outliers in the data. No special software or complex implementation is needed. The qualities of the genes selected by the present method were analysed through their gene expression data. Most of them were observed to be related to solve the classification issue since they were dominant in the diagnostic category of the dataset for which they were selected as features. CONCLUSION: In the present paper, the problem of feature selection of microarray gene expression data was considered. We analyzed the wavelet power spectrum of genes and proposed a clustering and feature selection method useful for classification based on Haar wavelet power spectrum. Application of this technique in this area is novel, simple, and faster than other methods, fit for a wide range of data types. The results are encouraging and throw light into the possibility of using this technique for problem domains like disease classification, gene network identification and personalized drug design.

Artificial Intelligence↗
PubMed2006

Sparse overcomplete Gabor wavelet representation based on local competitions.

Gabor representations present a number of interesting properties despite the fact that the basis functions are nonorthogonal and provide an overcomplete representation or a nonexact reconstruction. Overcompleteness involves an expansion of the number of coefficients in the transform domain and induces a redundancy that can be further reduced through computational costly iterative algorithms like Matching Pursuit. Here, a biologically plausible algorithm based on competitions between neighboring coefficients is employed for adaptively representing any source image by a selected subset of Gabor functions. This scheme involves a sharper edge localization and a significant reduction of the information redundancy, while, at the same time, the reconstruction quality is preserved. The method is characterized by its biological plausibility and promising results, but it still requires a more in depth theoretical analysis for completing its validation.

Algorithms↗
PubMed2005

Fourier wavelets from ultrasonic spectra: a new approach for detecting the onset of fouling during microfiltration of paper mill effluent.

Nondestructive (NDT) and noninvasive ultrasonic techniques have long been used to evaluate the properties and especially the thickness of thin layers. Here, we use this technique adding a new approach to investigate microfiltration of paper mill wastewater, which gives an unexpected sensitivity in the detection of membrane fouling. In situ ultrasonic reflections data can indicate an early fouling deposition at about 30 s after filtration starts, evident by an initial decline in permeate flux. By producing differential signals, obtained by comparing reference and testwaveforms, the fouling process can be detected and monitored. A linear relationship between fouling resistance and the amplitude of differential signals exists. In the case of fouling layer thickness, the resolution exceeds the theoretical limit of h/lambda +/- 0.25, where h is the layer thickness and lambda is the wavelength. When using differential signals, excellent results for thickness measurements were obtained, down to h/lambda = 0.04. Measurements on wavelet transforms support the findings and add quantitative information on other physical properties such as density and porosity of fouling layers and the fouling process. Measurement of early fouling allows (automated) remedial methods to be applied so as to maintain a high flux and therefore improve the filtration process.

Bioreactors↗
PubMed2005

Use of multiresolution wavelet feature pyramids for automatic registration of multisensor imagery.

The problem of image registration, or the alignment of two or more images representing the same scene or object, has to be addressed in various disciplines that employ digital imaging. In the area of remote sensing, just like in medical imaging or computer vision, it is necessary to design robust, fast, and widely applicable algorithms that would allow automatic registration of images generated by various imaging platforms at the same or different times and that would provide subpixel accuracy. One of the main issues that needs to be addressed when developing a registration algorithm is what type of information should be extracted from the images being registered, to be used in the search for the geometric transformation that best aligns them. The main objective of this paper is to evaluate several wavelet pyramids that may be used both for invariant feature extraction and for representing images at multiple spatial resolutions to accelerate registration. We find that the bandpass wavelets obtained from the steerable pyramid due to Simoncelli performs best in terms of accuracy and consistency, while the low-pass wavelets obtained from the same pyramid give the best results in terms of the radius of convergence. Based on these findings, we propose a modification of a gradient-based registration algorithm that has recently been developed for medical data. We test the modified algorithm on several sets of real and synthetic satellite imagery.

Algorithms↗
PubMed2001

Universality in the brain while listening to music.

The human brain, which is one of the most complex organic systems, involves billions of interacting physiological and chemical processes that give rise to experimentally observed neuroelectrical activity, which is called an electroencephalogram (EEG). The presence of non-stationarity and intermittency render standard available methods unsuitable for detecting hidden dynamical patterns in the EEG. In this paper, a method that is suitable for non-stationary signals and preserving the phase characteristics and that combines wavelet and Hilbert transforms was applied to multivariate EEG signals from human subjects at rest as well as in different cognitive states: listening to music, listening to text and performing spatial imagination. It was found that, if suitably rescaled, the gamma band EEG over distributed brain areas while listening to music can be described by a universal and homogeneous scaling, whereas this homogeneity in scale is reduced at resting conditions and also during listening to text and performing spatial imagination. The degree of universality is characterized by a Kullback-Leibler divergence measure. By statistical surrogate analysis, nonlinear phase interaction was found to play an important role in exhibiting universality among multiple cortical regions.

Auditory Perception↗
PubMed2005

CMDWave: conserved motifs detection using wavelets.

CMDWave (Conserved Motif Detection using WAVElets) is a web server that predicts conserved motifs in protein sequences. A set of query protein sequences are first aligned using ClustalW to obtain equal sized sequences. CMDWave then converts the sequences into a numerical representation using electron-ion interaction potential (EIIP). This is followed by a wavelet decomposition and reconstruction. A new similarity metric along with thresholding is then used to identify conserved motifs across all the query sequences. Users need not specify the number of motifs to be identified. For larger groups of sequences, results can be emailed to the users.

Amino Acid Motifs↗