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PubMed2005

Phase retrieval of optical fringe patterns from the ridge of a wavelet transform.

A new method for phase retrieval of optical fringe patterns is presented. This method is based on a wavelet transform and is capable of extracting the full 2D phase distribution from a single fringe pattern. An important conclusion that the phase of the optical fringe pattern is equal to the phase of its wavelet transform on the ridge of the wavelet transform is theoretically clarified. The method is compared with the Fourier transform and the integration methods. A numerical simulation and an experimental example of phase retrieval are shown.

Algorithms↗
PubMed2004

Optimal-sensitivity analysis of ion channel gating kinetics.

We propose a new approach to analysis of kinetic models for ion channel gating, based on application of fluctuating voltages through a voltage clamp, in addition to conventional techniques. We show that the channel kinetics can be probed in a much more sensitive way, leading to more efficient model selection and more reliable estimates of model parameters. We use wavelet transform as an analytic tool for fluctuating currents and parametric dispersion plots as a measure of model compatibility with experimental data.

Algorithms↗
PubMed2003

Coherent oscillations in neuronal activity of the supplementary motor area during a visuomotor task.

Neural activity recorded in behaving animals is nonstationary, making it difficult to determine factors influencing its temporal patterns. In the present study, rhesus monkeys were trained to produce a series of visually guided hand movements according to the changes in target locations, and multichannel single-neuron activity was recorded from the caudal supplementary motor area. Coherent oscillations in neural activity were analyzed using the wavelet cross-spectrum, and its statistical significance was evaluated using various methods based on surrogate spike trains and trial shuffling. A population-averaged wavelet cross-spectrum displayed a strong tendency for oscillatory activity in the gamma frequency range (30 approximately 50 Hz) to synchronize immediately before and after the onset of movement target. The duration of synchronized oscillations in the gamma frequency range increased when the onset of the next target was delayed. In addition, analysis of individual neuron pairs revealed that many neuron pairs also displayed coherent oscillations in the beta frequency range (15-30 Hz). Coherent beta frequency oscillations were less likely to be synchronized than gamma frequency oscillations, consistent with the fact that coherent beta frequency oscillations were not clearly seen in the population-averaged cross-spectrum. For a given neuron pair, the time course and phase of coherent oscillations were often similar across different movements. These results are consistent with the proposal that synchronized oscillations in the gamma frequency range might be related to the anticipation of behaviorally relevant events and the contextual control of cortical information flow.

Action Potentials↗
PubMed2006

Structural atrial remodeling alters the substrate and spatiotemporal organization of atrial fibrillation: a comparison in canine models of structural and electrical atrial remodeling.

Several animal models of atrial fibrillation (AF) have been developed that demonstrate either atrial structural remodeling or atrial electrical remodeling, but the characteristics and spatiotemporal organization of the AF between the models have not been compared. Thirty-nine dogs were divided into five groups: rapid atrial pacing (RAP), chronic mitral regurgitation (MR), congestive heart failure (CHF), methylcholine (Meth), and control. Right and left atria (RA and LA, respectively) were simultaneously mapped during episodes of AF in each animal using high-density (240 electrodes) epicardial arrays. Multiple 30-s AF epochs were recorded in each dog. Fast Fourier transform was calculated every 1 s over a sliding 2-s window, and dominant frequency (DF) was determined. Stable, discrete, high-frequency areas were seen in none of the RAP or control dogs, four of nine MR dogs, four of six CHF dogs, and seven of nine Meth dogs in either the RA or LA or both. Average DFs in the Meth model were significantly greater than in all other models in both LA and RA except LA DFs in the RAP model. The RAP model was the only one with a consistent LA-to-RA DF gradient (9.5 +/- 0.2 vs. 8.3 +/- 0.3 Hz, P < 0.00005). The Meth model had a higher spatial and temporal variance of DFs and lower measured organization levels compared with the other AF models, and it was the only model to show a linear relationship between the highest DF and dispersion (R(2) = 0.86). These data indicate that structural remodeling of atria (models known to have predominantly altered conduction) leads to an AF characterized by a stable high-frequency area, whereas electrical remodeling of atria (models known to have predominantly shortened refractoriness without significant conduction abnormalities) leads to an AF characterized by multiple high-frequency areas and multiple wavelets.

Animals↗
PubMed2002

Estimation of pulmonary arterial pressure by a neural network analysis using features based on time-frequency representations of the second heart sound.

The objective of the study was to develop a non-invasive method for the estimation of pulmonary arterial pressure (PAP) using a neural network (NN) and features extracted from the second heart sound (S2). To obtain the information required to train and test the NN, an animal model of pulmonary hypertension (PHT) was developed, and nine pigs were investigated. During the experiments, the electrocardiogram, phonocardiogram and PAP were recorded. Subsequently, between 15 and 50 S2 heart sounds were isolated for each PAP stage and for each animal studied. A Coiflet wavelet decomposition and a pseudo smoothed Wigner-Ville distribution were used to extract features from the S2 sounds and train a one-hidden-layer NN using two-thirds of the data. The NN performance was tested on the remaining one-third of the data. NN estimates of the systolic and mean PAPs were obtained for each S2 and then ensemble averaged over the 15-50 S2 sounds selected for each PAP stage. The standard errors between the mean and systolic PAPs estimated by the NN and those measured with a catheter were 6.0 mmHg and 8.4 mmHg, respectively, and the correlation coefficients were 0.89 and 0.86, respectively. The classification accuracy, using 23 mmHg mean PAP and 30 mmHg systolic PAP thresholds between normal PAP and PHT, was 97% and 91%, respectively.

Animals↗
PubMed1994

Time-frequency digital filtering based on an invertible wavelet transform: an application to evoked potentials.

This paper presents a method to analyze and filter digital signals of finite duration by means of a time-frequency representation. This is done by defining a purely invertible discrete transform, representing a signal either in the time or in the time-frequency domain, as simply as possible with the conventional discrete Fourier transform between the time and the frequency domains. The wavelet concept has been used to build this transform. To get a correct invertibility of this procedure, we have proposed orthogonal and periodic basic discrete wavelets. The properties of such a transform are described, and examples on brain-evoked potential signals are given to illustrate the time-frequency filtering possibilities.

Brain↗
PubMedDate not supplied

Mean square error approximation for wavelet-based semiregular mesh compression.

The objective of this paper is to propose an efficient model-based bit allocation process optimizing the performances of a wavelet coder for semiregular meshes. More precisely, this process should compute the best quantizers for the wavelet coefficient subbands that minimize the reconstructed mean square error for one specific target bitrate. In order to design a fast and low complex allocation process, we propose an approximation of the reconstructed mean square error relative to the coding of semiregular mesh geometry. This error is expressed directly from the quantization errors of each coefficient subband. For that purpose, we have to take into account the influence of the wavelet filters on the quantized coefficients. Furthermore, we propose a specific approximation for wavelet transforms based on lifting schemes. Experimentally, we show that, in comparison with a "naive" approximation (depending on the subband levels), using the proposed approximation as distortion criterion during the model-based allocation process improves the performances of a wavelet-based coder for any model, any bitrate, and any lifting scheme.

Algorithms↗
PubMed2002

Quantitative analysis of NO2 in the presence of CO using a single tungsten oxide semiconductor sensor and dynamic signal processing.

We demonstrate that NO2 can be quantitatively analysed in the presence of CO using a single tungsten oxide based resistive gas sensor. The working temperature of the sensor was modulated between 190 and 380 degrees C and its dynamic response to different concentrations of CO, NO2, and CO + NO2 mixtures was monitored. Either the fast Fourier transform (FFT) or the discrete wavelet transform (DWT) was used to extract important features from the sensor response. These features were then input to different (statistical and neural) pattern recognition methods. The species considered can be discriminated with a success rate higher than 90% using a Fuzzy ARTMAP or a radial basis function neural network. The concentrations of the gases studied can be accurately predicted, by using the DWT coupled to partial least squares (PLS) models. The correlation coefficients of the predicted versus real concentrations were 0.923, 0.870 and 0.866 for CO, NO2, and NO2 in CO + NO2 mixtures, respectively.

Journal Article↗
PubMed2002

Neural networks in recognition of electrical signals recorded from two facial muscle sites.

The present aim was to develop signal analysis methods to recognize muscle action potentials recorded with electromyography (EMG) from two facial muscle sites, corrugator supercilii (the muscle activated in frowning) and zygomaticus major (the muscle activated in smiling). Fourteen subjects produced volitional activations on both muscle sites, first, on a single muscle site activation basis and finally combinatory activations of both muscle sites. Wavelets and neural networks were used to analyse these voluntarily produced bursts of electric signals. Our results showed well over 90% recognition rates for all signal types. It is possible to utilize these communicative signals, for example, for multimodal human-computer interaction.

Action Potentials↗
PubMed2003

Analysis of event-related fMRI data using best clustering bases.

We explore a new paradigm for the analysis of event-related functional magnetic resonance images (fMRI) of brain activity. We regard the fMRI data as a very large set of time series x(i) (t), indexed by the position i of a voxel inside the brain. The decision that a voxel i(o) is activated is based not solely on the value of the fMRI signal at i(o), but rather on the comparison of all time series x(i) (t) in a small neighborhood Wi(o) around i(o). We construct basis functions on which the projection of the fMRI data reveals the organization of the time series x(i) (t) into activated and nonactivated clusters. These clustering basis functions are selected from large libraries of wavelet packets according to their ability to separate the fMRI time series into the activated cluster and a nonactivated cluster. This principle exploits the intrinsic spatial correlation that is present in the data. The construction of the clustering basis functions described in this paper is applicable to a large category of problems where time series are indexed by a spatial variable.

Brain↗
PubMed2005

Improving medical diagnostic accuracy of ultrasound Doppler signals by combining neural network models.

There are a number of different quantitative models that can be used in a medical diagnostic decision support system including parametric methods (linear discriminant analysis or logistic regression), nonparametric models (k nearest neighbor or kernel density) and several neural network models. The complexity of the diagnostic task is thought to be one of the prime determinants of model selection. Unfortunately, there is no theory available to guide model selection. This paper illustrates the use of combined neural network models to guide model selection for diagnosis of ophthalmic and internal carotid arterial disorders. The ophthalmic and internal carotid arterial Doppler signals were decomposed into time-frequency representations using discrete wavelet transform and statistical features were calculated to depict their distribution. The first-level networks were implemented for the diagnosis of ophthalmic and internal carotid arterial disorders using the statistical features as inputs. To improve diagnostic accuracy, the second-level networks were trained using the outputs of the first-level networks as input data. The combined neural network models achieved accuracy rates which were higher than that of the stand-alone neural network models.

Adult↗
PubMed2004

Simultaneous spectrophotometric determination of four metals by two kinds of partial least squares methods.

Simultaneous determination of Ni(II), Cd(II), Cu(II) and Zn(II) was studied by two methods, kernel partial least squares (KPLS) and wavelet packet transform partial least squares (WPTPLS), with xylenol orange and cetyltrimethyl ammonium bromide as reagents in the medium pH = 9.22 borax-hydrochloric acid buffer solution. Two programs, PKPLS and PWPTPLS, were designed to perform the calculations. Data reduction was performed using kernel matrices and wavelet packet transform, respectively. In the KPLS method, the size of the kernel matrix is only dependent on the number of samples, thus the method was suitable for the data matrix with many wavelengths and fewer samples. Wavelet packet representations of signals provide a local time-frequency description, thus in the wavelet packet domain, the quality of the noise removal can be improved. In the WPTPLS by optimization, wavelet function and decomposition level were selected as Daubeches 12 and 5, respectively. Experimental results showed both methods to be successful even where there was severe overlap of spectra.

Cadmium↗
PubMed2002

Development of a computer algorithm for the detection of phase singularities and initial application to analyze simulations of atrial fibrillation.

Atrial fibrillation (AF) is a common cardiac arrhythmia, but its mechanisms are incompletely understood. The identification of phase singularities (PSs) has been used to define spiral waves involved in maintaining the arrhythmia, as well as daughter wavelets. In the past, PSs have often been identified manually. Automated PS detection algorithms have been described previously, but when we attempted to apply a previously developed algorithm we experienced problems with false positives that made the results difficult to use directly. We therefore developed a tool for PS identification that uses multiple strategies incorporating both image analysis and mathematical convolution for automated detection with optimized sensitivity and specificity, followed by manual verification. The tool was then applied to analyze PS behavior in simulations of AF maintained in the presence of spatially distributed acetylcholine effects in cell grids of varying size. These analyses indicated that in almost all cases, a single PS lasted throughout the simulation, corresponding to the central-core tip of a single spiral wave that maintained AF. The sustained PS always localized to an area of low acetylcholine concentration. When the grid became very small and no area of low acetylcholine concentration was surrounded by zones of higher concentration, AF could not be sustained. The behavior of PSs and the mechanisms of AF were qualitatively constant over an 11.1-fold range of atrial grid size, suggesting that the classical emphasis on tissue size as a primary determinant of fibrillatory behavior may be overstated. (c) 2002 American Institute of Physics.

Journal Article↗
PubMed2006

Directionlets: anisotropic multidirectional representation with separable filtering.

In spite of the success of the standard wavelet transform (WT) in image processing in recent years, the efficiency of its representation is limited by the spatial isotropy of its basis functions built in the horizontal and vertical directions. One-dimensional (1-D) discontinuities in images (edges and contours) that are very important elements in visual perception, intersect too many wavelet basis functions and lead to a nonsparse representation. To efficiently capture these anisotropic geometrical structures characterized by many more than the horizontal and vertical directions, a more complex multidirectional (M-DIR) and anisotropic transform is required. We present a new lattice-based perfect reconstruction and critically sampled anisotropic M-DIR WT. The transform retains the separable filtering and subsampling and the simplicity of computations and filter design from the standard two-dimensional WT, unlike in the case of some other directional transform constructions (e.g., curvelets, contourlets, or edgelets). The corresponding anisotropic basis unctions (directionlets) have directional vanishing moments along any two directions with rational slopes. Furthermore, we show that this novel transform provides an efficient tool for nonlinear approximation of images, achieving the approximation power O(N(-1.55)), which, while slower than the optimal rate O(N(-2)), is much better than O(N(-1)) achieved with wavelets, but at similar complexity.

Algorithms↗
PubMed1979

Properties of single central Ia afferent fibres projecting to motoneurones.

1. Electrical potentials in the cat lumbosacral spinal cord evoked by the action of single medial gastrocnemius Ia afferent fibres were recorded using low impedance, bevelled micropipette electrodes and the spike triggered averaging technique. 2. Axonal potentials from the Ia fibres recorded extracellularly appeared as brief triphasic predominantly negative potentials. 3. Terminal potentials recorded in regions of Ia afferent termination appeared as brief diphasic positive-negative waves, often with additional wavelets. 4. Focal synaptic potentials, recorded extracellularly in regions of the medial gastrocnemius Ia afferent termination, appeared as slow (about 10 msec duration) negative potentials following terminal potentials. 5. Excitatory post-synaptic potentials, recorded intracellularly in Ia target cells of the medial gastrocnemius, appeared as slow (about 10 msec duration) positive potentials following terminal potentials. 6. Analysis of the temporal progression of these potentials through the spinal cord allowed calculations of the Ia conduction velocity in the dorsal funiculus stem axon (50-60 m/sec), in major collateral branches (8-19 m/sec) and in terminal branches (0.2-1.0 m/sec). 7. The number of major collateral branches (nine or fewer) and their spacing along the spinal cord (1071 micron mean value) were determined by analysing the extent of the triceps surae motoneurone column. 8. The structural and functional properties of medial gastrocnemius Ia afferent fibres are discussed in relation to recent single fibre anatomical data and the present single fibre electrophysiological data.

Action Potentials↗
PubMed1995

An augmented computer vision approach for enhanced image understanding.

This paper addresses the design concept of a spatially and spectrally augmented computer vision approach toward enhanced image analysis and understanding. The concept of spatially augmented computer vision refers to the inclusion of the stereo disparity measure (1/2-D) along with the two-dimensional (2-D) spatial coordinates of the images, together yielding the augmented (2 1/2-D) representation. The concept of spectrally augmented computer vision refers to the implementation of the multiresolution concept of the wavelet theory to analyze and assess in detail the local properties of the 2-D images. The principal objective in applying this augmented, and more revealing, computer vision approach is to provide the added dimension in spatial and spectral resolutions for the enhanced understanding of images. To this end, imaging techniques are developed to exploit, in an optimal fashion, the information acquired by the camera system to yield useful descriptions of the viewed scenes. Experimental results are provided in support of this research direction.

Image Processing, Computer-Assisted↗
PubMed2000

Phase transitions in the common brainstem and related systems investigated by nonstationary time series analysis.

Neuronal activities of the reticular formation (RF) of the lower brainstem and the nucleus tractus solitarii (NTS, first relay station of baroreceptor afferents) were recorded together in the anesthized dog with related parameters of EEG, respiration and cardiovascular system. The RF neurons are part of the common brainstem system (CBS) which participates in regulation and coordination of cardiovascular, respiratory, somatomotor systems, and vigilance. Multiple time series of these physiological subsystems yield useful information about internal dynamic coordination of the organism. Essential problems are nonlinearity and instationarity of the signals, due to the dynamic complexity of the systems. Several time-resolving methods are presented to describe nonlinear dynamic couplings in the time course, particularly during phase transitions. The methods are applied to the recorded signals representing the complex couplings of the physiological subsystems. Phase transitions in these systems are detected by recurrence plots of the instationary signals. The pointwise transinformation and the pointwise conditional coupling divergence are measures of the mutual interaction of the subsystems in the state space. If the signals show marked rhythms, instantaneous frequencies and their shiftings are demonstrated by time frequency distributions, and instantaneous phase differences show couplings of oscillating subsystems. Transient signal components are reconstructed by wavelet packet time selective transient reconstruction. These methods are useful means for analyzing coupling characteristics of the complex physiological system, and detailed analyses of internal dynamic coordination of subsystems become possible. During phase transitions of the functional organization (a) the rhythms of the central neuronal activities and the peripheral systems are altered, (b) changes in the coupling between CBS neurons and cardiovascular signals, respiration and the EEG, and (c) between NTS neurons (influenced by baroreceptor afferents) and CBS neurons occur, and (d) the processing of baroreceptor input at the NTS neurons changes. The results of this complex analysis, which could not be done formerly in this manner, confirm and complete former investigations on the dynamic organization of the CBS with its changing relations to peripheral and other central nervous subsystems.

Action Potentials↗
PubMed1997

System identification of closed-loop cardiovascular control: effects of posture and autonomic blockade.

We applied system identification to the analysis of fluctuations in heart rate (HR), arterial blood pressure (ABP), and instantaneous lung volume (ILV) to characterize quantitatively the physiological mechanisms responsible for the couplings between these variables. We characterized two autonomically mediated coupling mechanisms [the heart rate baroreflex (HR baroreflex) and respiratory sinus arrhythmia (ILV-HR)] and two mechanically mediated coupling mechanisms [the blood pressure wavelet generated with each cardiac contraction (circulatory mechanics) and the direct mechanical effects of respiration on blood pressure (ILV-->ABP)]. We evaluated the method in humans studied in the supine and standing postures under control conditions and under conditions of beta-sympathetic and parasympathetic pharmacological blockades. Combined beta-sympathetic and parasympathetic blockade abolished the autonomically mediated couplings while preserving the mechanically mediated coupling. Selective autonomic blockade and postural changes also altered the couplings in a manner consistent with known physiological mechanisms. System identification is an "inverse-modeling" technique that provides a means for creating a closed-loop model of cardiovascular regulation for an individual subject without altering the underlying physiological control mechanisms.

Adult↗