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PubMed2003

Discrimination analysis of discontinuous breath sounds using higher-order crossings.

The paper evaluates the performance of an automatic discrimination analysis (DA) method used to discriminate efficiently the types of discontinuous breath sound (DBS), i.e. fine crackles (FCs), coarse crackles (CCs) and squawks (SQs); this may lead to more accurate characterisation of the pulmonary acoustical changes due to the related pathology. Based on higher-order crossings (HOCs), the proposed method, HOC-DA, captured the differences in the oscillatory patterns of FCs, CCs and SQs, which are only exposed when higher (> 1) crossings are employed. Prior to HOC-DA, wavelet-based de-noising of DBSs was employed to eliminate the effects of the vesicular sound (background noise) from their oscillatory pattern. The HOC-DA was applied to 157 discontinuous breath sounds corresponding to 16 cases included in three lung sound databases. Results showed that the HOC-DA efficiently separated FCs from CCs, SQs from CCs (both with an accuracy of 100%), and SQs from FCs (accuracy of 80%), with the optimum order ranging from 9 to 11. When compared with other classification tools, the HOC-DA resulted in high discrimination accuracy without involving high computational complexity. Owing to its simplicity, it could be implemented in a real-time context and be used in clinical medicine as a module of an integrated intelligent patient evaluation system.

Auscultation↗
PubMed2005

Optimal block boundary pre/postfiltering for wavelet-based image and video compression.

This paper presents a pre/postfiltering framework to reduce the reconstruction errors near block boundaries in wavelet-based image and video compression. Two algorithms are developed to obtain the optimal filter, based on boundary filter bank and polyphase structure, respectively. A low-complexity structure is employed to approximate the optimal solution. Performances of the proposed method in the removal of JPEG 2000 tiling artifact and the jittering artifact of three-dimensional wavelet video coding are reported. Comparisons with other methods demonstrate the advantages of our pre/postfiltering framework.

Algorithms↗
PubMed2006

Robust regression of scattered data with adaptive spline-wavelets.

A coarse-to-fine data fitting algorithm for irregularly spaced data based on boundary-adapted adaptive tensor-product semi-orthogonal spline-wavelets has been proposed in Castaño and Kunoth, 2003. This method has been extended in Castaño and Kunoth, 2005 to include regularization in terms of Sobolev and Besov norms. In this paper, we develop within this least-squares approach some statistical robust estimators to handle outliers in the data. Our wavelet scheme yields a numerically fast and reliable way to detect outliers.

Algorithms↗
PubMed2006

Wavelet-based scalable L-infinity-oriented compression.

Among the different classes of coding techniques proposed in literature, predictive schemes have proven their outstanding performance in near-lossless compression. However, these schemes are incapable of providing embedded L(infinity)-oriented compression, or, at most, provide a very limited number of potential L(infinity) bit-stream truncation points. We propose a new multidimensional wavelet-based L(infinity)-constrained scalable coding framework that generates a fully embedded L(infinity)-oriented bit stream and that retains the coding performance and all the scalability options of state-of-the-art L2-oriented wavelet codecs. Moreover, our codec instantiation of the proposed framework clearly outperforms JPEG2000 in L(infinity) coding sense.

Algorithms↗
PubMed1994

Observations from intraatrial recordings on the termination of electrically induced atrial fibrillation in humans.

BACKGROUND: The circulating wavelet hypothesis suggests that atrial fibrillation could terminate by either progressive fusion or simultaneous block of all wavelets. METHODS: Intraatrial recordings from the right atrial free wall were made during procainamide induced (n = 8) or spontaneous (n = 7) termination of electrically induced atrial fibrillation in 14 patients. Atrial rate, mean magnitude squared coherence, and direction of activation during sequential electrograms were measured. Rate and coherence were calculated from the earliest point within 5 minutes prior to termination as well as from the 4-second interval just prior to termination. RESULTS: Termination was directly to sinus rhythm (13 episodes) or to atrial flutter (2 episodes). For the eight procainamide induced terminations, rate decreased between the first measurement and the measurement just prior to termination, from 443 +/- 127 beats/min to 322 +/- 119 beats/min. For the seven spontaneous terminations, rate also decreased from 373 +/- 119 beats/min to 323 +/- 88 beats/min; however, a slight increase in atrial rate prior to termination was observed in three episodes. No specific patterns of atrial cycle lengths were seen during the final few seconds of fibrillation. No increase in coherence was observed. In seven episodes, recordings were made using orthogonal bipoles in the x, y, and z directions, allowing direction of activation of wavefronts to be measured. Three episodes showed multiple instances where direction of activation remained similar over several electrograms as we have previously reported for chronic fibrillation. However, no such instances precipitated termination in any of the seven episodes. CONCLUSIONS: Atrial fibrillation usually terminates directly to sinus rhythm and does so abruptly and without forewarning. While we and others have previously reported that the rate of atrial fibrillation decreases with procainamide infusion, a decrease in the rate of atrial fibrillation is not required for the rhythm to terminate and consequently may not be a part of the termination process at all. Coherence does not demonstrate a progressive increase in the organization of atrial fibrillation prior to termination. Lack of stabilization in the direction of activation of wavefronts in the final few seconds also fails to support fusion of wavefronts as the mechanism of termination of atrial fibrillation. Simultaneous block of all wavelets is consistent with, but not proven by, our observations.

Adolescent↗
PubMed2005

[Comparative analysis of first and second order kernels of multifocal retinogram in normal subjects].

PURPOSE: To compare the features of waveform and retinal distribution between first order kernel (FOK) and second order kernel (SOK) of multifocal electro retinogram (mfERG) in normal subjects. METHODS: The mfERG were recorded in 17 normal eyes. The recordings were finished by the VERIS Science 4.0 system, with the condition of 96% contrast and 103 hexagons stimulating pattern. RESULTS: Compared with the FOK, the waveform of SOK was complex with more wavelets. Generally, there were 3 positive and 3 negative waves in the first slice of SOK, and 4 positive and 4 negative in the second slice of SOK. Except the latencies, the retinal distributions of amplitude responses of FOK and SOK were almost the same as the following. The amplitude densities from central hexagon were the highest, and gradually decreased as the acentric degree increased. The amplitudes were larger in upper and temporal retina than those of lower and nasal retina. But the total energy of it was much smaller than that of FOK. CONCLUSIONS: The features of retinal distribution of SOK were almost the same as FOK in normal people. It needs further research on whether it has the specificity for some kinds of fundus diseases or not.

Adult↗
PubMed2003

[Digital signal acquisition and processing software for atomic fluorescence spectrometry and its application to elemental speciation].

A program was developed to collect and process digital signal by using Visual C++ for atomic fluorescence spectrometry (AFS). The software solves the data acquisition problem of coupling AFS with HPLC and other successive separation techniques. This program employs three signal processing techniques, i.e. Savitzky-Golay smoothing, Fourier filter and wavelet denoising, to smooth and filter the noisy data. The processes and comparisons of these techniques have been discussed. The program was successfully applied to the preliminary study of Cd speciation by using cation exchange HPLC coupled to AFS, and will provide a new possibility for extending the application of atomic fluorescence spectrometry to the field of elemental speciation analysis.

Cadmium↗
PubMed2000

Diagnostic accuracy of film-based, TIFF, and wavelet compressed digital temporomandibular joint images.

The purpose of this research was to determine if digitization and the application of various compression routines to digital images of temporomandibular joint (TMJ) radiographs would diminish observer accuracy in the detection of specific osseous characteristics associated with TMJ degenerative joint disease (DJD). Nine observers viewed 6 cropped hard-copy radiographic films each of 34 TMJs (17 radiographic series). Regions of interest measuring 2 in x 2 in were digitized using an 8-bit scanner with transparency adapter at 300 dpi. The images were placed into a montage of 6 images and stored as tagged image file format (TIFF), compressed at 4 levels (25:1, 50:1, 75:1, and 100:1) using a wavelet algorithm, and displayed to the observers on a computer monitor. Their observations regarding condylar faceting, sclerosis, osteophyte formation, erosion, and abnormal shape were analyzed using ROC. Kappa values were determined for relative condylar size and condylar position within the glenoid fossa. Indices were compared using ANOVA at a significance level of P < .05. Although significant and substantial observer variability was demonstrated, there were no statistically significant differences between image modalities, except for condylar position, in which TIFF and wavelet (at all compression ratios) performed better than the original image. For faceting, wavelet 100:1 performed better than radiographic film images. Little actual image file reduction was achieved at compression ratios above 25:1.

Analysis of Variance↗
PubMed2005

Fractional Gaussian noise, functional MRI and Alzheimer's disease.

Fractional Gaussian noise (fGn) provides a parsimonious model for stationary increments of a self-similar process parameterised by the Hurst exponent, H, and variance, sigma2. Fractional Gaussian noise with H < 0.5 demonstrates negatively autocorrelated or antipersistent behaviour; fGn with H > 0.5 demonstrates 1/f, long memory or persistent behaviour; and the special case of fGn with H = 0.5 corresponds to classical Gaussian white noise. We comparatively evaluate four possible estimators of fGn parameters, one method implemented in the time domain and three in the wavelet domain. We show that a wavelet-based maximum likelihood (ML) estimator yields the most efficient estimates of H and sigma2 in simulated fGn with 0 < H < 1. Applying this estimator to fMRI data acquired in the "resting" state from healthy young and older volunteers, we show empirically that fGn provides an accommodating model for diverse species of fMRI noise, assuming adequate preprocessing to correct effects of head movement, and that voxels with H > 0.5 tend to be concentrated in cortex whereas voxels with H < 0.5 are more frequently located in ventricles and sulcal CSF. The wavelet-ML estimator can be generalised to estimate the parameter vector beta for general linear modelling (GLM) of a physiological response to experimental stimulation and we demonstrate nominal type I error control in multiple testing of beta, divided by its standard error, in simulated and biological data under the null hypothesis beta = 0. We illustrate these methods principally by showing that there are significant differences between patients with early Alzheimer's disease (AD) and age-matched comparison subjects in the persistence of fGn in the medial and lateral temporal lobes, insula, dorsal cingulate/medial premotor cortex, and left pre- and postcentral gyrus: patients with AD had greater persistence of resting fMRI noise (larger H) in these regions. Comparable abnormalities in the AD patients were also identified by a permutation test of local differences in the first-order autoregression AR(1) coefficient, which was significantly more positive in patients. However, we found that the Hurst exponent provided a more sensitive metric than the AR(1) coefficient to detect these differences, perhaps because neurophysiological changes in early AD are naturally better described in terms of abnormal salience of long memory dynamics than a change in the strength of association between immediately consecutive time points. We conclude that parsimonious mapping of fMRI noise properties in terms of fGn parameters efficiently estimated in the wavelet domain is feasible and can enhance insight into the pathophysiology of Alzheimer's disease.

Aged↗
PubMed2006

Control of SBR switching by fuzzy pattern recognition.

The sequencing batch reactor (SBR) is a widely used process for biological removal of nutrients (nitrogen and phosphorus) from wastewater. It is based on the metabolism of specialised bacteria, which under alternate anaerobic/aerobic conditions uptake phosphorus and perform denitrification. Intermittent operation is normally operated on a fixed switching schedule with ample margin for possible inaccuracies, with the result that the process operation is highly inefficient. This paper proposes a switching strategy based on the indirect observation of process state through simple physico-chemical measurements and the use of an inferential engine to determine the most appropriate switching schedule. In this way the duration of each phase is limited to the time strictly necessary for the actual loading conditions. Experimental results show that the treatment cycle can be significantly shortened, with the results that more wastewater can be treated. The switching strategy is based on innovative data-processing techniques applied to simple process signals including pH, oxido-reduction potential (ORP) and dissolved oxygen (DO). They include wavelet filtering for signal denoising and fuzzy clustering for features extraction and decision-making. The formation of a knowledge-base and its adaptation during the operation are also discussed.

Aerobiosis↗
PubMed2002

A method for spike sorting and detection based on wavelet packets and Shannon's mutual information.

Studying the dynamics of neural activity via electrical recording, relies on the ability to detect and sort neural spikes recorded from a number of neurons by the same electrode. We suggest the wavelet packets decomposition (WPD) as a tool to analyze neural spikes and extract their main features. The unique quality of the wavelet packets-adaptive coverage of both time and frequency domains using a set of localized packets, facilitate the task. The best basis algorithm utilizing the Shannon's information cost function and local discriminant basis (LDB) using mutual information are employed to select a few packets that are sufficient for both detection and sorting of spikes. The efficiency of the method is demonstrated on data recorded from in vitro 2D neural networks, placed on electrodes that read data from as many as five neurons. Comparison between our method and the widely used principal components method and a sorting technique based on the ordinary wavelet transform (WT) shows that our method is more efficient both in separating spikes from noise and in resolving overlapping spikes.

Action Potentials↗
PubMed2006

An experimental study on pedestrian classification.

Detecting people in images is key for several important application domains in computer vision. This paper presents an in-depth experimental study on pedestrian classification; multiple feature-classifier combinations are examined with respect to their ROC performance and efficiency. We investigate global versus local and adaptive versus nonadaptive features, as exemplified by PCA coefficients, Haar wavelets, and local receptive fields (LRFs). In terms of classifiers, we consider the popular Support Vector Machines (SVMs), feed-forward neural networks, and k-nearest neighbor classifier. Experiments are performed on a large data set consisting of 4,000 pedestrian and more than 25,000 nonpedestrian (labeled) images captured in outdoor urban environments. Statistically meaningful results are obtained by analyzing performance variances caused by varying training and test sets. Furthermore, we investigate how classification performance and training sample size are correlated. Sample size is adjusted by increasing the number of manually labeled training data or by employing automatic bootstrapping or cascade techniques. Our experiments show that the novel combination of SVMs with LRF features performs best. A boosted cascade of Haar wavelets can, however, reach quite competitive results, at a fraction of computational cost. The data set used in this paper is made public, establishing a benchmark for this important problem.

Algorithms↗
PubMed2005

Wavelet packet correlation methods in biometrics.

We introduce wavelet packet correlation filter classifiers. Correlation filters are traditionally designed in the image domain by minimization of some criterion function of the image training set. Instead, we perform classification in wavelet spaces that have training set representations that provide better solutions to the optimization problem in the filter design. We propose a pruning algorithm to find these wavelet spaces by using a correlation energy cost function, and we describe a match score fusion algorithm for applying the filters trained across the packet tree. The proposed classification algorithm is suitable for any object-recognition task. We present results by implementing a biometric recognition system that uses the NIST 24 fingerprint database, and show that applying correlation filters in the wavelet domain results in considerable improvement of the standard correlation filter algorithm.

Algorithms↗
PubMed2006

[The application of adaptive algorithm and wavelet transform in the filtering of ECG signal].

Electrocardiographic (ECG) signal are a kind of basic physiological signals of human body, and are very important in clinical diagnosis. But the ECG signals from body surface are often interfered by noises such as 50 Hz noise, baseline displacemant, electromyography (EMG) noise and edv. These noises bring obstacle to the diagnosis of cardiovascular diseases. To eliminate the ECG signals noises mentioned above,this paper adopts LMS adaptive algorithm and wavelet transform theory to design three kinds of digital adaptive filters-adaptive noise cancellation filter, wavelet transform filter and adaptive signal dividing filter to filter the corresponding noises. The results show that the three kinds of noises existing in the ECG signal have been efficiently eliminated.

Algorithms↗
PubMed1997

Mode matches and their locations in the hydrophobic free energy sequences of peptide ligands and their receptor eigenfunctions.

Patterns in sequences of amino acid hydrophobic free energies predict secondary structures in proteins. In protein folding, matches in hydrophobic free energy statistical wavelengths appear to contribute to selective aggregation of secondary structures in "hydrophobic zippers." In a similar setting, the use of Fourier analysis to characterize the dominant statistical wavelengths of peptide ligands' and receptor proteins' hydrophobic modes to predict such matches has been limited by the aliasing and end effects of short peptide lengths, as well as the broad-band, mode multiplicity of many of their frequency (power) spectra. In addition, the sequence locations of the matching modes are lost in this transformation. We make new use of three techniques to address these difficulties: (i) eigenfunction construction from the linear decomposition of the lagged covariance matrices of the ligands and receptors as hydrophobic free energy sequences; (ii) maximum entropy, complex poles power spectra, which select the dominant modes of the hydrophobic free energy sequences or their eigenfunctions; and (iii) discrete, best bases, trigonometric wavelet transformations, which confirm the dominant spectral frequencies of the eigenfunctions and locate them as (absolute valued) moduli in the peptide or receptor sequence. The leading eigenfunction of the covariance matrix of a transmembrane receptor sequence locates the same transmembrane segments seen in n-block-averaged hydropathy plots while leaving the remaining hydrophobic modes unsmoothed and available for further analyses as secondary eigenfunctions. In these receptor eigenfunctions, we find a set of statistical wavelength matches between peptide ligands and their G-protein and tyrosine kinase coupled receptors, ranging across examples from 13.10 amino acids in acid fibroblast growth factor to 2.18 residues in corticotropin releasing factor. We find that the wavelet-located receptor modes in the extracellular loops are compatible with studies of receptor chimeric exchanges and point mutations. A nonbinding corticotropin-releasing factor receptor mutant is shown to have lost the signatory mode common to the normal receptor and its ligand. Hydrophobic free energy eigenfunctions and their transformations offer new quantitative physical homologies in database searches for peptide-receptor matches.

Amino Acid Sequence↗
PubMed1999

Sparse code shrinkage: denoising of nongaussian data by maximum likelihood estimation

Sparse coding is a method for finding a representation of data in which each of the components of the representation is only rarely significantly active. Such a representation is closely related to redundancy reduction and independent component analysis, and has some neurophysiological plausibility. In this article, we show how sparse coding can be used for denoising. Using maximum likelihood estimation of nongaussian variables corrupted by gaussian noise, we show how to apply a soft-thresholding (shrinkage) operator on the components of sparse coding so as to reduce noise. Our method is closely related to the method of wavelet shrinkage, but it has the important benefit over wavelet methods that the representation is determined solely by the statistical properties of the data. The wavelet representation, on the other hand, relies heavily on certain mathematical properties (like self-similarity) that may be only weakly related to the properties of natural data.

Journal Article↗
PubMed2003

Haar transform analysis of photon time-of-flight measurements for quantification of optical properties in scattering media.

A method to independently quantify the absorption and the scattering properties of samples based on the analysis of the Haar transform (HT) of photon time-of-flight (TOF) distributions is described. A series of reflectance photon TOF measurements were acquired from absorbing/scattering milk samples of known composition (0 < mu(a) < 0.025 mm(-1); 100 < mu(s) < 250 mm(-1)). The HT of the profiles was calculated, and the regression based on the most parsimonious subset of wavelets was determined by the genetic algorithm (GA). In addition, the utility of computing the logarithm of the profiles or of the absolute value of the wavelet coefficients before the GA was studied. Results show that the absorption coefficient could be estimated with a coefficient of variation (C.V.) of 6.7% and an r2 of 0.99 by use of the log of selected wavelets of frequency less than 800 MHz. Scattering coefficients were estimated with a C.V. of 2.3% and an r2 of 0.99 with the log of wavelets of frequency less than 400 MHz. The above results suggest that a simplified instrument based on low-frequency switches could be developed to quantify the optical properties of highly scattering media.

Animals↗
PubMed1994

Nonlinear approaches for separation of slow and fast phase nystagmus signals.

Central nervous system (CNS) disorders and vestibulo-ocular reflex (VOR) degradation contribute to the loss of orientation and balance, typically causing the illusion of motion. VOR degradation may be caused by illnesses or common prescription medications, and this degradation may have serious repercussions in affecting one's ability to walk, maintain balance, or track moving objects. The two current VOR analysis techniques are applied to nystagmus signals (recordings of eye position during standardized tests) to isolate the two neuronal pathways used. These techniques are only partially successful because the VOR signal is passed through several filtering layers, to isolate the nystagmus signal, leaving it severely altered. In this paper, several alternate approaches are introduced: nonlinear robust ordering procedures, mathematical morphology, and wavelet filtering techniques are demonstrated. All approaches were as successful as the current techniques in isolating data from one neuronal pathway. Techniques for each of the three were also developed which would identify data from the second neuronal pathway as well. The nonlinear approach, which works using raw position data, bypasses layers of filtering. A robust technique is used to identify the points at which the data changes from that due to one neuronal pathway to the other, and the end result is obtained by concatenating the respective segments. The other two approaches work with velocity data obtained by differentiation, and because they are applied to non-original data, their results may be open to the possibility of error. Additionally, the two most recent approaches in the literature (using order statistic filters and fuzzy logic) have been implemented for a comparison of results.

Humans↗