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PubMed2005

Topological coarse graining of polymer chains using wavelet-accelerated Monte Carlo. I. Freely jointed chains.

We introduce a new, topologically-based method for coarse-graining polymer chains. Based on the wavelet transform, a multiresolution data analysis technique, this method assigns a cluster of particles to a coarse-grained bead located at the center of mass of the cluster, thereby reducing the complexity of the problem by dividing the simulation into several stages, each with a fraction of the number of beads as the overall chain. At each stage, we compute the distributions of coarse-grained internal coordinates as well as potential functions required for subsequent simulation stages. In this paper, we present the basic algorithm, and apply it to freely jointed chains; the companion paper describes its applications to self-avoiding chains.

Journal Article↗
PubMed2006

Intramolecular vibrational energy redistribution as state space diffusion: classical-quantum correspondence.

We study the intramolecular vibrational energy redistribution (IVR) dynamics of an effective spectroscopic Hamiltonian describing the four coupled high frequency modes of CDBrClF. The IVR dynamics ensuing from nearly isoenergetic zeroth-order states, an edge (overtone) and an interior (combination) state, is studied from a state space diffusion perspective. A wavelet based time-frequency analysis reveals an inhomogeneous phase space due to the trapping of classical trajectories. Consequently the interior state has a smaller effective IVR dimension as compared to the edge state.

Journal Article↗
PubMed2005

Imaging dose management using multi-resolution in CT-guided radiation therapy.

In image-guided radiation therapy, megavoltage computerized tomography (MVCT) delivers higher dose to the patient for lower image quality than diagnostic kilovoltage CT (kVCT). One way to reduce the mean imaging dose is to reduce the imaging volume, which is often sufficient for registration and dosimetry purposes. The filtered back projection using truncated data causes artefacts that degrade the image quality. Those artefacts can be effectively reduced by wavelet-based multi-resolution analysis (WMRA), in which the detail and approximate information are reconstructed separately to bypass the non-locality of filtered back projection. In this study, WMRA was used to reconstruct local images from both very low-dose kVCT scans from a bench-top tomotherapy unit and MVCT scans from helical tomotherapy. Results show that mean imaging dose can be significantly reduced by imaging a small region of interest. In simulation, the root-mean-square error brought by the truncation is smaller than 1-2% and depends on the level of dose reduction. On the other hand, the same mean dose that would have been delivered by a low-quality global CT can be conformed to a smaller volume to improve the visibility of low-contrast organs and fine structures using WMRA. Organs at risk can be avoided during repeated daily CT imaging when irregular-shaped reconstruction areas are used. WMRA does not involve computationally expensive iterations and is suitable for image-guided radiation therapy where imaging speed is essential. Compared with extrapolation methods, errors are further reduced to improve the detection of low contrast and fine structures.

Algorithms↗
PubMed2001

Epigenetic randomness, complexity and singularity of human iris patterns.

We investigated the randomness and uniqueness of human iris patterns by mathematically comparing 2.3 million different pairs of eye images. The phase structure of each iris pattern was extracted by demodulation with quadrature wavelets spanning several scales of analysis. The resulting distribution of phase sequence variation among different eyes was precisely binomial, revealing 244 independent degrees of freedom. This amount of statistical variability corresponds to an entropy (information density) of about 3.2 bits mm(-2) over the iris. It implies that the probability of two different irides agreeing by chance in more than 70% of their phase sequence is about one in 7 billion. We also compared images of genetically identical irides, from the left and right eyes of 324 persons, and from monozygotic twins. Their relative phase sequence variation generated the same statistical distribution as did unrelated eyes. This indicates that apart from overall form and colour, iris patterns are determined epigenetically by random events in the morphogenesis of this tissue. The resulting diversity, and the combinatorial complexity created by so many dimensions of random variation, mean that the failure of a simple test of statistical independence performed on iris patterns can serve as a reliable rapid basis for automatic personal identification.

Functional Laterality↗
PubMed2003

Standard pulse oximeters can be used to monitor respiratory rate.

BACKGROUND: One of the most important limitations of standard pulse oximeters is the inability to detect changes in respiratory rate until oxygenation is affected. This study sought to determine if analysis of the plethysmogram by wavelet transforms would enable the determination of changes in respiratory rate at an earlier stage. METHODS: Ten healthy adult volunteers were monitored, breathing at baseline and predetermined respiratory rates, using a standard pulse oximeter. Photo-plethysmograms captured in an attached lap top computer were then analysed using wavelet transforms. RESULTS: Determination of baseline respiratory rate and subsequent changes including apnoea were easily identified. COMMENT: Wavelet transforms permit the accurate determination of respiratory rate by a standard pulse oximeter.

Humans↗
PubMed1999

[Multiresolution analysis based denoising algorithm for dynamic esophageal pH monitoring signal].

A denoising algorithm for dynamic esophageal pH monitoring signal is first introduced in this paper. The algorithm was developed on the basis of multiresolution analysis. The pH monitoring signal was investigated in experiments, then the algorithm was formulated by multiresolution analysis based on discrete dyadic wavelet transform combining with the results of the investigation mentioned above. Application of this algorithm to clinical data processing has proved its satisfactory denoising effect.

Algorithms↗
PubMed2004

Bayesian classification of myocardial excitation abnormality using magnetocardiogram maps for mass screening.

We propose a novel classification method based on the Bayes rule to utilize the magnetocardiogram (MCG) in noninvasive mass screening. The cardiac excitation is directly tracked by maps of the MCG field generated by myocardial excitation current through the excited wave front. To adopt the characteristics of the excited wave fronts as a parameter for the Bayes theorem, we developed a parameterization procedure that consists of a two-dimensional wavelet approximation and a cluster analysis of magnetic field maps. With the parameter determined by this procedure, the probability of a subject to belong to a disease group or to the normal group is estimated by the Bayes theorem. The subject is classified into the group of the highest probability. We applied the proposed method to ST-T period of MCG data of 6 old myocardial infarction (OMI) patients and 15 normal controls. The method showed sensitivity of 83%; specificity, 100%; positive predictive value, 100%; and negative predictive value, 94% in the classification of OMI patients and normal controls. The processing time is less than 5 seconds per one subject. It suggests a possible application of the proposed method in mass screening of abnormal MCG patterns.

Bayes Theorem↗
PubMed1997

Fractal analyses of HRV signals: a comparative study.

In this paper, we have investigated the scaling behavior of the heart rate variability signals using the power spectral density (PSD), the discrete wavelet transform (DWT), and dispersive analysis (DA), and the maximum likelihood estimator (MLE) method. Results suggested the lowest variance for the MLE method, greatest variance for the PSD methods, the other methods somewhere in between.

Fractals↗
PubMed1990

Forward and backward running waves in the arteries: analysis using the method of characteristics.

The one-dimensional equations of flow in the elastic arteries are hyperbolic and admit nonlinear, wavelike solutions for the mean velocity, U, and the pressure, P. Neglecting dissipation, the solutions can be written in terms of wavelets defined as differences of the Riemann invariants across characteristics. This analysis shows that the product, dUdP, is positive definite for forward running wavelets and negative definite for backward running wavelets allowing the determination of the net magnitude and direction of propagating wavelets from pressure and velocity measured at a point in the artery. With the linearizing assumption that intersecting wavelets are additive, the forward and backward running wavelets can be separately calculated. This analysis, applied to measurements made in the ascending aorta of man, shows that forward running wavelets dominate during both the acceleration and deceleration phases of blood flow in the aorta. The forward and backward running waves calculated using the linearized analysis are similar to the results of an impedance analysis of the data. Unlike the impedance analysis, however, this is a time domain analysis which can be applied to nonperiodic or transient flow.

Aorta↗
PubMedDate not supplied

Fractal analysis of the uterine contractions.

The fractal dimension D may be calculated in many ways, since its strict definition, the Hausdorff definition is too complicated for practical estimation. In this paper we perform a comparative study often methods of fractal analysis of time series. In Benoit, a commercial program for fractal analysis, five methods of computing fractal dimension of time series (rescaled range analysis, power spectral analysis, roughness-length, variogram methods and wavelet method) are available. We have implemented some other algorithms for calculating D: Higuchi's fractal dimension, relative dispersion analysis, running fractal dimension, method based on mathematical morphology and method based on intensity differences. For biomedical signals results obtained by means of different algorithms are different, but consistent.

Algorithms↗
PubMed2006

Automated defect detection system using wavelet packet frame and Gaussian mixture model.

This paper proposes an approach for automated defect detection in homogeneous textiles using texture analysis. The texture features are extracted by the wavelet packet frame decomposition followed by the Karhunen-Loève transform. The texture feature vector for each pixel is used as an input to a Gaussian mixture model that determines whether or not each pixel is defective. The parameters of the Gaussian mixture model are estimated with nondefective textile images in supervised defect detection. An approach for unsupervised defect detection is also presented that can identify the heterogeneous subblocks on the basis of the Kullback-Leibler divergence between two Gaussian mixtures. The proposed method was evaluated on 25 different homogeneous textile image pairs, one of each pair with a defect and the other with no defect, and was compared with existing methods using texture analysis. The experimental results yielded visually good segmentation and an excellent detection rate with a low false alarm rate for both supervised and unsupervised defect detection. This confirms the validity of the proposed approach for automated defect detection and localization.

Journal Article↗
PubMed1997

Adaptive reduction of heart sounds from lung sounds using a wavelet-based filter.

A new adaptive method for heart sounds reduction from lung sounds, based on wavelet transform, is presented in this paper. The use of a wavelet transform domain filtering technique as an adaptive de-noising tool, implemented in lung sounds analysis, is introduced. The multiresolution representations of the signal, produced by wavelet transform, are used for signal structure extraction. Experimental results have shown that implementation of this wavelet-based filter in lung sound analysis results in an efficient reduction of heart sounds from lung sounds, producing an almost noise-free output signal.

Adult↗
PubMed2004

Fourier and wavelet analyses of dental radiographs detect trabecular changes in osteoporosis.

OBJECTIVES: Osteoporosis results in loss of bone mass and microarchitectural deterioration. Dental radiographs potentially offer a means of screening for osteoporosis as they are commonly made on adults. Spatial frequency analyses are well suited to detect subtle changes in image patterns. We hypothesize that individuals with osteoporosis exhibit an altered radiographic trabecular pattern that can be detected by spatial frequency and strut analysis. STUDY DESIGN: Maxillary and mandibular periapical radiographs of 26 women with osteoporosis and 23 controls were examined using one-dimensional discrete Fourier and wavelet analyses in both jaws to measure the spatial frequency distributions of trabecular structures. A strut analysis was also performed. RESULTS: Individuals with osteoporosis revealed an altered trabecular pattern compared to controls. Using Fourier and strut variables allows classification of subjects with 92% sensitivity, 96% specificity, and a 22% cross-validation error rate. Wavelet analysis was also useful but did not perform better than Fourier analysis for subject classification. CONCLUSIONS: Spatial frequency analysis of digitized dental radiographs, especially Fourier analysis, and strut analysis provide value for identifying individuals with osteoporosis.

Case-Control Studies↗
PubMedDate not supplied

A wavelet-based reduction of heart sound noise from lung sounds.

Heart sounds produce an incessant noise during lung sounds recordings. This noise severely contaminates the breath sounds signal and interferes in the analysis of lung sounds. In this paper, the use of a wavelet transform domain filtering technique as an adaptive de-noising tool, implemented in lung sounds analysis, is presented. The multiresolution representations of the signal, produced by wavelet transform, are used for signal structure extraction. In addition, the use of hard thresholding in the wavelet transform domain results in a separation of the nonstationary part of the input signal (heart sounds) from the stationary one (lung sounds). Thus, the location of the heart sound noise (1st and 2nd heart sound peaks) is automatically detected, without requiring any noise reference signal. Experimental results have shown that the implementation of this wavelet-based filter in lung sound analysis results in an efficient reduction of the superimposed heart sound noise, producing an almost noise-free output signal. Due to its simplicity and its fast implementation the method can easily be used in clinical medicine.

Adult↗
PubMed1994

Analysis of PTCA-induced ischemia using an ECG inverse solution or the wavelet transform.

In patients without significant collaterals, percutaneous transluminal coronary angioplasty (PTCA) produces acute transient ischemia that is detectable in both standard electrocardiograms (ECG) and body surface potential maps (BSPMs). Control recordings made before or between inflations provide personalized baselines, which isolate the effects of ischemia from interpatient differences, such as torso shape and electrode location. In this study, two methods of evaluating PTCA-induced ischemia from BSPM recordings are presented. In the first method, an ECG inverse solution that estimates epicardial potentials from body surface signals using a realistic model of torso geometry is applied. The strength of this method lies in its potential ability to localize areas of cardiac ischemia on the epicardial surface. In the second approach, wavelet transforms were used to perform a multiresolution decomposition of the BSPM data into different frequency bands. The basis functions of the wavelet transform are time-limited and narrow band and hence can be expected to be sensitive to features of the BSPM that originate in discrete electrophysiologic events, such as intrusion of the activation front onto regions of ischemia or arrhythmias due to local conduction abnormalities. The method also offers a means of temporal and frequency localization of cardiac events related to the initiation of injury currents and abnormal conduction due to PTCA-induced ischemia. The inverse solution and the wavelet transform each offer new views of the spatial and temporal courses of acute ischemia potentially leading to new diagnostic insights in ECG patient examination.

Angioplasty, Balloon, Coronary↗
PubMed2004

Embolic Doppler ultrasound signal detection using discrete wavelet transform.

Asymptomatic circulating emboli can be detected by Doppler ultrasound. Embolic Doppler ultrasound signals are short duration transient like signals. The wavelet transform is an ideal method for analysis and detection of such signals by optimizing time-frequency resolution. We propose a detection system based on the discrete wavelet transform (DWT) and study some parameters, which might be useful for describing embolic signals (ES). We used a fast DWT algorithm based on the Daubechies eighth-order wavelet filters with eight scales. In order to evaluate feasibility of the DWT of ES, two independent data sets, each comprising of short segments containing an ES (N = 100), artifact (N = 100) or Doppler speckle (DS) (N = 100), were used. After applying the DWT to the data, several parameters were evaluated. The threshold values used for both data sets were optimized using the first data set. While the DWT coefficients resulting from artifacts dominantly appear at the higher scales (five, six, seven, and eight), the DWT coefficients at the lower scales (one, two, three, and four) are mainly dominated by ES and DS. The DWT is able to filter out most of the artifacts inherently during the transform process. For the first data set, 98 out of 100 ES were detected as ES. For the second data set, 95 out of 100 ES were detected as ES when the same threshold values were used. The algorithm was also tested with a third data set comprising 202 normal ES; 198 signals were detected as ES.

Algorithms↗
PubMed1997

[Application of wavelet transform to the isolation of EXAFS oscilations from experiment data].

It is of significant importance to isolate lambda(k) form the total absorption cofficient data micro(E) for the analysis of EXAFS spectrum. Generally, least-squares procedure of polynomial spline or B spline was used for removing the smooth sbsorption background, which has been proved to be effective. But it suffers in that the trial of knot points is time-consuming and it is poor in reproducibility. In this paper, a new chemometrics method--wavelet transform analysis was used to separate the lambda(k) and background absorption from micro(E) and it is proved that the technique is a very good method for the analysis of EXAFS spectrum.

English Abstract↗
PubMed2006

Moving window-based double Haar wavelet transform for image processing.

Image denoising is a lively research field. The classical nonlinear filters used for image denoising, such as median filter, are based on a local analysis of the pixels within a moving window. Recently, the research of image denoising has been focused on the wavelet domain. Compared to the classical nonlinear filters, it is based on a global multiscale analysis of images. Apparently, the wavelet transform can be embedded in a moving window. Thus, a moving window-based local multiscale analysis is obtained. In this paper, based on the Haar wavelet, a class of nonorthogonal multi-channel filter bank with its corresponding wavelet shrinkage called Lee shrinkage is derived. As a special case of this filter bank, the double Haar wavelet transform is introduced. Examples show that it is suitable for a moving window-based local multiscale analysis used for image denoising, edge detection, and edge enhancement.

Algorithms↗