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PubMed2000

Detection of microcalcifications by means of multiscale methods and statistical techniques.

The detection of clustered microcalcifications can help the radiologist to detect early breast cancer. Microcalcifications exhibit some important characteristics, such as small size and high luminosity. Use of a computer-aided diagnosis (CAD) method can prevent them being overlooked. In this report, a multiresolution analysis is performed based on a multilevel wavelet transformation. Decomposition produces sub-band images which become visible only as details of the different scales. Thereafter, all the images will be combined in a final image, in order to obtain an image that contains all the interest details at the scale where microcalcifications tend to appear. Once the image, called detail image, is obtained, it is necessary to determine which details correspond with microcalcifications. Statistical analysis of the histogram permits classification of the zones likely to contain microcalcifications. Applying this statistical techniques over the whole image and representing the results in a two-dimensional map, clustered microcalcification regions are clearly distinguishable.

Breast Neoplasms↗
PubMed2006

Heart rate variability: a review.

Heart rate variability (HRV) is a reliable reflection of the many physiological factors modulating the normal rhythm of the heart. In fact, they provide a powerful means of observing the interplay between the sympathetic and parasympathetic nervous systems. It shows that the structure generating the signal is not only simply linear, but also involves nonlinear contributions. Heart rate (HR) is a nonstationary signal; its variation may contain indicators of current disease, or warnings about impending cardiac diseases. The indicators may be present at all times or may occur at random-during certain intervals of the day. It is strenuous and time consuming to study and pinpoint abnormalities in voluminous data collected over several hours. Hence, HR variation analysis (instantaneous HR against time axis) has become a popular noninvasive tool for assessing the activities of the autonomic nervous system. Computer based analytical tools for in-depth study of data over daylong intervals can be very useful in diagnostics. Therefore, the HRV signal parameters, extracted and analyzed using computers, are highly useful in diagnostics. In this paper, we have discussed the various applications of HRV and different linear, frequency domain, wavelet domain, nonlinear techniques used for the analysis of the HRV.

Alcohol Drinking↗
PubMed1995

Multiresolution decomposition of non-stationary EEG signals: a preliminary study.

Wavelet representation is a recent development in the analysis of non-stationary signals. Its possibilities for use in the description of time-frequency characteristics of both transients in spontaneous EEG and time-varying rhythms in event related brain activity are explored here. By way of illustration, multiresolution decompositions of a wide variety of EEG transients are carried out in this work, including spike-and-waves, single spikes, sharp waves, blink artifacts, frontal intermittent rhythmic delta activity (FIRDA) and paroxysmal delta activity. Also, the application of the wavelet representation to study related spectra perturbations is illustrated with data from psychophysical experiments on the perception of image motion. The results demonstrate the capabilities of the wavelet transform, as an alternative to the Fourier transform, for the representation and analysis of non-stationary EEG signals.

Artifacts↗
PubMed1998

Characterization of visually similar diffuse diseases from B-scan liver images using nonseparable wavelet transform.

This paper describes a new approach for texture characterization, based on nonseparable wavelet decomposition, and its application for the discrimination of visually similar diffuse diseases of liver. The proposed feature-extraction algorithm applies nonseparable quincunx wavelet transform and uses energies of the transformed regions to characterize textures. Classification experiments on a set of three different tissue types show that the scale/frequency approach, particularly one based on the nonseparable wavelet transform, could be a reliable method for a texture characterization and analysis of B-scan liver images. Comparison between the quincunx and the traditional wavelet decomposition suggests that the quincunx transform is more appropriate for characterization of noisy data, and practical applications, requiring description with lower rotational sensitivity.

Algorithms↗
PubMedDate not supplied

Application of the wavelet method for the simultaneous quantitative determination of benazepril and hydrochlorothiazide in their mixtures.

The discrete and continuous wavelet transforms were applied to the overlapping signal analysis of the ratio data signal for simultaneous quantitative determination of the title subject compounds in samples. The ratio spectra data of the binary mixtures containing benazepril (BE) and hydrochlorothiazide (HCT) were transferred as data vectors into the wavelet domain. Signal compression, followed by a 1-dimension continuous wavelet transform (CWT), was used to obtain coincident transformed signals for pure BE and HCT and their mixtures. The coincident transformed amplitudes corresponding to both maximum and minimum points allowed construction of calibration graphs for each compound in the binary mixture. The validity of CWT calibrations was tested by analyzing synthetic mixtures of the investigated compounds, and successful results were obtained. All calculations were performed within EXCEL, C++, and MATLAB6.5 softwares. The obtained results indicated that our approach was flexible and applicable for the binary mixture analysis.

Algorithms↗
PubMed2006

The multiscale character of evoked cortical activity.

Both the architecture and the dynamics of the brain have characteristic features at different spatial scales. However, the existence, nature and function of dynamical interdependencies between such scales have not been investigated. We studied the multiscale properties of functional magnetic resonance imaging (fMRI) data acquired while human subjects viewed a visual image. Traditional "region of interest" analysis of this data set revealed evoked activity in primary and extrastriate visual cortex. Wavelet transform in the spatial domain provides a multiscale representation of this evoked brain activity. Studying the correlation structure of this representation revealed strong and novel interdependencies in these data within and between different spatial scales. We found that such correlations are stronger than those evident in the original data and comparable in magnitude to those obtained after Gaussian smoothing. However, analysis of the data in the wavelet domain revealed additional structure such as positive correlations, strong anti-correlations and phase-lagged interdependencies. Statistical significance of these effects was inferred through nonparametric bootstrap techniques. We conclude that the spatial analysis of functional neuroimaging data in the wavelet domain provides novel information which may reflect complex spatiotemporal neuronal activity and information encoding. It also affords a quantitative means of testing hierarchical and multiscale models of cortical activity.

Adult↗
PubMed2006

Invariance principles for cochlear mechanics: hearing phases.

A functional model of the cochlea is devised on the basis of the results from classical experiments. The basilar membrane filter is investigated in detail. Its phase is close to linear in the region around the peak of the amplification. On one side this has consequences for the time analysis and on the other side this has led to a prediction on phase perception for very simple combinations of tones, a prediction which is now confirmed by experiments. Equivariance under the dilation group permits one to describe the model by a wavelet transform [Daubechies, Ten Lectures on Wavelets (SIAM, Philadelphia, 1992)]. The wavelet is discussed in reference to the phase analysis of the basilar membrane filter.

Acoustic Stimulation↗
PubMed2000

Wavelet change-point prediction of transmembrane proteins.

MOTIVATION: A non-parametric method, based on a wavelet data-dependent threshold technique for change-point analysis, is applied to predict location and topology of helices in transmembrane proteins. A new propensity scale generated from a transmembrane helix database is proposed. RESULTS: We show that wavelet change-point performs well for smoothing hydropathy and transmembrane profiles generated using different scales. We investigate which wavelet bases and threshold functions are overall most appropriate to detect transmembrane segments. Prediction accuracy is based on the analysis of two data sets used as standard benchmarks for transmembrane prediction algorithms. The analysis of a test set of 83 proteins results in accuracy per segment equal to 98.2%; the analysis of a 48 proteins blind-test set, i.e. containing proteins not used to generate the propensity scales, results in accuracy per segment equal to 97.4%. We believe that this method can also be applied to the detection of boundaries of other patterns such as G + Cisochores and dot-plots. AVAILABILITY: The transmembrane database, TMALN and source code are available upon request from the authors.

Bacterial Proteins↗
PubMed2000

Multiscale characterization of chronobiological signals based on the discrete wavelet transform.

To compensate for the deficiency of conventional frequency-domain or time-domain analysis, this paper presents a multiscale approach to characterize the chronobiological time series (CTS) based on a discrete wavelet transform (DWT). We have shown that the local modulus maxima and zero-crossings of the wavelet coefficients at different scales give a complete characterization of rhythmic activities. We further constructed a tree scheme to represent those interacting activities across scales. Using the bandpass filter property of the DWT in the frequency domain, we also characterized the band-related activities by calculating energy in respective rhythmic bands. Moreover, since there is a fast and easily implemented algorithm for the DWT, this new approach may simplify the signal processing and provide a more efficient and complete study of the temporal-frequency dynamics of the CTS. Preliminary results are presented using the proposed method on the locomotion of mice under altered lighting conditions, verifying its competency for CTS analysis.

Animals↗
PubMed2003

A new local multiscale Fourier analysis for medical imaging.

The Stockwell transform (ST), recently developed for geophysics, combines features of the Fourier, Gabor and wavelet transforms; it reveals frequency variation over time or space. This valuable information is obtained by Fourier analysis of a small segment of a signal at a time. Localization of the Fourier spectrum is achieved by filtering the signal with frequency-dependent Gaussian scaling windows. This multi-scale time-frequency analysis provides information about which frequencies occur and more importantly when they occur. Furthermore, the Stockwell domain can be directly inferred from the Fourier domain and vice versa. These features make the ST a potentially effective tool to visualize, analyze, and process medical imaging data. The ST has proven useful in noise reduction and tissue texture analysis. Herein, we focus on the theory and effectiveness of the ST for medical imaging. Its effectiveness and comparison with other linear time-frequency transforms, such as the Gabor and wavelet transforms, are discussed and demonstrated using functional magnetic resonance imaging data.

Algorithms↗
PubMedDate not supplied

Multilevel wavelet-transform encoding in MRI.

This paper explores an alternative to standard Fourier MRI called wavelet-transform encoding. Spatially selective radiofrequency (RF) pulses are used to excite slice profiles in the shape of the wavelet-basis functions of a discrete wavelet transform. Our implementation on a standard commercial whole body MRI system resolves one spatial dimension through wavelet encoding and orthogonal directions through conventional frequency encoding and rectangular-slice selection. The wavelet transform is described as a method for multiresolution analysis, and we show how a high resolution MR image can be constructed from lower resolution images, representing the approximation and detail structures of the object. A multiple level architecture of the wavelet reconstruction is described, thereby allowing wavelet-encoded images to be reconstructed through several modes. Multilevel wavelet-encoded images of a gel phantom are presented that show comparable image quality to Fourier-encoded images of similar signal-to-noise ratio. However, a chemical-shift artifact is identified with this particular implementation.

Artifacts↗
PubMed2002

EEG noise cancellation by a subspace method based on wavelet decomposition.

BACKGROUND: Noise reduction techniques play an essential role in EEG signal processing applications. A variety of methods are currently in use, including those based on linear filtering and adaptive noise cancellation, as well as subspace-based methods using singular value decomposition (SVD). SVD offers a robust method to decompose the data matrix into signal and noise subspaces. However, the SVD algorithm is characterized by high computational complexity, which restricts its use in real time EEG signal analysis. MATERIAL/METHODS: In this work we applied a wavelet transform to decompose the EEG signal into parallel subsignals. Noise was cancelled using the SVD-based method for each subsignal, and the noiseless EEG was reconstructed by using an inverse wavelet transform. EEGs were recorded in freely behaving rats from two different sites of the brain: 1). the hilar region of the dentate gyrus of the hippocampus, 2). the frontal cortex, with the electrode tip located in the vicinity of the epipial neocortical surface. RESULTS: Our noise suppression method had the most obvious effect on the EEG frequency spectrum, where random noise was considerably diminished. In the time domain, the reconstructed waveform closely resembled the original EEG signal, but it could clearly be seen that most of the transient spikes had been removed. CONCLUSIONS: The present method offers remarkable computational savings and is especially well adapted for the analysis of highly dynamic EEGs.

Algorithms↗
PubMedDate not supplied

Multicomponent quantitative resolution of binary mixtures by using continuous wavelet transform.

Continuous 1-dimensional wavelet transform (WT) was applied to the quantitative analysis of a vitamin combination of thiamine hydrochloride (THI) and pyridoxine hydrochloride (PYR) with strongly overlapping signals. Absorbance data from the UV-Vis absorption spectrum of width 1150 were subjected to Gauss1 and Gauss2 WTs. Because of its flexibility, data processing, and its high signal amplitude, the continuous WT method is a powerful tool for analysis of multicomponent mixtures. By measuring the amplitude signals corresponding to the selected zero-crossing points of the transformed signal, we obtained the calibration curve. The validation of the calibration graphs was confirmed with different mixtures of THI and PYR at various concentration ratios. A brief explanation of the continuous wavelet method is given. MATLAB 6.5 software was used to perform the calculations. The results of our study were compared with those obtained by spectroscopic, chemometric, and liquid chromatographic methods, and good agreement was found.

Calibration↗
PubMed2002

WAVEWAT-improved solvent suppression in NMR spectra employing wavelet transforms.

WAVEWAT is a new processing algorithm to suppress the on-resonance water signal in NMR spectra. It is based on a multiresolution analysis (MRA) of the free induction decay (FID) using a dyadic discrete wavelet transform (DWT). The width of the suppressed signal can be adjusted so that signals close to water are recovered without distortion of the signal shape and intensity. Computational efficiency is comparable to that of convolution filters employing a Fourier transform.

Algorithms↗
PubMed2003

Microarray image enhancement by denoising using stationary wavelet transform.

Microarray imaging is considered an important tool for large scale analysis of gene expression. The accuracy of the gene expression depends on the experiment itself and further image processing. It's well known that the noises introduced during the experiment will greatly affect the accuracy of the gene expression. How to eliminate the effect of the noise constitutes a challenging problem in microarray analysis. Traditionally, statistical methods are used to estimate the noises while the microarray images are being processed. In this paper, we present a new approach to deal with the noise inherent in the microarray image processing procedure. That is, to denoise the image noises before further image processing using stationary wavelet transform (SWT). The time invariant characteristic of SWT is particularly useful in image denoising. The testing result on sample microarray images has shown an enhanced image quality. The results also show that it has a superior performance than conventional discrete wavelet transform and widely used adaptive Wiener filter in this procedure.

Algorithms↗
PubMed2003

From wavelets to adaptive approximations: time-frequency parametrization of EEG.

This paper presents a summary of time-frequency analysis of the electrical activity of the brain (EEG). It covers in details two major steps: introduction of wavelets and adaptive approximations. Presented studies include time-frequency solutions to several standard research and clinical problems, encountered in analysis of evoked potentials, sleep EEG, epileptic activities, ERD/ERS and pharmaco-EEG. Based upon these results we conclude that the matching pursuit algorithm provides a unified parametrization of EEG, applicable in a variety of experimental and clinical setups. This conclusion is followed by a brief discussion of the current state of the mathematical and algorithmical aspects of adaptive time-frequency approximations of signals.

Algorithms↗
PubMed2002

Epileptic transient detection: wavelets and time-frequency approaches.

This paper is aimed at presenting the two main classes of nonstationary signal transforms that are currently used to analyze and to characterize EEG observations. Time-scale methods, or wavelet transforms, allow a time versus duration analysis to be performed whereas time-frequency methods allow spectral contents to be analyzed as a function of time. These two types of transform are well suited to the study of changes either localized or progressive that may be observed in EEG signal dynamics and that sign the evolution of underlying physiological mechanisms. The potential interest of these methods in nonstationary signal representation is illustrated through several academic examples. Then, methods are applied on real EEG signals to solve problems such that the detection of interictal transient signals (like spikes or spike-waves) and the recognition of signatures during ictal periods.

Algorithms↗
PubMed2006

Study on a quantitative electroencephalography power spectrum typical of Chinese Han Alzheimer's disease patients by using wavelet transforms.

Our objective was to investigate the quantitative electroencephalogram (EEG) power spectrum typical of Chinese Han ethnic Alzheimer's disease (AD) patients. A study on the resting EEG was carried out on 103 local AD (NINCDS-ADRDA criteria) patients, and 124 age-matched normal elderly subjects served as controls. A novel multi-resolution decomposition algorithm based on Daubechies wavelet transform was employed for EEG spectral analysis. This algorithm decomposed recorded EEG signals into components with five frequency subbands, which especially provided more electroneural activity details in comparison with the conventional four subbands. A significant prevalence of an EEG spectrum characterized by increased slow activity with decreased fast activity was found in these patients. Moreover, the spectral power increase/decrease was mainly centralized in the below-2 Hz/over-8 Hz band, whereas the 2-8 Hz band did not show any widespread change. In conclusion, this study may provide some evidence of specific spectral changes of EEG affected by AD in China.

Aged↗