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PubMed2006

An analysis of the efficiency of different SNR-scalable strategies for video coders.

In this paper, we analyze the efficiency of three signal-to-noise scalable strategies for video coders using single-loop motion-compensated prediction (MCP). In our analysis, we assume the video sequences have uniform and constant translational motion and we model MCP as a stochastic filter. We also assume an exponential model for the distortion-rate function of the intraframe coding. The analysis is divided into two parts: the steady-state analysis and the transient analysis. In the first part, only the steady-state response of the coders is taken into account, and, thus, this analysis allows us to asses approximately the efficiency of coders with long input sequences. The transitory analysis considers both the transient and the steady-state responses of the coders, which makes it appropriate to analyze coders using periodic intraframes or with short input sequences. To validate our analysis, theoretical results have been compared to results from encodings of real video sequences using the scalable adaptive motion compensated wavelet video coder. We show that our theoretical analysis effectively describes qualitatively the main trends of every video coding strategy.

Algorithms↗
PubMed2002

Long-range correlations between DNA bending sites: relation to the structure and dynamics of nucleosomes.

It has been established that the precise positioning of nucleosomes on genomic DNA can be achieved, at least for a minority of them, through sequence-dependent processes. However, to what extent DNA sequences play a role in the positioning of the major part of nucleosomes is still debated. The aim of the present study is to examine to what extent long-range correlations (LRC) are related to the presence of nucleosomes. Using the wavelet transform technique, we perform a comparative analysis of the DNA text and of the corresponding bending profiles generated with curvature tables based on nucleosome positioning data. The exploration of a number of eukaryotic and bacterial genomes through the optics of the so-called "wavelet transform microscope" reveals a characteristic scale of 100-200 bp that separates two regimes of different LRC. Here, we focus on the existence of LRC in the small-scale regime (10-200 bp) which are actually observed in eukaryotic genomes, in contrast to their absence in eubacterial genomes. Analysis of viral DNA genomes shows that, like their host's genomes, eukaryotic viruses present LRC but eubacterial viruses do not. There is one exception for genomes of poxviruses (Vaccinia and Melamoplus sanguinipes) which do not replicate in the cell nucleus and do not exhibit LRC. No small-scale LRC are detected in the genomes of all examined RNA viruses, with the exception of retroviruses. These results together with the observation of LRC between particular sequence motifs known to participate in the formation of nucleosomes (e.g. AA dinucleotides) strongly suggest that the 10-200 bp LRC are a signature of the sequence-dependence of nucleosome positioning. Finally, we discuss possible interpretations of these LRC in terms of the physical mechanisms that might govern the positioning and the dynamics of the nucleosomes along the DNA chain through cooperative processes.

Bacteria↗
PubMed2004

Kullback-Leibler clustering of continuous wavelet transform measures of heart rate variability.

Power spectral analysis of beat-to-beat heart rate variability (HRV) has provided a useful means of understanding the interplay between autonomic and cardiovascular functionality. Despite their utility, commonly employed frequency-domain techniques are limited in their prerequisite for stationary signals and their inability to account for temporal changes in the power spectral and/or frequency properties of signals. The purpose of this study is to develop an algorithm that utilizes continuous wavelet transform (CWT) parameters as inputs to a Kohonen self-organizing map (SOM), providing a method of clustering subjects with similar wavelet transform signatures. Continuous interbeat-intervals were recorded (Portapres monitor at 200 Hz) during a perception of affect test in 79 African-American volunteers (ages 21-83), where after a 5-min baseline, participants evaluated emotional expressions in sentences and pictures of faces, followed by a 5-min recovery. Individual HRV biosignals from each session were pre-processed (artifact replacement and signal resampling at 2 Hz) and a CWT was applied (db9 wavelet basis function over 32 scales). Standard deviations of resulting wavelet coefficients at each scale were calculated, normalized, and used as inputs into a SOM with Kullback-Leibler divergence as the dissimilarity measure used for clustering. Differences in subject demographics between two final clusters were assessed via two-independent-groups t-tests or chi-square or Fisher's exact tests of contingency tables. Significant differences were found for age, initial systolic blood pressure, smoking status, and mean s.d. of coefficients in the high frequency band (0.15-0.4 Hz). These findings may have clinical significance and the developed algorithm provides an alternative means of analyzing HRV data originating from populations with complex covariates.

Adult↗
PubMedDate not supplied

Representation of somatosensory evoked potentials using discrete wavelet transform.

OBJECTIVE: Somatosensory evoked potentials (SEP) have been shown to be a useful tool in monitoring of the central nervous system (CNS) during anaesthesia. SEP analysis is usually performed by an experienced human operator. For automatic analysis, appropriate parameter extraction and signal representation methods are required. The aim of this work is to evaluate the discrete wavelet transform (DWT) as such a method for an SEP representation. METHODS: Median nerve SEP were derived in 52 female patients, scheduled for elective surgery with SEP monitoring, under clinically proven conditions in the awake state. The discrete wavelet transform implemented as the multiresolution analysis was adopted for evaluating SEP. The suitability of the wavelet coefficients was investigated by calculating the error between the averaged response and the corresponding wavelet reconstructions. RESULTS: SEP can be represented by a very small number of wavelet coefficients. Although the individual SEP waveform has an influence on the number and selection of wavelet coefficients, in all subjects more than 84% of the SEP waveform energy can be represented by a set 16 wavelet coefficients. CONCLUSIONS: The discrete wavelet transformation provides an efficient tool for SEP representation and parameterisation. Depending on the specific problem the DWT, can be adjusted to the desired accuracy, which is important for the subsequent development of automatic SEP analysers.

Adult↗
PubMed1992

Single evoked potential reconstruction by means of wavelet transform.

We would like to propose a method of single evoked potential (EP) extraction free from assumptions and based on a novel approach--the wavelet representation of the signal. Wavelets were introduced by Grossman and Morlet in 1984. The method is based on the multiresolution signal decomposition. Wavelets are already used for speech recognition, geophysics investigations and fractal analysis. This method seems to be a useful improvement upon Fourier Transform analysis, since it provides simultaneous information on frequency and time localization of the signal. We would like to introduce wavelet formalism for the first time to brain signal analysis. One of the most important problems in this field is the analysis of evoked potentials. This signal has an amplitude several times smaller than EEG, therefore stimulus-synchronized averaging is commonly used. This method is based on several assumptions. Namely it is postulated that: 1) EP are characterized by a deterministic repeatable pattern, 2) EEG has purely stochastic character, 3) EEG and EP are independent. These assumptions have been challenged e.g. the variability of the EP pattern was demonstrated by John (1973) by means of factor analysis. In view of the works of Sayers et al. (1974) and Başar (1988) EP reflects the reorganization of the spontaneous activity under the influence of a stimulus and it is connected with the redistribution of EEG phases. Several attempts to overcome the limitation of the averaging method have been made. Heintze and Künkel (1984) used an autoregressive moving average (ARMA) model to extract evoked potentials from 2 segments. This was possible under two conditions: high signal to noise ratio and clear separation of the EEG and EP spectra.(ABSTRACT TRUNCATED AT 250 WORDS)

Algorithms↗
PubMed2005

Correction for broadening in Doppler blood flow spectrum estimated using wavelet transform.

The conventionally used spectral estimation technique for Doppler blood flow signal analysis is short-time Fourier transform (STFT). But this method requires stationarity of the signal during the window interval. Wavelet transform (WT), which has a flexible time-frequency window, is particularly suitable for nonstationary signals. In recently years, the WT has been used to investigate its advantages and limitations for the analysis of Doppler blood flow signals. In these studies, the estimated spectral width of Doppler blood flow signals using the WT might include significant window and nonstationarity broadening errors. These broadening errors of the time-varying spectrum were clearly undesirable since it would tend to mask the effect of flow disturbance on the spectra width. In this paper, a closed form expression for window and nonstationary root-mean-squared (rms) spectral width is given when using the WT to estimate the Doppler blood flow spectrum. The increases in the rms spectral width can be calculated and then the spectral width estimation based on the WT can be corrected.

Algorithms↗
PubMed2006

Feature extraction for systolic heart murmur classification.

Heart murmurs are often the first signs of pathological changes of the heart valves, and they are usually found during auscultation in the primary health care. Distinguishing a pathological murmur from a physiological murmur is however difficult, why an "intelligent stethoscope" with decision support abilities would be of great value. Phonocardiographic signals were acquired from 36 patients with aortic valve stenosis, mitral insufficiency or physiological murmurs, and the data were analyzed with the aim to find a suitable feature subset for automatic classification of heart murmurs. Techniques such as Shannon energy, wavelets, fractal dimensions and recurrence quantification analysis were used to extract 207 features. 157 of these features have not previously been used in heart murmur classification. A multi-domain subset consisting of 14, both old and new, features was derived using Pudil's sequential floating forward selection (SFFS) method. This subset was compared with several single domain feature sets. Using neural network classification, the selected multi-domain subset gave the best results; 86% correct classifications compared to 68% for the first runner-up. In conclusion, the derived feature set was superior to the comparative sets, and seems rather robust to noisy data.

Aged↗
PubMed2006

The t-CWT: a new ERP detection and quantification method based on the continuous wavelet transform and Student's t-statistics.

OBJECTIVE: This study was aimed at developing a method for extraction and assessment of event-related brain potentials (ERP) from single-trials. This method should be applicable in the assessment of single persons' ERPs and should be able to handle both single ERP components and whole waveforms. METHODS: We adopted a recently developed ERP feature extraction method, the t-CWT, for the purposes of hypothesis testing in the statistical assessment of ERPs. The t-CWT is based on the continuous wavelet transform (CWT) and Student's t-statistics. The method was tested in two ERP paradigms, oddball and semantic priming, by assessing individual-participant data on a single-trial basis, and testing the significance of selected ERP components, P300 and N400, as well as of whole ERP waveforms. The t-CWT was also compared to other univariate and multivariate ERP assessment methods: peak picking, area computation, discrete wavelet transform (DWT) and principal component analysis (PCA). RESULTS: The t-CWT produced better results than all of the other assessment methods it was compared with. CONCLUSIONS: The t-CWT can be used as a reliable and powerful method for ERP-component detection and testing of statistical hypotheses concerning both single ERP components and whole waveforms extracted from either single persons' or group data. SIGNIFICANCE: The t-CWT is the first such method based explicitly on the criteria of maximal statistical difference between two average ERPs in the time-frequency domain and is particularly suitable for ERP assessment of individual data (e.g. in clinical settings), but also for the investigation of small and/or novel ERP effects from group data.

Acoustic Stimulation↗
PubMed2006

Denoising and enhancing digital mammographic images for visual screening.

Dense regions in digital mammographic images are usually noisy and have low contrast, and their visual screening is difficult. This paper describes a new method for mammographic image noise suppression and enhancement, which can be effective particularly for screening image dense regions. Initially, the image is preprocessed to improve its local contrast and the discrimination of subtle details. Next, image noise suppression and edge enhancement are performed based on the wavelet transform. At each resolution, coefficients associated with noise are modelled by Gaussian random variables; coefficients associated with edges are modelled by Generalized Laplacian random variables, and a shrinkage function is assembled based on posterior probabilities. The shrinkage functions at consecutive scales are combined, and then applied to the wavelets coefficients. Given a resolution of analysis, the image denoising process is adaptive (i.e. does not require further parameter adjustments), and the selection of a gain factor provides the desired detail enhancement. The enhancement function was designed to avoid introducing artifacts in the enhancement process, which is essential in mammographic image analysis. Our preliminary results indicate that our method allows to enhance local contrast, and detect microcalcifications and other suspicious structures in situations where their detection would be difficult otherwise. Compared to other approaches, our method requires less parameter adjustments by the user.

Mammography↗
PubMed2000

Utility of the wavelet transform to analyze the stationarity of single ionic channel recordings.

Wavelet transform, a time-scale analysis, is presented as a new tool to analyze single-channel recordings. This method makes it possible to verify the stationarity, to identify episodes of change in the kinetic channel behavior (burst, flickering, cooperativity) or episodes of noise, and to localize stationary segments in long single-channel current recordings. It can help the conventional analysis of the kinetic behavior of ionic channels leading to better understand the gating mechanism.

Electrophysiology↗
PubMed2003

[Model function for population rhythm using wavelet transform].

Interindividual variability among a population induces some chronobiological analysis difficulties; to solve them we create a so called Model Function, which is associated to a biological rhythm. This model function is an image of a population and can be used directly for further studies. Its building has several steps: normalisation, wavelet transform, threshold application, summation of transforms and inverse wavelet transform. This algorithm is tested on activity rhythm of four inbred mouse strains: B, C, BC and CB; this has permitted to check that F1s have an heterotic effect and that this acrophasis indicates a B dominance. The Model Function permits to deal with some difficult problems, such as rhythm shape and position of a population acrophasis. Those elements are absolutely necessary in chronotherapy which needs the knowledge of maximum efficacy and toxicity hours.

Algorithms↗
PubMed2006

Characterizing glottal jet turbulence.

Air pressure associated with airflow from the lungs drives the vocal folds into oscillation and allows the air to exit the glottis as a turbulent jet, even though laminar flow may enter the glottis from the trachea. The separation of the turbulence from the deterministic portion of the glottal jet was investigated in the excised canine larynx model. The present study is methodological in that the main goal was to examine three methods of obtaining reasonable representations of both the deterministic signal and the residual turbulence portion: (a) smoothing, (b) wavelet denoising, and (c) ensemble averaging. Ensemble averaging resulted in a deterministic signal that disregarded gross cyclic alterations while exaggerating the turbulence intensity. Wavelet denoising can perform an excellent analysis and synthesis of the glottal velocity, but was problematic in determining which levels of analysis to choose to represent both the deterministic and turbulence appropriately. Smoothing appeared to be the most appropriate for phonation velocities because it preserved gross cyclic variations important to perturbations and modulations, while extracting turbulence at what appears to be reasonable levels.

Animals↗
PubMedDate not supplied

[Fractal properies of gating in potential-dependent K+-channels in Lymnaea stagnalis neurons].

Sets of the channel open times, [tau(o)], and closed times, [tau(c)], and the full set of the channel open and closed times, [tau(o), tau(c)], in the activity of single voltage-dependent K+-channels in mollusc L. stagnalis neurons were analyzed using the rescaled range analysis (Hurst method), fast Fourier and wavelet transforms. It was found that the Hurst dependence for each time series could be approximated by a polygonal line with at least two slopes: H1 and H2 (Hurst exponents). The averaged values of H1 and H2 for the sets [tau(o), tau(c)] were equal to 0.61 +/- 0.03 and 0.83 +/- 0.11, respectively; for the [tau(o)] sets H1 = 0.66 +/- 0.03 and H2 = 0.95 +/- 0.10; for the [tau(c)] sets, H1 = 0.62 +/- 0.05 and H2 = 0.85 +/- 0.10. In some cases, a third slope appeared on the Hurst dependences. It was very variable and ranged between 0.5 and 1. The Hurst exponents H1, H2, and H3 characterized short, intermediate, and long time ranges, respectively. The ranges greatly varied from experiment to experiment. The data obtained show that the channel openings and closings (gating process) represent a persistent process correlated in time. The randomization of the time sets resulted in a single slope, H, of 0.52 +/- 0.02 characteristic of random processes. The results were confirmed by the fast Fourier and wavelet transforms. In addition, possible voltage dependences of Hurst exponents and their correlation with tau(o) and tau(c) were investigated. As a whole, single channel activity may be characterized as a multifractal process with a slight voltage dependence of the Hurst exponents.

Animals↗
PubMed2004

Gabor-based kernel PCA with fractional power polynomial models for face recognition.

This paper presents a novel Gabor-based kernel Principal Component Analysis (PCA) method by integrating the Gabor wavelet representation of face images and the kernel PCA method for face recognition. Gabor wavelets first derive desirable facial features characterized by spatial frequency, spatial locality, and orientation selectivity to cope with the variations due to illumination and facial expression changes. The kernel PCA method is then extended to include fractional power polynomial models for enhanced face recognition performance. A fractional power polynomial, however, does not necessarily define a kernel function, as it might not define a positive semidefinite Gram matrix. Note that the sigmoid kernels, one of the three classes of widely used kernel functions (polynomial kernels, Gaussian kernels, and sigmoid kernels), do not actually define a positive semidefinite Gram matrix either. Nevertheless, the sigmoid kernels have been successfully used in practice, such as in building support vector machines. In order to derive real kernel PCA features, we apply only those kernel PCA eigenvectors that are associated with positive eigenvalues. The feasibility of the Gabor-based kernel PCA method with fractional power polynomial models has been successfully tested on both frontal and pose-angled face recognition, using two data sets from the FERET database and the CMU PIE database, respectively. The FERET data set contains 600 frontal face images of 200 subjects, while the PIE data set consists of 680 images across five poses (left and right profiles, left and right half profiles, and frontal view) with two different facial expressions (neutral and smiling) of 68 subjects. The effectiveness of the Gabor-based kernel PCA method with fractional power polynomial models is shown in terms of both absolute performance indices and comparative performance against the PCA method, the kernel PCA method with polynomial kernels, the kernel PCA method with fractional power polynomial models, the Gabor wavelet-based PCA method, and the Gabor wavelet-based kernel PCA method with polynomial kernels.

Algorithms↗
PubMedDate not supplied

Effect of supraglottic mechanoreceptors on spatial mapping of respiratory related evoked responses using wavelet and Laplacian methods.

Respiratory related evoked potentials (RREPs) were recorded from eight normal subjects in response to brief pressure pulses of -10 cm H2O with a duration of 200 ms to identify the contribution of supralaryngeal mechanoreceptors to the evoked responses by constraining application of the stimulus to the larynx and sublaryngeal regions through insertion of a laryngeal mask airway (LMA). Wavelet decomposition of RREP responses from 30 electrodes on the right side of the scalp was performed for eight frequency scales in time. The RREPs at each wavelet scale were enhanced by eliminating the wavelet coefficients due to artifacts and noise. After denoising, the third (125-150 Hz), fourth (62.5-125 Hz), fifth (31.25-62.5 Hz) and sixth (13.62-31.25 Hz) wavelet scales were used for estimation of the Laplacian. In addition, the global field power was calculated to quantify the wavelet-filtered RREP activity and to extract features for statistical analysis. Our results show that estimates of the global field power at the fourth and fifth wavelet scales are more significantly decreased after the insertion of the LMA than was true for scales 3 and 6. Further, after the LMA was inserted, the Laplacian showed reduced activity in the posterior-lateral region in four subjects and reductions in other places in the rest.

Biomedical Engineering↗
PubMed2004

Spectral broadening of ophthalmic arterial Doppler signals using STFT and wavelet transform.

In this study, short-time Fourier transform (STFT) and wavelet transform (WT) were used for spectral analysis of ophthalmic arterial Doppler signals. Using these spectral analysis methods, the variations in the shape of the Doppler spectra as a function of time were presented in the form of sonograms in order to obtain medical information. These sonograms were then used to compare the applied methods in terms of their frequency resolution and the effects in determination of spectral broadening in the presence of ophthalmic artery stenosis. A qualitative improvement in the appearance of the sonograms obtained using the WT over the STFT was noticeable. Despite the qualitative improvement in the individual sonograms, no quantitative advantage in using the WT over the STFT for the determination of spectral broadening index was obtained due to the poorer variance of the wavelet transform-based spectral broadening index and the additional computational requirements of the wavelet transform.

Adult↗
PubMed2006

[R-wave detection of ECG signal by using wavelet transform].

The detection of R-wave of ECG is essential to the analysis of the heart rate variability (HRV). In this paper, an R-wave detection method using wavelet transform(WT) is presented in line with the principle of discrete wavelet transform(DWT) and multi-resolution technique (MRT). We made use of the special properties of dbl wavelet in time-domain, decomposed the original ECG signals into 3-level detailed signals on different frequency bands by using DWT with Mallat algorithm, and got appropriate threshold values in different high frequency bands to distinguish R-wave. It is concluded that the algorithm had significant effects on it, which is verified by MIT/BIH (Massachusetts Institute of Technology/Boston's Beth Israel Hospital) ECG Database. The results show that R-wave could be detected accurately and localized precisely by this method, even when the patient was seriously sick or the signal was disturbed by noise. Consequently the method has a quite high locating precision (its error is not more than two sampled points and about 85 percent of the points of R-wave in ECG signal are localized precisely) and the correct detection rate of R-wave is 99.8% by using wavelet transform, so this method is quite feasible.

Algorithms↗
PubMed2006

Long-term signal detection, segmentation and summarization using wavelets and fractal dimension: a bioacoustics application in gastrointestinal-motility monitoring.

The current paper describes a wavelet-based method for long-term processing and analysis of gastrointestinal sounds (GIS). Windowing techniques are used to select sequential blocks of the prolonged multi-channel recordings and proceed to various wavelet-domain processing stages. De-noising, significant-activity detection, automated segmentation and extraction of summary curves are applied in an integrated mode, allowing for enhanced content manipulation and analysis. The proposed analysis scheme combines flexible long-term graphical representation tools, while maintaining the ability of quick browsing via visualization and auralization of the detected short-term events. This work is part of a project aiming to implement non-invasive diagnosis over gastrointestinal-motility (GIM) physiology. However, the proposed techniques might be applied to any study of long-term bioacoustics time series.

Algorithms↗