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At least 163 records · Page 9Linked to original sources
PubMed2000

Time-domain quantification of multiple-quantum-filtered (23)Na signal using continuous wavelet transform analysis.

The application of continuous wavelet transform (CWT) analysis technique is presented to analyze multiple-quantum-filtered (MQF) (23)Na magnetic resonance spectroscopy (MRS) data. CWT acts on the free-induction-decay (FID) signal as a time-frequency variable filter. The signal-to-noise ratio (SNR) and frequency resolution of the output filter are locally increased. As a result, MQF equilibrium longitudinal magnetization and the apparent fast and slow transverse relaxation times are accurately estimated. A developed iterative algorithm based on frequency signal detection and components extraction, already proposed, was used to estimate the values of the signal parameters by analyzing simulated time-domain MQF signals and data from an agarose gel. The results obtained were compared to those obtained by measurement of signal height in frequency domain as a function of MQF preparation time and those obtained by a simple time-domain curve fitting. The comparison indicates that the CWT approach provides better results than the other tested methods that are generally used for MQF (23)Na MRS data analysis, especially when the SNR is low. The mean error on the estimated values of the amplitude signal and the apparent fast and slow transverse relaxation times for the simulated data were 2.19, 6. 63, and 16.17% for CWT, signal height in frequency domain, and time-domain curve fitting methods, respectively. Another major advantage of the proposed technique is that it allows quantification of MQF (23)Na signal from a single FID and, thus, reduces the experiment time dramatically.

Magnetic Resonance Spectroscopy↗
PubMed2005

[Spectral signal recognition based on wavelet packet analysis].

Automated recognition of spectral signal is an importance part of spectral analysis, which jnvolves comparing spectral signals under the same conditions and obtaining relation of chemistry constitution according to similarity by pattern recognition method and computer technique, and is useful to determine the nature of spectral signal and validate spectral signal. This paper takes spectral signal according with Lambert-beer' law as object, introduces basic theory and method of recognition of spectral signal in brief, normalizes spectral signal to decrease difficulty, introduces wavelet analysis theory, puts forward the method of wavelet packet analysis to gain features of spectral signal, obtains formula to compute feature vector and error vector by statistics method, then makes use of binary tree' mode distinguishing step by step for quick recognition, and finally gives an example to explain.

Algorithms↗
PubMed2004

Enhanced feature analysis using wavelets for scanning probe microscopy images of surfaces.

In this work we develop wavelet theory for the analysis of surface topography images obtained by scanning probe microscopy (SPM) such as atomic force microscopy (AFM). Wavelet transformation is localized in space and frequency, which can offer an advantage for analyzing information on surface morphology and topography. Wavelet transformation is an ideal tool to detect trends, discontinuities, and short periodicities on a surface. Additionally, wavelets can be used to remove artifacts and noise from scanning microscopy images. In terms of 3-D image analysis, discrete wavelet transform can capture patterns at all relevant frequency scales, thus providing a level of image analysis that is not possible otherwise. It is also possible to use the methodology for analyzing surface structures at the molecular level. The results demonstrate superior capabilities of wavelet approach to scanning probe microscopy image analysis compared to traditional analysis techniques.

Journal Article↗
PubMed2000

[The noise-filtering of chemiluminescence spectra with wavelet multiresolution analysis].

In this paper, the wavelet multiresolution signal decomposition was successfully applied to the filtering of high-frequency noise in chemiluminescence spectra. The spectra signal-to-noise ratio was greatly improved while the noise was suppressed effectively. The effect of wavelet basis and time of decomposition are also discussed. It is shown that the wavelet analyses have many advantages in processing of discrete signals.

Algorithms↗
PubMed2001

Wavelet decomposition analysis of heart rate variability in aerobic athletes.

Heart rate variability (HRV) can be quantified, among others, in the frequency domain using digital signal processing (DSP) techniques. The wavelet transform is an alternative tool for the analysis of non-stationary signals. The implementation of perfect reconstruction digital filter banks leads to multi resolution wavelet analysis. Software was developed in LabVIEW. In this study, the average power was compared at each decomposition level of a tachogram, containing the consecutive RR-intervals of two groups of subjects: aerobic athletes and a control group. Compared to the controls, the aerobic athletes showed an increased power in all frequency bands. These results are in accordance with values obtained by spectral analysis using the Fourier transform, suggesting that wavelet analysis could be an appropriate tool to evaluate oscillating components in HRV, but in addition to classic methods, it also gives a time resolution.

Adolescent↗
PubMed2006

Emotional task-dependent low-frequency fluctuations and methylphenidate: Wavelet scaling analysis of 1/f-type fluctuations in fMRI of the cerebellar vermis.

UNLABELLED: Ion channel currents, neural firing patterns, and brain BOLD signals display 1/f-type fluctuations or fractal properties in time. By design, fMRI methods attempt to minimize the contribution of variance from low-frequency physiological 1/f-noise. New fMRI methods are described to visualize and measure 1/f-type BOLD fluctuations in volunteers recalling affectively neutral or emotional memories or meditating (i.e., attending to breathing) then retrospectively rating emotional content. A wavelet scaling exponent (alpha) was used to characterize signals from 0.015625 to 0.5Hz in cerebellar lobules VIII to X of the vermis (posterior inferior vermis; PIV), a region coordinating balance, eye tracking, locomotion, and vascular tone, and a possible site of pathology in attention deficit hyperactivity disorder (ADHD). RESULTS: Changes in alpha and emotional measures were correlated in PIV voxels (r = 0.622, d.f .= 14, P < 0.0005), but not other regions examined. In contrast, conventional means and standard deviations of PIV voxels were unchanged. Methylphenidate, shown to decrease slow oscillations in rodent basal ganglia [Ruskin DN, Bergstrom DA, Shenker A, Freeman LE, Baek D, Walters JR. Drugs used in the treatment of attention-deficit/hyperactivity disorder affect postsynaptic firing rate and oscillation without preferential dopamine autoreceptor action. Biol Psychiatry 2001;49:340-50.], abolished task-dependent alpha changes in the PIV of an adult with ADHD. Wavelet analysis of long BOLD time series appears well suited to fractal physiology and studies of pharmacologically modulated cerebellar-thalamic-cortical function in ADHD or other psychiatric disorders.

Adult↗
PubMed2002

Wavelet-crosscorrelation analysis can help predict whether bursts of pulse stimulation will terminate afterdischarges.

OBJECTIVE: Extraoperative cortical localizing stimulation (LS) is a standard clinical tool used to assess brain function before epilepsy surgery. However, LS can produce unwanted afterdischarges (ADs). We previously have shown that brief pulses of electrical stimulation (BPS) can terminate ADs caused by cortical stimulation. Our objective was to assess whether wavelet-crosscorrelation analysis could help predict the conditions under which BPS would be most likely to terminate ADs. METHODS: We used wavelet-crosscorrelation analysis to get wavelet-correlation coefficients (WCC), and determine time lag (TL) and absolute value of TL (ATL) between two electrodes. For Analysis-1, we compared WCC and ATL in epoch 1 which was before LS, epoch 2 which was after LS but before BPS, and epoch 3 which was after BPS. For Analysis 2, we compared WCC and ATL during epoch 1 under 4 conditions: epochs when ADs subsequently terminated within 2 s after the end of BPS (1A), terminated within 2-5 s (1B), did not terminate within 5 s (1C), and when ADs did not appear (1D). RESULTS: We found that BPS efficacy in terminating ADs was predicted by (1) low correlation and (2) slow propagation speed between electrode pairs in the 2-10 s period before stimulation. CONCLUSIONS: Wavelet-crosscorrelation analysis can help predict conditions during which BPS can abort ADs. It is possible that similar analyses could help predict when BPS or other interventions could abort clinical seizures.

Adolescent↗
PubMed2006

[Analysis of several methods for wavelet denoising used in near infrared spectrum pretreatment].

Based on the wavelet analysis, the noise of the near infrared first derivative spectrum of rapeseed oil is eliminated. Several popular wavelet denoising methods are introduced, including wavelet decomposition and reconstruction method, nonlinear wavelet soft-threshold denoising method, and wavelet transform modulus maxima method, The results show that the wavelet transform modulus maxima method is the best, the nonlinear wavelet soft-threshold method is the second, and other are worse. The wavelet transform modulus maxima method can produce more precise model with little deviation.

Fatty Acids, Monounsaturated↗
PubMed2000

Dynamic architecture of the yeast cell cycle uncovered by wavelet decomposition of expression microarray data.

Wavelet analysis has been applied to yeast cell cycle expression microchip data to reveal large-scale temporal structures and ubiquitous oscillations in mRNA levels. Discrete intervals in time within the cell cycle when expression levels changed were visualized as contour maps in which points of transition in gene expression among all 6178 genes were plotted as a function of cell cycle time. Time-frequency analysis using wavelet transforms supported the direct visualization and led to the conclusion that the predominant period is not the cell cycle but a higher frequency, 40 min, submultiple of the cycle. Each of the 6178 gene expression profiles was dissected by wavelet decomposition into all permitted frequencies from the Nyquist limit to roughly twice the cell cycle length. Transitions associated with maximum up- or down-regulation of mRNA levels appear as bands at circa 40-min intervals, half the length of the cycle, through two cell cycles. More than two thirds of the genes, including many of the cyclins, showed this half-cycle periodicity. Gene expression and events within the yeast cell cycle may be regulated by an attractor whose fundamental period is an emergent property of dynamic interactions within the yeast transcriptome.

Cell Cycle↗
PubMed2005

Estimation of the interplay between groups of fast and slow muscle fibers of the tibialis anterior and gastrocnemius muscle while running.

Electromyograms recorded from the lower limbs of humans while running were submitted to a time/frequency analysis using wavelets. The results of the wavelet analysis yielded intensity spectra at every time point during the swing and the stance phase. It was previously shown that more or less high frequency components get activated during different periods of the movement. The purpose of this study was to test to what extent the spectra can be reconstructed by a linear superposition of two generating spectra that were associated to groups of fast and slow muscle fibers. The terms fast and slow do not only refer to the conduction velocity but also to the shape of the motor unit action potential and are used to characterize the groups in a broader sense. The principal component analysis of the spectra confirmed that a two dimensional spectral space was appropriate. A parametric spectral decomposition was used to extract the generating spectra within the two dimensional spectral space. The generating spectra were in turn used to compute the power with which the groups of muscle fibers contribute to the measured spectra and thus to the overall muscular activity. The power that was obtained for the different time points during the movement reflects the biomechanically important interplay between the groups of muscle fibers while running.

Electromyography↗
PubMed2003

Analysing the ventricular fibrillation waveform.

The surface electrocardiogram associated with ventricular fibrillation has been of interest to researchers for some time. Over the last few decades, techniques have been developed to analyse this signal in an attempt to obtain more information about the state of the myocardium and the chances of successful defibrillation. This review looks at the implications of analysing the VF waveform and discusses the various techniques that have been used, including fast Fourier transform analysis, wavelet transform analysis and mathematical techniques such as chaos theory.

Electrocardiography↗
PubMed2002

Ship recognition via its radiated sound: the fractal based approaches.

Due to the complexity of its radiated sound, ship recognition is difficult. Fractal approaches are proposed in this study, including fractal Brownian motion based analysis, fractal dimension analysis, and wavelet analysis, to augment existing feature extraction methods that are based on spectrum analysis. Experimental results show that fractal approaches are effective. When used to augment two traditional features, line and average spectra, fractal approaches led to better classification results. This implies that fractal approaches can capture some information not detected by traditional approaches alone.

Journal Article↗
PubMed2004

[Wavelet denoising and optimization of two-dimensional correlation IR spectroscopy].

Wavelet analysis and wavelet denoising was applied to analyse the data of two dimensional correlation IR spectroscopy of traditional Chinese medicine for the first time in this article. The results showed that, after denoising, the authors can decrease the interference to the synchronous spectra of two dimensional correlation IR spectroscopy effectively, and tone up the signal peaks and separate the peaks which overlap together before denoising. In this way, the authors can not only improve the spectral revolution, but also get more spectral informations.

English Abstract↗
PubMed1998

Wavelet decomposition analysis of the signal averaged electrocardiogram used for risk stratification of patients with hypertrophic cardiomyopathy.

AIMS: To study the predictive value of wavelet decomposition, as demonstrated by the signal-averaged ECG, in order to identify patients with hypertrophic cardiomyopathy at increased risk for malignant ventricular arrhythmias or sudden death. METHODS AND RESULTS: Two hundred and forty-six patients with hypertrophic cardiomyopathy were studied. During a mean follow-up of 68 +/- 17 months 32 patients died, of whom 17 died suddenly. Patients with sudden death, together with eight patients with a history of ventricular fibrillation (sudden death/ventricular fibrillation group) were analysed and compared to the other 221 patients as well as to a subgroup of 82 patients without a history of syncope, ventricular arrhythmias on a long-term ECG recording or a family history of sudden death. There were no differences in mean values of the four wavelet decomposition parameters among patients in the sudden death/ ventricular fibrillation group, those without sudden death/ ventricular fibrillation or patients in the low risk group. There were, however, significant differences between patients dying non-suddenly and patients being alive at the end of follow-up. Eighty-seven patients (35%) demonstrated evidence of non-sustained ventricular tachycardia on a long-term ECG. Analysis of wavelet decomposition resulted in abnormal findings in these patients more often than in those without ventricular arrhythmias. CONCLUSION: The usefulness of wavelet decomposition analysis in predicting sudden death or ventricular fibrillation is limited in patients with hypertrophic cardiomyopathy. It may, however, play a role in identifying patients at risk of dying non-suddenly and of patients with non-sustained ventricular tachycardia.

Adolescent↗
PubMed2005

ROC evaluation of statistical wavelet-based analysis of brain activation in [15O]-H2O PET scans.

This paper presents and evaluates a wavelet-based statistical analysis of PET images for the detection of brain activation areas. Brain regions showing significant activations were obtained by performing Student's t tests in the wavelet domain, reconstructing the final image from only those wavelet coefficients that passed the statistical test at a given significance level, and discarding artifacts introduced during the reconstruction process. Using Receiver Operating Characteristic (ROC) curves, we have compared this statistical analysis in the wavelet domain to the conventional image-domain Statistical Parametric Mapping (SPM) method. For obtaining an accurate assessment of sensitivity and specificity, we have simulated realistic single subject [15O]-H2O PET studies with different hyperactivation levels of the thalamic region. The results obtained from an ROC analysis show that the wavelet approach outperforms conventional SPM in identifying brain activation patterns. Using the wavelet method, activation areas detected were closer in size and shape to the region actually activated in the reference image.

Algorithms↗
PubMed2006

Wavelet transform analysis predicts outcome of DC cardioversion for atrial fibrillation patients.

The aim of this study was to examine whether wavelet transform analysis of the electrocardiogram (ECG) can improve the prediction of the maintenance of sinus rhythm in patients with atrial fibrillation (AF) after external DC cardioversion. We examined a variety of wavelet transform-based statistical markers as potential candidates for the prediction of patient status post-cardioversion. Considering a 'success' as a patient who remains in normal sinus rhythm for one month post cardioversion and 'failure' as a patient who does not, it was shown the proposed non-parametric classification system can achieve 89% specificity at 100% sensitivity using a non-parametric classification method.

Algorithms↗
PubMed2004

Wavelet-based analysis of heart-rate-dependent ECG features.

BACKGROUND: Wavelet-based methods of analyzing ECG signals have been used to identify specific features in cardiac arrhythmias. Since some of these features are rate dependent, it is a requirement that they are examined across a range of physiological heart rates. The wavelet transform is a signal analysis tool that can elucidate spectral and temporal information simultaneously from complex signals, including the ECG. The aim of this study was to identify the local frequency characteristics of the ECG using a real-time wavelet scalogram and to study the rate dependence of these features. METHODS: We examined the spectral temporal behavior of the local characteristics of the electrocardiogram (ECG) of 10 patients, in whom precise control of heart rate was achieved using right atrial pacing. Temporary reprogramming was used to adjust the paced atrial rate to predetermined values so that a rate-controlled rhythm was produced that closely resembled sinus rhythm. RESULTS: Rate-dependent features are seen on time-frequency scalograms. As the rate increases, the temporal spacing of features decrease and the frequency bands shift upward on the plot. Two patients with abnormal atrioventricular conduction demonstrate features of Wenckebach conduction and fusion. CONCLUSIONS: Characterization of the rate-dependent features of the ECG in a paced atrial rhythm by wavelet transform techniques has revealed some additional information not readily seen on single lead ECG analysis. This model provides a surrogate for changes that might be expected during rate changes in physiological sinus rhythm. It is envisaged that this method will offer advantages in detecting features of clinical significance that may not be readily seen by existing methods.

Arrhythmias, Cardiac↗
PubMed2002

Wavelet transform analysis of heart rate variability during myocardial ischaemia.

Analysis of heart rate variability (HRV) is a valuable, non-invasive method for quantifying autonomic cardiac control in humans. Frequency-domain analysis of HRV involving myocardial ischaemic episodes should take into account its non-stationary behaviour. The wavelet transform is an alternative tool for the analysis of non-stationary signals. Fourteen patients have been analysed, ranging from 40 to 64 years old and selected from the European Electrocardiographic ST-T Database (ESDB). These records contain 33 ST episodes, according to the notation of the ESDB, with durations of between 40s and 12 min. A method for analysing HRV signals using the wavelet transform was applied to obtain a time-scale representation for very low-frequency (VLF), low-frequency (LF) and high-frequency (HF) bands using the orthogonal multiresolution pyramidal algorithm. The design and implementation using fast algorithms included a specially adapted decomposition quadrature mirror filter bank for the frequency bands of interest. Comparing a normality zone against the ischaemic episode in the same record, increases in LF (0.0112 +/- 0.0101 against 0.0175 +/- 0.0208 s2 Hz(-1); p<0.1) and HF (0.0011 +/- 0.0008 against 0.00 17 +/- 0.0020 s2 Hz(-1); p<0.05) were obtained. The possibility of using these indexes to develop an ischaemic-episode classifier was also tested. Results suggest that wavelet analysis provides useful information for the assessment of dynamic changes and patterns of HRV during myocardial ischaemia.

Adult↗