Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Wavelet Analysis”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 127 records · Page 7Linked to original sources
PubMed1997

Wavelet analysis of electromyography for back muscle fatigue detection during dynamic constant-torque exertions.

The fatigue of the back muscles appears to be strongly implicated as a risk factor for acquisition of low back pain, which is one of the leading ills of our industrial society. Previously, researchers have successfully measured the level of muscular fatigue by using the Fourier transform to analyze the frequency content of the electromyogram (EMG). However, due to the requirement that the EMG signal be stationary, the Fourier transform is suitable only for the analysis of static muscle exertions in which the muscle is held at constant length and tension. Because the majority of industrial work tasks are not static in nature, new methods for quantifying fatigue during dynamic work are needed. The wavelet transform is a novel, although mathematically well developed, technique for analyzing non-stationary signals that has only recently been applied to the study of EMG. Consequently, the main objective of this project is to develop techniques, using the wavelet transform, for the quantification of back muscle fatigue during dynamic repetitive working conditions.

Back Pain↗
PubMed2001

Genome wide oscillations in expression. Wavelet analysis of time series data from yeast expression arrays uncovers the dynamic architecture of phenotype.

A reanalysis of expression arrays in yeast cells synchronized by alpha factor blockade or through the use of temperature sensitive mutants uncovered a genome wide pattern of oscillations in mRNA concentrations. Using wavelet decomposition as a signal processing technique and enhancement strategies borrowed from image processing, noise and trends in the Stanford yeast cell cycle data were partitioned away from time series profiles to uncover genome-wide oscillations in expression. These oscillations which were typically of cell cycle or half cell cycle duration, 40 and 80 minutes in the Stanford data set suggest that there are large-scale temporal structures and high frequency oscillations in mRNA levels through the cell cycle. Wavelet decomposition, which acts like a band pass filter bank, was used to determine where most of the power appeared in the decomposition. The approximately 40-min oscillation is mirrored in continuous chemostat cultures. In these cultures, metabolic synchrony involving an unknown proportion of the transcriptome can be monitored by measurement of oxygen consumption and can be sustained for weeks. These 40-min oscillations are stable and precise with coefficients of variation less than 1% for both period and amplitude. The hypothesis that high and low amplitude oscillations are a ubiquitous property of the genetic regulatory circuitry was supported by the observation of period doubling bifurcations in the distribution of population doubling times in yeast.

Cell Cycle↗
PubMed2003

[Wavelet analysis of ECG in mouse with acute myocardial ischemia].

The myocardial ischemia model was established by ligating the left anterior descending artery of mice for 10 min. The ECG signal were collected from the mouse with acute myocardial ischemia and from the healthy mouse as controls. The frequency of data acquisition was 500 Hz. The measurement time around 20 seconds was chosen. The wavelet transform technique, a time-frequency method with logarithmic frequency resolution, was used to analyze the ECG signal. Mexican Hat wavelet was used and the scale factor was chosen from an interval [0.00125, 2.5] for evaluating its wavelet coefficient at corresponding scale respectively, thus the central frequency of the band-pass filter was within a range of 200-0. 1Hz. Then we evaluated the signal energy in the bandwidth delta fat frequency f. A variational rule was obtained, i.e. rat's ECG signal energy varies with frequency before and after myocardial ischemia. The results showed that in the mice suffering from acute myocardial ischemia the average signal energy increases in 0.1-1. 0Hz and 1-10 Hz segments and decreases in 10-200 Hz segment.

Acute Disease↗
PubMed2005

Study of myocardial glucose metabolism in rats with PET using wavelet analysis techniques.

The parametric images are attractive in PET imaging since they provide a global view of the imaged tissue including healthy and diseased structures. However these images are hampered by the noise due to the limited injected dose of the tracer. In this work we compare myocardial metabolic rates of glucose (MMRG) in rats obtained in the spatial and in the wavelet domains with the usual graphical method. The images were decomposed in the wavelet domain and all correlated pixels in approximations and details images were considered. Mean MMRG obtained from regions-of-interest drawn on parametric images in both methods showed values 15% higher while the variance was about 18% lower in the wavelet images. In conclusion, the wavelet filtering process allowed to enhance the image quality and to reduce the variance in the parametric images while preserving the spatial resolution.

Animals↗
PubMed2003

Post-processing noise removal algorithm for magnetic resonance imaging based on edge detection and wavelet analysis.

A post-processing noise suppression technique for biomedical MRI images is presented. The described procedure recovers both sharp edges and smooth surfaces from a given noisy MRI image; it does not blur the edges and does not introduce spikes or other artefacts. The fine details of the image are also preserved. The proposed algorithm first extracts the edges from the original image and then performs noise reduction by using a wavelet de-noise method. After the application of the wavelet method, the edges are restored to the filtered image. The result is the original image with less noise, fine detail and sharp edges. Edge extraction is performed by using an algorithm based on Sobel operators. The wavelet de-noise method is based on the calculation of the correlation factor between wavelet coefficients belonging to different scales. The algorithm was tested on several MRI images and, as an example of its application, we report the results obtained from a spin echo (multi echo) MRI image of a human wrist collected with a low field experimental scanner (the signal-to-noise ratio, SNR, of the experimental image was 12). Other filtering operations have been performed after the addition of white noise on both channels of the experimental image, before the magnitude calculation. The results at SNR = 7, SNR = 5 and SNR = 3 are also reported. For SNR values between 5 and 12, the improvement in SNR was substantial and the fine details were preserved, the edges were not blurred and no spikes or other artefacts were evident, demonstrating the good performances of our method. At very low SNR (SNR = 3) our result is worse than that obtained by a simpler filtering procedure.

Algorithms↗
PubMed2003

Wavelet analysis of epileptic spikes.

Interictal spikes and sharp waves in human EEG are characteristic signatures of epilepsy. These potentials originate as a result of synchronous pathological discharge of many neurons. The reliable detection of such potentials has been the long standing problem in EEG analysis, especially after long-term monitoring became common in investigation of epileptic patients. The traditional definition of a spike is based on its amplitude, duration, sharpness, and emergence from its background. However, spike detection systems built solely around this definition are not reliable due to the presence of numerous transients and artifacts. We use wavelet transform to analyze the properties of EEG manifestations of epilepsy. We demonstrate that the behavior of wavelet transform of epileptic spikes across scales can constitute the foundation of a relatively simple yet effective detection algorithm.

Algorithms↗
PubMed1998

Forecasting generalized epileptic seizures from the EEG signal by wavelet analysis and dynamic unsupervised fuzzy clustering.

Dynamic state recognition and event-prediction are fundamental tasks in biomedical signal processing. We present a new, electroencephalogram (EEG)-based, brain-state identification method which could form the basis for forecasting a generalized epileptic seizure. The method relies on the existence in the EEG of a preseizure state, with extractable unique features, a priori undefined. We exposed 25 rats to hyperbaric oxygen until the appearance of a generalized EEG seizure. EEG segments from the preexposure, early exposure, and the period up to and including the seizure were processed by the fast wavelet transform. Features extracted from the wavelet coefficients were imputed to the unsupervised optimal fuzzy clustering (UOFC) algorithm. The UOFC is useful for classifying similar discontinuous temporal patterns in the semistationary EEG to a set of clusters which may represent brain-states. The unsupervised selection of the number of cluster overcomes the a priori unknown and variable number of states. The usually vague brain state transitions are naturally treated by assigning each temporal pattern to one or more fuzzy clusters. The classification succeeded in identifying several, behavior-backed, EEG states such as sleep, resting, alert and active wakefulness, as well as the seizure. In 16 instances a preseizure state, lasting between 0.7 and 4 min was defined. Considerable individual variability in the number and characteristics of the clusters may postpone the realization of an early universal epilepsy warning. University may not be crucial if using a dynamic version of the UOFC which has been taught the individual's normal vocabulary of EEG states and can be expected to detect unspecified new states.

Algorithms↗
PubMed2000

Wavelet analysis of dynamic PET data: application to the parametric imaging of benzodiazepine receptor concentration.

Receptor density and ligand affinity can be assessed using positron emission tomography (PET). Biological parameters (B(max)('), k(1), k(2), k(on)/V(R), k(off)) are estimated using a compartmental model and a multi-injection protocol. Parametric imaging of the ligand-receptor model has been shown to be of special interest to study certain brain disorders. However, the low signal-to-noise ratio in kinetic curves at the pixel level hampers an adequate estimation of model parameters during the optimization procedure. For this reason, mapping requires a spatial filter, resulting in a loss of resolution. Filtering the kinetic curves in the frequency domain using the Fourier transform is not appropriate, because of difficulties in choosing a correct and efficient cutoff frequency. A wavelet-based filter is more appropriate to such tracer kinetics. The purpose of this study is to build up parametric images at the pixel level while conserving the original spatial resolution, using wavelet-based filtering. Data from [(11)C]flumazenil studies, mapping the benzodiazepine receptor density, were used. An invertible discrete wavelet transform was used to calculate the time-frequency signals of the time-concentration PET curves on a pixel-by-pixel basis. Kinetic curves observed from large regions of interest in high and low receptor-density regions were used to calibrate the threshold of wavelet coefficients. The shrunken wavelet coefficients were then transformed back to the original domain in order to obtain the filtered PET signal. Maps of all binding parameters were obtained at the pixel level with acceptable coefficients of variation of less than 30% for the B(max)(') parameter in most of the gray matter. A strong correlation between model parameter estimates using the usual regions of interest and parametric imaging was observed for all model parameters (r = 0.949 for the parameter B(max)(')). We conclude that wavelet-based filters are useful for building binding parameter maps without loss of the original spatial resolution of the PET scanner. The use of the wavelet-based filtering method can be extended far beyond the multi-injection protocol. It is likely to be also effective for other dynamic PET studies.

Brain↗
PubMed2003

Wavelet analysis of surface electromyography to determine muscle fatigue.

Muscle fatigue is often a result of unhealthy work practice. It has been known for some time that there is a significant change in the spectrum of the electromyography (EMG) of the muscle when it is fatigued. Due to the very complex nature of this signal however, it has been difficult to use this information to reliably automate the process of fatigue onset determination. If such a process implementation were feasible, it could be used as an indicator to reduce the chances of work-place injury. This research report on the effectiveness of the wavelet transform applied to the EMG signal as a means of identifying muscle fatigue. We report that with the appropriate choice of wavelet functions and scaling factors, it is possible to achieve reliable discrimination of the fatigue phenomenon, appropriate to an automated fatigue identification system.

Algorithms↗
PubMed2006

Chromatically dispersed interferometry with wavelet analysis.

A new white-light interferometry point sensor utilizing a chromatically dispersed depth detection field is addressed. Monitoring the interference in the optical frequency domain allows for microscopic height detection without the necessity of a mechanical axial scan. The problem of limited dynamic range in previously reported spectral interferometric schemes is solved by forming a high-contrast interference window due to the chromatically dispersed focusing of the detection field. In a proof-of-principle experiment, the position of a reflecting object could be retrieved with a focus of 0.8 NA over an axial range of 30 microm by analyzing the phase of the emerging interference wavelets.

Journal Article↗
PubMed1997

CT image enhancement with wavelet analysis for the detection of small airways disease.

Bronchiolar obstruction is commonly manifested in computed tomography (CT) images as areas of decreased attenuation relative to adjacent normal lung parenchyma. The certain identification of such areas is difficult in practice, particularly if they are poorly marginated. This paper presents a novel approach to the enhancement of feature differences between normal and diseased lung parenchyma so that reliable visual assessment can be made. The method relies on a hybrid structural filtering technique which removes pulmonary vessels appearing in the CT cross-sectional images without affecting intrinsic subtle intensity details of the lung parenchyma. In order to restore possible structural distortions introduced by the hybrid filter, a feature localization process based on wavelet reconstruction of feature extrema is used. After contrast enhancement the resultant images are used to delineate region borders of the diseased areas and quantification is made with regard to the extent of the disease.

Bronchiolitis Obliterans↗
PubMed2004

Wavelet analysis of blood flow dynamics: effect on the individual oscillatory components of iontophoresis with pharmacologically neutral electrolytes.

Iontophoresis currents are used in the transcutaneous delivery of vasoactive substances for noninvasive assessment of skin vascular properties. The blood flow rate can be recorded by laser Doppler flowmetry (LDF), its average value and the amplitudes of its oscillatory components being used to evaluate the effect of the drugs. Because non-drug-specific, current-induced, vasodilation could confound the interpretation of the response, we have investigated the effect of currents of both polarities on the spectral components of the LDF signal in the absence of vasoactive substances. It was recorded for healthy volunteers with both high conductance (5 mol/l NaCl) and low conductance (deionized water) electrolytes. The oscillatory components were analysed by wavelet transform within 0.0095-1.6 Hz, divided into five sub-intervals. Only cathodal iontophoresis with deionized water increased the oscillatory energy and amplitude. It did so at all frequencies, but none of the sub-intervals associated with vasodilation (0.0095-0.145 Hz) was selectively affected compared to the others.

Adult↗
PubMed2000

[Analysis of wavelet scalogram of blood flow ultrasonic Doppler signal].

OBJECTIVE: To study the power spectrum density of blood flow ultrasonic Doppler signal using wavelet analysis. METHOD: Doppler signal of blood flow in human common carotid artery was studied with wavelet scalogram analysis, an affine smoothing Wigner-Ville transform with a constant "Q" character. RESULT: Wavelet scalogram of blood flow ultrasonic signal had a higher distinguishability than STFT spectrogram in the low frequency portion, but had poorer decussation terms than spectrogram. CONCLUSION: It is practicable to estimate the spectrum of Doppler signals of blood flow by the wavelet analysis method. And this can be used in low speed blood flow for higher resolution of spectrum in the low frequency portion.

Aerospace Medicine↗
PubMed2006

Multiresolution wavelet analysis for efficient analysis, compression and remote display of long-term physiological signals.

Increased inter-equipment connectivity coupled with advances in Web technology allows ever escalating amounts of physiological data to be produced, far too much to be displayed adequately on a single computer screen. The consequence is that large quantities of insignificant data will be transmitted and reviewed. This carries an increased risk of overlooking vitally important transients. This paper describes a technique to provide an integrated solution based on a single algorithm for the efficient analysis, compression and remote display of long-term physiological signals with infrequent short duration, yet vital events, to effect a reduction in data transmission and display cluttering and to facilitate reliable data interpretation. The algorithm analyses data at the server end and flags significant events. It produces a compressed version of the signal at a lower resolution that can be satisfactorily viewed in a single screen width. This reduced set of data is initially transmitted together with a set of 'flags' indicating where significant events occur. Subsequent transmissions need only involve transmission of flagged data segments of interest at the required resolution. Efficient processing and code protection with decomposition alone is novel. The fixed transmission length method ensures clutter-less display, irrespective of the data length. The flagging of annotated events in arterial oxygen saturation, electroencephalogram and electrocardiogram illustrates the generic property of the algorithm. Data reduction of 87% to 99% and improved displays are demonstrated.

Algorithms↗