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 937 records · Page 52Linked to original sources
PubMed2004

Determining correspondence in 3-D MR brain images using attribute vectors as morphological signatures of voxels.

Finding point correspondence in anatomical images is a key step in shape analysis and deformable registration. This paper proposes an automatic correspondence detection algorithm for intramodality MR brain images of different subjects using wavelet-based attribute vectors (WAVs) defined on every image voxel. The attribute vector (AV) is extracted from the wavelet subimages and reflects the image structure in a large neighborhood around the respective voxel in a multiscale fashion. It plays the role of a morphological signature for each voxel, and our goal is, therefore, to make it distinctive of the respective voxel. Correspondence is then determined from similarities of AVs. By incorporating the prior knowledge of the spatial relationship among voxels, the ability of the proposed algorithm to find anatomical correspondence is further improved. Experiments with MR images of human brains show that the algorithm performs similarly to experts, even for complex cortical structures.

Aged↗
PubMed2006

Analysis of environmental samples by application of multivariate curve resolution on fused high-performance liquid chromatography-diode array detection mass spectrometry data.

Multivariate curve resolution-alternating least squares (MCR-ALS) is applied to solve coelution problems in liquid chromatograpy with diode array detection (DAD) and mass spectrometry (MS). Fusion of DAD and MS detector signals improved results versus those obtained using only one of the two detector signals. Application of wavelet transform to MS data before its fusion with DAD data, further help to facilitate the resolution and quantitation of the coeluted compounds under study, besides a decrease of time of analysis. Mixtures of biocide compounds in standard mixtures and in environmental samples (sediment and wastewater samples) were analyzed with estimated quantitation errors below 12%.

Chromatography, High Pressure Liquid↗
PubMed2001

Automatic analysis of image of surface structure of cell wall-deficient EVC.

Some computer applications for cell characterization in medicine and biology, such as analysis of surface structure of cell wall-deficient EVC (El Tor Vibrio of Cholera), operate with cell samples taken from very small areas of interest. In order to perform texture characterization in such an application, only a few texture operators can be employed: the operators should be insensitive to noise and image distortion and be reliable in order to estimate texture quality from images. Therefore, we introduce wavelet theory and mathematical morphology to analyse the cellular surface micro-area image obtained by SEM (Scanning Electron Microscope). In order to describe the quality of surface structure of cell wall-deficient EVC, we propose a fully automatic computerized method. The image analysis process is carried out in two steps. In the first, we decompose the given image by dyadic wavelet transform and form an image approximation with higher resolution, by doing so, we perform edge detection of given images efficiently. In the second, we introduce many operations of mathematical morphology to obtain morphological quantitative parameters of surface structure of cell wall-deficient EVC. The obtained results prove that the method can eliminate noise, detect the edge and extract the feature parameters validly. In this work, we have built automatic analytic software named "EVC.CELL".

Cell Membrane↗
PubMed2006

Advantages of the Hilbert Huang transform for marine mammals signals analysis.

While marine mammals emit variant signals (in time and frequency), the Fourier spectrogram appears to be the most widely used spectral estimator. In certain cases, this approach is suboptimal, particularly for odontocete click analysis and when the signal-to-noise ratio varies during the continuous recordings. We introduce the Hilbert Huang transform (HHT) as an efficient means for analysis of bioacoustical signals. To evaluate this method, we compare results obtained from three time-frequency representations: the Fourier spectrogram, the wavelet transform, and the Hilbert Huang transform. The results show that HHT is a viable alternative to the wavelet transform. The chosen examples illustrate certain advantages. (1) This method requires the calculation of the Hilbert transform; the time-frequency resolution is not restricted by the uncertainty principle; the frequency resolution is finer than with the Fourier spectrogram. (2) The original signal decomposition into successive modes is complete. If we were to multiply some of these modes, this would contribute to attenuate the presence of noise in the original signal and to being able to select pertinent information. (3) Frequency evolution for each mode can be analyzed as one-dimensional (1D) signal. We not need a complex 2D post-treatment as is usually required for feature extraction.

Acoustics↗
PubMed1999

[Detection of ECG characteristic using wavelet transform].

This paper discusses the characteristic detection of modulus maxima of the loavelet transform, based on the properties of the singularity of the signal and noise. Theoretical analysis and experimental results show That the method is fitting for ECG signal detection and positioning.

Algorithms↗
PubMed2001

Examination of the myoelectric activity of back muscles during random vibration--methodical approach and first results.

OBJECTIVE: To elaborate methods for an elimination of artefacts and the analysis of the relationship between random whole-body vibration and electromyographic responses of back muscles. DESIGN: A procedure involving wavelets and digital filtering has been used for the removal of artefacts from the electromyogram during whole-body vibration. BACKGROUND: Back muscle forces contribute essentially to the whole-body vibration-induced spinal load. The electromyogram can help to estimate these forces during whole-body vibration. METHODS: 38 subjects were exposed to identical random low-frequency whole-body vibration. Artefacts caused by the electrocardiogram in the electromyogram were identified by appropriate wavelets and eliminated in the time-domain. After averaging the individual high-pass filtered and rectified undistorted electromyograms across subjects, the transfer function from seat acceleration to the average electromyogram was determined and used for the prediction of the electromyogram. RESULTS: A sufficient procedure involving wavelets and digital filtering has been elaborated for the removal of artefacts from the electromyogram of back muscles during whole-body vibration. A systematic relationship between random vibration and back muscle-response was obtained and described. The transfer function suggests two different reflex-mechanisms - one elicited below, the other above 4 Hz. CONCLUSIONS: The approach of analysing and predicting the muscle-response to random vibration by using the transfer function seems to be promising and could be a valuable tool for the future calculation of muscle forces as an input to active models. RELEVANCE: The knowledge of the extent and timing of the back muscle-response to random whole-body vibration is relevant for an improved evaluation of whole-body vibration with respect to health.

Adult↗
PubMed2004

Signal verification of middle latency auditory evoked potentials by automated detection of the brainstem response.

BACKGROUND: The midlatency components of auditory evoked potentials (AEPs) are gradually suppressed with increasing concentrations of anesthetics. Thus, they have been proposed as a monitor of anesthetic depth. However, undetected malfunction or disconnection of headphones and undetected hearing loss also result in suppressed midlatency AEPs that in turn may be misinterpreted as signs of deep anesthesia. As the brainstem component of the AEP is minimally influenced by anesthetics, its presence or absence can be used to verify that the recorded signal is a true AEP rather than an artifact. In this study, an online-capable procedure for detection of the brainstem component of the AEP was developed. METHODS: One hundred and ninety perioperatively recorded AEPs (binaural stimuli, 500 sweeps) were selected from a database with electroencephalographic and concomitant AEP stimulus information. Identical electroencephalogram regions were used to produce nonstimulus synchronized averaged signals (500 sweeps, "non-AEP"). The 190 AEPs and 190 "non-AEPs" were used to develop a detector of the brainstem component of AEPs. AEPs and "non-AEPs" were wavelet transformed (discrete wavelet decomposition, biorthogonal 2.2 mother-wavelet), and the coefficient with the best separation of the two classes of signals was selected. Receiver operating characteristic curve analysis was performed to determine the optimum threshold value for this coefficient. RESULTS: The third coefficient of the third level was selected. In AEP signals, retransform of this coefficient produces a peak that resembles peak V of the brainstem response. The developed detector of the brainstem component of AEP had a sensitivity of 97.90% and a specificity of 99.48%. CONCLUSIONS: This detector of the AEP brainstem component can be used to verify that the signal reflects the response to an auditory stimulus. An alternative approach, used in the Danmeter AEP monitor, is based on the signal-to-noise ratio of the midlatency components of the AEP. Because the midlatency components of AEP are suppressed by anesthesia, a false alarm "low AEP/no AEP" is generated during deep anesthesia. This, in turn, may suggest disconnection of headphones or technical problems whenever anesthesia is deep. This disadvantage has been overcome by our detector, which is based on the identification of the brainstem component of AEP.

Acoustic Stimulation↗
PubMed2006

Information extraction from sound for medical telemonitoring.

Today, the growth of the aging population in Europe needs an increasing number of health care professionals and facilities for aged persons. Medical telemonitoring at home (and, more generally, telemedicine) improves the patient's comfort and reduces hospitalization costs. Using sound surveillance as an alternative solution to video telemonitoring, this paper deals with the detection and classification of alarming sounds in a noisy environment. The proposed sound analysis system can detect distress or everyday sounds everywhere in the monitored apartment, and is connected to classical medical telemonitoring sensors through a data fusion process. The sound analysis system is divided in two stages: sound detection and classification. The first analysis stage (sound detection) must extract significant sounds from a continuous signal flow. A new detection algorithm based on discrete wavelet transform is proposed in this paper, which leads to accurate results when applied to nonstationary signals (such as impulsive sounds). The algorithm presented in this paper was evaluated in a noisy environment and is favorably compared to the state of the art algorithms in the field. The second stage of the system is sound classification, which uses a statistical approach to identify unknown sounds. A statistical study was done to find out the most discriminant acoustical parameters in the input of the classification module. New wavelet based parameters, better adapted to noise, are proposed in this paper. The telemonitoring system validation is presented through various real and simulated test sets. The global sound based system leads to a 3% missed alarm rate and could be fused with other medical sensors to improve performance.

Activities of Daily Living↗
PubMed1998

Automated spectral analysis III: application to in vivo proton MR spectroscopy and spectroscopic imaging.

An automated method for analysis of in vivo proton magnetic resonance (MR) spectra and reconstruction of metabolite distributions from MR spectroscopic imaging (MRSI) data is described. A parametric spectral model using acquisition specific, a priori information is combined with a wavelet-based, nonparametric characterization of baseline signals. For image reconstruction, the initial fit estimates were additionally modified according to a priori spatial constraints. The automated fitting procedure was applied to four different examples of MRS data obtained at 1.5 T and 4.1 T. For analysis of major metabolites at medium TE values, the method was shown to perform reliably even in the presence of large baseline signals and relatively poor signal-to-noise ratios typical of in vivo proton MRSI. Identification of additional metabolites was also demonstrated for short TE data. Automated formation of metabolite images will greatly facilitate and expand the clinical applications of MR spectroscopic imaging.

Aspartic Acid↗
PubMed1996

Spatial resolution requirements in digital radiography of scaphoid fractures. An ROC analysis.

PURPOSE: To investigate the spatial resolution requirements in digital radiography of scaphoid fractures. MATERIAL AND METHODS: Included in the study were 60 scaphoid radiographs with and 60 without fractures of the scaphoid bone. The film-screen images were digitized using pixel sizes of 115, 170, and 340 microns along with 170 microns with a 10:1 wavelet compression. The digital images were displayed on a 1280 x 1024 x 8 bits monitor, and 5 observers evaluated the images in 5 randomized sessions. The results for each pixel size were then compared to the film-screen images by ROC analysis. RESULTS: The mean area under the ROC curves was larger for the film-screen images than for the digital images at all resolutions. However, this difference was not significant when the areas under the ROC curves for the film-screen images were compared to the digital images of 115, 170, and 170 microns with 10:1 compression. There was a significant difference for the 340-microns pixel size in favour of the film-screen images. The mean ROC curves for the digital images were very similar for the 115 and 170 microns pixel sizes, although slightly better for 115 microns. At 170 microns, the compression seemed to have a relatively small negative effect on the diagnostic performance; the deterioration was greater when the pixel size was increased to 340 microns. There was no obvious correlation between diagnostic performance and the experience of the observers in using work-stations. CONCLUSIONS: The pixel size of 170 microns is adequate for the detection of subtle fractures, even after wavelet compression by a ratio of 10:1.

Angiography, Digital Subtraction↗
PubMed2006

Detection of breast lesion regions in ultrasound images using wavelets and order statistics.

Accurate detection and segmentation of suspicious regions within the complex and irregular tissues of the breast, as depicted with ultrasonic B scans, typically require human analysis and decision making. Tissue characterization methods for classifying suspicious regions often depend on identifying and then accurately segmenting these regions. Motivated by an ultimate goal to automate this critical identification and segmentation step for tissue characterization problems, this work examines ultrasonic signal characteristics between various regions of breast tissue broadly classified as normal tissue and breast lesions. This paper introduces a nonparametric model based on order statistics (OS) estimated from multiresolution (MR) decompositions of energy-normalized subregions. Experimental results demonstrate the classification performance of the OS-based features extracted from the tumor and normal tissue regions in multiple scans from 84 patients, which resulted in a total of 204 tumor regions (from 43 malignant and 161 benign) and 816 normal tissue regions. Performance results indicate that OS-based features achieved an area under the receiver-operator characteristic curve of 91% in the discrimination between breast lesions and surrounding normal tissues.

Algorithms↗
PubMed1996

The future of computer-assisted investigation of the polysomnogram: sleep microstructure.

Previous attempts at automated analysis of sleep were mainly directed towards imitating the Rechtschaffen and Kales rules (RKR) in order to save scoring time and further objectify the procedure. RKR, however, do not take into consideration the sleep microstructure of REM, stage 2, and SWS. While the microstructure of stage 2 has been analyzed in the past decade, the microstructure of REM and SWS are virtually unknown. In stage 2 the amount and distribution of spindles, K complexes, and arousal reactions have been studied. At least two types of spindles (12/s and 14/s) with different dynamics and locations have been identified. Two different shapes for K complexes have been described: one related to external sensory stimuli with similarities to evoked potentials and another one more related to sinusoidal slow wave activity seen in SWS. These two different K complex shapes have different distributions and, obviously, different functions. The authors also suggest that one should differentiate between arousal reactions and true arousals. Recent investigations suggest two types of delta waves in SWS. The more sinusoidal 1-3/s delta waves with a frontal maximum are already seen with lower amplitude in late stage 2 and increase their amplitude and incidence towards stage 3 and Stage 4. The other delta-wave type is slower (< 1/s), polymorphic, and has varying amounts of theta and higher frequency waves superimposed. During REM sleep it seems to be important to separate phases with rapid eye movements from those with none (REM sine REM), and count the amount and distribution of sawtooth activity. Background activity during REM and REM sine REM, as well as intra- and interhemispheric coherence should be analyzed separately. Only if the microstructure of the sleep EEG can be analyzed automatically using newer techniques such as transformation into wavelets and pattern classification with neuronal networks, and only if we learn more about the importance of microstructure elements, can automated sleep analysis go beyond the limited information obtained from scoring according to RKR.

Arousal↗
PubMed2006

Multiresolution analysis of heart rate variability as investigational tool in experimental fetal cardiac surgery.

Multiresolution analysis of heart rate variability derived from aortic blood pressure, acquired before and after (30 and 60 min) experimental fetal cardiac bypass performed on five ewe's fetuses, was used to investigate the physiological response to an invasive clinical approach. Tachograms were implemented and analyzed by wavelet transform in order to verify the existence of a quantitative relationship between arterial blood gases and time series in the very-low (0.021<f<0.084 Hz) and low (0.084<f<0.337 Hz) frequency band. Multiresolution analysis showed an average decreasing trend from basal condition for all the fetuses investigated in the very-low frequency band, while an opposite trend was highlighted in the low frequency band: this resulting behavior could be related to the temporal evolution of blood gas data. Finally, a slight decrease of sympatho-vagal balance was monitored 30 min after the cardiac bypass was discontinued compared to basal condition. Multiresolution analysis could give more insights on fetal hypoxemia and could also represent a minimally invasive monitoring tool to limit the damage to the fetoplacental unit during experimental fetal cardiac surgery.

Algorithms↗
PubMedDate not supplied

Two applications of wavelets and related techniques in medical imaging.

We present two applications of wavelet and related techniques to problems arising in medical imaging. Both make considerable use of the edge detection and classification properties of wavelet-type representations. First we describe simple and effective techniques for image denoising and contrast enhancement based on the multiscale edge representation of images. These techniques are sufficiently flexible to successfully address the varying requirements posed by several different medical imaging modalities in common use today. Experimental results are presented to illustrate the application of these techniques to various types of medical images. Next we describe adapted waveform encoding, a technique for magnetic resonance imaging. One advantage of this technique is that it can be used to efficiently encode edge features of the object being imaged. This has a particular diagnostic application in tracking heart wall thickness during the cardiac cycle, which we present along with some experimental results along this line. We also present an analysis of the signal-to-noise ratios of images formed with this technique, as this is a factor of paramount importance in MRI. The fact that wavelet schemes tend to concentrate energy near edge features makes the result rather different than that found in standard Fourier based approaches. We indicate an exciting potential application of our technique: reducing spectral leakage in phosphorus spectroscopy.

Algorithms↗
PubMed2004

Fast automatic registration of images using the phase of a complex wavelet transform: application to proteome gels.

Image registration describes the process of manipulating a distorted version of an image such that its pixels overlay the equivalent pixels in a clean, master or reference image. The need for it has assumed particular prominence in the analysis of images of electrophoretic gels used in the analysis of protein expression levels in living cells, but also has fundamental applications in most other areas of image analysis. Much of the positional information of a data feature is carried in the phase of a complex transform, so a complex transform allows explicit specification of the phase, and hence of the position of features in the image. Registration of a test gel to a reference gel is achieved by using a multiresolution movement map derived from the phase of a complex wavelet transform (the Q-shift wavelet transform) to dictate the warping directly via movement of the nodes of a Delaunay-triangulated mesh of points. This warping map is then applied to the original untransformed image such that the absolute magnitude of the spots remains unchanged. The technique is general to any type of image. Results are presented for a simple computer simulated gel, a simple real gel registration between similar "clean" gels with local warping vectors distributed about one main direction, a hard problem between a reference gel and a "dirty" test gel with multi-directional warping vectors and many artifacts, and some typical gels of present interest in post-genomic biology. The method compares favourably with others, since it is computationally rapid, effective and entirely automatic.

Animals↗
PubMed1992

Advanced time-frequency methods for signal-averaged ECG analysis.

Frequency-domain techniques have been extensively investigated for the analysis of high-resolution electrocardiograms (ECGs), although the merit of frequency-domain analysis is still subject to controversy. Time-frequency analysis methods, which estimate the frequency content of a signal as a function of time, potentially provide even more information for improved ECG analysis. Some researchers report impressive results in predicting the outcome of electrophysiologic studies using the short-time Fourier transform (spectrogram). Other time-frequency representations, such as the Wigner distribution, short-time spectral estimators, and the wavelet transform, have also been investigated. The authors present a unified overview of time-frequency representations, showing that only four classes characterize most time-frequency representations. The authors describe the advantages and drawbacks of the various approaches and speculate on their promise for ECG analysis. Very preliminary experiments in applying some of these techniques to the prediction of the outcome of electrophysiologic studies have suggested some possible new research directions.

Electrocardiography↗
PubMed1994

Spike discharges of skeletomotor neurons during random noise modulated transmembrane current stimulation and muscle stretch.

Spike discharges of skeletomotor neurons innervating triceps surae muscles elicited by white noise modulated transmembrane current stimulation and muscle stretch were studied in decerebrated cats. The white noise modulated current intensity ranged from 4.3 to 63.2 nA peak-to-peak, while muscle stretches ranged from 100 microns to 4.26 mm peak-to-peak. The neuronal responses were studied by averaging the muscle length records centered at the skeletomotor action potentials (peri-spike average, PSA) and by Wiener analysis. Skeletomotor spikes appeared after a sharp peak in PSA of the injected current, preceded by a longer-lasting smaller wavelet of either depolarizing or hyperpolarizing direction. The PSA amplitude was not related to the injected current amplitude nor showed any differences related to the motor unit type. The PSA amplitudes were virtually independent of the stretching amplitude sigma, after an initial increase with stretching amplitudes in the range of 15-40 microns (S.D.), or 100-270 microns peak-to-peak. Analyses of cross-spectra indicated a small or absent increase in gain with frequency in response to injected current, but about 20 dB/decade in the range 10-100 Hz in response to muscle stretch. The peaks of both Wiener kernels in response to current injection appear to decrease with the amplitude of injected current, but this decrease was not statistically significant. The narrow first-order kernels suggest that the transfer function between the current input and spike discharge is lowpass with a wide passband, i.e. there is very little change in dynamics. The values of the second-order kernels appear to be nonzero only along the main diagonal.(ABSTRACT TRUNCATED AT 250 WORDS)

Action Potentials↗
PubMed2003

Alpha and beta oscillatory changes during stimulus-induced movement paradigms: effect of stimulus predictability.

We studied the effect of stimulus predictability on the alpha and beta changes observed in central regions during stimulus-induced movement paradigms. Six young volunteers were instructed to extend briskly their dominant wrist as soon as possible after hearing a 2000 Hz sound. Two sequences of stimuli were presented to each subject, the first rhythmic at 1/6 s and the second with random intervals between 5 and 13s. A time-frequency analysis of nonphase-locked activity in the 7-37 Hz range was performed on stimulus-centred EEG sweeps using wavelet filters and Gabor transforms. During the sequence of predictable rhythmic stimuli, stimulus-induced movements were accompanied by a decrease in beta activity that began contralaterally about 1 s prior to the stimulus and extended to both sides later on. This decrease was followed by a rebound after the end of the movement. In the alpha band, the decrease observed started just after the sound. During the sequence of non-predictable, random stimuli, stimulus-induced movements were accompanied by a shorter and smaller alpha and beta-ERD, that started after the stimulus. The presence of a pre-stimulus beta ERD only in the rhythmic predictable paradigm suggests that central pre-movement beta ERD may be an indicator of motor preparation, and could be used for objective evaluation of time estimation and motor timing. The minimal differences observed in the alpha changes in both paradigms suggest that alpha-ERD may not be linked to motor preparation.

Acoustic Stimulation↗