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PubMed1998

[Research and development of the surface EMG detection and analysis system].

To meet the requirement of developing myoelectric controller for a man-machine systems, a surface electromyography (EMG) detection and analysis system is discussed in this paper. This system consists of two parts--data acquisition hardware and signal processing software. In the surface EMG acquisition system both wire and wireless TELEMG are available. In the surface EMG processing system various methods are integrated such as the methods in time domain, the methods for frequency analysis, and also including some newly developed methods, e.g., wavelet transform and fractal analysis. This system provides a useful platform for research and development of the myoelectric controller that is widely used in rehabilitation engineering products.

Electromyography↗
PubMed2006

A comparison of wavelet, ridgelet, and curvelet-based texture classification algorithms in computed tomography.

The research presented in this article is aimed at the development of an automated imaging system for classification of normal tissues in medical images obtained from computed tomography (CT) scans. This article focuses on comparing the discriminating power of several multi-resolution texture analysis techniques using wavelet, ridgelet, and curvelet-based texture descriptors. The approach consists of two steps: automatic extraction of the most discriminative texture features of regions of interest and creation of a classifier that automatically identifies the various tissues. The algorithms are extensively tested and results are compared with standard texture classification algorithms. Tests indicate that using curvelet-based texture features significantly improves the classification of normal tissues in CT scans.

Algorithms↗
PubMed2000

Analysis of synaptic quantal depolarizations in smooth muscle using the wavelet transform.

The time-frequency characteristics of synaptic potentials contain valuable information about the process of neurotransmission between nerves and their target organs. For example, at the synapse between autonomic nerves and smooth muscle, two central issues of neurophysiology, i.e., 1) the probability of neurotransmitter release and 2) the quantal behavior of transmission can be deduced from analysis of the rising phases of evoked excitatory junction potentials (eEJP's) recorded from smooth muscle. eEJP rising phases are marked by prominent inflexions, which reflect these features of neuronal activity. Since these inflexions contain time-varying frequency information, we have applied recent techniques of time-frequency analysis based upon wavelet transforms to eEJP's recorded from the guinea-pig vas deferens in vitro. We find that these techniques allow accurate and convenient characterization of neuronal release sites, and that their probability of release falls between 0.001-0.004. We have also analyzed eEJP's recorded in the presence of the chemical 1-heptanol, which reveals quantal depolarizations. These results have helped clarify the nature of the quantal depolarizations that underly eEJP's. The present method offers significant advantages over those previously employed for these tasks, and holds promise as a novel approach to the analysis of synaptic potentials.

Animals↗
PubMed2005

On the use of wavelet filtering and correlation techniques in atmospheric condensed phase spectroscopy.

The application of wavelet filtering and analysis in spectroscopy is discussed in relation to the analysis of complex atmospheric spectra, where contributions from condensed phase particles and gas phase molecules are present in the form of broad-band features and narrow lines, respectively. The broad-band contribution can be extracted as the 'smooth signal' component of the wavelet transform, with a large reduction in the size of the corresponding data files. This procedure is applied to an investigation of the H2SO4 aerosol content of a series of atmospheric spectra measured in the ATMOS missions. The sulfate content of the smooth signal is analysed by means of correlation techniques, using a set of laboratory reference spectra of varying sulfuric acid concentration and temperature. Correlation density maps and correlation curves are used to select the most appropriate spectral zones for sulfate analysis and to assess the sulfate aerosol content in the atmosphere subsequent to the eruption of the Mount Pinatubo volcano.

Aerosols↗
PubMed1998

Statistical analysis of functional MRI data in the wavelet domain.

The use of the wavelet transform is explored for the detection of differences between brain functional magnetic resonance images (fMRI's) acquired under two different experimental conditions. The method benefits from the fact that a smooth and spatially localized signal can be represented by a small set of localized wavelet coefficients, while the power of white noise is uniformly spread throughout the wavelet space. Hence, a statistical procedure is developed that uses the imposed decomposition orthogonality to locate wavelet-space partitions with large signal-to-noise ratio (SNR), and subsequently restricts the testing for significant wavelet coefficients to these partitions. This results in a higher SNR and a smaller number of statistical tests, yielding a lower detection threshold compared to spatial-domain testing and, thus, a higher detection sensitivity without increasing type I errors. The multiresolution approach of the wavelet method is particularly suited to applications where the signal bandwidth and/or the characteristics of an imaging modality cannot be well specified. The proposed method was applied to compare two different fMRI acquisition modalities. Differences of the respective useful signal bandwidths could be clearly demonstrated; the estimated signal, due to the smoothness of the wavelet representation, yielded more compact regions of neuroactivity than standard spatial-domain testing.

Adult↗
PubMed2006

Wavelet-based medical image compression with adaptive prediction.

A lossless wavelet-based image compression method with adaptive prediction is proposed. Firstly, we analyze the correlations between wavelet coefficients to identify a proper wavelet basis function, then predictor variables are statistically test to determine which relative wavelet coefficients should be included in the prediction model. At last, prediction differences are encoded by an adaptive arithmetic encoder. Instead of relying on a fixed number of predictors on fixed locations, we proposed the adaptive prediction approach to overcome the multicollinearity problem. The proposed innovative approach integrating correlation analysis for selecting wavelet basis function with predictor variable selection is fully achieving high accuracy of prediction. Experimental results show that the proposed approach indeed achieves a higher compression rate on CT, MRI and ultrasound images comparing with several state-of-the-art methods.

Data Compression↗
PubMed1999

A wavelet packet model of evoked potentials.

The standard methods for decomposition and analysis of evoked potentials are bandpass filtering, identification of peak amplitudes and latencies, and principal component analysis (PCA). We discuss the limitations of these and other approaches and introduce wavelet packet analysis. Then we propose the "single-channel wavelet packet model," a new approach in which a unique decomposition is achieved using prior time-frequency information and differences in the responses of the components to changes in experimental conditions. Orthogonal sets of wavelet packets allow a parsimonious time-frequency representation of the components. The method allows energy in some wavelet packets to be shared among two or more components, so the components are not necessarily orthogonal. The single-channel wavelet packet model and PCA both require constraints to achieve a unique decomposition. In PCA, however, the constraints are defined by mathematical convenience and may be unrealistic. In the single-channel wavelet packet model, the constraints are based on prior scientific knowledge. We give an application of the method to auditory evoked potentials recorded from cats. The good frequency resolution of wavelet packets allows us to separate superimposed components in these data. Our present approach yields estimates of component waveforms and the effects of experiment conditions on the amplitude of the components. We discuss future extensions that will provide confidence intervals and p values, allow for latency changes, and represent multichannel data.

Animals↗
PubMed2006

Singularity characteristics of needle EMG IP signals.

Clinical electromyography (EMG) interference pattern (IP) signals can reveal more diagnostic information than their constituents, the motor unit action potentials (MUAPs). Singularities and irregular structures typically characterize the mathematically defined content of information in signals. In this paper, a wavelet transform method is used to detect and quantify the singularity characteristics of EMG IP signals using the Lipschitz exponent (LE) and measures derived from it. The performance of the method is assessed in terms of its ability to discriminate healthy, myopathic and neuropathic subjects and how it compares with traditionally used Turns Analysis (TA) methods and a method recently developed by the authors, interscale wavelet maximum (ISWM). Highly significant intergroup differences were found using the LE method. Most of the singularity measures have a performance similar to that of ISWM and considerably better than that of TA. Some measures such as the ratio of the mean LE value to the number of singular points in the signal have considerably superior performance to both methods. These findings add weight to the view that wavelet analysis methods offer an effective way forward in the quantitative analysis of EMG IP signal to assist the clinician in the diagnosis of neuromuscular disorders.

Action Potentials↗
PubMed2000

Water peak suppression: time-frequency vs time-scale approach.

Wavelets are the most popular time-scale analysis tool. A well-known application of wavelets in nuclear magnetic resonance spectroscopy is water peak extraction/suppression. However, spectroscopists are more familiar with frequency than scale. So, from a spectroscopist point of view, a time-scale analysis tool (i.e., wavelets) is not natural and a time-frequency approach would be much more satisfactory. We explain a time-frequency solution to this problem based on Gabor analysis. As the two formalisms are closely linked together we continuously emphasize their similarities and differences. In particular we show that, here, the Gabor method is as efficient as the wavelet approach, and we give some examples. Those remarks also apply to other NMR problems solved previously with the continuous wavelet transform, such as quantification or dynamical phase correction.

Algorithms↗
PubMed2000

Predictive value of wavelet decomposition of the signal-averaged electrocardiogram in idiopathic dilated cardiomyopathy.

BACKGROUND: Wavelet decomposition of the signal-averaged electrocardiogram has been proposed as a method of detecting small and transient irregularities hidden within the QRS complex and of overcoming some of the limitations of time domain analysis of the signal-averaged electrocardiogram. AIM: This study evaluated the potential utility of wavelet decomposition analysis in the risk stratification of patients with idiopathic dilated cardiomyopathy. METHODS AND RESULTS: Both wavelet decomposition and time domain analysis were applied to the signal-averaged electrocardiogram recordings of 82 patients with idiopathic dilated cardiomyopathy (mean age 43 +/- 14 years, 60 men) and 72 normal controls (mean age 44 +/- 15 years, 48 men). Three conventional time domain indices and four wavelet decomposition analysis parameters (QRS length, maximum count, surface area, and relative length) were derived from each recording using a Del Mar CEWS system and an in-house software package, respectively. The results showed that (1) more patients with idiopathic dilated cardiomyopathy than without had late potentials, and that the filtered QRS duration was significantly longer in patients than in controls (P<0.001). Similarly, abnormal wavelet decomposition analysis was more common in patients and wavelet decomposition measurements were significantly different between patients and controls (P<0.01); (2) conventional time domain analysis did not distinguish between clinically stable patients and patients who developed progressive heart failure, or between patients with and without arrhythmic events; (3) wavelet decomposition analysis identified patients who went on to develop progressive heart failure but failed to distinguish patients with arrhythmic events from those without; (4) survival analyses of a mean follow-up of 23 months showed that patients with late potentials tended to develop progressive heart failure more frequently than others (P=0.06). Patients with an abnormal wavelet decomposition result more frequently developed progressive heart failure than those with a normal wavelet decomposition result (P=0.027); (5) in a univariate analysis (Cox model), wavelet decomposition measurements but not time domain indices significantly correlated with the development of progressive heart failure (P=0.01). Multivariate analysis showed that only left ventricular end-diastolic dimension and peak oxygen consumption during exercise remained significant predictors of progressive heart failure. CONCLUSION: Wavelet decomposition analysis of the signal-averaged electrocardiogram is superior to conventional time domain analysis for identifying patients with idiopathic dilated cardiomyopathy at increased risk of clinical deterioration. Wavelet decomposition analysis, however, is unlikely to prospectively distinguish patients at a high risk of arrhythmic events in idiopathic dilated cardiomyopathy in its present form.

Adult↗
PubMed2006

Image analysis using a dual-tree M-band wavelet transform.

We propose a two-dimensional generalization to the M-band case of the dual-tree decomposition structure (initially proposed by Kingsbury and further investigated by Selesnick) based on a Hilbert pair of wavelets. We particularly address: 1) the construction of the dual basis and 2) the resulting directional analysis. We also revisit the necessary pre-processing stage in the M-band case. While several reconstructions are possible because of the redundancy of the representation, we propose a new optimal signal reconstruction technique, which minimizes potential estimation errors. The effectiveness of the proposed M-band decomposition is demonstrated via denoising comparisons on several image types (natural, texture, seismics), with various M-band wavelets and thresholding strategies. Significant improvements in terms of both overall noise reduction and direction preservation are observed.

Algorithms↗
PubMed2000

Selection of biorthogonal filters for image compression of MR images using wavelet packets.

We present an analysis of different filter banks for the compression of magnetic resonance (MR) images of the human brain using wavelet packets based on biorthogonal filters. Initially, peak signal to noise ratio (PSNR) and normalized root mean square (RMS) error criteria are calculated for a series of images compressed with a 33:1 ratio, using filter banks based on biorthogonal wavelet packets. The results lead us to choose a few of these filter banks as optimal for image compression. One of these filters is employed to compress several images at four different compression ratios: 12.5:1, 25:1, 37.5:1 and 50:1. The quality of these images was evaluated by visual analysis by a group of seven experts who graded image quality on a 0-7 scale. Results show that using these filters, we can compress images to a rate of around 30:1 without introducing noticeable differences. Other applications for these filters are currently under study and include the compression/fusion of MR image stacks in order to obtain even better reductions in the amount of data needed to reconstruct complete MRI studies.

Brain↗
PubMedDate not supplied

A method for fully automated quantitative analysis of arterial flow using flow-sensitized MR images.

While the recent developments in the velocity-sensitive MR imaging offer great potential for non-invasive assessment of blood flow in major blood vessels, the clinical applications of this technique have been hampered by tedious, human-assisted data processing techniques. In this paper, we describe a robust system for automated extraction of quantitative as well as qualitative flow information from velocity-sensitive, phase contrast MR images. The algorithm accomplishes reliable segmentation of blood vessels using multiresolution analysis based on wavelet transform, and employs a multivariate scoring criterion to minimize the impact of imaging artifacts such as partial volume averaging and flow turbulence, which normally cause incomplete or inaccurate detection of vascular boundaries.

Algorithms↗
PubMed2005

The measure of randomness by leave-one-out prediction error in the analysis of EEG after laser painful stimulation in healthy subjects and migraine patients.

OBJECTIVE: We aimed to perform a quantitative analysis of event-related modulation of EEG activity, resulting from a not-warned and a warned paradigm of painful laser stimulation, in migraine patients and controls, by the use of a novel analysis, based upon a parametric approach to measure predictability of short and noisy time series. METHODS: Ten migraine patients were evaluated during the not-symptomatic phase and compared to seven age and sex matched controls. The dorsum of the right hand and the right supraorbital zone were stimulated by a painful CO(2) laser, in presence or in absence of a visual warning stimulus. An analysis time of 1s after the stimulus was submitted to a time-frequency analysis by a complex Morlet wavelet and to a cross-correlation analysis, in order to detect the development of EEG changes and the most activated cortical regions. A parametric approach to measure predictability of short and noisy time series was applied, where time series were modeled by leave-one-out (LOO) error. RESULTS: The averaged laser-evoked potentials features were similar between the two groups in the alerted and not alerted condition. A strong reset of the beta rhythms after the painful stimuli was seen for three groups of electrodes along the midline in patients and controls: the predictability of the series induced by the laser stimulus changed very differently in controls and patients. The separation was more evident after the warning signal, leading to a separation with P-values of 0.0046 for both the hand and the face. DISCUSSION: As painful stimulus causes organization of the local activity in cortex, EEG series become more predictable after stimulation. This phenomenon was less evident in migraine, as a sign of an inadequate cortical reactivity to pain. SIGNIFICANCE: The LOO method enabled to show in migraine subtle changes in the cortical response to pain.

Adult↗
PubMed2006

A novel method for analysis of single ion channel signal based on wavelet transform.

A single ion channel signal was analysed by the power distribution fraction constructed by a discrete wavelet transform. Average opening time and energy distribution of the signal can be obtained directly by this method. The method can also be used when the signal is corrupted by noise. By contrast, the conventional frequency domain analysis method--power spectral density--is less effective. Power distribution fraction will therefore give more useful information in analysis of experimental ion channel signals, principally by giving values of the mean channel opening time. The method may be applied to distinguish different ion channels more efficiently and to find their reactions to drugs.

Algorithms↗
PubMedDate not supplied

Automated cardiac auscultation for detection of pathologic heart murmurs.

Experienced cardiologists can usually recognize pathologic heart murmurs with high sensitivity and specificity, although nonspecialists with less clinical experience may have more difficulty. Harsh, pansystolic murmurs of intensity grade > or = 3 at the left upper sternal border (LUSB) are likely to be associated with pathology. In this study, we designed a system for automatically detecting systolic murmurs due to a variety of conditions and examined the correlation between relative murmur intensity and likelihood of pathology. Cardiac auscultatory examinations of 194 children and young adults were recorded, digitized, and stored along with corresponding echocardiographic diagnoses, and automated spectral analysis using continuous wavelet transforms was performed. Patients without heart disease and either no murmur or an innocent murmur (n = 95) were compared to patients with a variety of cardiac diagnoses and a pathologic systolic murmur present at the LUSB (n = 99). The sensitivity and specificity of the automated system for detecting pathologic murmurs with intensity grade > or = 2 were both 96%, and for grade > or = 3 murmurs they were 100%. Automated cardiac auscultation and interpretation may be useful as a diagnostic aid to support clinical decision making.

Adolescent↗
PubMed2005

Sexual dimorphism in the Japanese cranial base: a Fourier-wavelet representation.

An approach, computational shape analysis (CSA), is presented here which utilizes a Fourier-wavelet representation to numerically describe shape features of biological forms. Two elements are involved: 1) elliptical Fourier functions (EFFs), to provide estimates of global aspects, and 2) continuous wavelet transforms (CWTs) to generate an objective estimate of localized features. EFFs are computed, using a set of pseudohomologous points, to create a precise analog of the boundary. This computed contour is then normalized by scaling and rotated in two-dimensional space to insure a representation that is invariant with respect to starting point, size, and orientation. The predicted point coordinates derived from the EFFs are submitted to CWT for further processing. Wavelet coefficients are then computed to identify localized features, localization being a subjective process with EFFs. The advantage of wavelets is that they eliminate the inevitable subjectivity inherent in the choice of measurements. To test the usefulness of CSA, a sample of cranial base (CB) lateral radiographic outlines was available. Five archaeological periods, Yayoi, Kofun, Kamakura, Edo, and Modern, were utilized (n = 297). Statistically significant differences in sex and archaeological age were found. Although archaeological age differences were present, they were small and random in character, suggesting stability in the CB structures. In contrast, sexually dimorphic differences were present in every group from the Yayoi to the Modern period. This presence of sexually dimorphic differences in shape was consistent with earlier studies of M. nemestrina, G. gorilla, and P. troglodytes. Consequently, it is suggested that the pattern of sexual dimorphism documented in the Japanese CB is a primate pattern with an ancient evolutionary history. The results demonstrate, both visually and numerically, that CSA is a powerful approach for describing both global and localized features of craniofacial structures such as the CB.

Anthropology, Physical↗