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PubMed2005

Effects and wavelet spectral entropy analysis of rhubarb extracts rhein on synaptic transmission in rat hippocampal ca1 area in vitro.

BACKGROUND: 5-dihydroxyanthraquinone-2-carboxylic acid (rhein) inhibits oxidoreduction induced by reducing nicotingamide adenine dinucleotide in the mitochondria and reducing reactive oxygen species, it also suppresses lipid peroxidation in rat brain homogenates. This study was to assess the effects of anthraquinone derivatives, rhein on synaptic transmission in the rat hippocampal CA1 pyramidal cell layer by intracellular recording. METHODS: The excitatory postsynaptic potential (EPSP) evoked by stimulation of the Schaffer collaterals in the presence of bicuculline (15 micromol/L) was depressed by application of rhein (0.3 - 30 micromol/L). The amplitude of the EPSP was restored within 20 minutes after removal of rhein from the supernatant. At a concentration of 30 micromol/L, rhein reduced the amplitude of the EPSP to 42% +/- 3.7% (n = 24) of the control. Subsequently, wavelet spectral entropy was used to analyze the EPSP. RESULTS: A strong positive correlation was observed between the wavelet spectral entropy and other parameters such as amplitude, slope of rising phase and slope of descending phase of the EPSP. The paired-pulse facilitation (PPF) of the EPSP was significantly increased by rhein (30 micromol/L). The inhibitory postsynaptic potential (IPSP) recorded in the presence of CNQX (20 micromol/L) and APV (40 micromol/L) is not altered by rhein (30 micromol/L). CONCLUSIONS: Rhein (30 micromol/L) can decrease the frequency but not the amplitude of the miniature EPSP (mEPSP). It is suggested that rhein inhibits excitatory synaptic transmission by decreasing the release of glutamate in rat hippocampal CA1 pyramidal neurons.

Animals↗
PubMed2004

Neurons as sensors: individual and cascaded chemical sensing.

A single neuron sensor has been developed based on the interaction of gradient electric fields and the cell membrane. Single neurons are rapidly positioned over individual microelectrodes using positive dielectrophoretic traps. This enables the continuous extracellular electrophysiological measurements from individual neurons. The sensor developed using this technique provides the first experimental method for determining single cell sensitivity; the speed of response and the associated physiological changes to a broad spectrum of chemical agents. Binding of specific chemical agents to a specific combination of receptors induces changes to the extracellular membrane potential of a single neuron, which can be translated into unique "signature patterns" (SP), which function as identification tags. Signature patterns are derived using Fast Fourier Transformation (FFT) analysis and Wavelet Transformation (WT) analysis of the modified extracellular action potential. The validity and the sensitivity of the system are demonstrated for a variety of chemical agents ranging from behavior altering chemicals (ethanol), environmentally hazardous agents (hydrogen peroxide, EDTA) to physiologically harmful agents (pyrethroids) at pico- and femto-molar concentrations. The ability of a single neuron to selectively identify specific chemical agents when injected in a serial manner is demonstrated in "cascaded sensing".

Action Potentials↗
PubMed2006

Performance analysis of different wavelet feature vectors in quantification of oral precancerous condition.

This paper presents an automatic method for classification of progressive stages of oral precancerous conditions like oral submucous fibrosis (OSF). The classifier used is a three-layered feed-forward neural network and the feature vector, is formed by calculating the wavelet coefficients. Four wavelet decomposition functions, namely GABOR, HAAR, DB2 and DB4 have been used to extract the feature vector set and their performance has been compared. The samples used are transmission electron microscopic (TEM) images of collagen fibers from oral subepithelial region of normal and OSF patients. The trained network could classify normal fibers from less advanced and advanced stages of OSF successfully.

Humans↗
PubMed2000

Echocardiographic texture analysis using the wavelet transform: differentiation of early heart muscle disease.

Echocardiographic quantitation of myocardial texture for diagnosis of early cardiomyopathy (CMP) remains problematic. Conventional statistical methods are limited, contributed by a small image region-of-interest (ROI) and difficulty in discrimination from noise. This study was performed to evaluate the 2-D Haar wavelet decomposition method as a tool to identify textural changes in a rat model of early CMP, focusing on changes that occur before development of M-mode structural abnormalities. Early diabetic CMP, ethanol CMP and diabetic-ethanol CMP rat models were evaluated. Echocardiography was performed on two groups of rats. Group I (5 week cohort, n = 4 per subgroup) included controls, rats on 12% ethanol, a diabetic subgroup, and diabetic rats on 4% ethanol. Group II (10 week cohort, n = 5 per subgroup) included the same categories as group I with an additional subgroup taking 4% ethanol was also studied. M-mode left ventricular measurements were comparable in all subgroups of group I. However, diabetic rats in group II had an increased left ventricular dimension (LVD) compared to all others and an increased septal dimension (IVSD) and posterior wall dimension (PWD) were noted in the 4% and 12% ethanol groups. End-diastolic digital images of all rats in the parasternal short axis view, at the papillary muscle level, were downloaded to a computer. A 16 x 16 (ROI) was selected from the anterior interventricular septum. Although standard statistical methods could not differentiate any of the groups, calculation of textural energy and normalized textural energy with the 2-D Haar wavelet decomposition method found at 5 weeks increased normalized texture energy in diabetics compared to all others. At 10 weeks increased texture energy was noted in diabetics. Diabetic-ethanol rats at both 5 and 10 weeks revealed a blunted textural energy compared to diabetic rats. In a rat model of diabetic cardiomyopathy, the 2-D wavelet decomposition method identified textural energy changes before development of echocardiographic structural changes. Ethanol-associated blunting of textural changes in diabetic rats was also noted. This method for quantitation of ventricular texture may be relevant for diagnosis of early cardiomyopathy.

Animals↗
PubMed2001

Experimental and theoretical analysis of wavelet-based denoising filter for echocardiographic images.

One of the most significant features of diagnostic echocardiographic images is to reduce speckle noise and make better image quality. In this paper we proposed a simple and effective filter design for image denoising and contrast enhancement based on multiscale wavelet denoising method. Wavelet threshold algorithms replace wavelet coefficients with small magnitude by zero and keep or shrink the other coefficients. This is basically a local procedure, since wavelet coefficients characterize the local regularity of a function. After we estimate distribution of noise within echocardiographic image, then apply to fitness Wavelet threshold algorithm. A common way of the estimating the speckle noise level in coherent imaging is to calculate the mean-to-standard-deviation ratio of the pixel intensity, often termed the Equivalent Number of Looks(ENL), over a uniform image area. Unfortunately, we found this measure not very robust mainly because of the difficulty to identify a uniform area in a real image. For this reason, we will only use here the S/MSE ratio and which corresponds to the standard SNR in case of additivie noise. We have simulated some echocardiographic images by specialized hardware for real-time application;processing of a 512*512 images takes about 1 min. Our experiments show that the optimal threshold level depends on the spectral content of the image. High spectral content tends to over-estimate the noise standard deviation estimation performed at the finest level of the DWT. As a result, a lower threshold parameter is required to get the optimal S/MSE. The standard WCS theory predicts a threshold that depends on the number of signal samples only.

Algorithms↗
PubMed2005

Analysis of scanning probe microscope images using wavelets.

The utility of wavelet transforms for analysis of scanning probe images is investigated. Simulated scanning probe images are analyzed using wavelet transforms and compared to a parallel analysis using more conventional Fourier transform techniques. The wavelet method introduced in this paper is particularly useful as an image recognition algorithm to enhance nanoscale objects of a specific scale that may be present in scanning probe images. In its present form, the applied wavelet is optimal for detecting objects with rotational symmetry. The wavelet scheme is applied to the analysis of scanning probe data to better illustrate the advantages that this new analysis tool offers. The wavelet algorithm developed for analysis of scanning probe microscope (SPM) images has been incorporated into the WSxM software which is a versatile freeware SPM analysis package.

Algorithms↗
PubMed2003

Data reduction using a discrete wavelet transform in discriminant analysis of very high dimensionality data.

We present a method of data reduction using a wavelet transform in discriminant analysis when the number of variables is much greater than the number of observations. The method is illustrated with a prostate cancer study, where the sample size is 248, and the number of variables is 48,538 (generated using the ProteinChip technology). Using a discrete wavelet transform, the 48,538 data points are represented by 1271 wavelet coefficients. Information criteria identified 11 of the 1271 wavelet coefficients with the highest discriminatory power. The linear classifier with the 11 wavelet coefficients detected prostate cancer in a separate test set with a sensitivity of 97% and specificity of 100%.

Data Compression↗
PubMed1997

A DSP based real time system for analysis of bundle of His and late potentials using wavelet transforms.

A new system for analysis of Bundle of HIS and Late Potentials has been developed using the wavelet approach. The objective of the present research work is to develop a real-time system which does not rely on averaged data and has the capability to detect beat to beat variations in the cardiac micro-volt signals from the body surface recordings. Multiresolution wavelet analysis gives better time and frequency resolution of the signal and its implementation on DSP hardware makes the system real time. The clinical applicability of the system developed is currently being investigated with initial success on pre clinical data.

Bundle of His↗
PubMed2000

Analysis of ground reaction forces by means of wavelet transform.

OBJECTIVE: To identify details of clinical relevance in ground reaction forces by means of wavelet transform. DESIGN: A retrospective analysis of gait tests performed by total knee replacement patients and a control population has been performed. METHODS: The ground reaction forces have been processed by means of wavelet transform. Results of the wavelet analysis are represented, in a time-frequency plane, by tiles. These are coloured in different grey levels associated to the values of a suitable energy function of the expansion coefficients, resulting from the wavelet transform. RESULTS: The high frequency tiles revealed the presence of irregularities with clinical significance in the first part of the stance phase. These transients in ground reaction forces are described in a quantitative manner allowing to monitor their evolution during the patient observation time. The heel strike transient has been identified by the tile representation of the vertical component of ground reaction forces and confirmed by its correlation with corresponding irregularities in the other components. CONCLUSIONS: The tile representation allows to detect and to quantify details not easily perceivable by the examiner through traditional techniques. The wavelet transform seems particularly appealing for clinical applications such as outcome assessment or treatment evaluation and can assist in the definition of normative models of ground reaction forces. The effectiveness of the procedure suggest to try to automate it. RELEVANCE: The tile representation allows to identify and to keep the records of ground reaction forces clinically significant details, such as heel strike transient.

Arthroplasty, Replacement, Knee↗
PubMed2001

Landau-Kleffner Syndrome: Localization of Epileptogenic Lesion Using Wavelet- Cross-Correlation Analysis.

Magnetoencephalographic findings in a 6-year-old patient suffering from acquired aphasia with convulsive disorder (Landau-Kleffner Syndrome, LKS) are presented. The data were analyzed using wavelet-cross-correlation analysis, a nonstationary analysis method developed to analyze the localization of an epileptogenic lesion and the propagation of epileptiform discharges. The results indicate that LKS might be a disorder of the primary temporal cortex, and that the auditory neural network may function as the circuit for the epileptic discharge propagation.

Journal Article↗
PubMed2000

Automatic on-line electronic portal image analysis with a wavelet-based edge detector.

A fully automatic method for on-line electronic portal image analysis is proposed. The method uses multiscale edge detection with wavelets for both the field outline and the anatomical structures. An algorithm to extract and combine the information from different scales has been developed. The edges from the portal image are aligned with the edges from the reference image using chamfer matching. The reference is the first portal image of each treatment. The matching is applied first to the field and subsequently to the anatomy. The setup deviations are quantified as the displacement of the anatomical structures relative to the radiation beam boundaries. The performance of the algorithm was investigated for portal images with different contrast and noise level. The automatic analysis was used first to detect simulated displacements. Then the automatic procedure was tested on anterior-posterior and lateral portal images of a pelvic phantom. In both sets of tests the differences between the measured and the actual shifts were used to quantify the performance. Finally we applied the automatic procedure to clinical images of pelvic and lung regions. The output of the procedure was compared with the results of a manual match performed by a trained operator. The errors for the phantom tests were small: average standard deviation of 0.39 mm and 0.26 degrees and absolute mean error of 0.31 mm and 0.2 degrees were obtained. In the clinical cases average standard deviations of 1.32 mm and 0.6 degrees were found. The average absolute mean errors were 1.09 mm and 0.39 degrees. Failures were registered in 2% of the phantom tests and in 3% of the clinical cases. The algorithm execution is approximately 5 s on a 168 MHz Sun Ultra 2 workstation. The automatic analysis tool is considered to be a very useful tool for on-line setup corrections.

Automation↗
PubMed2001

Wavelet Transform in the analysis of the frequency composition of evoked potentials.

This technical paper deals with the application of the Wavelet Transform to the study of evoked potentials. In particular, Wavelet Transform gives an optimal time-dependent frequency decomposition of the evoked responses, something difficult to be achieved with previous methods such as the Fourier Transform. We describe in detail the protocol for implementing the decomposition based on the Wavelet Transform and apply it to two different types of evoked potentials. In the first case we study alpha responses in pattern visual evoked potentials and in the second case, we study gamma responses to bimodal (auditory and visual) stimulation. Although in this study we focus on methodological issues, we briefly discuss physiological implications of the present time-frequency analysis. Furthermore, we show examples of the better performance of the wavelet decomposition in comparison with Fourier-based methods.

Alpha Rhythm↗
PubMed2003

Multiresolution fMRI activation detection using translation invariant wavelet transform and statistical analysis based on resampling.

A new method is proposed for activation detection in event-related functional magnetic resonance imaging (fMRI). The method is based on the analysis of selected resolution levels (a subspace) in translation invariant wavelet transform (TIWT) domain. Using a priori knowledge about the activation signal and trends, we analyze their power in different resolution levels in TIWT domain and select an optimal set of resolution levels. A randomization-based statistical test is then applied in the wavelet domain for activation detection. This approach suppresses the effects of trends and enhances the detection sensitivity. In addition, since TIWT is insensitive to signal translations, the power analysis is robust with respect to signal shifts. The randomization test alleviates the need for assumptions about fMRI noise. The method has been applied to simulated and experimental fMRI datasets. Comparisons have been made between the results of the proposed method, a similar method in the time domain and the cross-correlation method. The proposed method has shown superior sensitivity compared to the other methods.

Algorithms↗
PubMed2004

Effect of time-dependent basis functions and their superposition error on atom-centered density matrix propagation (ADMP): connections to wavelet theory of multiresolution analysis.

We present a rigorous analysis of the primitive Gaussian basis sets used in the electronic structure theory. This leads to fundamental connections between Gaussian basis functions and the wavelet theory of multiresolution analysis. We also obtain a general description of basis set superposition error which holds for all localized, orthogonal or nonorthogonal, basis functions. The standard counterpoise correction of quantum chemistry is seen to arise as a special case of this treatment. Computational study of the weakly bound water dimer illustrates that basis set superposition error is much less for basis functions beyond the 6-31+G(*) level of Gaussians when structure, energetics, frequencies, and radial distribution functions are to be calculated. This result will be invaluable in the use of atom-centered Gaussian functions for ab initio molecular dynamics studies using Born-Oppenheimer and atom-centered density matrix propagation.

Journal Article↗
PubMed2006

Wavelet time-frequency analysis and least squares support vector machines for the identification of voice disorders.

This work describes a novel algorithm to identify laryngeal pathologies, by the digital analysis of the voice. It is based on Daubechies' discrete wavelet transform (DWT-db), linear prediction coefficients (LPC), and least squares support vector machines (LS-SVM). Wavelets with different support-sizes and three LS-SVM kernels are compared. Particularly, the proposed approach, implemented with modest computer requirements, leads to an adequate larynx pathology classifier to identify nodules in vocal folds. It presents over 90% of classification accuracy and has a low order of computational complexity in relation to the speech signal's length.

Adolescent↗
PubMed2006

[Research for De-noising in the detection of chromatic visual evoked potential based on wavelet].

The signal analysis technology based on wavelet was used to detect the chromatic visual evoked potential (VEP). The method was based on wavelet transform modulus maxima reconstruction. The test signal was decomposed using the dyadic discrete time wavelet transform "Mallat" algorithm, and the modulus maxima distribution of each scale was obtained. Signal was de-noised as the Lipschitz coefficient. The POCS method was used to reconstruction the original signal. The news method proved not only good for de-noising but also for decreasing the times of test by the simulation result.

Color↗
PubMed2006

Measuring heart rate variability with wavelet thresholds and energy components in healthy subjects and patients with congestive heart failure.

BACKGROUND: Analysis of wavelet thresholds and energy components can be used to differentiate patients with congestive heart failure (CHF) from healthy subjects and to demonstrate the improvement of heart rate variability after treatment with beta-blockers in the patient group. METHODS: Nine patients with CHF without concurrent beta-blocker therapy and 15 healthy volunteers were recruited and received 24-hour Holter recording. Wavelet thresholds and energy components were calculated. The patients with CHF were then treated with atenolol for 3 months. Another Holter recording was repeated and associated wavelet parameters were estimated. RESULTS: Wavelet thresholds were statistically higher in normal subjects than those derived from patients with CHF (62.40 +/- 20.65 vs. 16.90 +/- 9.72, p < 0.001). The difference was still significant after control of heart rate and the total number of heart beats (74.85 +/- 22.68 vs. 32.51 +/- 18.73, p < 0.001). The energy component of HRV in the finest wavelet domain was also significantly higher in the normal subjects (243.01 +/- 237.69 x 10(-6) vs. 40.65 +/- 39.65 x 10(-6), p = 0.001). These two measures increased after treatment with beta-blockers in the patients with CHF, but not to the extent of those of normal subjects. CONCLUSION: Analysis of wavelet thresholds and energy components has provided robust and accurate measurements for background HRV.

Adrenergic beta-Antagonists↗
PubMed2005

A wavelet-like filter based on neuron action potentials for analysis of human scalp electroencephalographs.

This paper describes the development and testing of a wavelet-like filter, named the SNAP, created from a neural activity simulation and used, in place of a wavelet, in a wavelet transform for improving EEG wavelet analysis, intended for brain-computer interfaces. The hypothesis is that an optimal wavelet can be approximated by deriving it from underlying components of the EEG. The SNAP was compared to standard wavelets by measuring Support Vector Machine-based EEG classification accuracy when using different wavelets/filters for EEG analysis. When classifying P300 evoked potentials, the error, as a function of the wavelet/filter used, ranged from 6.92% to 11.99%, almost twofold. Classification using the SNAP was more accurate than that with any of the six standard wavelets tested. Similarly, when differentiating between preparation for left- or right-hand movements, classification using the SNAP was more accurate (10.03% error) than for four out of five of the standard wavelets (9.54% to 12.00% error) and internationally competitive (7% error) on the 2001 NIPS competition test set. Phenomena shown only in maps of discriminatory EEG activity may explain why the SNAP appears to have promise for improving EEG wavelet analysis. It represents the initial exploration of a potential family of EEG-specific wavelets.

Action Potentials↗