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 757 records · Page 42Linked to original sources
PubMed2002

Dynamics of coherent anharmonic phonons in bismuth using high density photoexcitation.

We have investigated the dynamical properties of the coherent anharmonic phonons generated in Bi under high density excitation. The time-resolved reflectivity in the intensely photoexcited Bi film is modulated by the coherent A(1g) phonon oscillation with a time-dependent oscillation period. As the pump power density is increased, the line shape of the A(1g) mode in the Fourier transformed spectra becomes asymmetric, and the redshift of the phonon frequency is observed. Analysis of the transient redshift with a wavelet transform reveals that the frequency of the A(1g) mode depends on the squared amplitude of the oscillation, which is attributed to an anharmonicity of the lattice potential.

Journal Article↗
PubMed2005

Wavelet-based enhancement of lung and bowel sounds using fractal dimension thresholding--Part I: methodology.

An efficient method for the enhancement of lung sounds (LS) and bowel sounds (BS), based on wavelet transform (WT), and fractal dimension (FD) analysis is presented in this paper. The proposed method combines multiresolution analysis with FD-based thresholding to compose a WT-FD filter, for enhanced separation of explosive LS (ELS) and BS (EBS) from the background noise. In particular, the WT-FD filter incorporates the WT-based multiresolution decomposition to initially decompose the recorded bioacoustic signal into approximation and detail space in the WT domain. Next, the FD of the derived WT coefficients is estimated within a sliding window and used to infer where the thresholding of the WT coefficients has to happen. This is achieved through a self-adjusted procedure that iteratively "peels" the estimated FD signal and isolates its peaks produced by the WT coefficients corresponding to ELS or EBS. In this way, two new signals are constructed containing the useful and the undesired WT coefficients, respectively. By applying WT-based multiresolution reconstruction to these two signals, a first version of the desired signal and the background noise is provided, accordingly. This procedure is repeated until a stopping criterion is met, finally resulting in efficient separation of the ELS or EBS from the background noise. The proposed WT-FD filter introduces an alternative way to the enhancement of bioacoustic signals, applicable to any separation problem involving nonstationary transient signals mixed with uncorrelated stationary background noise. The results from the application of the WT-FD filter to real bioacoustic data are presented and discussed in an accompanying paper.

Algorithms↗
PubMed1996

Automated detection of breast masses on mammograms using adaptive contrast enhancement and texture classification.

This paper presents segmentation and classification results of an automated algorithm for the detection of breast masses on digitized mammograms. Potential mass regions were first identified using density-weighted contrast enhancement (DWCE) segmentation applied to single-view mammograms. Once the potential mass regions had been identified, multiresolution texture features extracted from wavelet coefficients were calculated, and linear discriminant analysis (LDA) was used to classify the regions as breast masses or normal tissue. In this article the overall detection results for two independent sets of 84 mammograms used alternately for training and test were evaluated by free-response receiver operating characteristics (FROC) analysis. The test results indicate that this new algorithm produced approximately 4.4 false positive per image at a true positive detection rate of 90% and 2.3 false positives per image at a true positive rate of 80%.

Automation↗
PubMed2006

Complexity analysis of electrocardiographic signals.

Two types of electrocardiographic data series were investigated using appropriate tests based on a selection of semi-quantitative analysis algorithms. Distribution histograms, power spectra, auto-correlation functions, state-space portraits, Lyapunov exponents and wavelet transformations were applied to electrocardiograms of normal and stressed subjects. Statistical analysis using the Student's t-test revealed significant and non-significant alterations in stress-loaded cases compared to normal ones. Higher levels of adrenaline may account for a more complex dynamics (deterministic chaos) revealed in the stressed subjects.

Algorithms↗
PubMed2005

Cardiovascular variability during periodic leg movements: a spectral analysis approach.

OBJECTIVE: Changes in cardiovascular measures have been advocated as sensitive markers of phasic events arising from sleep. The current study was aimed to analyse the effects of periodic leg movements (PLMS) on heart rate variability (HRV) during NREM sleep in patients having restless legs syndrome and periodic leg movements during sleep. METHODS: The absolute and normalized high- and low-frequency peaks from spectral analysis (FFT) of R-R intervals, the HRV changes using wavelet transform, the geometric and time domain HRV were measured in 14 patients with restless legs syndrome and PLMS. The analysis was done comparing one hundred, 10 min periods with PLMS (PLMS+) and 60 periods without PLMS (PLMS-) in stage 2 of NREM sleep. In 8 patients analysis was also done in slow wave sleep (SWS). RESULTS: Occurrence of PLMS induced changes in geometrical indices of HRV, with a rise of the triangular index and the triangular interpolation of R-R intervals in PLMS+ periods (P < 0.0001). Small changes in time domain indices were found during PLMS+ periods, while the SD of the R-R intervals (SDNN), reflecting global HRV, was significantly higher (P = 0.001). While the low frequency (LF) power significantly increased in PLMS+ periods (P < 0.0001), high frequency (HF) power showed a weak and not significant reduction. The rise in sympathetic activity as detected by frequency domain HRV analysis was related to density and interval of PLMS. Comparison between sleep stages of NREM sleep demonstrated lower values of HRV measures when PLMS+ periods occur in SWS. CONCLUSIONS: Overall, PLMS occurrence was associated with a shift to increased sympathetic activity without significant changes in cardiac parasympathetic activity. The frequency domain analysis of HRV appears to be an easy tool to estimate the autonomic changes related to PLMS and PLMS- arousals and to differentiate their occurrence during stage 2 and deep sleep. SIGNIFICANCE: Spectral HRV measures may offer a simple approach to estimate the degree of autonomic changes occurring in relation to periodic leg movements in restless legs patients.

Adult↗
PubMedDate not supplied

A multiscale wavelet data treatment for reliable localization of inflection points for analytical purposes.

Instrumental analysis techniques that employ measurements based on inflection points may have their accuracy compromised due to the need for signal differentiation, which is very sensitive to instrumental noise. This paper presents a strategy for localizing inflection points that exploits the multiscale processing capability of the Wavelet Transform and avoids the need for explicit signal differentiation. The strategy is illustrated in simulated examples and also in a real analytical problem involving the determination of Pb and Cd by potentiometric stripping analysis. In this application, the results were in good agreement with the expected values and were slightly better than those obtained from the first derivative of the curves after smoothing by a Windowed Fourier Transform.

Journal Article↗
PubMed2004

Normalization of cDNA microarray data using wavelet regressions.

Normalization is an essential step in microarray data mining and analysis. For cDNA microarray data, the primary purpose of normalization is removing the intensity-dependent bias across different slides within an experimental group or between multiple groups. The locally weighted regression (lowess) procedure has been widely used for this purpose but can be comparatively time consuming when the dataset becomes relatively large. In this study, we applied wavelet regressions, a new smoothing method for recovering a regression function from data that is supposed to outperform other methods in many cases, such as spline or local polynomial fitting, to normalize two cDNA microarray datasets. Relative to the lowess procedure, we found that wavelet regressions not only produced reliable normalization results but also ran much faster. The computing speed represents one of the most important advantages over other algorithms, especially when one is interested in analyzing a large microarray experiment involving hundreds of slides.

Algorithms↗
PubMed1998

Automated breast tumor diagnosis and grading based on wavelet chromatin texture description.

In this paper, wavelets were employed for multi-scale image analysis to extract parameters for the description of chromatin texture in the cytological diagnosis and grading of invasive breast cancer. Their value was estimated by comparing the performance of co-occurrence, densitometric, and morphometric parameters in an automated K-nearest neighbor (Knn) classification scheme based on light microscopic images of isolated nuclei of paraffin-embedded tissue. This design allowed a multifaceted cytological retrospective study of which the practical value can be judged easily. Results show that wavelets perform excellently with classification scores comparable with densitometric and co-occurrence features. Moreover, because wavelets showed a high additive value with the other textural groups, this panel allowed a very profound description with higher recognition scores than previously reported (76% for individual nuclei, 100% for cases). Morphometric parameters performed less well and only slightly increased correct classification. The major drawback, besides image segmentation errors demanding operator supervision, emanated to be the few false-negative cases, which restrict the immediate practical use. However, an enlargement of the parameter set may avoid this misclassification, resulting in an applicable expert system of practical use.

Automation↗
PubMed2006

Classification of EMG signals using wavelet neural network.

An accurate and computationally efficient means of classifying electromyographic (EMG) signal patterns has been the subject of considerable research effort in recent years. Quantitative analysis of EMG signals provides an important source of information for the diagnosis of neuromuscular disorders. Following the recent development of computer-aided EMG equipment, different methodologies in the time domain and frequency domain have been followed for quantitative analysis. In this study, feedforward error backpropagation artificial neural networks (FEBANN) and wavelet neural networks (WNN) based classifiers were developed and compared in relation to their accuracy in classification of EMG signals. In these methods, we used an autoregressive (AR) model of EMG signals as an input to classification system. A total of 1200 MUPs obtained from 7 normal subjects, 7 subjects suffering from myopathy and 13 subjects suffering from neurogenic disease were analyzed. The success rate for the WNN technique was 90.7% and for the FEBANN technique 88%. The comparisons between the developed classifiers were primarily based on a number of scalar performance measures pertaining to the classification. The WNN-based classifier outperformed the FEBANN counterpart. The proposed WNN classification may support expert decisions and add weight to EMG differential diagnosis.

Adolescent↗
PubMed2004

[Simultaneous spectrophotometric determination of multicomponent mixtures by a soft thresholding wavelet-based radial basis function neural network].

A Soft Thresholding Wavelet-based Radial Basis Function Neural network (STWRBFN) method was developed to perform simultaneous quantitative analysis of multicomponent mixtures. The quality of noise removal and regression was improved by combining wavelet soft thresholding with radial basis function neural network. Through optimization, the wavelet function, wavelet decomposition level (L), thresholding method and spread parameter sigma of RBFN were selected. Two-programs, i.e. PSTWRBFN and PRBFN, were designed to perform STWRBFN and RBFN calculations. Experimental results showed the STWRBFN method to be successful and better than RBFN. Comparing with classical multivariate linear regression, both the methods were more powerful.

Algorithms↗
PubMed1997

Preprocessing of HPLC trace impurity patterns by wavelet packets for pharmaceutical fingerprinting using artificial neural networks.

The immediate objective of this research program is to evaluate several computer-based classifiers as potential tools for pharmaceutical fingerprinting based on analysis of HPLC trace organic impurity patterns. In the present study, wavelet packets (WPs) are investigated for use as a preprocessor of the chromatographic data taken from commercial samples of L-tryptophan (LT) to extract input data appropriate for classifying the samples according to manufacturer using artificial neural networks (ANNs) and the standard classifiers KNN and SIMCA. Using the Haar function, WP decompositions for levels L = 0-10 were generated for the trace impurity patterns of 253 chromatograms corresponding to LT samples that had been produced by six commercial manufacturers. Input sets of N = 20, 30, 40, and 50 inputs were constructed, each one consisting of the first N/2 WP coefficents and corresponding positions from the overall best level (L = 2). The number of hidden nodes in the ANNs was also varied to optimize performance. Optimal ANN performance based on percent correct classifications of test set data was achieved by ANN-30-30-6 (97%) and ANN-20-10-6 (94%), where the integers refer to the numbers of input, hidden, and output nodes, respectively. This performance equals or exceeds that obtained previously (Welsh, W.J.; et al.Anal.Chem. 1996, 68, 3473) using 46 inputs from a so-called Window preprocessor (93%). KNN performance with 20 inputs (97%) or 30 inputs (90%) from the WP preprocessor also exceeded that obtained from the Window preprocessor (85%), while SIMCA performance with 20 inputs (86%) or 30 inputs (82%) from the WP preprocessor was slightly inferior to that obtained from the Window preprocessor (87%). These results indicate that, at least for the ANN and KNN classifiers considered here, the WP preprocessor can yield superior performance and with fewer inputs compared to the Window preprocessor.

Chromatography, High Pressure Liquid↗
PubMedDate not supplied

Wavelet-based characterization of spectral fluctuations in normal, benign, and cancerous human breast tissues.

Fluorescence intensity fluctuations in the visible wavelength regime in normal, benign, and cancerous human breast tissue samples are studied through wavelet transform. The analyses have been carried out in unpolarized, parallel and perpendicularly polarized channels, for optimal tissue characterization. It has been observed that polarized fluorescence data, particularly the perpendicular components, differentiate various tissue types quite well. Wavelet transform, because of its ability for multiresolution analysis, provides the ideal tool to separate and characterize fluctuations in the fluorescence spectra at different scales. We quantify these differences and find that the fluctuations in the perpendicular channel of the cancerous tissues are more randomized as compared to their normal counterparts. Furthermore, for cancerous tissues, the same is very well described by the normal distribution, which is not the case for normal and benign samples. It has also been observed that, up to a certain point, fluctuations at larger scales are more sensitive to tissue types. The differences in the average, low-pass wavelet coefficients of normal, cancerous, pericanalicular, and intracanalicular benign tissues are also pointed out.

Adolescent↗
PubMed2003

A brain-computer interface (BCI) for the locked-in: comparison of different EEG classifications for the thought translation device.

OBJECTIVE: The Thought Translation Device (TTD) for brain-computer interaction was developed to enable totally paralyzed patients to communicate. Patients learn to regulate slow cortical potentials (SCPs) voluntarily with feedback training to select letters. This study reports the comparison of different methods of electroencephalographic (EEG) analysis to improve spelling accuracy with the TTD on a data set of 6,650 trials of a severely paralyzed patient. METHODS: Selections of letters occurred by exceeding a certain SCP amplitude threshold. To enhance the patient's control of an additional event-related cortical potential, a filter with two filter characteristics ('mixed filter') was developed and applied on-line. To improve performance off-line the criterion for threshold-related decisions was varied. Different types of discriminant analysis were applied to the EEG data set as well as on wavelet transformed EEG data. RESULTS: The mixed filter condition increased the patients' performance on-line compared to the SCP filter alone. A threshold, based on the ratio between required selections and rejections, resulted in a further improvement off-line. Discriminant analysis of both time-series SCP data and wavelet transformed data increased the patient's correct response rate off-line. CONCLUSIONS: It is possible to communicate with event-related potentials using the mixed filter feedback method. As wavelet transformed data cannot be fed back on-line before the end of a trial, they are applicable only if immediate feedback is not necessary for a brain-computer interface (BCI). For future BCIs, wavelet transformed data should serve for BCIs without immediate feedback. A stepwise wavelet transformation would even allow immediate feedback.

Communication Devices for People with Disabilities↗
PubMed1998

Myocardial tissue characterization after acute myocardial infarction with wavelet image decomposition: a novel approach for the detection of myocardial viability in the early postinfarction period.

BACKGROUND: Only a few texture measures can be used for texture characterization of infarcted myocardium and detection of reperfused myocardium early after infarction. This study was conducted to establish the relationship between texture properties of infarcted myocardium and infarct-related artery patency by quantitative computer analysis of 2-dimensional echocardiographic images with the wavelet-based method for texture characterization, evaluate the relationship between texture properties and myocardial viability, and correlate histopathologic changes after experimental infarction with the texture measures. METHODS AND RESULTS: We analyzed 2-dimensional transthoracic echocardiographic images in 18 patients at different time points after infarction using the wavelet transform method. Regional wall motion of infarcted segments was analyzed on a follow-up echocardiographic study obtained 6 months after infarction. To verify the accuracy of the proposed texture measure and energy difference cutoff value, we prospectively evaluated another group of 19 patients. In addition, histopathologic changes in 9 dogs with experimental infarction were correlated with the texture measures. Sensitivity, specificity, and accuracy of the wavelet method for detection of reperfusion in the study group were 73%, 86%, and 78%, respectively, on day 2; 91%, 86%, and 89%, at 1 week; and 100%, 100%, and 100% at 3 weeks. Among 9 patients with improvement in regional wall motion on a follow-up study, 7 on day 2, 8 at 1 week, and 9 at 3 weeks were classified into the reperfused group by the wavelet method. Histopathologic features associated with the classification of reperfusion by the wavelet method were infarct transmurality (P=0.024) and degree of necrosis (P=0.028). CONCLUSIONS: Our clinical and experimental data suggest that the wavelet method can be used to differentiate between viable myocardium with recovery potential and definite myocardial necrosis in the early postinfarction period.

Animals↗
PubMed2003

Discrimination of benign common nevi from malignant melanoma lesions by use of features based on spectral properties of the wavelet transform.

OBJECTIVE: To evaluate the possibilities of describing and discriminating common nevi and malignant melanoma tissue with features based on spectral properties of the Daubechies 4 wavelet transform. STUDY DESIGN: Images of common nevi and malignant melanoma were dissected in square elements. The wavelet coefficients were calculated inside the square elements. The diagonal coefficients and related power spectra were used for further analysis. The analysis results served as guide for the selection of features, including standard deviations of wavelet coefficients inside the frequency bands and the energy of the frequency bands. These features describe properties of the frequency bands, representing information on different scales. To test the usefulness of the features for discrimination, a study set of 80 cases was classified by classification and regression trees analysis. The set was divided into a training set and a test set. RESULTS: In the case of benign common nevi, the energies of the lower frequency bands and higher, whereas malignant melanoma tissue shows more variability of the coefficients in higher-frequency bands. The influence on the detail properties of the images was studied by suppression of coefficients with low values, which are concentrated mainly in higher-frequency bands. In the case of benign common nevi the main information is contained in 15% of the coefficients and in the case of malignant melanoma, in 39%. The results of classification show a clear-cut difference between the cases. The classification correctly classified 95.78% of nevi elements and 94.22% of melanoma elements in the training set and 100% of cases of benign nevi and 80% of cases of malignant melanoma in the test set. CONCLUSION: Features based on the wavelet power spectrum contain sufficient information for differentiation between common nevi and malignant melanomas.

Artificial Intelligence↗
PubMed2000

Optimal zonal wavelet-based ECG data compression for a mobile telecardiology system.

A new integrated design approach for an optimal zonal wavelet-based ECG data compression (OZWC) method for a mobile telecardiology model is presented. The hybrid implementation issues of this wavelet method with a GSM-based mobile telecardiology system are also introduced. The performance of the mobile system with compressed ECG data segments selected from the MIT-BIH arrhythmia data base is evaluated in terms of bit error rate (BER), percent rms difference (PRD), and visual clinical inspection. The compression performance analysis of the OZWC is compared with another wavelet-based (Discrete Symmetric Wavelet Compression) approach. The optimal wavelet algorithm achieved a maximum compression ratio of 18: 1 with low PRD ratios. The mobile telemedical simulation results show the successful compressed ECG transmission at speeds of 100 (km/h) with BER rates of less than 10(-15), providing a 73% reduction in total mobile transmission time with clinically acceptable reconstruction of the received signals. This approach will provide a framework for the design and functionality issues of GSM-based wireless telemedicine systems with wavelet compression techniques and their future integration for the next generation of mobile telecardiology systems.

Cardiology↗
PubMed1997

A new analysis method for the evaluation of transient evoked otoacoustic emissions.

An analysis system with the Matlab 4.2 system with a filter bank is presented that provides graphically created 2D and 3D views of responses in the frequency, time and amplitude coordinates. Smoothed spectral and noise responses are presented. The start end and maximum TEOAE amplitude latencies are presented graphically and calculated numerically. The fast Fourier transformation (FFT), filter bank and wavelet transforms were developed and compared for the TEOAE analysis purposes.

Acoustic Stimulation↗