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PubMed2004

Wavelet-based neural network analysis of ophthalmic artery Doppler signals.

In this study, ophthalmic artery Doppler signals were recorded from 115 subjects, 52 of whom had ophthalmic artery stenosis while the rest were healthy controls. Results were classified using a wavelet-based neural network. The wavelet-based neural network model, employing the multilayer perceptron, was used for analysis of ophthalmic artery Doppler signals. A multilayer perceptron neural network (MLPNN) trained with the Levenberg-Marquardt algorithm was used to detect stenosis in ophthalmic arteries. In order to determine the MLPNN inputs, spectral analysis of ophthalmic artery Doppler signals was performed using wavelet transform. The MLPNN was trained, cross validated, and tested with training, cross validation, and testing sets, respectively. All data sets were obtained from ophthalmic arteries of healthy subjects and subjects suffering from ophthalmic artery stenosis. The correct classification rate was 97.22% for healthy subjects, and 96.77% for subjects having ophthalmic artery stenosis. The classification results showed that the MLPNN trained with the Levenberg-Marquardt algorithm was effective to detect ophthalmic artery stenosis.

Adult↗

PubMed1997

Wavelet image extension for analysis and classification of infarcted myocardial tissue.

Some computer applications for tissue characterization in medicine and biology, such as analysis of the myocardium or cancer recognition, operate with tissue 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 yet be reliable in order to estimate texture quality from the small number of image points available. In order to describe the quality of infarcted myocardial tissue, we propose a new wavelet-based approach for analysis and classification of texture samples with small dimensions. The main idea of this method is to decompose the given image with a filter bank derived from an orthonormal wavelet basis and to form an image approximation with higher resolution. Texture energy measures calculated at each output of the filter bank as well as energies of synthesized images are used as texture features in a classification procedure. We propose an unsupervised classification technique based on a modified statistical t-test. The method is tested with clinical data, and the classification results obtained are very promising. The performance of the new method is compared with the performance of several other transform-based methods. The new algorithm has advantages in classification of small and noisy input samples, and it represents a step toward structural analysis of weak textures.

Algorithms↗

PubMed2001

On the wavelet formalism for multifractal analysis.

It is proved that the multifractal characterizations of diametrically regular measures that are provided by the wavelet and by the Hentschel-Procaccia formalisms are identical. (c) 2001 American Institute of Physics.

Journal Article↗

PubMed2004

Continuous wavelet transformation applied to the simultaneous quantitative analysis of two-component mixtures.

In this paper we developed a graphical method based on Haar (HA) and Mexican (MEX) one-dimensional continuous wavelet transforms and we applied it to a mixture of hydrochlorothiazide (HCT) and spironolactone (SP) in the presence of strongly overlapping signals. Keeping in mind to obtain an appropriately transformed spectrum, we tested several values of the scaling parameter a and the point number of the analysed spectrum in the concentration range of 2-22 microg/ml for both active compounds. The optimal values of the scale parameters and the corresponding frequencies were found to be a = 32 and 0.031 for HA and a = 30 and 0.008 for MEX corresponding to 400 points. HA and MEX methods based on a zero crossing technique were applied to the analysed signal and their regression lines at the selected points were obtained. The validation of the above methods was carried out by analysing different synthetic mixtures containing HCT and SP. MATLAB 6.5 software was used for one-dimensional wavelet analysis and the basic concepts about wavelet method were briefly explained. The method developed in this paper is rapid, easy to apply, inexpensive and is suitable for analysing the overlapping signals of compounds in their mixtures without any chemical pre-treatment.

Algorithms↗

PubMed2005

Investigating couples' sleep: an evaluation of actigraphic analysis techniques.

'Blip' analysis, fast wavelet transformations (FWT) and correlation analysis have all been used to actigraphically assess the impact one person is having on another's sleep, yet no review exists as to the differences between, and applicability of, these methods for investigating couples' sleep. Using actigraphy data and audio sleep diaries collected from 18 couples, this paper provides such a review. This paper constructs and assesses two novel, analytical methods: Lotjonen's sleep/wake algorithm, and the partner impact on sleep wake analysis (PISWA). Both 'blip' analysis and correlation suggest that the strongest relationship between bed partners occurs on an epoch-to-epoch basis. However, 'blips' deal strictly with onset of movement and fail to incorporate strength and duration of movement. Conversely, correlation analysis incorporates some elements of strength and duration of movement but makes identification of onset problematic. FWT offer useful 'relativistic' pattern recognition, identifying onset, strength and duration of movement, but are difficult to quantify. Although audio diary data support the potential of Lotjonen's sleep/wake algorithm to identify sleep non-movement, sleep movement, wake non-movement (or quiet wakefulness) and wake movement, the problem remains that this method also relies on visualization. Of most promise, we argue, is the PISWA, which examines 'impact' of bed partners through incorporating elements of 'blip' analysis and the sleep/wake algorithm.

Adult↗

PubMed2006

Wavelet-based neural network analysis of internal carotid arterial Doppler signals.

In this study, internal carotid arterial Doppler signals recorded from 130 subjects, where 45 of them suffered from internal carotid artery stenosis, 44 of them suffered from internal carotid artery occlusion and the rest of them were healthy subjects, were classified using wavelet-based neural network. Wavelet-based neural network model, employing the multilayer perceptron, was used for analysis of the internal carotid arterial Doppler signals. Multi-layer perceptron neural network (MLPNN) trained with the Levenberg-Marquardt algorithm was used to detect stenosis and occlusion in internal carotid arteries. In order to determine the MLPNN inputs, spectral analysis of the internal carotid arterial Doppler signals was performed using wavelet transform (WT). The MLPNN was trained, cross validated, and tested with training, cross validation, and testing sets, respectively. All these data sets were obtained from internal carotid arteries of healthy subjects, subjects suffering from internal carotid artery stenosis and occlusion. The correct classification rate was 96% for healthy subjects, 96.15% for subjects having internal carotid artery stenosis and 96.30% for subjects having internal carotid artery occlusion. The classification results showed that the MLPNN trained with the Levenberg-Marquardt algorithm was effective to detect internal carotid artery stenosis and occlusion.

Adult↗

PubMed2006

Bileaflet mechanical valve sound analysis using a continuous wavelet transform.

Bileaflet mechanical valve closing sounds have splits, the duration of which is not constant in normally functioning valves. However, no reports have discussed the influences of valve malfunction on the split interval, neither have any studies discussed the fact that mechanical valve closing sound signals must be analyzed using a time-frequency analysis because they are nonstationary signals. The continuous wavelet transform (CWT), a time-frequency analyzing method using mother wavelets modified by scale numbers, was selected in this study for analyzing bileaflet valve closing sounds because it is easy to understand and has no limitations such as the cross-terms in the Wigner-Ville distribution or the tradeoff between time and frequency resolutions of the short-time Fourier transform. This study compares the properties of the mother wavelets of various CWTs and selects one that is suitable for detection of the clear split in bileaflet mechanical valve closing sound signals. This article also establishes a standard frequency analyzing system for bileaflet mechanical valve sounds. A preliminary study with chirp Doppler signals for comparing the frequency properties of the mother wavelets of various CWTs suggested that Ishikawa's modified Morlet CWT has better time and frequency resolution at the highest frequency scale. Morlet/power CWT analysis of normal in vivo bileaflet valve closing sounds of the ST. Jude Medical (SJM), ATS, and Carbomedics (CM) valves demonstrated clear splits of very short interval at the highest level of frequency. Detection of the disappearance of the split by using this analytical method may be the key to identifying bileaflet mechanical valve malfunction in outpatient departments.

Aortic Valve↗

PubMedDate not supplied

Quantitative assessment of the use of continuous wavelet transform in the analysis of the fundamental frequency disturbance of the synthetic voice.

The aim of this work is to investigate quantitatively the capability of the Continuous Wavelet Transform (CWT) as a tool to estimate (calculate) Jitter and Shimmer, assessing the error between these indices calculated in each Wavelet decomposition and the ones for the original signal, for several dilatation levels. Two synthetic vowels /a/ were generated with the fundamental frequencies of 120 Hz for male and 220 Hz for female, by an autoregressive 22 coefficient all-pole model, and Jitter and Shimmer were introduced to the signal using five different percentage variations. The signals were decomposed by CWT in eight levels of dilatation (1, 2, 4, 8, 16, 32, 64 and 128), using the Mexican Hat, Meyer and Morlet real bases. Jitter and Shimmer were calculated for the original signals and all eight levels of decompositions and then the errors between the indices in the decompositions and the original signals were calculated. It can be concluded that CWT can be used as a tool for pre-processing the signal to measure Shimmer preferentially, and Jitter, instead of using the original signal to do that. The Mexican Hat base provided the lowest errors for Shimmer analysis, where the best dilatation level was 8 (error below 0.1%). In addition, the errors associated with Shimmer index, in general, are lower than the ones associated with Jitter index.

Algorithms↗

PubMed1998

Automated spectral analysis II: application of wavelet shrinkage for characterization of non-parameterized signals.

An iterative method for differentiating between known resonances and uncharacterized baseline contributions in MR spectra is described. The method alternates parametric modeling, using a priori knowledge of spectral parameters, with non-parametric characterization of remaining signal components, using wavelet shrinkage and denoising. Rapid convergence of the iterative method is demonstrated, and examples are shown for analysis of simulated data and an in vivo 1H spectrum from the brain. Results show good separation between metabolite signals and strong baseline contributions.

Algorithms↗

PubMed2002

Comparison of wavelet transform and FFT methods in the analysis of EEG signals.

In this study, whether the wavelet transform method is better for spectral analysis of the brain signals is investigated. For this purpose, as a spectral analysis tool, wavelet transform is compared with fast Fourier transform (FFT) applied to the electroencephalograms (EEG), which have been used in the previous studies. In addition, the time-domain characteristics of the wavelet transform are also detected. The comparison results show that the wavelet transform method is better in detecting brain diseases.

Brain Diseases↗

PubMed2005

[Near infrared spectra (NIR) analysis of octane number by wavelet denoising-derivative method].

Derivative can correct baseline effects and also increase the level of noise. Wavelet transform has been proven an efficient tool for de-noising. This paper is directed to the application of wavelet transfer and derivative in the NIR analysis of octane number (RON). The derivative parameters, as well as their effects on the noise level and analytic accuracy of RON, have been studied in detail. The results show that derivative can correct the baseline effects and increase the analytic accuracy. Noise from the derivative spectra has great detriment to the analysis of RON. De-noising of wavelet transform can increase the S/N and improve the analytical accuracy.

Algorithms↗

PubMedDate not supplied

Frequent sampling reveals dynamic responses by the transcriptome to routine media replacement in HepG2 cells.

Cultured cell lines are employed extensively for biological research. Large-scale differential gene expression (LSDGE) is being used to study mechanisms of toxicity in such cultures. 'Normal' gene expression dynamics could have a major impact on the design and interpretation of these studies. In order to provide understanding of such dynamics, we investigated LSDGE responses to media replacement in human hepatoblastoma cells (HepG2) using 5-minute sampling frequencies for 6 hours post routine media replacement. Each mRNA transcript was found to exhibit a characteristic 'operating range' based on signal intensity. Following media replacement, which replenishes nutrients (eg, glucose and glutamate) and removes excretory products (eg, lactate), a complex set of gene expression changes was observed. Some transcripts appeared to switch on from a quiescent state to a very active one (eg, CYP1A1), others exhibited 'clocklike' oscillations (eg, asparagine synthetase), or a synchronous burst (chirp) of expression up regulation (eg, timeless). Mathematical analysis (Fourier Transform, Singular Value Decomposition, Wavelets, Phase Analysis) of oscillating expression patterns identified cycle lengths ranging from 11.8 to 210 minutes. There were prominent 36.5- and 17.4-minute cycles, for subsets of genes, and transcript-specific differences in phase angle with respect to these cycles. The functional consequences of these novel observations remain to be determined. It is clear that dense time-course studies provide a valuable approach to the investigation of physiological responses to nutrients, toxicants, and other environmental variables. This research also highlights the need for an understanding of biological dynamics when using cell culture systems. An Excel data file representing individual transcripts from the respective Clontech cDNA arrays referred to in this article is available at http://taylorandfrancis.metapress.com/openurl.asp?genre=journal&issn=0192-6233. Rows represent data for individual transcripts and columns represent the time-points from 0 to 360 minutes. To access this file, click on the issue link for 31(4), then select this article. In order to access the full article online, you must either have an individual subscription or a member subscription accessed through www.toxpath.org.

Cell Culture Techniques↗

PubMed2004

Autonomic nervous system response to epidural analgesia in laboring patients by wavelet transform of heart rate and blood pressure variability.

BACKGROUND: Epidurals are effective in relieving labor pain but result in a sympathectomy that may compromise maternal hemodynamic stability and fetal perfusion. Decreases in blood pressure and heart rate can be corrected, but markers of autonomic activity would be useful to predict and prevent such changes. The goal of this study was to find markers describing the changes in autonomic nervous system activity with epidural anesthesia in laboring patients. METHODS: The authors analyzed heart rate variability and blood pressure variability in 13 laboring patients using wavelet transform, a time-frequency analysis that accommodates rapid changes in autonomic activity. Heart rate and blood pressure variability were obtained 5 min before and 10 min after injection of 20 ml bupivacaine, 0.125%, and 50 microg fentanyl in the epidural space. RESULTS: Blood pressure and heart rate were not affected by epidural analgesia. However, high-frequency power of heart rate variability increased after epidural (increase in parasympathetic drive). The ratio of low-frequency:high-frequency power of heart rate variability decreased. High- and low-frequency power of blood pressure variability decreased (decrease in sympathetic outflow). CONCLUSIONS: Indices of parasympathetic and sympathetic activity after neuraxial blockade in laboring patients can be obtained by analysis of both heart rate variability and blood pressure variability. The analysis by wavelet transform can discern changes in autonomic activity when values of blood pressure and heart rate do not vary significantly. Whether this technique could be used to predict and prevent hemodynamic compromise after neuraxial blockade merits further studies.

Adult↗

PubMed1999

Single-sweep analysis of event-related potentials by wavelet networks--methodological basis and clinical application.

OBJECTIVE: Trial-to-trial variabilities in event-related potentials (ERP's), which are neglected by investigating averaged ERP's, can be important to establish group-specific effects in clinical studies. Single ERP responses have to be analyzed to quantify these variations. In order to overcome the disadvantages of existing single-sweep estimators, we have developed a new procedure based on wavelet networks (WN's) and applied this novel approach in a study concerning attention deficit hyperactivity disorder (ADHD) in children. METHOD: WN's represent signals as a linear combination of wavelet nodes, i.e., components characterized by time-frequency features related to the wavelet transformation. In single-sweep analysis, each wavelet node is restricted to a specific region of the time-frequency plane during the recursive WN training process. This is achieved by means of tapering and bandpass filtering with Gaussian functions which are automatically adapted and closely related to the Morlet basis wavelet. The time course of a single event-related response can be reliably estimated. Furthermore, the WN method automatically provides well-defined parameters for single event-related responses, respectively ERP trial-to-trial variabilities. RESULTS: In a psychophysiological study on ADHD using auditory evoked potentials (AEP's), latency and amplitude parameters extracted from averaged ERP's did not reveal any significant differences between 25 control and 25 ADHD boys. In contrast, interesting group-specific differences could be established by WN single-sweep analysis. CONCLUSION: WN single-sweep analysis can be recommended as a sensitive tool for clinical ERP studies which should be applied in addition to the investigation of averaged responses. INDEX TERMS: Attention deficit hyperactivity disorder (ADHD), event-related potentials, single-sweep estimation, single-sweep parameterization, time-frequency method, wavelet networks.

Adolescent↗

PubMed2003

Wavelet-packet-based texture analysis for differentiation between benign and malignant liver tumours in ultrasound images.

The purpose of this study was to apply a novel method of multiscale echo texture analysis for distinguishing benign (hemangiomas) from malignant (hepatocellular carcinomas (HCCs) and metastases) focal liver lesions in B-mode ultrasound images. In this method, regions of interest (ROIs) extracted from within the lesions were decomposed into subimages by wavelet packets. Multiscale texture features that quantify homogeneity of the echogenicity were calculated from these subimages and were combined by an artificial neural network (ANN). A subset of the multiscale features was selected that yielded the highest performance in the classification of lesions measured by the area under the receiver operating characteristic curve (Az). In an analysis of 193 ROIs consisting of 50 hemangiomas, 87 hepatocellular carcinomas and 56 metastases, the multiscale features yielded a high A: value of 0.92 in distinguishing benign from malignant lesions, 0.93 in distinguishing hemangiomas from HCCs and 0.94 in distinguishing hemangiomas from metastases. Our new multiscale texture analysis method can effectively differentiate malignant from benign lesions, and thus has the potential to increase the accuracy of diagnosis of focal liver lesions in ultrasound images.

Carcinoma, Hepatocellular↗

PubMed2006

Nonlinear analysis of wheezes using wavelet bicoherence.

Wheezes, as being abnormal breath sounds, are observed in patients with obstructive pulmonary diseases, such as asthma. The aim of this study was to capture and analyze the nonlinear characteristics of asthmatic wheezes, reflected in the quadrature phase coupling of their harmonics, as they evolve over time within the breathing cycle. To achieve this, the continuous wavelet transform (CWT) was combined with third-order statistics/spectra. Wheezes from patients with diagnosed asthma were drawn from a lung sound database and analyzed in the time-bi-frequency domain. The analysis results justified the efficient performance of this combinatory approach to reveal and quantify the evolution of the nonlinearities of wheezes with time.

Algorithms↗

PubMed2006

Wavelet transforms in the analysis of mechanical heart valve cavitation.

Cavitation is known to cause blood element damage and may introduce gaseous emboli into the cerebral circulation, increasing the patient's risk of stroke. Discovering methods to reduce the intensity of cavitation induced by mechanical heart valves (MHVs) has long been an area of interest. A novel approach for analyzing MHV cavitation is presented. A wavelet denoising method is explored because currently used analytical techniques fail to suitably unmask the cavitation signal from other valve closing sounds and noise detected with a hydrophone. Wavelet functions are used to denoise the cavitation signal during MHV closure and rebound. The wavelet technique is applied to the signal produced by closure of a 29-mm Medtronic-Hall MHV in degassed water with a gas content of 5 ppm. Valve closing dynamics are investigated under loading conditions of 500, 2500, and 4500 mm Hg/s. The results display a marked improvement in the quantity and quality of information that can be extracted from acoustic cavitation signals using the wavelet technique compared to conventional analytical techniques. Time and frequency data indicate the likelihood and characteristics of cavitation formation under specified conditions. Using this wavelet technique we observe an improved signal-to-noise ratio, an enhanced time-dependent aspect, and the potential to minimize valve closing sounds, which disguise individual cavitation events. The overall goal of this work is to eventually link specific valves with characteristic waveforms or distinct types of cavitation, thus promoting improved valve designs.

Algorithms↗

PubMed2000

Attenuation of acoustic emission body waves in acrylic bone cement and synthetic bone using wavelet time-scale analysis.

This article addresses acoustic emission (AE) wave attenuation in acrylic bone cement and wave attenuation due to the existence of material interface between acrylic bone cement and sawbone. In this article, a series of tests were performed using standard pencil lead break sources. The attenuation features inside acrylic bone cement and sawbone materials and the attenuation due to the existence of material interfaces such as cement-cement and cement-sawbone were studied. In addition, the discrete wavelet-based signal energy decomposition method was introduced to examine detail breakdown of signal energy distribution of attenuation. From the tests, it was observed that the attenuation of AE signal was linear with respect to the wave travel distance for both energy and amplitude. Furthermore, AE energy attenuation was more sensitive than AE amplitude attenuation. In the analysis of attenuation due to material interfaces, the couplant plays a major role in reducing the attenuation at the interfaces. The attenuation at an interface composed by different material is less compared with an interface composed by the same material.

Acoustics↗