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PubMed2004

Wavelet analysis of cutaneous blood flow in melanocytic skin lesions.

Laser Doppler flowmetry (LDF) is frequently used to study the microcirculation. Usually LDF time series are analyzed by conventional linear methods, mainly Fourier analysis. The aim of this study was to observe dynamic blood perfusion of the skin in malignant and benign melanocytic skin lesions. Wavelet transformation was performed on each LDF time series in order to calculate a vasomotion field. First, the differences in vasomotion between healthy and pigmented skin were evaluated visually on six different time scales of the vasomotion field. In order to quantify the findings, vasomotion scale variance (VSV) was calculated for each scale plane of the vasomotion field. These VSV were compared using contrast DeltaVSV to determine the difference between healthy skin and a pigmented skin lesion in the same patient. After the measurements, the skin lesions were excised and examined histologically. We found that wavelet analysis of LDF time series is a specific, sensitive method for the in vivo identification of malignant melanoma. It is a non-invasive procedure and takes minimal time to be carried out.

Humans↗
PubMed2006

[Research on a novel watershed algorithm combining with wavelet analysis].

Due to noise and irregularity of the gradient image, watershed algorithm used to segment images generally leads to over-segmentation, which is unacceptable. A novel watershed algorithm, which combines with some concepts of wavelet analysis and mathematic morphology, is proposed in this paper. The test shows that this method can solve the problem of over-segmentation and achieve the anticipative goal.

Algorithms↗
PubMed2003

Wavelet analysis for neonatal electroencephalographic seizures.

Electroencepholographs (EEGs) of neonatal seizures differ from those of children and adults. This study evaluated whether wavelet transform analysis, a nonstationary frequency analysis of EEG, can recognize and characterize neonatal seizures. Twenty-second segments were analyzed from 69 EEG seizures in 15 neonatal patients whose seizures lasted 10 seconds or longer. The wavelet transform results were examined, as were EEG seizure durations and dominant frequencies. The wavelet transform results were correlated with the occurrence, after an 18-month follow-up, of postneonatal seizures. Wavelet transform analysis identified 40 seizures (58%) with a "sustained dominant frequency component" that lasted 10 seconds or longer and 29 seizures without a sustained dominant frequency component. The mean seizure duration of the 40 seizures with sustained dominant frequency components was 63.3 seconds, longer than the mean duration (33.6 seconds) of the seizures without sustained dominant frequency components, P < 0.01. Eleven patients manifested postneonatal epileptic seizures. Fifty-two EEG seizures in these 11 patients revealed more sustained dominant frequency components (74%) than 17 seizures in the 4 patients without postneonatal seizures (only 12%), P < 0.05. Wavelet transform analysis can identify neonatal EEG seizures and characterize their epileptic components. The presence of sustained dominant frequency components may predict postneonatal epileptic seizures.

Asphyxia Neonatorum↗
PubMedDate not supplied

Fractal fingerprinting of chromatographic profiles based on wavelet analysis and its application to characterize the quality grade of medicinal herbs.

Extracting chemical fingerprints is an important step for representing and interpreting chromatographic data. In this paper, the chromatographic profile is decomposed into components at different resolution levels using wavelet analysis, then the fractal dimensions of these components are computed as the chemical fingerprints. The chromatographic fingerprint is characterized by the vector composed of these chemical fingerprints, which can represent the chemical patterns of different categories of complex samples. Computer simulations reveal that the fractal fingerprints are more stable than the original chromatographic profile data with respect to variations of peak retention time. To demonstrate the validity of this method, the evaluation of the quality of the medicinal herb Angelica sinensis (Oliv.) diels is investigated. Principal component analysis of the fractal fingerprints indicates that samples belonging to the same quality grade are clustered together, while those belonging to different quality grades are separated. Using these fractal fingerprints taken from the chromatographic scans as inputs for an artificial neural network (ANN). The quality grades of two sets of the herbs were verified by cross-validation, indicating that 96.7% of the herbs are correctly identified with respect to their quality grades evaluated by experienced experts, and 100.0% of the herbs are correctly identified with respect to their quality grades determined by pharmacodynamical evaluation.

Algorithms↗
PubMed2004

Effect of medication in Parkinson's disease: a wavelet analysis of EMG signals.

The improvements in the motor ability in patients with Parkinson's disease due to antiparkinsonian medication is well-known and widely documented. Recent results, based both on kinematic parameters and standard electromyographic (EMG) signal analysis, clearly indicated that the medication reduced, as expected, the clinical signs of Parkinson's disease, but did not restore agonist burst duration modulation with distance in elbow flexion movements. The main aim of the present work is to shed more light on this medication effect using a wavelet analysis approach on multiple EMG signals recorded both on shoulder and elbow muscles in ballistic or rapid movements. The wavelet cross-correlation information allows us to evidence some important quantitative features of the EMG signals due to medication.

Aged↗
PubMed2004

Wavelet analysis in a canine model of gastric electrical uncoupling.

Abnormal gastric motility function could be related to gastric electrical uncoupling, the lack of electrical, and respectively mechanical, synchronization in different regions of the stomach. Therefore, non-invasive detection of the onset of gastric electrical uncoupling can be important for diagnosing associated gastric motility disorders. The aim of this study is to provide a wavelet-based analysis of electrogastrograms (EGG, the cutaneous recordings of gastric electric activity), to detect gastric electric uncoupling. Eight-channel EGG recordings were acquired from 16 dogs in basal state and after each of two circular gastric myotomies. These myotomies simulated mild and severe gastric electrical uncoupling, while keeping the separated gastric sections electrophysiologically active by preserving their blood supply. After visual inspection, manually selected 10 min EGG segments were submitted to wavelet analysis. Quantitative methodology to choose an optimal wavelet was derived. This 'matching' wavelet was determined using the Pollen parametrization for 6-tap wavelet filters and error minimization criteria. After a wavelet-based compression, the distortion of the approximated EGG signals was computed. Statistical analysis on the distortion values allowed us to significantly (p < 0.05) distinguish basal state from mild and severe gastric electrical uncoupling groups in particular EGG channels.

Algorithms↗
PubMed2006

Combining wavelet analysis and Bayesian networks for the classification of auditory brainstem response.

The auditory brainstem response (ABR) has become a routine clinical tool for hearing and neurological assessment. In order to pick out the ABR from the background EEG activity that obscures it, stimulus-synchronized averaging of many repeated trials is necessary, typically requiring up to 2000 repetitions. This number of repetitions can be very difficult, time consuming and uncomfortable for some subjects. In this study, a method combining wavelet analysis and Bayesian networks is introduced to reduce the required number of repetitions, which could offer a great advantage in the clinical situation. 314 ABRs with 64 repetitions and 155 ABRs with 128 repetitions recorded from eight subjects are used here. A wavelet transform is applied to each of the ABRs, and the important features of the ABRs are extracted by thresholding and matching the wavelet coefficients. The significant wavelet coefficients that represent the extracted features of the ABRs are then used as the variables to build the Bayesian network for classification of the ABRs. In order to estimate the performance of this approach, stratified ten-fold cross-validation is used.

Algorithms↗
PubMed2002

Computerised intrapartum diagnosis of fetal hypoxia based on fetal heart rate monitoring and fetal pulse oximetry recordings utilising wavelet analysis and neural networks.

OBJECTIVE: To develop a computerised system that will assist the early diagnosis of fetal hypoxia and to investigate the relationship between the fetal heart rate variability and the fetal pulse oximetry recordings. DESIGN: Retrospective off-line analysis of cardiotocogram and FSpO2 recordings. SETTING: The Maternity Unit of the 2nd Department of Obstetrics and Gynaecology, Aretaieion Hospital, University of Athens. POPULATION: Sixty-one women of more than 37 weeks of gestation were monitored throughout labour. METHODS: Multiresolution wavelet analysis was applied in each 10-minute period of second stage of labour focussing on long term variability changes in different frequency ranges and statistical analysis was performed in the associated 10-minute FSpO2 recordings. Self-organising map neural network was used to categorise the different 10-minute fetal heart rate patterns and the associated 10-minute FSpO2 recordings. MAIN OUTCOME MEASURES: Umbilical artery pH of < or = 7.20 and Apgar score at 5 minutes of < or = 7 formed the inclusion criteria of the risk group. RESULTS: After using k-means clustering algorithm, the two-dimensional output layer of the self-organising map neural network was divided into three distinct clusters. All the cases that mapped in cluster 3 belonged in the risk group except one. The sensitivity of the system was 83.3% and the specificity 97.9% for the detection of risk group cases. CONCLUSIONS: A relationship between the fetal heart rate variability in different frequency ranges and the time in which FSpO2 is less than 30% was noticed. Fetal pulse oximetry seems to be an important additional source of information. Computerised analysis of the fetal heart rate monitoring and pulse oximetry recordings is a promising technique in objective intrapartum diagnosis of fetal hypoxia. Further evaluation of this technique is mandatory to evaluate its efficacy and reliability in interpreting fetal heart rate recordings.

Adult↗
PubMed2006

Application of dynamic phase shifting with wavelet analysis to electronic speckle contouring.

Dynamic phase shifting is a temporal phase unwrapping method, i.e., a method in which a sequence of speckle patterns is analyzed along the time axis. Each pixel can thus be considered as an independent detector, which is of particular interest for the study of complex surfaces. I report the application of this technique, which is based on a wavelet analysis, to contouring measurements with a dual-beam illumination electronic speckle pattern interferometry setup. I present a new, more general geometric model of the setup. I also investigate the possibility of enhancing the accuracy by using the intermediate phase values. Tests are performed on a simply described object and compared with coordinate measuring machine measurements.

Journal Article↗
PubMed2000

[Nonlinear dynamics, chaos theory and wavelet analysis of the heart].

Over the past years, techniques of nonlinear dynamics have gained increasing attention in cardiology. This kind of partly interdisciplinary research is supposed to provide a better understanding of cardiac disease, especially in the field of tachyarrhythmia. This article intends to introduce the principles of diagnostic procedures in terms of nonlinear dynamics, chaos theory and stochastic processes related to cardiology. An introduction to a new technique of wavelet analysis is given, and possible applications in the quantitative classification of high-risk patients for sudden cardiac death are discussed.

Arrhythmias, Cardiac↗
PubMed2002

Wavelet analysis of nonequilibrium ionic currents in human heart sodium channel (hH1a).

Nonequilibrium response spectroscopy (NRS), the technique of using rapidly fluctuating voltage pulses in the study of ion channels, is applied here. NRS is known to drive an ensemble of ion channels far from equilibrium where, it has been argued, new details of ion channel kinetics can be studied under nonequilibrium conditions. In this paper, a single-pulse NRS technique with custom-designed waveforms built from wavelets is used. The pulses are designed to produce different responses from two competing models of a human heart isoform of the sodium channel (hH1a). Experimental data using this new type of pulses are obtained through whole-cell recordings from mammalian cells (HEK 293). Wavelet analysis of the model response and the experimental data is introduced to show how these NRS pulses can aid in distinguishing the better of the two models and thus introduces another important application of this new technique.

Cell Line↗
PubMed2001

Quantifying image quality at breast periphery vs mammary gland in mammography using wavelet analysis.

Use of high contrast film-screen systems in mammography, in combination with the fact that exposure parameters are selected to ensure good visualization of the mammary gland, results in overexposure of the film area corresponding to the breast periphery, therefore decreasing image quality. The aim of this work was to provide a quantitative evaluation of image quality at the breast periphery compared with the mammary gland. To deal with the difficulties in quantification of image quality introduced by low contrast encountered at the breast periphery, wavelet analysis has been used for derivation of a contrast indicator (CI) and a noise indicator (NI), taking into account local grey level variations. Gradient magnitude coefficients corresponding to region of interest (ROI) grey level values are the basis of CI definition. Mammary gland and breast periphery were sampled by equally spaced ROIs, the quantity of which was determined by a heuristic method. For NI definition, the power values of gradient magnitude coefficients corresponding to the ROI were utilized. Image quality at the breast periphery compared with the mammary gland was evaluated using 150 craniocaudal images from the Digital Database for Screening Mammography. Measurements were carried out using a tool developed in our department. A 50% contrast decrease at the breast periphery was observed, while noise decreased by approximately 2%.

Breast Diseases↗
PubMed2002

Wavelet analysis of shoreline change on the Outer Banks of North Carolina: an example of complexity in the marine sciences.

The horizontal, shore-perpendicular change in shoreline position along the Outer Banks of North Carolina is found to be a self-affine signal. We measure shoreline change by determining the horizontal change in position of the 0.8-m contour sampled from shore-perpendicular profiles spaced at 20-m intervals along the coast. The profiles are obtained from two light detection and ranging surveys performed in September 1997 and September 1998. For six selected sections of coast, wavelet analysis of the shoreline change signal indicates the signal is self-affine with a scaling exponent that varies from 1.2 to 2.1. This self-affine behavior indicates that the shoreline change signal is nonstationary with long-range persistence. A stochastic diffusion model of sediment transport replicates the observed self-affine behavior observed south of Cape Hatteras (scaling exponent between 1.2 and 1.6) whereas a random walk model replicates the signal observed north of Cape Hatteras (scaling exponent approximately 2.0). Because of the finite nature of the data set, there are limits in space and time to the power law behavior of the system. Characteristics of such systems can be described by upper-truncated power laws, which yield the upper limits of power law behavior. Applying an upper-truncated power law to the data for one section of coast, we find an upper limit of 7 km for the maximum continuous alongshore distance eroding or accreting. For the same section of coast, we find upper limits of 25 m for the maximum shore-perpendicular erosion and 11 m for the maximum shore-perpendicular accretion during the study period.

Journal Article↗
PubMed2005

A study of wavelet analysis based error compensation for the angular measuring system of high-precision test turntables.

An angular measuring system is the most important component of high-precision test turntables; its function and precision determine the turntable's function and precision. The angular measuring system's error was considered as a stationary signal in the past. An autocorrelation function and spectrum characteristics of the angular measuring system error are analyzed using the cyclostationary signal theory. The idea that the error in the angular measuring system is nonstationary is first put forward; theory is provided to reconstruct the angular measuring system's error signal using wavelet analysis. The error signal is reconstructed using one-dimensional Mallat's algorithm. The standard deviation between the reconstructed and the original signal is much less than the angular measuring system's accuracy. The reconstruction signal is used to compensate the system error instead of the original error signal; the angular measuring system accuracy is improved.

Journal Article↗
PubMed2004

The use of ultrasonic guided waves and wavelets analysis in pipe inspection.

One of the main applications of guided waves is for pipe inspection, since its one-dimensional geometry allows inspecting long distances in a short time. As with most configurations (frequency, thickness) ultrasonic waves with many modes of propagation are generated, recent research focuses mostly on the generation and reception of specific modes, mainly by means of sophisticated arrays of transducers. In this paper this problem is addressed with a different approach, by processing ultrasonic signals with a low signal/noise ratio acquired with a single transducer in an pulse-echo configuration. In order to improve the evaluation of results, frequency bandpass filters and wavelet analysis were tested. Results showed that even when very noisy signals are utilized, signal processing improve the signal/noise (S/N) ratio up to 12 dB approximately and enhance the analysis of the results, thus demonstrating its usefulness.

Journal Article↗
PubMed2005

WaveRead: automatic measurement of relative gene expression levels from microarrays using wavelet analysis.

Gene expression microarrays monitor the expression levels of thousands of genes in an experiment simultaneously. To utilize the information generated, each of the thousands of spots on a microarray image must be properly quantified, including background correction. Most present methods require manual alignment of grids to the image data, and still often require additional minor adjustments on a spot by spot basis to correct for spotting irregularities. Such intervention is time consuming and also introduces inconsistency in the handling of data. A fully automatic, tested system would increase throughput and reliability in this field. In this paper, we describe WaveRead, a fully automated, standalone, open-source system for quantifying gene expression array images. Through the use of wavelet analysis to identify the spot locations and diameters, the system is able to automatically grid the image and quantify signal intensities and background corrections without any user intervention. The ability of WaveRead to perform proper quantification is demonstrated by analysis of both simulated images containing spots with donut shapes, elliptical shapes, and Gaussian intensity distributions, as well as of standard images from the National Cancer Institute.

Algorithms↗
PubMed2001

Wavelet analysis of middle latency auditory evoked responses: calculation of an index for detection of awareness during propofol administration.

BACKGROUND: Middle latency auditory evoked responses (MLAER) as a measure of depth of sedation are critically dependent on data quality and the analysis technique used. Manual peak labeling is subject to observer bias. This study investigated whether a user-independent index based on wavelet transform can be derived to discriminate between awake and unresponsive states during propofol sedation. METHODS: After obtaining ethics committee approval and written informed consent, 13 volunteers and 40 patients were studied. In all subjects, propofol was titrated to loss of response to verbal command. The volunteers were allowed to recover, then propofol was titrated again to the same end point, and subjects were finally allowed to recover. From three MLAER waveforms at each stage, latencies and amplitudes of peaks Pa and Nb were measured manually. In addition, wavelet transform for analysis of MLAER was applied. Wavelet transform gives both frequency and time information by calculation of coefficients related to different frequency contents of the signal. Three coefficients of the so-called wavelet detail level 4 were transformed into a single index (Db3d4) using logistic regression analysis, which was also used for calculation of indices for Pa, Nb, and Pa/Nb latencies. Prediction probabilities for discrimination between awake and unresponsive states were calculated for all MLAER indices. RESULTS: During propofol infusion, subjects were unresponsive, and MLAER components were significantly depressed when compared with the awake states (P < 0.001). The wavelet index Db3d4 was positive for awake and negative for unresponsive subjects with a prediction probability of 0.92. CONCLUSION: These data show that automated wavelet analysis may be used to differentiate between awake and unresponsive states. The threshold value for the wavelet index allows easy recognition of awake versus unresponsive subjects. In addition, it is independent of subjective peak identification and offers the advantage of easy implementation into monitoring devices.

Adult↗
PubMed2004

On wavelet analysis of auditory evoked potentials.

OBJECTIVE: To determine a preferred wavelet transform (WT) procedure for multi-resolution analysis (MRA) of auditory evoked potentials (AEP). METHODS: A number of WT algorithms, mother wavelets, and pre-processing techniques were examined by way of critical theoretical discussion followed by experimental testing of key points using real and simulated auditory brain-stem response (ABR) waveforms. Conclusions from these examinations were then tested on a normative ABR dataset. RESULTS: The results of the various experiments are reported in detail. CONCLUSIONS: Optimal AEP WT MRA is most likely to occur when an over-sampled discrete wavelet transformation (DWT) is used, utilising a smooth (regularity >or=3) and symmetrical (linear phase) mother wavelet, and a reflection boundary extension policy. SIGNIFICANCE: This study demonstrates the practical importance of, and explains how to minimize potential artefacts due to, 4 inter-related issues relevant to AEP WT MRA, namely shift variance, phase distortion, reconstruction smoothness, and boundary artefacts.

Adult↗