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PubMed2003

Robust deconvolution of high-frequency ultrasound images using higher-order spectral analysis and wavelets.

Deconvolution of high-frequency (30-40 MHz) ultrasonic images of human skin was studied in vivo. Separate one-dimensional (1-D) functions for the axial and lateral profiles were first estimated using higher-order spectral methods. Subsequently, deconvolution was implemented us ing a regularized inverse Wiener filtering of the wavelet and scaling coefficients that were obtained after a wavelet decomposition of the RF signals. Deconvolution was first performed in the axial direction, then in the lateral direction. The methods were applied to data obtained from the skin of 16 volunteers using three different transducers. Significant improvements in both the axial and lateral resolutions were obtained in all the cases. Features such as hair follicles in the dermis and fingerprints on the surface of the finger were more clearly displayed in the processed images compared to the original images. The results indicate that the deconvolution method using higher-order spectral methods and wavelet analysis could significantly improve the quality of high-frequency ultrasonic skin images.

Adult↗
PubMed2005

Alzheimer's disease: models of computation and analysis of EEGs.

In a recent article the authors presented a comprehensive review of research performed on computational modeling of Alzheimer's disease (AD) and its markers with a focus on computer imaging, classification models, connectionist neural models, and biophysical neural models. The popularity of imaging techniques for detection and diagnosis of possible AD stems from the relative ease with which neurological markers can be converted to visual markers. However, due to the expense of specialized experts and equipment involved in the use of imaging techniques, a subject of significant research interest is detecting markers in EEGs obtained from AD patients. In this article, the authors present a state-of-the-art review of models of computation and analysis of EEGs for diagnosis and detection of AD. This review covers three areas: time-frequency analysis, wavelet analysis, and chaos analysis. The vast number of physiological parameters involved in the poorly understood processes responsible for AD yields a large combination of parameters that can be manipulated and studied. A combination of parameters from different investigation modalities seems to be more effective in increasing the accuracy of detection-and diagnosis.

Alzheimer Disease↗
PubMed2005

Wavelet-crosscorrelation analysis: Non-stationary analysis of neurophysiological signals.

OBJECTIVE: Wavelet-crosscorrelation analysis is a new application of wavelet analysis used to show the propagation of epileptiform discharges and to localize the corresponding lesions. We have shown previously that this analysis can help predict brain conditions statistically (Mizuno-Matsumoto et al. 2002). Our objective was to assess whether wavelet-crosscorrelation analysis reveals the initiation and propagation of epileptiform activity in human patients. METHODS: The data obtained from three patients with simple partial seizures (SPS) using whole-head magnetoencephalography (MEG) were analyzed by the wavelet-crosscorrelation method. Wavelet-crosscorrelation coefficients (WCC), the coherent structure of each possible pair of signals from 64 MEG channels forvarious periods, and the time lag (TL) in two related signals, were ascertained. RESULTS: We clearly demonstrated both localization of the irritative zone and propagation of the epileptiform discharges. CONCLUSIONS: Wavelet-crosscorrelation analysis can help reveal and visualize the dynamic changes of brain conditions. The method of this analysis can compensate for other existing methods for the analysis of MEG, electroencephalography (EEG) or Elecotrocorticography (ECoG). SIGNIFICANCE: Our proposed method suggests that revealing and visualizing the dynamic changes of brain conditions can help clinicians and even patients themselves better understand such conditions.

Adult↗
PubMed1995

Multiresolution analysis of event-related potentials by wavelet decomposition.

Wavelet analysis is presented as a new tool for analyzing event-related potentials (ERPs). The wavelet transform expands ERPs into a time-scale representation, which allows the analyst to zoom in on the small scale, fine structure details of an ERP or zoom out to examine the large scale, global waveshape. The time-scale representation is closely related to the more familiar time-frequency representation used in spectrograms of time-varying signals. However, time-scale representations have special properties that make them attractive for many ERP applications. In particular, time-scale representations permit theoretically unlimited time resolution for the detection of short-lived peaks and permit a flexible choice of wavelet basis functions for analyzing different types of ERPs. Generally, time-scale representations offer a formal basis for designing new, specialized filters for various ERP applications. Among recently explored applications of wavelet analysis to ERPs are (a) the precise identification of the time of occurrence of overlapping peaks in the auditory brainstem evoked response; (b) the extraction of single-trial ERPs from background EEG noise; (c) the decomposition of averaged ERP waveforms into orthogonal detail functions that isolate the waveform's experimental behavior in distinct, orthogonal frequency bands; and (d) the use of wavelet transform coefficients to concisely extract important information from ERPs that predicts human signal detection performance. In this tutorial we present an intuitive introduction to wavelets and the wavelet transform, concentrating on the multiresolution approach to wavelet analysis of ERP data. We then illustrate this approach with real data. Finally, we offer some speculations on future applications of wavelet analysis to ERP data.

Brain↗
PubMed2006

[Wavelet property analysis of near infrared spectra].

Wavelet analysis (WT) was conducted on the simulated near infrared spectra (NIR) obtained by adding simulated background and simulated noise into diesel NIR Results show that the background components are mainly located in the low frequency region, while noise in the high frequency region, and useful signal in the middle frequency region. Background and noise components can be simultaneously subtracted from the spectra by WT. The WT coefficients in mid-frequency details can be selected as variables to build the multivariate calibration model, which can improve analytic accuracy and reduce the analysis time.

Algorithms↗
PubMedDate not supplied

Epiphyseal fusion assessment based on wavelets decomposition analysis.

Epiphyseal fusion is a finding frequently analysed in hand wrist radiographs. It is tested in the bone age assessment, the gonadal dysgenesis, etc. The computerized fusion analysis is performed on an automatically selected region of interest containing the lower edge of epiphysis and the upper edge of metaphysis. In the analysis a wavelets decomposition approach is employed. The wavelets decomposition components are first subjected to a preliminary test which rejects the overexposed images whose analysis would not give reasonable results. This increases the accuracy of the algorithm and a chance for an unsupervised application. Then, a quantitative measure is found. Its value decreases while the epiphyseal fusion proceeds. The analysis yields an assignment of fusion to one of four stages: no fusion, early stage of fusion, advanced stage of fusion, and fusion completed. The results show that wavelets decomposition components may efficiently be applied to a texture analysis.

Age Determination by Skeleton↗
PubMed2006

HBM functional imaging analysis contest data analysis in wavelet space.

An analysis of the Functional Imaging Analysis Contest (FIAC) data is presented using spatial wavelet processing. This technique allows the image to be filtered adaptively according to the data itself, rather than relying on a predetermined filter. This adaptive filtering leads to better estimation of the parameters and contrasts in terms of mean squared error. It will be shown that by introducing a slight bias into the estimation, a large reduction in the variance can be achieved, leading to better overall mean squared error estimates. As no single filter needs to be preselected, results containing many scales of information can be found. In the FIAC data, it is shown that both small-scale and large-scale (smoother, more dispersed) effects occur. The combination of small- and large-scale effects detected in the FIAC data would be easy to miss using conventional single filter analysis.

Algorithms↗
PubMed2004

[Feature extraction and classification of EEG for mental tasks based on wavelet packet analysis].

This paper explores the use of wavelet packet analysis to extract features from spontaneous electroencephalogram (EEG) during three different mental tasks. Artifact-free EEG segments are transformed to multi-scale representations by dyadic wavelet packet decomposition channel by channel. Their feature vectors formed by energy values of different sub-spaces EEG components are used as inputs of a radial basis function network to test the classification accuracies of three task pairs. The results indicate that the classification accuracies of the wavelet packet analysis method are significantly better than those of autoregressive model method. Wavelet packet analysis would be a promising method to extract features from EEG signals.

Electroencephalography↗
PubMed2005

Analysis of GDx-VCC polarimetry data by Wavelet-Fourier analysis across glaucoma stages.

PURPOSE: The purpose of this study was to apply shape-based analysis techniques of retinal nerve fiber layer (RNFL) thickness to GDx-VCC (variable corneal and lens compensator; Laser Diagnostic Technologies, Inc., San Diego, CA) polarimetry data and to evaluate the techniques' ability to detect glaucoma in its earliest stages. Wavelet-based (wavelet-Fourier analysis [WFA]), Fourier-based (fast Fourier analysis [FFA]), and several previous variations of shape-based analysis were considered, as well as the standard metric nerve fiber indicator (NFI), and all were compared as a function of disease stage. METHODS: GDx-VCC scans of one eye of each of 67 patients with glaucoma and each of 67 healthy age-matched subjects provided RNFL thickness estimates at a fixed distance from the optic disc. Severity of disease was graded according to the Glaucoma Staging System and also by mean deviation (MD) from standard automated perimetry. WFA, FFA, and NFI procedures were performed including the following variations: use of signed or unsigned phase, inclusion of interocular or intraocular asymmetry of analysis parameters, and combination of features by principle components analysis or Wilks lambda. Independent samples (k-fold variation) were used for training and testing. Sensitivity, specificity, and receiver operating characteristic (ROC) area were obtained. RESULTS: Classification performance of WFA (ROC = 0.978) was significantly better than FFA (ROC = 0.938) and NFI (ROC = 0.900). This difference was largest for the earliest stages of glaucoma. Shape-based analysis methods performed better than NFI overall. Adding between-eye asymmetry measures helped FFA but not WFA. CONCLUSIONS: Shape-based analysis, and WFA in particular, makes an important improvement in detecting earliest glaucoma with polarimetry.

Aged↗
PubMed2001

Spontaneous arousals during quiet sleep in piglets: a visual and wavelet-based analysis.

STUDY OBJECTIVES: The objectives of the study were to characterize spontaneous arousals during NREM sleep in piglets and to compare two methods of identifying these events: a "visual" technique using spectral analysis and an automated technique using wavelets. Our goal was to understand the benefits and limits of these methods when applied to sleep in human infants. DESIGN: Arousals were identified by evaluating rapid changes in EEG low frequency activity, blood pressure (BP), and heart rate (HR). A cortical arousal was defined as a rapid decrease in EEG low frequency activity. An autonomic arousal was defined by a transient increase in heart rate or a transient change in mean arterial BP (MAP). SETTING: Laboratory study in sleeping and awake piglets. PARTICIPANTS: Five 1-2 week old piglets. INTERVENTIONS: Chronically instrumented with a femoral arterial line, EEG, EOG, EMG electrodes, and a micro-dialysis probe with its tip located in the rostral ventral medulla. Artificial CSF (aCSF) was dialyzed into the RVM throughout the experiments Measurements: For the visual analysis, the average delta power (0.5-4 Hz) for each 5-second epoch was determined using spectral analysis. MAP and HR were analyzed in 1-second bins. Video images were analyzed for body movements and eye openings. Transient changes in blood pressure, HR, and delta power were then visually identified. For the wavelet analysis, a quantitative, automated technique with a defined "wakefulness threshold" was used to identify rapid decreases in EEG low frequency activity and the rate of change of MAP. RESULTS: Using the visual method, 117 episodes associated with stereotypical hemodynamic, EEG, and behavioral changes (startle) were identified. Seventy five events occurred in isolation or were first in a series of "multiple" events, 41 "multiple" events were defined as events occurring <20 seconds following a previous event. Eighteen events were associated with the termination of apnea. In isolated events or those occurring first in a series, the onset of changes in HR and BP clearly preceded the decrease in EEG amplitude and delta power. Using wavelet analysis, 73 EEG arousals and 115 MAP transients were identified independently; 62% of the EEG events were associated with a transient change in MAP and HR, and in these cases the onset of the hemodynamic events preceded EEG arousals. EEG arousals and MAP transients, however, also occurred alone and not associated with a stereotypical pattern of a startle, changes in MAP and HR and the EEG. CONCLUSIONS: Many of these spontaneous arousals represent integrated EEG, hemodynamic, and behavioral processes similar to arousal phenomena described in adult rats and human infants, but the pattern of spontaneous arousals appears to be more heterogeneous than has been described for arousals induced by exogenous stimuli. Both the visual and wavelet analysis identified these events, but the wavelet technique has the potential advantage of better time resolution and automation of the analysis.

Animals↗
PubMedDate not supplied

Wavelet based analysis and characterization of the ECG signal.

This paper reports the use of a wavelet analysis technique based on the Mexican Hat wavelet to identify the onset and termination points and the duration of the principal constituent components of the human electrocardiogram (ECG). ECG recordings were obtained from 21 healthy subjects aged between 13 and 65 years, over a wide range of heart rates extending from 46 to 184 beats min(-1). A wavelet transform method was then used to locate precisely the positions of the onset, termination and the durations of individual components in the ECG. Component times were then classified according to the heart rate associated with the cardiac cycle to which the component belonged. Second order equations of the form [formula in text] were fitted to the data obtained for each component to characterize its timing variation.

Adolescent↗
PubMed2006

Evaluation of blood access dysfunction based on a wavelet transform analysis of shunt murmurs.

We investigated shunt murmurs based on wavelet transform analysis as a new method for assessing vascular access function. In the present study, in patients with venous stenosis near an arteriovenous fistula (A-V fistula), a sensor was placed at different positions around the stenosis and shunt murmur signals obtained using a measurement system were subjected to time-frequency analysis based on wavelet transforms. The shunt murmurs obtained from the stenotic region closely represented some features of murmurs that are often referred to as "high-pitch" murmurs in the clinical setting. In contrast, shunt murmurs obtained about 5 cm downstream of the stenotic region closely represented some features of murmurs that are often referred to as "low-pitch" murmurs in the clinical setting. Furthermore, with the aim of extending the lifespan of arteriovenous grafts (A-V grafts) by detecting and treating stenotic lesions before the A-V graft becomes occluded, we evaluated the possibility of utilizing the present shunt murmur analysis for monitoring stenosis in such A-V grafts. When shunt murmurs from patients with A-V grafts were analyzed, the results suggested that the blood flow through the venous anastomosis of the graft was the most turbulent. This present method whereby blood flow in an A-V fistula is assessed based on the frequency distribution on a time-frequency plane by wavelet transform analysis is advantageous because findings are not markedly affected by sensor attachment. Furthermore, because the sensor is attached using an adhesive collar, measurements can be taken over a short period of time before each dialysis session.

Anastomosis, Surgical↗
PubMed2003

Scaling properties and symmetrical patterns in the epidemiology of rotavirus infection.

The rich epidemiological database of the incidence of rotavirus, as a cause of severe diarrhoea in young children, coupled with knowledge of the natural history of the infection, can make this virus a paradigm for studies of epidemic dynamics. The cyclic recurrence of childhood rotavirus epidemics in unvaccinated populations provides one of the best documented phenomena in population dynamics. This paper makes use of epidemiological data on rotavirus infection in young children admitted to hospital in Melbourne, Australia from 1977 to 2000. Several mathematical methods were used to characterize the overall dynamics of rotavirus infections as a whole and individually as serotypes G1, G2, G3, G4 and G9. These mathematical methods are as follows: seasonal autoregressive integrated moving-average (SARIMA) models, power spectral density (PSD), higher-order spectral analysis (HOSA) (bispectrum estimation and quadratic phase coupling (QPC)), detrended fluctuation analysis (DFA), wavelet analysis (WA) and a surrogate data analysis technique. Each of these techniques revealed different dynamic aspects of rotavirus epidemiology. In particular, we confirm the existence of an annual, biannual and a quinquennial period but additionally we found other embedded cycles (e.g. ca. 3 years). There seems to be an overall unique geometric and dynamic structure of the data despite the apparent changes in the dynamics of the last years. The inherent dynamics seems to be conserved regardless of the emergence of new serotypes, the re-emergence of old serotypes or the transient disappearance of a particular serotype. More importantly, the dynamics of all serotypes is multiple synchronized so that they behave as a single entity at the epidemic level. Overall, the whole dynamics follow a scale-free power-law fractal scaling behaviour. We found that there are three different scaling regions in the time-series, suggesting that processes influencing the epidemic dynamics of rotavirus over less than 12 months differ from those that operate between 1 and ca. 3 years, as well as those between 3 and ca. 5 years. To discard the possibility that the observed patterns could be due to artefacts, we applied a surrogate data analysis technique which enabled us to discern if only random components or linear features of the incidence of rotavirus contribute to its dynamics. The global dynamics of the epidemic is portrayed by wavelet-based incidence analysis. The resulting wavelet transform of the incidence of rotavirus crisply reveals a repeating pattern over time that looks similar on many scales (a property called self-similarity). Both the self-similar behaviour and the absence of a single characteristic scale of the power-law fractal-like scaling of the incidence of rotavirus infection imply that there is not a universal inherently more virulent serotype to which severe gastroenteritis can uniquely be ascribed.

Australia↗