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[Laser posterior cordectomy in bilateral vocal cord paralysis].

OBJECTIVES: We evaluated the functional results of laser posterior cordectomy with respect to respiration, phonation, and deglutition. PATIENTS AND METHODS: Unilateral laser posterior cordectomy was performed in nine patients (7 women, 2 men; mean age 56 years; range 46 to 61 years) with bilateral vocal cord paralysis. Postoperatively, respiration, deglutition, and phonation were evaluated with the use of an exercise tolerance scale, the Pearson scale, and wavelet analysis, respectively. The mean follow-up period was 14.2 months. RESULTS: Respiration was satisfactory in eight patients. Of these, four patients had excellent, and four patients had good exercise tolerance. The procedure resulted in success without tracheotomy in four patients. No deglutition or aspiration problems developed. Of eight patients with satisfactory respiration, voice quality remained unchanged in six patients, whereas an improved voice quality was noted in two patients who received postoperative phoniatric rehabilitation. CONCLUSION: Laser posterior cordectomy for the treatment of bilateral vocal cord paralysis resolves respiration problems, it restores phonation and deglutition functions, as well.

Deglutition↗
PubMed2004

An expert system for cutaneous blood flow in melanocytic skin lesions.

A basic tool in microcirculation research is laser Doppler fluxmetry (LDF). The chaotic behaviour of the measured LDF-time series acquires mathematical tools like, for instance, Wavelets. The notion of contrast is known as useful tool to measure differences between two LDF-time series [K. Bräuer, Chaos, Attraktoren und Fraktale, Logos, Berlin, 2002]. The one time series arises from the blood flow in healthy skin and the other from a pigmented symmetric contra lateral skin lesion. Our approach is based on taking the contrast from all shorter non-overlapping time intervals of approximate length 5 or 10 seconds. This gives a sample or more precisely, a time series of contrast values. Our goal is an expert system to decide between malign and beligne lesions by estimating the probability for a maligne lesion. As a data base we again use the same data set as [H.-M. Häfner, K. Bräuer, M. Eichner, A. Steins, M. Möhrle, A. Blum and M. Jünger, Wavelet analysis of cutaneous blood flow in melanocytic skin lesions, J. Vasc. Res., submitted]. The statistical tool is logistic regression. We can show that 93% of data are correctly classified. If we check the expert system against the independent data base of the Greifswald dermatology department we get 78% correctly classified cases. Further work must be done to find a well distributed data base for an expert release system.

Blood Flow Velocity↗
PubMed2004

Enhanced neural responses correlated with perceptual binding of color and motion.

When both color and motion direction of visual stimuli are alternated in physical synchrony at a relatively higher frequency (approximately 2 Hz), the changes in motion direction are perceived to be delayed. On the other hand, color and motion direction changes are perceived to be in phase when the motion direction changes precede the color changes by about 100 ms [Moutoussis, 1997]. In the present study, we utilized this phenomenon to investigate the neural mechanisms underlying the binding of color and motion based on the temporal synchrony. Magnetoencephalogram (MEG) was recorded for ten human subjects under the following four conditions: color change (color), motion direction change (motion), and simultaneous color and motion direction changes (color+motion) in perceptual synchrony (physical asynchrony) or in perceptual asynchrony (physical synchrony). The wavelet analysis was applied on these MEGs to study the neural responses in time-frequency domain. The interactions of color and motion responses, defined by [color+motion]-([color]+[motion]), were calculated in time-frequency domain for both perceptually synchronous and asynchronous conditions. The results showed significantly larger interactions at gamma band (30-35 Hz) under the condition of perceptual synchrony than under the condition of perceptual asynchrony, suggesting that synchronized neural responses at gamma band are related to the synchrony-based binding of visual attributes. This result is consistent with previous studies reporting the correlation of gamma band responses with perceptual grouping [Castelo-Branco, 2000] [Tallon-Baudry, 1996].

Color Perception↗
PubMed1998

[Computer analysis of electroencephalogram].

Many computing systems have been developed and applied to the sleep studies. The most famous system is EEG sleep stage determination system. It becomes possible to process the PSG data during one night in a few minutes by these systems. We also have been studying for such system and analyzing method of EEG. This paper presents the feature extraction and recognition methods of EEG waves. For the feature extraction methods, wave shape analysis, wavelet transformation, AR (autoregressive) model and damped system with Poisson impulse input sequences are used. For the recognition methods, statistical pattern recognition method and artificial neural networks are used. The effectiveness of these methods is confirmed through the experimental studies.

Electroencephalography↗
PubMed2002

Wavelet and receiver operating characteristic analysis of heart rate variability.

Multiresolution wavelet analysis has been used to study the heart rate variability in two classes of patients with different pathological conditions. The scale dependent measure of Thurner et al. was found to be statistically significant in discriminating patients suffering from hypercardiomyopathy from a control set of normal subjects. We have performed Receiver Operating Characteristc (ROC) analysis and found the ROC area to be a useful measure by which to label the significance of the discrimination, as well as to describe the severity of heart dysfunction.

Analysis of Variance↗
PubMed2005

Hilbert transform assisted complex wavelet transform for neuroelectric signal analysis.

In this work, we present a new approach for shift invariant complex wavelet analysis of neuroelectric signals. A key idea is to preprocess the signal with the Hilbert transformer to yield an analytic signal, which is then wavelet transformed using the linear phase complex scaling and wavelet filters. In different scales, the total energy of the wavelet transform coefficients is shift invariant. The decimated analytic wavelet coefficients suffer no aliasing effects, which are predominant in conventional wavelet analysis. We show the usefulness of the present method in multi-scale analysis of the neuroelectric signal waveforms.

Action Potentials↗
PubMed2005

A wavelet-based statistical analysis of FMRI data: I. motivation and data distribution modeling.

We propose a new method for statistical analysis of functional magnetic resonance imaging (fMRI) data. The discrete wavelet transformation is employed as a tool for efficient and robust signal representation. We use structural magnetic resonance imaging (MRI) and fMRI to empirically estimate the distribution of the wavelet coefficients of the data both across individuals and spatial locations. An anatomical subvolume probabilistic atlas is used to tessellate the structural and functional signals into smaller regions each of which is processed separately. A frequency-adaptive wavelet shrinkage scheme is employed to obtain essentially optimal estimations of the signals in the wavelet space. The empirical distributions of the signals on all the regions are computed in a compressed wavelet space. These are modeled by heavy-tail distributions because their histograms exhibit slower tail decay than the Gaussian. We discovered that the Cauchy, Bessel K Forms, and Pareto distributions provide the most accurate asymptotic models for the distribution of the wavelet coefficients of the data. Finally, we propose a new model for statistical analysis of functional MRI data using this atlas-based wavelet space representation. In the second part of our investigation, we will apply this technique to analyze a large fMRI dataset involving repeated presentation of sensory-motor response stimuli in young, elderly, and demented subjects.

Adult↗
PubMed2003

Hidden corrosion detection in aircraft aluminum structures using laser ultrasonics and wavelet transform signal analysis.

Preliminary results of hidden corrosion detection in aircraft aluminum structures using a noncontact laser based ultrasonic technique are presented. A short laser pulse focused to a line spot is used as a broadband source of ultrasonic guided waves in an aluminum 2024 sample cut from an aircraft structure and prepared with artificially corroded circular areas on its back surface. The out of plane surface displacements produced by the propagating ultrasonic waves were detected with a heterodyne Mach-Zehnder interferometer. Time-frequency analysis of the signals using a continuous wavelet transform allowed the identification of the generated Lamb modes by comparison with the calculated dispersion curves. The presence of back surface corrosion was detected by noting the loss of the S(1) mode near its cutoff frequency. This method is applicable to fast scanning inspection techniques and it is particularly suited for early corrosion detection.

Aircraft↗
PubMed2006

The influence of wavelets on multiscale analysis and parametrization of midlatency auditory evoked potentials.

This work shows methodological aspects of heuristic pattern recognition in auditory evoked potentials. A linear and a nonlinear transformation based on wavelet transform are presented. They result in a statistical error model and an entropy function related to the Gibbs function and describe changes in midlatency auditory evoked potentials induced by general anaesthesia. The same transformations were calculated using 12 common wavelets. We present a method to compare the two defined parametrizations with respect to their ability to discriminate two defined states which is responsive and unresponsive depending on the wavelet used for the analysis. Auditory evoked potentials of 60 patients undergoing general anaesthesia were analysed. We propose the defined statistical error model and the entropy function as a very robust measure of changes in auditory evoked potentials. The influence of the wavelets suggest that for each parametrization the goodness of the wavelet should be validated.

Acoustic Stimulation↗
PubMed2004

Analysis of wavelet-filtered tonic-clonic electroencephalogram recordings.

EEG signals obtained during tonic-clonic epileptic seizures can be severely contaminated by muscle and physiological noise. Heavily contaminated EEG signals are hard to analyse quantitatively and also are usually rejected for visual inspection by physicians, resulting in a considerable loss of collected information. The aim of this work was to develop a computer-based method of time series analysis for such EEGs. A method is presented for filtering those frequencies associated with muscle activity using a wavelet transform. One of the advantages of this method over traditional filtering is that wavelet filtering of some frequency bands does not modify the pattern of the remaining ones. In consequence, the dynamics associated with them do not change. After generation of a 'noise free' signal by removal of the muscle artifacts using wavelets, a dynamic analysis was performed using non-linear dynamics metric tools. The characteristic parameters evaluated (correlation dimension D2 and largest Lyapunov exponent lambda1) were compatible with those obtained in previous works. The average values obtained were: D2=4.25 and lambda1=3.27 for the pre-ictal stage; D2=4.03 and lambda1=2.68 for the tonic seizure stage; D2=4.11 and lambda1=2.46 for the clonic seizure stage.

Artifacts↗
PubMed2001

Comparison between the Fourier and Wavelet methods of spectral analysis applied to stationary and nonstationary heart period data.

The aim of this study was to assess the error made by violating the assumption of stationarity when using Fourier analysis for spectral decomposition of heart period power. A comparison was made between using Fourier and Wavelet analysis (the latter being a relatively new method without the assumption of stationarity). Both methods were compared separately for stationary and nonstationary segments. An ambulatory device was used to measure the heart period data of 40 young and healthy participants during a psychological stress task and during periods of rest. Surprisingly small differences (<1%) were found between the results of both methods, with differences being slightly larger for the nonstationary segments. It is concluded that both methods perform almost identically for computation of heart period power values. Thus, the Wavelet method is only superior for analyzing heart period data when additional analyses in the time-frequency domain are required.

Adolescent↗
PubMed2005

Wavelet-based cluster analysis: data-driven grouping of voxel time courses with application to perfusion-weighted and pharmacological MRI of the rat brain.

MRI time series experiments produce a wealth of information contained in two or three spatial dimensions that evolve over time. Such experiments can, for example, localize brain response to pharmacological stimuli, but frequently the spatiotemporal characteristics of the cerebral response are unknown a priori and variable, and thus difficult to evaluate using hypothesis-based methods alone. Here we used features in the temporal dimension to group voxels with similar time courses based on a nonparametric discrete wavelet transform (DWT) representation of each time course. Applying the DWT to each voxel decomposes its temporal information into coefficients associated with both time and scale. Discarding scales in the DWT that are associated with high-frequency oscillations (noise) provided a straight-forward data reduction step and decreased the computational burden. Optimization-based clustering was then applied to the remaining wavelet coefficients in order to produce a finite number of voxel clusters. This wavelet-based cluster analysis (WCA) was evaluated using two representative classes of MRI neuroimaging experiments. In perfusion-weighted MRI, following occlusion of the middle cerebral artery (MCAO), WCA differentiated healthy tissue and different regions within the ischemic hemisphere. Following an acute cocaine challenge, WCA localized subtle differences in the pharmacokinetic profile of the cerebral response. We conclude that WCA provides a robust method for blind analysis of time series image data.

Animals↗
PubMed2000

Analysis of acceleration signals using wavelet transform.

In this study, we attempted to discriminate the acceleration signal for horizontal level and stairway walking using wavelet-based fractal analysis method. The acceleration signal was measured close to the center of gravity of the body, while the subjects walked continuously in the corridor and up and down the stairs. We used the wavelet-based fractal analysis method to discriminate walking pattern. The parameter H which is related directly to the fractal dimension was estimated by the wavelet coefficient and was changed into low value during walking upstairs. By manually setting the threshold level for individual, it was possible to discriminate walking upstairs from the other walking type. However, the common feature among subjects was not shown between level walking and walking downstairs.

Acceleration↗
PubMed2005

Multi-resolution wavelet-transformed image analysis of histological sections of breast carcinomas.

Multi-resolution images of histological sections of breast cancer tissue were analyzed using texture features of Haar- and Daubechies transform wavelets. Tissue samples analyzed were from ductal regions of the breast and included benign ductal hyperplasia, ductal carcinoma in situ (DCIS), and invasive ductal carcinoma (CA). To assess the correlation between computerized image analysis and visual analysis by a pathologist, we created a two-step classification system based on feature extraction and classification. In the feature extraction step, we extracted texture features from wavelet-transformed images at 10x magnification. In the classification step, we applied two types of classifiers to the extracted features, namely a statistics-based multivariate (discriminant) analysis and a neural network. Using features from second-level Haar transform wavelet images in combination with discriminant analysis, we obtained classification accuracies of 96.67 and 87.78% for the training and testing set (90 images each), respectively. We conclude that the best classifier of carcinomas in histological sections of breast tissue are the texture features from the second-level Haar transform wavelet images used in a discriminant function.

Breast Neoplasms↗
PubMed2001

Electroencephalogram analysis using fast wavelet transform.

The continuous wavelet transform is a new approach to the problem of time-frequency analysis of signals such as electroencephalogram (EEG) and is a promising method for EEG analysis. However, it requires a convolution integral in the time domain, so the amount of computation is enormous. In this paper, we propose a fast wavelet transform (FWT) that the corrected basic fast algorithm (CBFA) and the fast wavelet transform for high accuracy (FWTH). As a result, our fast wavelet transform can achieve high computation speed and at the same time to improve the computational accuracy. The CBFA uses the mother wavelets whose frequencies are 2 octaves lower than the Nyquist frequency in the basic fast algorithm. The FWT for high accuracy is realized by using upsampling based on a L-Spline interpolation. The experimental results demonstrate advantages of our approach and show its effectiveness for EEG analysis.

Algorithms↗
PubMed2005

Multiscale 3D shape analysis using spherical wavelets.

Shape priors attempt to represent biological variations within a population. When variations are global, Principal Component Analysis (PCA) can be used to learn major modes of variation, even from a limited training set. However, when significant local variations exist, PCA typically cannot represent such variations from a small training set. To address this issue, we present a novel algorithm that learns shape variations from data at multiple scales and locations using spherical wavelets and spectral graph partitioning. Our results show that when the training set is small, our algorithm significantly improves the approximation of shapes in a testing set over PCA, which tends to oversmooth data.

Algorithms↗
PubMed2000

Application of spline wavelet transformation to the analysis of extended energy-loss fine structure.

We propose a new analysis method of the extended energy-loss fine structure (EXELFS), applying the multi-resolution analysis (MRA) in the wavelet transformation. We select the cardinal spline wavelet where the rank-4 cardinal spline function is used as a scaling function to construct the mother wavelet. In contrast with the conventional analysis method, the present method is efficient in filtering out the high-frequency noise and low-frequency components introduced by the numerical analysis process without distorting the original signal components, which promises quantitative analyses with less uncertainties.

Letter↗
PubMed2005

Wavelet transform for analysis of heart rate variability preceding ventricular arrhythmias in patients with ischemic heart disease.

INTRODUCTION: Studies evaluating changes in HRV preceding the onset of ventricular arrhythmias using conventional techniques have shown inconsistent results. Time-frequency analysis of HRV is traditionally performed using short-term Fourier transform (STFT). Wavelet transform (WT) may however be better suited for analyzing non-stationary signals such as heart rate recordings. METHODS AND RESULTS: We studied patients with a history of myocardial infarction implanted with a defibrillator with an extended memory. The RR intervals during the 51 min preceding ventricular events requiring electrical therapy were retrieved, and HRV studied by WT and STFT. 111 episodes of ventricular arrhythmia were retrieved from 41 patients (38 males, age 64 +/- 8 years). Heart rate increased significantly before arrhythmia. There was no significant variation in low frequency / high frequency components (LF/HF) observed for the group as a whole, probably due to a great degree of heterogeneity amongst individuals. A subset of 30 patients also had heart rate recordings performed during normal ICD follow-up. WT did not show any difference in HRV before arrhythmia onset and during control conditions. CONCLUSION: Variations in HRV before onset of ventricular arrhythmias were not apparent in this large dataset, despite use of optimal tools for studying time-frequency analysis.

Aged↗