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PubMed2002

[Application of kalman filtering based on wavelet transform in ICP-AES].

Kalman filtering is a recursive algorithm, which has been proposed as an attractive alternative to correct overlapping interferences in ICP-AES. However, the noise in ICP-AES contaminates the signal arising from the analyte and hence limits the accuracy of kalman filtering. Wavelet transform is a powerful technique in signal denoising due to its multi-resolution characteristics. In this paper, first, the effect of noise on kalman filtering is discussed. Then we apply the wavelet-transform-based soft-thresholding as the pre-processing of kalman filtering. The simulation results show that the kalman filtering based on wavelet transform can effectively reduce the noise and increase the accuracy of the analysis.

Algorithms↗
PubMed2004

[The algorithms and development for the extraction of evoked potentials].

The extraction of evoked potentials is a main subject in the area of brain signal processing. In recent years, the single-trial extraction of evoked potentials has been focused on by many studies. In this paper, the approaches based on the wavelet transform, the neural network, the high order acumulants and the independent component analysis are briefly reviewed.

Algorithms↗
PubMed2005

Quantum computation and analysis of Wigner and Husimi functions: toward a quantum image treatment.

We study the efficiency of quantum algorithms which aim at obtaining phase-space distribution functions of quantum systems. Wigner and Husimi functions are considered. Different quantum algorithms are envisioned to build these functions, and compared with the classical computation. Different procedures to extract more efficiently information from the final wave function of these algorithms are studied, including coarse-grained measurements, amplitude amplification, and measure of wavelet-transformed wave function. The algorithms are analyzed and numerically tested on a complex quantum system showing different behavior depending on parameters: namely, the kicked rotator. The results for the Wigner function show in particular that the use of the quantum wavelet transform gives a polynomial gain over classical computation. For the Husimi distribution, the gain is much larger than for the Wigner function and is larger with the help of amplitude amplification and wavelet transforms. We discuss the generalization of these results to the simulation of other quantum systems. We also apply the same set of techniques to the analysis of real images. The results show that the use of the quantum wavelet transform allows one to lower dramatically the number of measurements needed, but at the cost of a large loss of information.

Journal Article↗
PubMed2001

Classification of endocardial electrograms using adapted wavelet packets and neural networks.

The discrimination of ventricular tachycardias with 1:1 retrograde conduction from sinus tachycardia still remains a challenge for rate based algorithms commonly used in dual-chamber implantable cardioverter defibrillators. Morphology based analysis techniques for a classification of antegrade and retrograde atrial activation patterns can be used to cope with this problem. Here time-domain template matching techniques are known approaches. However, a time-domain representation of endocardial electrograms is not optimal for classification tasks as the dimensionality of the underlying signal space is high and features being irrelevant for a signal characterization are involved in the analysis. Therefore, the aim of this study is to develop an enhanced morphological analysis tool for a classification of antegrade and retrograde atrial activation by using a transform domain representation of endocardial electrograms. For this, we applied an adapted wavelet-packet decomposition to extract discriminating features in endocardial electrograms representing antegrade and retrograde activation patterns. Further, a feed-forward neural network was utilized to produce a classification based on the extracted information. In using our hybrid method, no false classification of the physiological and pathological cardiac state was made. It is concluded that the proposed classification scheme represents a highly efficient approach for a classification of antegrade and retrograde atrial activation.

Algorithms↗
PubMed2004

Characterization of event related potentials using information theoretic distance measures.

Analysis of event-related potentials (ERPs) using signal processing tools has become extremely widespread in recent years. Nonstationary signal processing tools such as wavelets and time-frequency distributions have proven to be especially effective in characterizing the transient phenomena encountered in event-related potentials. In this paper, we focus on the analysis of event-related potentials collected during a psychological experiment where two groups of subjects, spider phobics and snake phobics, are shown the same set of stimulus: A blank stimulus, a neutral stimulus and a spider stimulus. We introduce a new approach, based on time-frequency distributions, for analyzing the ERPs. The difference in brain activity before and after a stimulus is presented is quantified using distance measures as adapted to the time-frequency plane. Three different distance measures, including a new information theoretic distance measure, are applied on the time-frequency plane to discriminate between the responses of the two groups of subjects. The results illustrate the effectiveness of using distance measures combined with time-frequency distributions in differentiating between the two classes of subjects and the different regions of the brain.

Algorithms↗
PubMed2000

Medical image compression by cosine and wavelet transforms.

The investigation results for improving lossy compression techniques for ultrasound and angiographic images are presented. The goal was to clear out where the compression process could be improved for the medical application, and to make efforts to improve it. It is proved that wavelet transform outperforms discrete cosine transform applied for ultrasound and angiographic images. A lot of wavelet classes were tried for choosing the best one suited for corresponding image classes, which were characterized by content complexity criterion. The analysis of international image compression standards was carried out. Special attention was paid to algorithmical and high level service structure of a new still image compression standard JPEG2000. Its open architecture enables including some wavelet classes which we would like to suggest for medical images.

Algorithms↗
PubMed2002

Time-frequency and time-varying analysis for assessing the dynamic responses of cardiovascular control.

Time-frequency or time-variant methods have been extensively applied in the study of the heart-rate variability (HRV) signal. In fact, the frequency content of HRV signal has a strong correlation with the control system assessing heart rate. In particular, the power related to the low-frequency (LF) and high-frequency (HF) components have been demonstrated to correlate to the action of sympathetic and parasympathetic branches of the autonomic nervous system. However, the analysis is restricted to stationary conditions, unless time-frequency methods are employed for detecting dynamic changes that may occur during physiological and pathological conditions. This article reviews the most diffused tools for time-frequency analysis, starting from linear decomposition of the signal (including short-time Fourier transform and wavelet and wavelet packet decomposition), to quadratic time-frequency distributions (including Wigner-Ville transform and Cohen's class of distributions), and finally to adaptive or time-variant autoregressive (AR) models, in both the mono- and bivariate forms. In the past few years, these approaches have been applied in several studies related to cardiovascular responses during nonstationary pathophysiological events. Among them, we will recall and discuss myocardial ischemia (spontaneous or induced), drug infusion, rest-tilt maneuver and syncope, neurophysiological, and sleep investigations.

Animals↗
PubMed1998

Time-scale segmentation of respiratory sounds.

Respiratory sounds are composed of various events: normal and so-called adventitious sounds. These phenomena present a wide range of characteristics which make difficult their analysis with a single technique. Adapted time-frequency and time-scale techniques allow to fit best, under constraints, the accuracy of analysis of a time segmentation and, by the way, make feasible the study of complex signals. We present here new approaches based only on the wavelet packet decomposition to segment respiratory sounds.

Algorithms↗
PubMed2004

Folding dynamics of proteins from denatured to native state: principal component analysis.

Several trajectories starting from random configurations and ending in the native state for chymotrypsin inhibitor 2, CI2, are generated using a Go-type model where the backbone torsional angles execute random jumps on which a drift towards their native values is superposed. Bond lengths and bond angles are kept fixed, and the size of the backbone atoms and side groups are recognized. The large datasets obtained are analyzed using a particular type of principal component analysis known as Karhunen-Loeve expansion (KLE). Trajectories are decomposed separately into modes in residue space and time space. General features of different folding trajectories are compared in the modal space and relationships between the structure of CI2 and its folding dynamics are obtained. Dynamic scaling and order reduction of the folding trajectories are discussed. A continuous wavelet transform is used to decompose the nonstationary folding trajectories into windows exhibiting different features of folding dynamics. Analysis of correlations confirms the known two-state nature of folding of CI2. All of the conserved residues of the protein are shown to be stationary in the small modes of the residue space. The sequential nature of folding is shown by examining the slow modes of the trajectories. The present model of protein folding dynamics is compared with the simple Rouse model of polymer dynamics. Principal component analysis is shown to be a very effective tool for the characterization of the general folding features of proteins.

Computational Biology↗
PubMed2003

Wavelets filtering for classification of very noisy electron microscopic single particles images--application on structure determination of VP5-VP19C recombinant.

BACKGROUND: Images of frozen hydrated [vitrified] virus particles were taken close-to-focus in an electron microscope containing structural signals at high spatial frequencies. These images had very low contrast due to the high levels of noise present in the image. The low contrast made particle selection, classification and orientation determination very difficult. The final purpose of the classification is to improve the signal-to-noise ratio of the particle representing the class, which is usually the average. In this paper, the proposed method is based on wavelet filtering and multi-resolution processing for the classification and reconstruction of this very noisy data. A multivariate statistical analysis (MSA) is used for this classification. RESULTS: The MSA classification method is noise dependent. A set of 2600 projections from a 3D map of a herpes simplex virus--to which noise was added--was classified by MSA. The classification shows the power of wavelet filtering in enhancing the quality of class averages (used in 3D reconstruction) compared to Fourier band pass filtering. A 3D reconstruction of a recombinant virus (VP5-VP19C) is presented as an application of multi-resolution processing for classification and reconstruction. CONCLUSION: The wavelet filtering and multi-resolution processing method proposed in this paper offers a new way for processing very noisy images obtained from electron cryo-microscopes. The multi-resolution and filtering improves the speed and accuracy of classification, which is vital for the 3D reconstruction of biological objects. The VP5-VP19C recombinant virus reconstruction presented here is an example, which demonstrates the power of this method. Without this processing, it is not possible to get the correct 3D map of this virus.

Capsid Proteins↗
PubMed2002

De-noising of left ventricular myocardial borders in magnetic resonance images.

In short axis left ventricular MR images, endocardial borders are the major parameters in evaluation of cardiovascular functions such as end diastolic volume, end systolic volume, and ejection fraction. Functional analysis captures the dynamic behavior of the cardiovascular system as revealed by the movement of the endocardial borders over time. Because of the huge number of MR images, an effective computerized tool is required for real time applications. One of the widely used automatic border detection algorithm-dynamic programming-generates zigzag borderlines, which lead to measurement errors. This paper surveys the performance of the wavelet adaptive filter, the snake, and the medial filter in smoothing over the zigzag borders generated by dynamic programming. Statistical analysis of two hundred and sixty four images from sixteen subjects show that all three algorithms can reduce the border line errors in terms of Hausdorff distance and border area error; however, only the wavelet adaptive filter is effective in providing the physiological measurements such as ejection fraction, end systolic volume and end diastolic volume.

Algorithms↗
PubMed1997

Prescreening entire mammograms for masses with artificial neural networks: preliminary results.

RATIONALE AND OBJECTIVES: The authors evaluated the feasibility of combining wavelet transform and artificial neural network (ANN) technologies to prescreen mammograms for masses. METHODS AND MATERIALS: Fifty-five mammograms (29 with masses and 26 without) were digitized to 100-mm resolution and processed by using wavelet transformation. These wavelets were subjected to a linear output sequential recursive auto-associative memory ANN and cluster analysis with feature vector formation. These vectors were used in two separate experiments-one with 13 cases and another with seven cases held out in a test set-to train feed-forward ANNs to detect the mammograms with a mass. The experiments were repeated with rerandomization of the data, four and six times, respectively. RESULTS: There was a statistically significant correlation (P < .01) between the network's prediction of a mass and the presence of a mass. With majority voting, the feed-forward ANNs detected masses with 79% sensitivity and 50% specificity. CONCLUSION: Although preliminary, the combination of wavelet transform and ANN is promising and may provide a viable method to prescreen mammograms for masses with high sensitivity and reasonable specificity.

Breast Neoplasms↗
PubMed2001

Ultrasound and angio image compression by cosine and wavelet transforms.

The investigation results for improving lossy compression techniques for ultrasound and angio images are presented. The goal was to determine where the compression process could be improved for the medical application, and to make efforts to improve it. It is proved that the wavelet transform outperforms the discrete cosine transform applied to ultrasound and angio images. A lot of wavelet classes were tried for choosing the best one suited for corresponding image classes, which were characterised by a content complexity criterion. The analysis of international image compression standards was carried out. Special attention was paid to an algorithmical and high level service structure of a new still image compression standard JPEG2000. Its open architecture enables including some wavelet classes which we would like to suggest for medical images. A set of recommendations for acceptable compression ratio for different medical image modalities was developed. It was carried out on the base of compression study performed by the group of angiologists and cardiologists.

Algorithms↗
PubMed1997

Effect of cage size on ultradian locomotor rhythms of laboratory mice.

The effect of cage size on spontaneous locomotor rhythms of laboratory mice was studied under simulated light-dark (12:12) cycles. On-line image analysis of bodily displacement yielded a locomotor signal over a period of 3 days. Continuous wavelet transform was applied to the signal, and ensemble averaging of eight mice revealed in the time-frequency plot bouts of increased motor activities. Notably, there were two bouts in the dark corresponding to ultradians of periods below 5 h: a first bout at the dark onset (at 0.6-1.0 cycle/h), and a second bout during the second half of the dark period (at 0.4-0.7 cycle/h). These increases of activity were more intense and distinct when the animals were kept inside the larger cage. Furthermore, the first bout disappeared when the animals were kept in the small cage for 3 days.

Activity Cycles↗
PubMed2006

Optimization algorithms and weighting factors for analysis of dynamic PET studies.

Positron emission tomography (PET) pharmacokinetic analysis involves fitting of measured PET data to a PET pharmacokinetic model. The fitted parameters may, however, suffer from bias or be unrealistic, especially in the case of noisy data. There are many optimization algorithms, each having different characteristics. The purpose of the present study was to evaluate (1) the performance of different optimization algorithms and (2) the effects of using incorrect weighting factors during optimization in terms of both accuracy and reproducibility of fitted PET pharmacokinetic parameters. In this study, the performance of commonly used optimization algorithms (i.e. interior-reflective Newton methods) and a simulated annealing (SA) method was evaluated. This SA algorithm, known as basin hopping, was modified for the present application. In addition, optimization was performed using various weighting factors. Algorithms and effects of using incorrect weighting factors were studied using both simulated and clinical time-activity curves (TACs). Input data, taken from [(15)O]H(2)O, [(11)C]flumazenil and [(11)C](R)-PK11195 studies, were used to simulate time-activity curves at various variance levels (0-15% COV). Clinical evaluation was based on studies with the same three tracers. SA was able to produce accurate results without the need for selecting appropriate starting values for (kinetic) parameters, in contrast to the interior-reflective Newton method. The latter gave biased results unless it was modified to allow for a range of starting values for the different parameters. For patient studies, where large variability is expected, both SA and the extended Newton method provided accurate results. Simulations and clinical assessment showed similar results for the evaluation of different weighting models in that small to intermediate mismatches between data variance and weighting factors did not significantly affect the outcome of the fits. Large errors were observed only when the mismatch between weighting model and data variance was large. It is concluded that selection of specific optimization algorithms and weighting factors can have a large effect on the accuracy and precision of PET pharmacokinetic analysis. Apart from carefully selecting appropriate algorithms and variance models, further improvement in accuracy might be obtained by using noise reducing strategies, such as wavelet filtering, provided that these methods do not introduce significant bias.

Algorithms↗
PubMed2005

Acoustic diagnosis of aortic stenosis.

BACKGROUND AND AIM OF THE STUDY: Phonocardiography is a promising non-invasive diagnostic tool for the assessment of aortic stenosis (AS), and time-frequency representation is a potential tool to extract information from the phonocardiogram (PCG) signal. The study aim was to develop an acoustical method to predict the severity of AS. METHODS: Normalized continuous wavelet transform (NCWT) and fast Fourier Transform (FFT) were used to perform a spectral analysis of the PCG signal. A multi-peak detection algorithm was developed to determine the dominant frequency (DF) of systolic murmurs (SM). The spectral ratio of the SM, integration of the NCWT of SM (SI), and combined information of SM and second heart sound, were also calculated. RESULTS: The DF correlated best with the hemodynamic data: r = -0.72 with aortic valve (AV) area; r = 0.63 with maximal blood velocity through the AV; and r = 0.57 with mean pressure gradient across the AV. Based on DF and SI data, the study subjects (n = 59) were classified into three categories: severe AS; moderate AS; and other cases. The acoustical and echo classifications were in agreement in 50 subjects (85%). CONCLUSION: The acoustical method developed cannot predict accurately the severity of AS, but is valuable when conducting a screening classification before an invasive method is used.

Aged↗
PubMed2000

[Analysis of ultrasonic backscattering microstructure feature of human spleen based on "WD cepstrum"].

A new method "WD cepstrum" for analysing ultrasonic scattered signals based on wavelets decomposition before cepstrum has been developed and then applied to process the backscattered signals of normal and pathological human spleen in vitro. The mean scatterer spacing(MSS) has been estimated. The results show that the MSS data of the two tissues are quite different and "WD cepstrum" is more effective for reflecting the microstructure feature of tissue. The results also illustrate that "WD cepstrum" is an effective method for signal analysis of ultrasonic scattering and characterization of tissue scatterers.

Humans↗
PubMed2006

Gabor-based kernel PCA with doubly nonlinear mapping for face recognition with a single face image.

In this paper, a novel Gabor-based kernel principal component analysis (PCA) with doubly nonlinear mapping is proposed for human face recognition. In our approach, the Gabor wavelets are used to extract facial features, then a doubly nonlinear mapping kernel PCA (DKPCA) is proposed to perform feature transformation and face recognition. The conventional kernel PCA nonlinearly maps an input image into a high-dimensional feature space in order to make the mapped features linearly separable. However, this method does not consider the structural characteristics of the face images, and it is difficult to determine which nonlinear mapping is more effective for face recognition. In this paper, a new method of nonlinear mapping, which is performed in the original feature space, is defined. The proposed nonlinear mapping not only considers the statistical property of the input features, but also adopts an eigenmask to emphasize those important facial feature points. Therefore, after this mapping, the transformed features have a higher discriminating power, and the relative importance of the features adapts to the spatial importance of the face images. This new nonlinear mapping is combined with the conventional kernel PCA to be called "doubly" nonlinear mapping kernel PCA. The proposed algorithm is evaluated based on the Yale database, the AR database, the ORL database and the YaleB database by using different face recognition methods such as PCA, Gabor wavelets plus PCA, and Gabor wavelets plus kernel PCA with fractional power polynomial models. Experiments show that consistent and promising results are obtained.

Algorithms↗