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PubMed2000

Finding pathogenicity islands and gene transfer events in genome data.

MOTIVATION: There is a growing literature on wavelet theory and wavelet methods showing improvements on more classical techniques, especially in the contexts of smoothing and extraction of fundamental components of signals. G+C patterns occur at different lengths (scales) and, for this reason, G+C plots are usually difficult to interpret. Current methods for genome analysis choose a window size and compute a chi(2) statistics of the average value for each window with respect to the whole genome. RESULTS: Firstly, wavelets are used to smooth G+C profiles to locate characteristic patterns in genome sequences. The method we use is based on performing a chi(2) statistics on the wavelet coefficients of a profile; thus we do not need to choose a fixed window size, in that the smoothing occurs at a set of different scales. Secondly, a wavelet scalogram is used as a measure for sequence profile comparison; this tool is very general and can be applied to other sequence profiles commonly used in genome analysis. We show applications to the analysis of Deinococcus radiodurans chromosome I, of two strains of Helicobacter pylori (26695, J99) and two of Neisseria meningitidis (serogroup B strain MC58 and serogroup A strain Z2491). We report a list of loci that have different G+C content with respect to the nearby regions; the analysis of N. meningitidis serogroup B shows two new large regions with low G+C content that are putative pathogenicity islands. AVAILABILITY: Software and numerical results (profiles, scalograms, high and low frequency components) for all the genome sequences analyzed are available upon request from the authors.

Base Composition↗
PubMed2007

Usefulness of texture analysis for computerized classification of breast lesions on mammograms.

This work presents the usefulness of texture features in the classification of breast lesions in 5,518 images of regions of interest, which were obtained from the Digital Database for Screening Mammography that included microcalcifications, masses, and normal cases. Sixteen texture features were used, i.e., 13 were based on the spatial gray-level dependence matrix and 3 on the wavelet transform. The nonparametric K-NN classifier was used in the classification stage. The results obtained from receiver operating characteristic analysis indicated that the texture features can be used for separating normal regions and lesions with masses and microcalcifications, yielding the area under the curve (AUC) values of 0.957 and 0.859, respectively. However, the texture features were not very effective for distinguishing between malignant and benign lesions because the AUC was 0.617 for masses and 0.607 for microcalcifications. The study showed that the texture features can be used for the detection of suspicious regions in mammograms.

Breast Neoplasms↗
PubMed2004

Image representation by complex cell responses.

We present an analysis of the representation of images as the magnitudes of their transform with complex-valued Gabor wavelets. Such a representation is a model for complex cells in the early stage of visual processing and of high technical usefulness for image understanding, because it makes the representation insensitive to small local shifts. We show that if the images are band limited and of zero mean, then reconstruction from the magnitudes is unique up to the sign for almost all images.

Algorithms↗
PubMed2004

Analysis of cardiac signals using spatial filling index and time-frequency domain.

BACKGROUND: Analysis of heart rate variation (HRV) has become a popular noninvasive tool for assessing the activities of the autonomic nervous system (ANS). HRV analysis is based on the concept that fast fluctuations may specifically reflect changes of sympathetic and vagal activity. It shows that the structure generating the signal is not simply linear, but also involves nonlinear contributions. These signals are essentially non-stationary; may contain indicators of current disease, or even warnings about impending diseases. The indicators may be present at all times or may occur at random in the time scale. However, to study and pinpoint abnormalities in voluminous data collected over several hours is strenuous and time consuming. METHODS: This paper presents the spatial filling index and time-frequency analysis of heart rate variability signal for disease identification. Renyi's entropy is evaluated for the signal in the Wigner-Ville and Continuous Wavelet Transformation (CWT) domain. RESULTS: This Renyi's entropy gives lower 'p' value for scalogram than Wigner-Ville distribution and also, the contours of scalogram visually show the features of the diseases. And in the time-frequency analysis, the Renyi's entropy gives better result for scalogram than the Wigner-Ville distribution. CONCLUSION: Spatial filling index and Renyi's entropy has distinct regions for various diseases with an accuracy of more than 95%.

Electrocardiography↗
PubMed2005

Optimization of dynamic measurement of receptor kinetics by wavelet denoising.

The most important technical limitation affecting dynamic measurements with PET is low signal-to-noise ratio (SNR). Several reports have suggested that wavelet processing of receptor kinetic data in the human brain can improve the SNR of parametric images of binding potential (BP). However, it is difficult to fully assess these reports because objective standards have not been developed to measure the tradeoff between accuracy (e.g. degradation of resolution) and precision. This paper employs a realistic simulation method that includes all major elements affecting image formation. The simulation was used to derive an ensemble of dynamic PET ligand (11C-raclopride) experiments that was subjected to wavelet processing. A method for optimizing wavelet denoising is presented and used to analyze the simulated experiments. Using optimized wavelet denoising, SNR of the four-dimensional PET data increased by about a factor of two and SNR of three-dimensional BP maps increased by about a factor of 1.5. Analysis of the difference between the processed and unprocessed means for the 4D concentration data showed that more than 80% of voxels in the ensemble mean of the wavelet processed data deviated by less than 3%. These results show that a 1.5x increase in SNR can be achieved with little degradation of resolution. This corresponds to injecting about twice the radioactivity, a maneuver that is not possible in human studies without saturating the PET camera and/or exposing the subject to more than permitted radioactivity.

Algorithms↗
PubMed1997

Multichannel wavelet-type decomposition of evoked potentials: model-based recognition of generator activity.

Scalp recording of electrical events allows the evaluation of human cerebral function, but contributions of the specific brain structures generating the recorded activity are ambiguous. This problem is ill-posed and cannot be solved without physiological constraints based on the spatio-temporal characteristics of the generators' activity. In our model-based analysis of evoked potentials for the purpose of generator activity detection, multichannel scalp-recorded signals are decomposed into a combination of wavelets, each of which can describe the neural mass coherent activity of cell assemblies. Elimination of contributions of specific generators and/or distributed background activity can produce physiologically motivated time-frequency filtering. The decomposition and filtering procedures are demonstrated by three examples; simulation of the surface manifestation of known intracranial generators; decomposition and reconstruction of auditory brainstem evoked potentials which reflect the differences among generators of these potentials; and cognitive components of evoked potentials which are diminished in the averaged recording but are clearly detected in single-trial signals.

Algorithms↗
PubMed2006

A method for quantifying dynamic muscle dysfunction in children and young adults with cerebral palsy.

Cerebral palsy (CP) is caused by a lesion to the brain resulting in adaptations to the structure and function of the muscles and compromised mobility. Spastic cerebral palsy is commonly assessed by the limb kinematics and kinetics measured in a gait laboratory. However, these measures do not directly quantify the patterns of muscle dysfunction that occur during movements. Recent studies have shown that electromyographic (EMG) signals from children with CP have abnormal magnitude, timing and frequency content. Here we demonstrate how wavelet decomposition of the EMG signals into time-frequency space coupled to principal component analysis of the EMG spectra can be used as a powerful tool to quantify the patterns of muscle dysfunction. Data were compared between 17 children with spastic diplegic CP and 36 asymptomatic controls for the rectus femoris, semimembranosus, medial gastrocnemius and tibialis anterior muscles. CP muscle generated higher mean EMG frequencies. Imbalances in activity between the tibialis anterior and medial gatrocnemius contributed to equinus ankle during the swing phase. Patterns of co-activations between antagonistic muscles differed between CP and asymptomatic patients and were EMG frequency dependent. Muscle dysfunction was greater in the distal compared to the proximal lower limb. Muscle dysfunction between the tibialis anterior and medial gastrocnemius was distinguished with 96% sensitivity at 95% specificity.

Adolescent↗
PubMed1998

Analysis-synthesis of the phonocardiogram based on the matching pursuit method.

The matching pursuit method of Mallat and Zhang is applied to the analysis and synthesis of phonocardiograms (PCG's). The method is based on a classical Gabor wavelet or time-frequency atom which is the product of a sinusoid and a Gaussian window function. It decomposes a signal into a series of time-frequency atoms by an iterative process based on selecting the largest inner product of the signal (and the subsequent residues) with atoms from a redundant dictionary. The Gaussian window controls the envelope duration and time position of each atom; and the sinusoid represents the frequency. The method was applied to two sets of PCG's: one with very low-noise level and the other with 10% noise energy. Each data base includes 11 PCG's representing the normal and the pathological conditions of the heart. The normalized root-mean-square error (NRMSE) was computed between the original and the reconstructed signals. The results show that the matching pursuit method is very suitable to the transient and complex properties of the PCG's, as it yielded excellent NRMSE's around 2.2% for the two sets of 11 PCG's tested.

Heart Murmurs↗
PubMed2001

An artificial intelligent algorithm for tumor detection in screening mammogram.

Cancerous tumor mass is one of the major types of breast cancer. When cancerous masses are embedded in and camouflaged by varying densities of parenchymal tissue structures, they are very difficult to be visually detected on mammograms. This paper presents an algorithm that combines several artificial intelligent techniques with the discrete wavelet transform (DWT) for detection of masses in mammograms. The AI techniques include fractal dimension analysis, multiresolution markov random field, dogs-and-rabbits algorithm, and others. The fractal dimension analysis serves as a preprocessor to determine the approximate locations of the regions suspicious for cancer in the mammogram. The dogs-and-rabbits clustering algorithm is used to initiate the segmentation at the LL subband of a three-level DWT decomposition of the mammogram. A tree-type classification strategy is applied at the end to determine whether a given region is suspicious for cancer. We have verified the algorithm with 322 mammograms in the Mammographic Image Analysis Society Database. The verification results show that the proposed algorithm has a sensitivity of 97.3% and the number of false positive per image is 3.92.

Algorithms↗
PubMed2006

Chromosomal patterns of gene expression from microarray data: methodology, validation and clinical relevance in gliomas.

BACKGROUND: Expression microarrays represent a powerful technique for the simultaneous investigation of thousands of genes. The evidence that genes are not randomly distributed in the genome and that their coordinated expression depends on their position on chromosomes has highlighted the need for mathematical approaches to exploit this dependency for the analysis of expression data-sets. RESULTS: We have devised a novel mathematical technique (CHROMOWAVE) based on the Haar wavelet transform and applied it to a dataset obtained with the Affymetrix HG-U133_Plus_2 array in 27 gliomas. CHROMOWAVE generated multi-chromosomal pattern featuring low expression in chromosomes 1p, 4, 9q, 13, 18, and 19q. This pattern was not only statistically robust but also clinically relevant as it was predictive of favourable outcome. This finding was replicated on a data-set independently acquired by another laboratory. FISH analysis indicated that monosomy 1p and 19q was a frequent feature of tumours displaying the CHROMOWAVE pattern but that allelic loss on chromosomes 4, 9q, 13 and 18 was much less common. CONCLUSION: The ability to detect expression changes of spatially related genes and to map their position on chromosomes makes CHROMOWAVE a valuable screening method for the identification and display of regional gene expression changes of clinical relevance. In this study, FISH data showed that monosomy was frequently associated with diffuse low gene expression on chromosome 1p and 19q but not on chromosomes 4, 9q, 13 and 18. Comparative genomic hybridisation, allelic polymorphism analysis and methylation studies are in progress in order to identify the various mechanisms involved in this multi-chromosomal expression pattern.

Biomarkers, Tumor↗
PubMed2001

Extraction and analysis of early ictal activity in subdural electroencephalogram.

Subdural electroencephalograms (SEEGs) are of great value in localizing primary epileptogenic regions in patients undergoing evaluation for focal resective epilepsy surgery. The data segments which contain a transition from interictal to ictal activity carry the most critical diagnostic information. Computer signal extraction within this transition period represents a challenging signal processing problem. In this work a two-step method is presented to extract early ictal activity. In the first step we employ a nonlinear signal decomposition technique in the wavelet domain to separate SEEG data into ictal and background components. In the second step we use time-frequency analysis and a novel integration algorithm to extract the desired information. Our experiments on clinically recorded data indicate that this method is highly effective allowing us to reveal important hidden features in the data which could not otherwise be observable.

Adult↗
PubMed2006

Classification of surface EMG signals using optimal wavelet packet method based on Davies-Bouldin criterion.

In this paper we present an optimal wavelet packet (OWP) method based on Davies-Bouldin criterion for the classification of surface electromyographic signals. To reduce the feature dimensionality of the outputs of the OWP decomposition, the principle components analysis was employed. Then we chose a neural network classifier to discriminate four types of prosthesis movements. The proposed method achieved a mean classification accuracy of 93.75%, which outperformed the method using the energy of wavelet packet coefficients (with mean classification accuracy 86.25%) and the fuzzy wavelet packet method (87.5%).

Electromyography↗
PubMed2005

Wavelet-based compression of M-FISH images.

Multiplex fluorescence in situ hybridization (M-FISH) is a recently developed technology that enables multi-color chromosome karyotyping for molecular cytogenetic analysis. Each M-FISH image set consists of a number of aligned images of the same chromosome specimen captured at different optical wavelength. This paper presents embedded M-FISH image coding (EMIC), where the foreground objects/chromosomes and the background objects/images are coded separately. We first apply critically sampled integer wavelet transforms to both the foreground and the background. We then use object-based bit-plane coding to compress each object and generate separate embedded bitstreams that allow continuous lossy-to-lossless compression of the foreground and the background. For efficient arithmetic coding of bit planes, we propose a method of designing an optimal context model that specifically exploits the statistical characteristics of M-FISH images in the wavelet domain. Our experiments show that EMIC achieves nearly twice as much compression as Lempel-Ziv-Welch coding. EMIC also performs much better than JPEG-LS and JPEG-2000 for lossless coding. The lossy performance of EMIC is significantly better than that of coding each M-FISH image with JPEG-2000.

Algorithms↗
PubMed2005

[Analysis on the multi-distribution and the major influencing factors on severe acute respiratory syndrome in Beijing].

OBJECTIVE: To analyse the multi-dimension nature of severe acute respiratory syndrome (SARS) transmission. METHODS: Based on the data of SARS in 2003 and the geographic information system of Beijing, as well as under the broad range of the theorems and techniques of data-driven and model-driven knowledge mining, hierarchical techniques were used to test the hot spots. Wavelet technique was also used to decompose Moran's I frequency to survey the spatial clustering process of SARS. For factors analysis, BW test was used to distinguish factors which influencing SARS process. In temporal aspects, susceptive-infective-removal model (SIR) without Taylor expansion was solved by a genetic-simulated annealing algorithm, that directly provided a new approach to obtain epidemic parameters from the SIR model. RESULTS: Different order of spatial hot spots were noticed and the clustering were relevant with the means of transportation. Diffusion dynamics were changed along with the temporal process of SARS. Regarding factor analysis, geographic relationship, population density, the amount of doctors and hospitals appeared to be the key elements influencing the transmission of SARS. The predictable number of SARS cases evolving with time were also calculated. CONCLUSIONS: Cluster detection of close contacts of SARS infective in Beijing revealed the spatial characters of urban population flow and having important implications in the prevention and control of this communicable diseases. Some human and physical environment factors played statistical significant roles in different periods during SARS epidemics. An efficient algorithm was developed to solve SIR model directly, enabling the estimation of epidemic parameters from SIR and early forecast.

Algorithms↗
PubMed2000

Wavelet-based enhancement for detection of left ventricular myocardial boundaries in magnetic resonance images.

MRI is noninvasive and generates clear images, giving it great potential as a diagnostic instrument. However, current methods of image analysis are too time-consuming for dynamic systems such as the cardiovascular system. Since dynamic imagery generate a huge number of images, a computer aided machine vision diagnostic tool is essential for implementing MRI-based measurement. In this paper, a wavelet-based image technique is applied to enhance left ventricular endocardial and epicardial profiles as the preprocessor for a dynamic programming-based automatic border detection algorithm. Statistical tests are conducted to verify the performance of the enhancement technique by comparing borders manually drawn with 1. borders generated from the enhanced images, and 2. borders generated for the original images.

Algorithms↗
PubMed1999

Evaluation of frequency and time-frequency spectral analysis of heart rate variability as a diagnostic marker of the sleep apnoea syndrome.

The sleep apnoea/hypopnoea syndrome (SAHS) elicits a unique heart rate rhythm that may provide the basis for an effective screening tool. The study uses the receiver operator characteristic (ROC) to assess the diagnostic potential of spectral analysis of heart rate variability (HRV) using two methods, the discrete Fourier transform (DFT) and the discrete harmonic wavelet transform (DHWT). These two methods are compared over different sleep stages and spectral frequency bands. The HRV results are subsequently compared with those of the current screening method of oximetry. For both the DFT and the DHWT, the most diagnostically accurate frequency range for HRV spectral power calculations is found to be 0.019-0.036 Hz (denoted by AB2). Using AB2, 15 min sections of non-REM sleep data in 40 subjects produce ROC areas, for the DFT, DHWT and oximetry, of 0.94, 0.97 and 0.67, respectively. In REM sleep, ROC areas are 0.78, 0.79 and 0.71, respectively. In non-REM sleep, spectral analysis of HRV appears to be a significantly better indicator of the SAHS than the current screening method of oximetry, and, in REM sleep, it is comparable with oximetry. The advantage of the DHWT over the DFT is that it produces a greater time resolution and is computationally more efficient. The DHWT does not require the precondition of stationarity or interpolation of raw HRV data.

Adult↗