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PubMed1999

Partial wavelet encoding: a new approach for accelerating temporal resolution in contrast-enhanced MR imaging.

We propose a new approach using wavelet encoding to improve temporal resolution in contrast-enhanced magnetic resonance (MR) imaging. Exploiting the unique property of wavelets localized in space and frequency, we construct an efficient encoding scheme to capture signal changes due to contrast agent uptake, which in general is spatially localized with low- and mid-range frequency components. On the basis of space-frequency analysis, we describe mathematical formulations of our method and discuss its theoretical advantages over Fourier-based phase-encoding methods (the keyhole and reduced-encoding imaging by generalized-series reconstruction [RIGR] techniques). The results obtained in computer simulations and a phantom study demonstrate the feasibility and practical advantages of our approach.

Computer Simulation↗
PubMed2006

Classification enhancible grey relational analysis for cardiac arrhythmias discrimination.

This paper proposes a method for electrocardiogram (ECG) heartbeat recognition using classification enhancible grey relational analysis (GRA). The ECG beat recognition can be divided into a sequence of stages, starting with feature extraction and then according to characteristics to identify the cardiac arrhythmias including the supraventricular ectopic beat, bundle branch ectopic beat, and ventricular ectopic beat. Gaussian wavelets are used to enhance the features from each heartbeat, and GRA performs the recognition tasks. With the MIT-BIH arrhythmia database, the experimental results demonstrate the efficiency of the proposed non-invasive method. Compared with artificial neural network, the test results also show high accuracy, good adaptability, and faster processing time for the detection of heartbeat signals.

Arrhythmias, Cardiac↗
PubMed2002

A comparison of two compression algorithms and the detection of caries.

OBJECTIVES: To assess the effect of two compression algorithms (JPEG and wavelet) on the detection of approximal caries. METHODS: Fifteen bitewing radiographs were generated using 100 posterior teeth mounted in blocks. The images were produced on conventional films (Ektaspeed Plus) and scanned at 300 d.p.i. Digital images were then compressed 9:1 with JPEG and wavelet methods. Nine observers detected the presence and depth of approximal caries recorded on a 5-point confidence scale and a 4-point depth scale from images viewed in random order. Histological examination provided the true depth of the lesions. Data were analysed by means of ANOVA. The null hypothesis was that there is no significant difference between the two compression algorithms and the original uncompressed images. RESULTS: JPEG performed significantly worse than the original and the wavelet algorithm (P<0.001) for the detection of dentinal lesions. However, no significant differences were found for the detection of sound surfaces, enamel lesions, and lesions up to the DEJ between JPEG-compressed images and each of the other two modalities. There was also no significant difference between the wavelet-compressed images and the original for all lesion depths. CONCLUSIONS: At a compression ratio of 9:1, there were no significant differences among the original images, JPEG and wavelet compressed images for the detection of enamel caries. JPEG-compressed images performed inferiorly to the original and wavelet-compressed images for the detection of dentinal lesions. Wavelet compression is a better choice than JPEG at the compression ratio investigated in this study.

Algorithms↗
PubMed2003

Instantaneous frequency and its application to strain extraction in moire interferometry.

Moire interferometry is an effective experimental technique for measurement of in-plane deformation. However, it is information on the derivatives of the deformation, i.e., strains, that is usually desired in experimental mechanics. It is shown that the desired strains are the instantaneous frequencies of the fringe pattern and that either an energy operator or wavelet ridges can be used to extract the instantaneous frequencies from a single fringe pattern. The energy operator is a pixelwise processor; thus the strain extraction can be done on the fly, but it is sensitive to noise. The wavelet ridges extract the local features in the fringe pattern. The strain extraction is thus insensitive to noise, and good results are obtainable at the cost of longer computation time. The two methods can thus be chosen for different needs in strain analysis. The properties of the two methods as well as their applications to a real fringe pattern are given. The effectiveness of the proposed methods is illustrated by their comparison with traditional methods.

Journal Article↗
PubMed2004

Rapid detection of conserved regions in protein sequences using wavelets.

We present an algorithm to detect protein sub-structural motifs from primary sequence. The input to the algorithm is a set of aligned multiple protein sequences. It uses wavelet transforms to decompose protein sequences represented numerically by different indices (such as polarity, accessible surface area or electron-ion integration potentials of the amino acids). The numerical representation of a protein sequence has significant correlation with its biological activity, thus common motifs are expected to be observable from the wavelet spectrum. The decomposed signals are then up-sampled and similarity search techniques are used to identify similar regions across all the proteins at multiple scales. Results indicate that wavelet transform techniques are a promising approach for rapid motif detection.

Algorithms↗
PubMed1998

Analysis of oscillatory potentials of flash electroretinogram in frequency domain and time domain in retinal vein occlusion.

PURPOSE: To analyse the parameters of frequency domain and time domain of the OPs in branch retinal vein occlusion (BRVO) and central retinal vein occlusion (CRVO). METHODS: OPs from 9 eyes of BRVO, 9 eyes of CRVO and 40 normal control eyes were tested. The frequency spectra of the OPs were derived through Fast Fourier Transform (FFT). The mean frequency spectra, the total power, the maximum magnitude, the maximum power and the dominant frequency in frequency domain of the OPs from the eyes of BRVO, CRVO and the normal control group were compared with each other. The OPs waveforms were reconstructed with the OPs frequency spectra data by counter-FFT. In time domain, the latencies, the amplitudes and the sum of the amplitudes of the four wavelets of OPs were measured and compared in the above subjects. RESULTS: There were no statistically significant differences in the magnitudes of frequency spectra, the total power, the maximum magnitude and the maximum power in the frequency range from 110 to 250 Hz between BRVO and normal subjects. However, in BRVO, the dominant frequency decreased significantly, the latencies of the first to the third OPs wavelet prolonged significantly, the amplitudes of the first and the second OPs wavelet and the summed amplitude of the four OPs wavelets attenuated significantly. Comparing the results of CRVO with those of normal subjects, there were statistically significant decrease in the magnitudes of frequency spectra from 150 to 180 Hz, there were statistically significant decrease in the total power, the maximum magnitude, the maximum power and the dominant frequency in the frequency range from 110 to 250 Hz, the latencies of the first to the fourth OPs wavelet prolonged significantly, the amplitudes of the first to the third OPs wavelet and the summed amplitude of the four OPs wavelets attenuated significantly. CONCLUSION: Some parameters of OPs in frequency domain and time domain are abnormal in branch retinal vein occlusion and in central retinal vein occlusion, which is more marked in the latter.

Aged↗
PubMed2006

Limitations and applications of ICA for surface electromyogram.

Surface electromyogram (SEMG) has numerous applications, but the presence of artefacts and noise, especially at low level of muscle activity make the recordings unreliable. Spectral and temporal overlap can make the removal of artefacts and noise, or separation of relevant signals from other bioelectric signals extremely difficult. Individual muscles may be considered as independent at the local level and this makes an argument for separating the signals using independent component analysis (ICA). In the recent past, due to the easy availability of ICA tools, numbers of researchers have attempted to use ICA for this application. This paper reports research conducted to evaluate the use of ICA for the separation of muscle activity and removal of the artefacts from SEMG. It discusses some of the conditions that could affect the reliability of the separation and evaluates issues related to the properties of the signals and number of sources. The paper also identifies the lack of suitable measure of quality of separation for bioelectric signals and it recommends and tests a more robust measure of separation. The paper also reports tests using Zibulevsky's technique of temporal plotting to identify number of independent sources in SEMG recordings. The theoretical analysis and experimental results demonstrate that ICA is suitable for SEMG signals. The results identify the unsuitability of ICA when the number of sources is greater than the number of recording channels. The results also demonstrate the limitations of such applications due to the inability of the system to identify the correct order and magnitude of the signals. The paper determines the suitability of the use of error measure using simulated mixing matrix and the estimated unmixing matrix as a means identifying the quality of separation of the output. The work demonstrates that even at extremely low level of muscle contraction, and with filtering using wavelets and band pass filters, it is not possible to get the data sparse enough to identify number of independent sources using Zibulevs.ky's technique.

Algorithms↗
PubMedDate not supplied

New developments in PEST shape/property hybrid descriptors.

Recent investigations have shown that the inclusion of hybrid shape/property descriptors together with 2D topological descriptors increases the predictive capability of QSAR and QSPR models. Property-Encoded Surface Translator (PEST) descriptors may be computed using ab initio or semi-empirical electron density surfaces and/or electronic properties, as well as atomic fragment-based TAE/RECON property-encoded surface reconstructions. The RECON and PEST algorithms also include rapid fragment-based wavelet coefficient descriptor (WCD) computation. These descriptors enable a compact encoding of chemical information. We also briefly discuss the use of the RECON/PEST methodology in a virtual high-throughput mode, as well as the use of TAE properties for molecular surface autocorrelation analysis.

Algorithms↗
PubMed2001

Variability of the QRS signal in high-resolution electrocardiograms and magnetocardiograms.

The variability of electric and magnetic signals from the heart during the depolarization phase is investigated. A signal processing method is developed, which provides estimates for the beat-to-beat variability of the QRS-complex. The method is based on the decomposition of the depolarization signal into bandpass signals by means of the Morlet wavelet transform. The beat variability of the depolarization signal is estimated by normalized variances of the envelope and instantaneous frequency of bandpass signals. Time intervals of the bandpass filtered depolarization signals having a high signal-to-noise ratio are selected applying an analysis based on phase statistics. The method was tested by computer simulation and experimental data taken from electrocardiographic and magnetocardiographic measurements of healthy persons and patients prone to malignant ventricular tachycardia (VT) or ventricular fibrillation (VF). Results suggest that the calculated variance parameters permit the characterization of beat variable depolarization signals and distinguish VT/VF patients from healthy persons.

Algorithms↗
PubMed2003

Analysis of event-related fMRI data using best clustering bases.

We explore a new paradigm for the analysis of event-related functional magnetic resonance images (fMRI) of brain activity. We regard the fMRI data as a very large set of time series x(i)(t), indexed by the position i of a voxel inside the brain. The decision that a voxel i0 is activated is based not solely on the value of the fMRI signal at i0, but rather on the comparison of all time series x(i)(t) in a small neighborhood Wi0 around i0. We construct basis functions on which the projection of the fMRI data reveals the organization of the time-series x(i)(t) into "activated", and "non-activated" clusters. These "clustering basis functions" are selected from large libraries of wavelet packets according to their ability to separate the fMRI time-series into the activated cluster and a non activated cluster. This principle exploits the intrinsic spatial correlation that is present in the data.

Algorithms↗
PubMed1998

Data compression for diffraction patterns.

Efficient coding (lossless) and compression (lossy) of diffraction patterns is important in protein crystallography experiments because of storage and transmission limitations. The goal is to reduce the bit-rate significantly while keeping diffraction peak intensity distortion at an acceptable level. This paper presents an overview of coding and compression techniques more or less adapted to such problems. A large part of this study is dedicated to time-frequency-transform based compression algorithms and some of their extensions. Wavelet based software has been developed and tested. Results are compared with the discrete cosine transform (DCT) and other classical algorithms. These tools seem attractive and very promising for analyzing and compressing signals with singularities and transient phenomena such as diffraction peaks. Tests were performed on a standard protein crystallography data set coming from the CCD detector of D2AM beamline at the European Synchrotron Radiation Facility at Grenoble. These were compressed with DCT and wavelet-based algorithms. It appears that alterations of the result of the processing of restored images remain very weak for compression rates up to 10. These preliminary results indicate that the proposed wavelet method is a good standard technique for efficient compression of diffraction patterns.

Algorithms↗
PubMed2005

Sparse geometric image representations with bandelets.

This paper introduces a new class of bases, called bandelet bases, which decompose the image along multiscale vectors that are elongated in the direction of a geometric flow. This geometric flow indicates directions in which the image gray levels have regular variations. The image decomposition in a bandelet basis is implemented with a fast subband-filtering algorithm. Bandelet bases lead to optimal approximation rates for geometrically regular images. For image compression and noise removal applications, the geometric flow is optimized with fast algorithms so that the resulting bandelet basis produces minimum distortion. Comparisons are made with wavelet image compression and noise-removal algorithms.

Algorithms↗
PubMed2006

Wavelet-based tachycardia discrimination in ICDs: Impact of posture and electrogram configuration.

BACKGROUND: Inappropriate therapy delivery is an important concern in the management of patients with implantable cardioverter defibrillators (ICDs). Recently, a morphology-based algorithm (wavelet feature) has been introduced for differentiation of ventricular and supraventricular tachycardia. In this study, we evaluated the performance of the wavelet algorithm using various electrogram (EGM) configurations during different body positions. METHODS: Patients with a single-chamber Medtronic model 7230 ICD (Minneapolis, MN, USA) and a double-coil lead were included. EGM templates were collected during baseline rhythm in supine position for different EGM sources (right ventricular [RV] coil-can, RV coil-superior vena cava [SVC] coil, tip-ring, SVC coil-can). For each EGM configuration, morphologic similarity (match percentage) of EGMs obtained during different body positions (supine, left and right lateral, sitting, standing, walking) were compared with the templates. RESULTS: Twenty-eight patients (24 males; age 58 +/- 17 years) were studied. A total of 9,775 intracardiac EGMs were analyzed. Median match percentage (interquartile range) was 88% (85-94), 88% (82-94), 82% (76-88), and 73 (58-85) for the RV coil-can, RV coil-SVC coil, tip-ring, and SVC coil-can configurations, respectively. Correct classification rates, as defined by match percentage of 70% or higher, were significantly higher with the RV coil-can, RV coil-SVC coil, and tip-ring EGM configurations, as compared to the SVC coil-can configuration (95, 91, and 91 vs 58% > or =70% match percent, P < 0.001). CONCLUSION: Wavelet-based morphology scores in ICDs may change with various body positions. These variations are relatively minor using the nominal configuration (RV coil-can), as well as by using RV coil-SVC coil and tip-ring. However, morphology scores can vary considerably when SVC coil-can is used; therefore, this configuration should be avoided while using the wavelet algorithm.

Algorithms↗
PubMed1998

ECG compression: evaluation of FFT, DCT, and WT performance.

This work investigates a set of ECG data compression schemes to compare their performances in compressing and preparing ECG signals for automatic cardiac arrhythmia classification. These schemes are based on transform methods such as fast Fourier transform (FFT), discrete cosine transform (DCT), wavelet transform (WT), and their combinations. Each specific transform is applied to a pre-selected data segment from the MIT-BIH database and then compression is performed in the new domain. These transformation methods are known as an important class of ECG compression techniques. The WT has been shown as the most efficient method for further improvement. A compression ratio of 7.98 to 1 has been achieved with a percent of root mean square difference (PRD) of 0.25%, indicating that the wavelet compression technique offers the best performance over the other evaluated methods.

Algorithms↗
PubMed2005

A fuzzy theory of cortical computation: neuropoietic engrams, fuzzy hypercubes, and the nature of consciousness.

We apply fuzzy logic to a theory of memory representation and computation in the human cerebral cortex. The theory termed neuropoiesis is based on the hypothetical transfer of mRNA polyribosomes from the post-synaptic dendritic spine of cortical pyramidal neurons to the presynaptic boutons of connecting axons through a hypothetical process termed retroduction. The net effect of this process is a vast increase in predicted memory storage. The fuzzification of memory engrams permits this expansion in memory capacity and requires multiplex signaling which, in turn, requires the formation of a spike packet whose length is determined by the EEG frequencies generated by the thalamo-cortical rhythm generators. The role of the EEG frequencies is to provide a wavelet-like transform of the multiplex signal which in turn determines the degree of data compression that is required for memory recruitment at a given level of vigilance during cortical computation. In this conceptual model cortical computation is hypothesized to be a form of cluster analysis that can be represented by a fuzzy hypercube in which each dimension of the unit hypercube represents an apical dendrite of a layer 5 pyramidal cell in a cortical fascicle. The tuftal area of the apical dendrite in cortical layer one corresponds to the MIN or zero point of the hypercube's dimension and the cell body in layer 5 corresponds to the MAX or one point of that dimension in the unit hypercube. The neuroanatomical location of synapses on the apical dendrites in the fascicle is mapped onto the fuzzy hypercube. These synapses form clusters composed of both bottom-up and top-down signals represented as metasynaptic fuzzy sets-as-points in the hypercube. Soft winner-take-all gating by inhibitory neurons is proposed to supply the only non-linear operation needed for cortical computation. Feed-forward inhibition is envisioned to play the decisive role of spicing or de-fuzzifying the output signal. Proper transmission of the multiplex signal that carries the fuzzy engram requires synchrony of neuronal firing. For this fuzzy cortical model, synchrony of firing, multiplex signaling, winner-take-all gating, and the known spectrum of EEG frequencies are all derivable from the fundamental mechanism termed synaptopoiesis as described in the theory of neuropoiesis. Finally, this theory predicts that the neural correlate of consciousness must include inhibitory subcortical connections and that its function is largely that of limiting coherence to a narrow range of cortical engrams.

Cerebral Cortex↗
PubMed2006

Adaptive dynamic surface control of flexible-joint robots using self-recurrent wavelet neural networks.

A new method for the robust control of flexible-joint (FJ) robots with model uncertainties in both robot dynamics and actuator dynamics is proposed. The proposed control system is a combination of the adaptive dynamic surface control (DSC) technique and the self-recurrent wavelet neural network (SRWNN). The adaptive DSC technique provides the ability to overcome the "explosion of complexity" problem in backstepping controllers. The SRWNNs are used to observe the arbitrary model uncertainties of FJ robots, and all their weights are trained online. From the Lyapunov stability analysis, their adaptation laws are induced, and the uniformly ultimately boundedness of all signals in a closed-loop adaptive system is proved. Finally, simulation results for a three-link FJ robot are utilized to validate the good position tracking performance and robustness against payload uncertainties and external disturbances of the proposed control system.

Journal Article↗
PubMed2006

Fuzzy clustering of Raman spectral imaging data with a wavelet-based noise-reduction approach.

Raman spectral imaging has been widely used for extracting chemical information from biological specimens. One of the challenges is to cluster the chemical groups from the vast amount of hyperdimensional spectral imaging data so that functionally similar groups can be identified. In this paper, we present an approach that combines a differential wavelet-based data smoothing with a fuzzy clustering algorithm for the classification of Raman spectral images. The preprocessing of the spectral data is facilitated by decomposing them in the differential wavelet domain, where the discrimination of true spectral features and noise can be easily performed using a multi-scale pointwise product (MPP) criterion. This approach is applied to the classification of spectral data collected from adhesive/dentin interface specimens where the spectral data exhibit different signal-to-noise ratios. The proposed wavelet approach has been compared to several conventional noise-removal algorithms.

Adhesives↗
PubMed2006

Improved signal and reduced noise in neural recordings from close-spaced electrode arrays using independent component analysis as a preprocessor.

Noise can greatly complicate the isolation of individual cell action potential waveforms for the sake of electrophysiological analysis. For an experiment involving recording in the thalamus/subthalamus areas of a rat brain, a hybrid hardware/software method was utilized to improve the signal-to-noise quality of the recorded signal on each recording channel. The procedure uses closely spaced recording electrode arrays and independent component analysis (ICA) to fortify the signal energy of a single spike by combining input from several channels, and concurrently to reduce the noise on each channel by isolating common mode components such as artifacts, slow waves, and correlated distant spike activation. In the next step, a wavelet denoising-based signal-to-noise assessment is used to quantify the improvement in data quality for each data record. The data presented here demonstrate that this method, which can be applied off-line as a preprocessor to other time domain or transform domain spike sorting methods, is consistently effective at improving data quality and facilitating subsequent detection and classification of neurons.

Action Potentials↗