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PubMed2006

Improving SVT discrimination in single-chamber ICDs: a new electrogram morphology-based algorithm.

INTRODUCTION: Wide-spread adoption of ICD therapy has focused efforts on improving the quality of life for patients by reducing "inappropriate" shock therapies. To this end, distinguishing supraventricular tachycardia from ventricular tachycardia remains a major challenge for ICDs. More sophisticated discrimination algorithms based on ventricular electrogram morphology have been made practicable by the increased computational ability of modern ICDs. METHODS AND RESULTS: We report results from a large prospective study (1,122 pts) of a new ventricular electrogram morphology tachycardia discrimination algorithm (Wavelet Dynamic Discrimination, Medtronic, Minneapolis, MN, USA) operating at minimal algorithm setting (RV coil-can electrogram, match threshold of 70%). This is a nonrandomized cohort study of ICD patients using the morphology discrimination of the Wavelet algorithm to distinguish SVT and VT/VF. The Wavelet criterion was required ON in all patients and all other supraventricular tachycardia discriminators were required to be OFF. Spontaneous episodes (N = 2,235) eligible for ICD therapy were adjudicated for detection algorithm performance. The generalized estimating equations method was used to remove bias introduced when an individual patient contributes multiple episodes. Inappropriate therapies for supraventricular tachycardia were reduced by 78% (90% CI: 72.8-82.9%) for episodes within the range of rates where Wavelet was programmed to discriminate. Sensitivity for sustained ventricular tachycardia was 98.6% (90% CI: 97-99.3%) without the use of high-rate time out. CONCLUSIONS: Results from this prospective study of the Wavelet electrogram morphology discrimination algorithm operating as the sole discriminator in the ON mode demonstrate that inappropriate therapy for supraventricular tachycardia in a single-chamber ICD can be dramatically reduced compared to rate detection alone.

Algorithms↗
PubMed2001

Nonlinear and extra-classical receptive field properties and the statistics of natural scenes.

The neural mechanisms of early vision can be explained in terms of an information-theoretic optimization of the neural processing with respect to the statistical properties of the natural environment. Recent applications of this approach have been successful in the prediction of the linear filtering properties of ganglion cells and simple cells, but the relations between the environmental statistics and cortical nonlinearities, like those of end-stopped or complex cells, are not yet fully understood. Here we present extensions of our previous investigations of the exploitation of higher-order statistics by nonlinear neurons. We use multivariate wavelet statistics to demonstrate that a strictly linear processing would inevitably leave substantial statistical dependencies between the outputs of the units. We then consider how the basic nonlinearities of cortical neurons--gain control and ON/OFF half-wave rectification--can exploit these higher-order statistical dependencies. We first show that gain control provides an adaptation to the polar separability of the multivariate probability density function (PDF), and, together with an output nonlinearity, enables an overcomplete sparse coding. We then consider how the remaining higher-order dependencies between different units can be exploited by a combination of basic ON/OFF point nonlinearities and subsequent weighted linear combinations. We consider two statistical optimization schemes for the computation of the optimal weights: principal component analysis (PCA) and independent component analysis (ICA). Since the intermediate nonlinearities transform some of the higher-order dependencies into second-order dependencies even the basic PCA approach is able to exploit part of the redundancies. ICA ignores this second-order structure, but can exploit higher-order dependencies. Both schemes yield a variety of nonlinear units which comprise the typical nonlinear processing properties, such as end-stopping, side-stopping, complex-cell properties and extra-classical receptive field properties, but the 'ideal' complex cells seem only to occur with PCA. Thus, a combination of ON/OFF nonlinearities with an integrated PCA-ICA strategy seems necessary to exploit the statistical properties of natural images.

Algorithms↗
PubMed2006

A wavelet-based method for local phase extraction from a multi-frequency oscillatory signal.

One of the challenges in analyzing neuronal activity is to correlate discrete signal, such as action potentials with a signal having a continuous waveform such as oscillating local field potentials (LFPs). Studies in several systems have shown that some aspects of information coding involve characteristics that intertwine both signals. An action potential is a fast transitory phenomenon that occurs at high frequencies whereas a LFP is a low frequency phenomenon. The study of correlations between these signals requires a good estimation of both instantaneous phase and instantaneous frequency. To extract the instantaneous phase, common techniques rely on the Hilbert transform performed on a filtered signal, which discards temporal information. Therefore, time-frequency methods are best fitted for non-stationary signals, since they preserve both time and frequency information. We propose a new algorithmic procedure that uses wavelet transform and ridge extraction for signals that contain one or more oscillatory frequencies and whose oscillatory frequencies may shift as a function of time. This procedure provides estimates of phase, frequency and temporal features. It can be automated, produces manageable amounts of data and allows human supervision. Because of such advantages, this method is particularly suitable for analyzing synchronization between LFPs and unitary events.

Action Potentials↗
PubMed2004

Improved prediction of defibrillation success for out-of-hospital VF cardiac arrest using wavelet transform methods.

We report an improved method for the estimation of shock outcome prediction based on novel wavelet transform-based time-frequency methods. Wavelet-based peak frequency, energy, mean frequency, spectral flatness and a new entropy measure were studied to predict shock outcome. Of these, the entropy measure provided optimal results with 60 +/- 6% specificity at 91 +/- 2% sensitivity achieved for the prediction of return of spontaneous circulation (ROSC). These results represent a major improvement in shock prediction in human ventricular fibrillation.

Electric Countershock↗
PubMed2005

Resolution enhancement of composite spectra using wavelet-based derivative spectrometry.

An approach based on the using of the continuous wavelet transform (CWT) in derivative spectrometry (DS) is considered. Within the framework of the approach we develop a numerical differentiation algorithm with continuous wavelets for improving resolution of composite spectra. The wavelet-based derivative spectrometry (WDS) method results in best contrast in differential curves compared to the conventional derivative spectrometry method. A main advantage is that, as opposed to DS, WDS gives stable estimations of derivative in the wavelet domain without using the regularization. A wavelet shape and the information redundancy are of the greatest importance when the continuous wavelet transform is used. As an appropriate wavelet we offer to utilize the nth derivative of a component with a priori known shape. The energy distribution into scales allows one to determine a unique wavelet projection and in that way to avoid the information redundancy. A comparative study of WDS and DS with the statistical regularization method (SRM) is made; in particular, limits of applicability of these are given. Examples of the application of both DS and WDS for improving resolution of synthetic composite bands and real-world composite ones coming from molecular spectroscopy are given.

Algorithms↗
PubMed1994

Ventricular late potentials characterization in time-frequency domain by means of a wavelet transform.

The main transforms of Cohen's class allow signal representation simultaneously in time and frequency domains. Wavelet transforms make it possible to link the temporal window width to the analyzing frequency and leads to a "modified wavelet transform" which improves resolution both in time and frequency. A simulation study illustrates the artifacts of every time-frequency representation (TFR) on pure sinusoids and gives performance evaluation of the different methods when searching a sinusoid embedded in a QRS complex. Analyses of real signals from healthy and pathological subjects confirm the simulation results and complete the characterization of ventricular late potentials (VLP) yet detected by signal averaging.

Action Potentials↗
PubMed2002

Multiscale contrast enhancement for radiographies: Laplacian Pyramid versus fast wavelet transform.

Contrast enhancement of radiographies based on a multiscale decomposition of the images recently has proven to be a far more versatile and efficient method than regular unsharp-masking techniques, while containing these as a subset. In this paper, we compare the performance of two multiscale-methods, namely the Laplacian Pyramid and the fast wavelet transform (FWT). We find that enhancement based on the FWT suffers from one serious drawback-the introduction of visible artifacts when large structures are enhanced strongly. By contrast, the Laplacian Pyramid allows a smooth enhancement of large structures, such that visible artifacts can be avoided. Only for the enhancement of very small details, for denoising applications or compression of images, the FWT may have some advantages over the Laplacian Pyramid.

Algorithms↗
PubMed2006

Wavelet approximation-based affine invariant shape representation functions.

In this paper, new wavelet-based affine invariant functions for shape representation are presented. Unlike the previous representation functions, only the approximation coefficients are used to obtain the proposed functions. One of the derived functions is computed by applying a single wavelet transform; the other function is calculated by applying two different wavelet transforms with two different wavelet families. One drawback of the previously derived detail-based invariant representation functions is that they are sensitive to noise at the finer scale levels, which limits the number of scale levels that can be used. The experimental results in this paper demonstrate that the proposed functions are more stable and less sensitive to noise than the detail-based functions.

Algorithms↗
PubMed2005

Three-dimensional image fusion by use of multiwavelength digital holography.

We present three-dimensional (3D) image fusion by use of digital holography. We demonstrate experimentally that, through the image fusion technique with multiresolution wavelet decomposition, it is possible to increase the details and contrast of 3D reconstructed images obtained by multiwavelength digital holography. Although there is substantial activity in the fields of image fusion and holography, to the best of our knowledge, this is the first report of 3D image fusion by use of digital holography.

Algorithms↗
PubMed2004

[Identification of official rhubarb samples based on NIR spectra and neural networks].

Rhubarb is one of the most widely used Chinese medicinal herbs in China. Fast and accurate identification of official and unofficial rhubarb samples is most critical for quality control of Chinese medicine production. In the present paper near-infrared reflectance spectrometry (NIRS) and artificial neural network (ANN) were combined to develop classification models for identifying 52 official and unofficial rhubarb samples. The measured spectra were compressed by wavelet transformation (WT) and then the ANN classification models were trained with the reduced-variables spectral data. The rate of correct classification was over 96%. The effects of neurons in hidden layer and the momentum were also discussed. Owing to its fast and nondestructive properties, NIRS is a promising approach to identifying Chinese medicinal herbs.

Artificial Intelligence↗
PubMed2006

[A method to estimate the short-term fractal dimension of heart rate variability based on wavelet transform].

A new method of calculating fractal dimension of short-term heart rate variability signals is presented. The method is based on wavelet transform and filter banks. The implementation of the method is: First of all we pick-up the fractal component from HRV signals using wavelet transform. Next, we estimate the power spectrum distribution of fractal component using auto-regressive model, and we estimate parameter 7 using the least square method. Finally according to formula D = 2- (gamma-1)/2 estimate fractal dimension of HRV signal. To validate the stability and reliability of the proposed method, using fractional brown movement simulate 24 fractal signals that fractal value is 1.6 to validate, the result shows that the method has stability and reliability.

Algorithms↗
PubMed2000

Tomographic inversion for sediment parameters in shallow water

This article discusses inversions for bottom geoacoustic properties using broadband acoustic signals obtained from explosive sources. The experimental data used for the inversions are SUS charge explosions acquired on a vertical hydrophone array during the Shelf Break Primer Experiment conducted south of New England in the Middle Atlantic Bight in August 1996. The SUS signals were analyzed for their time-frequency behavior using wavelets. The group speed dispersion curves were obtained from the wavelet scalogram of the SUS signals. A genetic algorithm (GA) was used for the inversion of sound speeds in the water column and compressional wave speeds in the sediment layers. The variations in the sound speeds in the water column were represented using empirical orthogonal functions (EOFs). A range-independent normal mode routine was used to construct the replica fields corresponding to the parameters. Comparison of group speeds for modes 1 to 9 and for a range of frequencies 8 to 200 Hz was used to arrive at the best parameter fit. An efficient hybrid optimization scheme using the GA and a Levenberg-Marquardt algorithm is presented. Linear perturbation methods were also used to "fine tune" the inversions and to obtain resolution and variance estimates. Analysis was also done to compute the degree of convergence of each of the parameters by explicitly calculating the Hessian matrices numerically. A posteriori estimation of mean and covariance was also done to obtain error estimates. Group speeds for the inverted sound speed fields provide an excellent match to the experimental data. The inverted sediment compressional speed profile compares well with in situ measurements.

Journal Article↗
PubMed2005

Adaptive neural control for a class of nonlinearly parametric time-delay systems.

In this paper, an adaptive neural controller for a class of time-delay nonlinear systems with unknown nonlinearities is proposed. Based on a wavelet neural network (WNN) online approximation model, a state feedback adaptive controller is obtained by constructing a novel integral-type Lyapunov-Krasovskii functional, which also efficiently overcomes the controller singularity problem. It is shown that the proposed method guarantees the semiglobal boundedness of all signals in the adaptive closed-loop systems. An example is provided to illustrate the application of the approach.

Algorithms↗
PubMed2004

Wavelet packet transform and artificial neural network applied to simultaneous kinetic multicomponent determination.

This paper presents a novel method, named wavelet packet transform based multilayer feedforward neural network with Levenberg-Marquardt and back propagation algorithm (WPTLMBP), developed for simultaneous kinetic determination of Cu(II), Fe(III), and Ni(II). Wavelet packet representations of signals provided a local time-frequency description, thus in the wavelet packet domain the quality of noise removal can be improved. The artificial neural network was applied for non-linear multivariate calibration. In this study, by optimization, wavelet packet function, decomposition level and number of hidden nodes for WPTLMBP method were selected as Db2, 2, and 4 respectively. A program PWPTLMBP was designed to perform simultaneous kinetic determination of Cu(II), Fe(III), and Ni(II). The relative standard error of prediction (RSEP) for all components with WPTLMBP, LM-BP-MLFN, and PLS methods were 6.39, 10.4, and 8.30%, respectively. Experimental results showed the proposed method to be successful and better than the others.

Copper↗
PubMed2005

Identification of coding and non-coding sequences using local Holder exponent formalism.

MOTIVATION: Accurate prediction of genes in genomes has always been a challenging task for bioinformaticians and computational biologists. The discovery of existence of distinct scaling relations in coding and non-coding sequences has led to new perspectives in the understanding of the DNA sequences. This has motivated us to exploit the differences in the local singularity distributions for characterization and classification of coding and non-coding sequences. RESULTS: The local singularity density distribution in the coding and non-coding sequences of four genomes was first estimated using the wavelet transform modulus maxima methodology. Support vector machines classifier was then trained with the extracted features. The trained classifier is able to provide an average test accuracy of 97.7%. The local singularity features in a DNA sequence can be exploited for successful identification of coding and non-coding sequences. CONTACT: Available on request from bd.kulkarni@ncl.res.in.

Algorithms↗
PubMed2006

Model fitting in two dimensions to small angle diffraction patterns from soft tissue.

In our research programme small angle x-ray scattering (SAXS) is used to provide information on the axial arrangement of collagen molecules as well as data about the state of other components of the extra cellular matrix (ECM) in human tissues. Derivation of parameters to describe and simplify the data is required for much of the SAXS patterns analysis. A method is presented here to achieve function fitting to collagen diffraction peaks along with a representation of the underlying diffuse scatter. A simple model was used which proved reliable in fitting a variety of 2D diffraction patterns. The logarithm of the scatter intensity over the area of the scatter image was taken to reduce the range and improve fitting accuracy. Our model was then used to fit the log data. The model consisted of a radial exponential diffuse scatter component added to a specified number of Gaussian peaks. In 2D the peak model is toroidal, each component being rotated about a common specified centre. Initial search parameters from a 1D averaged sector were supplied to the iterative 2D fitting routine. With the aid of data weighting and basic wavelet filtering, successful and reliable fitting of a specified 2D model to real data is achievable. The process is easily automated. Multiple SAXS patterns can be fitted without operator intervention. As described the model is simple enough to converge rapidly and yet allows image data to be parameterized to a form suitable for extracting the requisite information. The fitting method is flexible enough to be extended to achieve a more comprehensive and complex pattern fitting in two dimensions if this turns out to be necessary. It is our intention to implement orientation distribution functions in the near future by including an angular scaling factor.

Animals↗
PubMed2003

Application of wavelet modulus maxima in microarray spots recognition.

This paper presents a novel approach to recognize the microarray image spots. The approach is based on the detection of wavelet modulus maxima in the microarray images. The detected maxima is actually the contour of the spots and thus the spots are recognized precisely. Then, the intensities within the contour of the spots can be obtained with low error rate. The test results on example image show this is an effective approach, especially for those spots with low intensities.

Algorithms↗
PubMed2003

Attempt to distinguish electric signals of a dichotomous nature.

Three types of electric signals were analyzed: Ion current fluctuations in membrane channels (ICFMC), Seismic electric signals activities (SES), and "artificial" noises (AN). The wavelet transform, when applied to the conventional time domain, does not allow a classification of these signals, but does so in the "natural" time domain. A classification also becomes possible, if we study - (q) versus q, where chi stands for the "natural" time. For q values approximately between 1 and 2 the signals are classified and ICFMC lies between the other two types. For q=1, the "entropy" S identical with - ln of ICFMC almost equals that of a "uniform" distribution, while the AN and SES have larger and smaller S values, respectively. The recent [P. Varotsos, N. Sarlis, and E. Skordas, Phys. Rev. E 67, 021109 (2003)] finding that, in short time scales, both SES and AN (which are shown to be non-Markovian) result in comparable detrended fluctuation analysis exponents alpha in (1.0,1.5) is revisited. Even a Markovian dichotomous time series, in short time scales, leads to similar alpha exponents.

Journal Article↗