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PubMed1996

Are serial Holter QT, late potential, and wavelet measurement clinically useful?

Clinical centers are increasingly using new techniques such as Holter QT, late potential, and wavelet measurements. However, we lack validated databases for the assessment of the performance of the signal-processing methods and their reproducibility. Failure of the QT interval to adapt to changes in the heart rate is considered to be a more meaningful parameter than QT prolongation itself. In this study, different factors that may affect the reproducibility of QT and QTm (onset of the QRS to the maximum of T) measurement are analyzed: the incidence of sympathetic tone and parasympathetic activity on low- and high-frequency QT variability, the very low frequency dependency of the QT interval to changes in the R-R interval, changes in the heart's position, and measurement errors. Typical root-mean-square values of the beat-to-beat measurement errors in upright-position Holter recordings are only 1.5 ms for QT versus 3.4 ms for QTm. Although the dependence of the QT interval on the heart rate is well established, the method for rate correction of the QT interval remains controversial. None of the formulas for heart rate adjustment of the QT previously proposed provide complete correction for all of the rate influences involved due to "memory phenomenon"; that is, there is a time delay, ranging up to 3-4 minutes, between a change in heart rate and the subsequent change in the QT interval. This problem has been solved by developing patient-specific neural networks that are trained to "identify" the dynamic behavior of the QT interval (or QTm) as a function of the R-R interval in order to predict the beat-to-beat changes of the QT interval as a function of the measured beat-to-beat changes of the R-R interval. Computing the differences between the predicted and the measured QT interval will allow for the detection of any significant deviations, both in the steady-state and transient conditions. Recent developments in the analysis of the high-resolution electrocardiogram (HRECG) in the time domain and frequency domain, with emphasis on the assessment of the reproducibility of late potential and wavelet measurements, are also reported in this study. The two main causes of variability in HRECG analysis are physiology and, for time-domain analysis, intermanufacturer variability. Physiologic changes can be overcome by standardizing the clinical protocols and repeating the recordings. The most important technical requirement for the proper use of late potentials is to standardize the algorithm for the detection of QRS offset among different late potential analyzing machines so that clinical data can be exchanged. The recently introduced wavelet transform provides a fruitful alternative to the more classical time-domain methods. Preliminary results show an 8 to 15% performance improvement over conventional time-domain analysis for the stratification of the HRECG after myocardial infarction. Reproducibility is excellent, up to 100%, but needs to be assessed on larger populations matched for age, sex, and pathology.

Artifacts↗
PubMed2001

Time-frequency analysis of neonatal click-evoked otoacoustic emissions.

Click-evoked otoacoustic emissions (CEOAEs) of full-term babies were considered. By means of the wavelet transform, each CEOAE was decomposed into frequency bands. The rms amplitude and test-retest correlation were computed from these bands. The results indicated that both the amplitude and correlation were not invariant with time but reached a maximum in specific time windows, depending on the frequency of the component. For all components, the correlation was greatly decreased for latencies > 12.5 ms. As a result, comparison between the performance of the default ILO 88 window (2.5-20 ms) and the 2.5-12.5 ms window showed that for all frequencies in the 1.5-6 kHz range there was a statistically significant improvement in the correlation.

Factor Analysis, Statistical↗
PubMed2006

Mean frequency derived via Hilbert-Huang transform with application to fatigue EMG signal analysis.

The mean frequency (MNF) of surface electromyography (EMG) signal is an important index of local muscle fatigue. The purpose of this study is to improve the mean frequency (MNF) estimation. Three methods to estimate the MNF of non-stationary EMG are compared. A novel approach based on Hilbert-Huang transform (HHT), which comprises the empirical mode decomposition (EMD) and Hilbert transform, is proposed to estimate the mean frequency of non-stationary signal. The performance of this method is compared with the two existing methods, i.e. autoregressive (AR) spectrum estimation and wavelet transform method. It is observed that our method shows low variability in terms of robustness to the length of the analysis window. The time-varying characteristic of the proposed approach also enables us to accommodate other non-stationary biomedical data analysis.

Adult↗
PubMedDate not supplied

Localized measures for nonstationary time-series of physiological data.

We will discuss localized measures related to the concepts of dimension, Lyapunove exponents (entropy), and recurrence diagrams. We stress the relevance of localized events and coincidences in physiological time series that often are lost when statistical averaging methods are applied. We suggest event-based statistics as an alternative to spectral or averaged-based statistics. The use of wavelets bases for characterizing localized structures is discussed as a potential alternative to Fourier-based analysis. Finally we mention how local domains in state space could be applied as triggers for external stimuli and thereby improve the statistics of ERP recordings.

Animals↗
PubMed2006

Visualization of synchronization of the uterine contraction signals: running cross-correlation and wavelet running cross-correlation methods.

In physiological research, we often study multivariate data sets, containing two or more simultaneously recorded time series. The aim of this paper is to present the cross-correlation and the wavelet cross-correlation methods to assess synchronization between contractions in different topographic regions of the uterus. From a medical point of view, it is important to identify time delays between contractions, which may be of potential diagnostic significance in various pathologies. The cross-correlation was computed in a moving window with a width corresponding to approximately two or three contractions. As a result, the running cross-correlation function was obtained. The propagation% parameter assessed from this function allows quantitative description of synchronization in bivariate time series. In general, the uterine contraction signals are very complicated. Wavelet transforms provide insight into the structure of the time series at various frequencies (scales). To show the changes of the propagation% parameter along scales, a wavelet running cross-correlation was used. At first, the continuous wavelet transforms as the uterine contraction signals were received and afterwards, a running cross-correlation analysis was conducted for each pair of transformed time series. The findings show that running functions are very useful in the analysis of uterine contractions.

Dysmenorrhea↗
PubMed2006

A comparison of recent parametric neuroreceptor mapping approaches based on measurements with the high affinity PET radioligands [11C]FLB 457 and [11C]WAY 100635.

In positron emission tomography (PET) studies, the detailed mapping of neuroreceptor binding is a trade-off between parametric accuracy and spatial precision. Logan's graphical approach is a straightforward way to quickly obtain binding potential values at the voxel level but it has been shown to have a noise-dependent negative bias. More recently suggested approaches claim to improve parametric accuracy with retained spatial resolution. In the present study, we used PET measurements on regional D2 dopamine and 5-HT1A serotonin receptor binding in man to compare binding potential (BP) estimates of six different parametric imaging approaches to the traditional Logan ROI-based approach which was used as a "gold standard". The parametric imaging approaches included Logan's reference tissue graphical analysis (PILogan), its version recently modified by Varga and Szabo (PIVarga), two versions of the wavelet-based approach, Gunn's basis function method (BFM) and Gunn et al.'s recent compartmental theory-based approach employing basis pursuit strategy for kinetic modeling (called DEPICT). Applicability for practical purposes in basic and clinical research was also considered. The results indicate that the PILogan and PIVarga approaches fail to recover the correct values, the wavelet-based approaches overcome the noise susceptibility of the Logan fit with generally good recovery of BP values, and BFM and DEPICT seem to produce values with a bias dependent on receptor density. Further investigations on this bias and other phenomena revealed fundamental issues regarding the use of BFM and DEPICT on noisy voxel-wise data. In conclusion, the wavelet-based approaches seem to provide the most valid and reliable estimates across regions with a wide range of receptor densities. Furthermore, the results support the use of receptor parametric imaging in applied studies in basic or clinical research.

Algorithms↗
PubMed2000

The matching pursuit: a new method of characterizing microembolic signals?

Detection of clinically silent circulating microemboli within cerebral arteries by transcranial Doppler ultrasound (US) is now being widely investigated in the hope of identifying patients at increased risk for stroke. However, the widespread application of embolus detection is still limited in clinical practice because current transcranial Doppler systems have not the required sensitivity and specificity to analyze microembolic signals, particularly to distinguish between gaseous, or solid brain emboli and artefacts. In this work, we proposed to investigate the potential of a new approach for the analysis of microembolic signals via the so-called matching pursuit, which is closely related to wavelet transform and is not subject to the same limitations as the fast Fourier transform. Our preliminary results clearly indicate that matching pursuit is well suited to this task.

Algorithms↗
PubMedDate not supplied

Statistical shape model based segmentation of medical images.

This paper reviews several kinds of 2D shape representations by a set of parameters based on labeled points, Fourier descriptors and wavelet descriptors. Seven shape models for axial slices of spinal vertebra are derived by a statistical analysis of parameters corresponding to a set of example shapes and are subsequently compared. Two of the developed models are incorporated into methods for interactive segmentation of 2D gray level images. The first method is founded on Fourier descriptors, the second one is based on normalized sets of labeled points. Both methods are based on a model guided shape exploration.

Data Interpretation, Statistical↗
PubMed2005

Topological coarse graining of polymer chains using wavelet-accelerated Monte Carlo. II. Self-avoiding chains.

In the preceding paper [A. E. Ismail, G. C. Rutledge, and G. Stephanopoulos J. Chem. Phys. (in press)] we introduced wavelet-accelerated Monte Carlo (WAMC), a coarse-graining methodology based on the wavelet transform, as a method for sampling polymer chains. In the present paper, we extend our analysis to consider excluded-volume effects by studying self-avoiding chains. We provide evidence that the coarse-grained potentials developed using the WAMC method obey phenomenological scaling laws, and use simple physical arguments for freely jointed chains to motivate these laws. We show that coarse-grained self-avoiding random walks can reproduce results obtained from simulations of the original, more-detailed chains to a high degree of accuracy, in orders of magnitude less time.

Journal Article↗
PubMed2007

Character independent font recognition on a single Chinese character.

A novel algorithm for font recognition on a single unknown Chinese character, independent of the identity of the character, is proposed in this paper. We employ a wavelet transform on the character image and extract wavelet features from the transformed image. After a Box-Cox transformation and LDA (Linear Discriminant Analysis) process, the discriminating features for font recognition are extracted and classified through a MQDF (Modified Quadric Distance Function) classifier with only one prototype for each font class. Our experiments show that our algorithm can achieve a recognition rate of 90.28 percent on a single unknown character and 99.01 percent if five characters are used for font recognition. Compared with existing methods, all of which are based on a text block, our method can provide a higher recognition rate and is more flexible and robust, since it is based on a single unknown character. Additionally, our method demonstrates that it is possible to extract subtle yet discriminative signals embedded in a much larger noisy background.

Algorithms↗
PubMed2002

Comparison of feature extraction and selection methods in mammogram recognition.

This paper presents a comparison of feature extraction and selection methods in the design of mammogram recognition systems. Mammographic images were classified into two categories, normal and cancerous. The following methods of feature extraction were investigated: two-dimensional Haar wavelets, histograms, and singular value decomposition. The feature patterns were reduced and selected using principal component analysis (PCA) and rough sets. The rough sets methods were applied to the final selection of the pattern features. Classification of mammograms was realized using an error backpropagation neural network.

Breast↗
PubMed2001

Wavelet transform filtering and nonlinear anisotropic diffusion assessed for signal reconstruction performance on multidimensional biomedical data.

Computer tomography (CT) techniques are the most widely applicable noninvasive methods for obtaining two- and three-dimensional insights into biological objects. They comprise CT for medical applications, as well as electron tomography used for investigating macromolecular and cellular specimens. Recent advances in the recording schemes improve the speed and resolution frontiers and provide new insights into structural organizations of different objects. However, many data sets suffer from a poor signal-to-noise ratio, which severely hinders the application of methods for automated data analysis, such as feature extraction, segmentation, and visualization. We propose the multidimensional implementation of two powerful signal reconstruction techniques, namely invariant wavelet filtering and nonlinear anisotropic diffusion. We establish quantitative measures to assess the signal reconstruction performance on synthetic data and biomedical images. The appropriate multidimensional implementations of wavelet and diffusion techniques allow for a superior performance over conventional noise-reduction methods. We derive the conditions for the choice between wavelet and diffusion techniques with respect to an optimal signal reconstruction performance. Results of applying the proposed methods in two very different imaging domains-molecular biology and clinical research-are provided.

Anisotropy↗
PubMed2006

Centre-specific multichannel electrogastrographic testing utilizing wavelet-based decomposition.

Although the principles of electrogastrography (EGG) have been known for years, the clinical utility of EGG has not been clearly demonstrated, and EGG recording and analysis techniques have not been fully standardized. The aim of this study was to develop a multichannel EGG technique for detecting abnormal gastric motility using an EGG database specifically designed for a particular testing centre, maximizing the sensitivity and the specificity of the test. Eight healthy volunteers formed a reference group to which 4 study groups (17 gastro-oesophageal reflux disease (GORD) patients, 7 functional dyspepsia patients, 8 post-fundoplication patients and 12 healthy volunteers) were compared. Eight-channel EGG was recorded in the postprandial and fasting states for 30 min each. The recorded signals were wavelet compressed and the resulting error (per cent root mean square difference (PRD)) after the compression was utilized to compare the study groups to the reference group. A threshold in the number of channels with significantly different PRD values was introduced. Sensitivity (SE), specificity (SP) and correct classification rate (CC) of the test in recognizing each clinical condition in the study groups for several channel thresholds and compressions were calculated, and were maximized. Increasing the compression and channel threshold levels improved the specificity, but decreased the sensitivity of the multichannel EGG test. An optimal combination region was identified based on a centre-specific adjustment of the channel threshold and the wavelet compression. The achieved maximum sensitivity, specificity and correct classification for this region in our test centre were as follows: GORD--SE 82.4%, SP 83.3%, CC 82.8%; functional dyspepsia--SE 100%, SP 75%, CC 84.2%; post-fundoplication--SE 75.0%, SP 83.3%, CC 80.0%. The utilization of a wavelet-based decomposition technique to process multichannel EGG signals can be a very effective method for enhancing the clinical utility of EGG, provided it is specifically developed for a given testing centre.

Adult↗
PubMed1998

Electroretinographic oscillatory potentials in different aged rats: an analysis in the domains of time and frequency.

OBJECTIVES: Electroretinographic Oscillatory Potentials (Ops) are considered to be the optimal parameter to study the retinal circulation disturbances the present we compared parameters of OPs between different aged rats under dark adaptation. METHODS: The scotopic ERG were performed in anaesthetized rats aged 3, 12, 24 months. According a window of collective power spectrum field of the OPs was isolated by using the FFT and IFFT of the spectrum analysis program. The parameters in the time and frequency domains were calculated. RESULTS: Results showed that the amplitudes of wavelets and the summed amplitudes of OPs declined with the increase of age. A reduction of the total power and dominated power of OPs was also found after the FFT. But implicit time of each OP and dominant frequency did not change obviously. CONCLUSIONS: The present study indicated that change of OPs of rat might be related to senile degeneration of the retina and circulative deficiency in retina in elder rats.

Aging↗
PubMed2006

Motivation and synthesis of the FIAC experiment: Reproducibility of fMRI results across expert analyses.

The Functional Imaging Analysis Contest (FIAC) culminated in the FIAC Workshop held at the 11th Annual Meeting of the Organization for Human Brain Mapping in Toronto in 2005. This special issue summarizes various analyses used by contestants with a single functional magnetic resonance imaging (fMRI) study, a cortical-language study using sentence repetition. The results from the cognitive neuroscientists who developed the test-base language study, and report their data analysis, are complemented by expert analyses of the same test-base data by most of the major groups actively developing fMRI software packages. Analyses include many variants of the general linear model (GLM), cutting-edge spatial- and temporal-wavelets, permutation-based, and ICA approaches. A number of authors also include surface-based approaches. Several articles describe the important emerging areas of diagnostics for GLM analysis, multivariate predictive modeling, and functional connectivity analysis. While the FIAC did not achieve all of its goals, it helped identify new activation regions in the test-base data, and more important, through this special issue it illustrates the significant methods-driven variability that potentially exists in the literature. Variable results from different methods reported here should provide a cautionary note and motivate the Human Brain Mapping community to explore more thoroughly the methodologies they use for analyzing fMRI data.

Brain Mapping↗
PubMed2002

Stimulus artifact cancellation in the serosal recordings of gastric myoelectric activity using wavelet transform.

Previous studies have shown that electrical stimulation of the stomach (i.e., gastric pacing) with appropriate parameters is a promising method for treatment of gastroparetic patients. The recording of gastric myoelectric activity (GMA) by serosal electrodes is often used to evaluate the effect of stimulation. However, the major problem with the measurement of GMA during gastric pacing is the stimulus artifacts which are often superimposed on the serosal recording and make analysis difficult. The frequency-domain adaptive filter has been used to reduce the stimulus artifacts but only with limited success. This paper describes a wavelet transform-based method for the reduction of stimulus artifacts in the serosal recordings of GMA. The key of this method lies in the use of the fuzzy set theory to select the stimulus artifact-related modulus maxima in the wavelet domain. Both quantitative and qualitative measures show that significant stimulus artifact cancellation was achieved through a series of computer simulations. Results from both single- and multichannel serosally recorded myoelectric signals during gastric pacing are presented to demonstrate the efficiency of the proposed method for the cancellation of stimulus artifacts.

Algorithms↗