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PubMed1997

Analysis of pattern reversal visual evoked potentials (PRVEP's) by spline wavelets.

In this study, the pattern-reversal visual evoked potentials (PRVEP's) collected from normal and demented subjects are investigated by applying the quadratic spline wavelet analysis. The data are decomposed into six octave frequency bands. For quantitative purposes, the wavelet coefficients in the residual waveform representing the delta-theta band activity (0-8 Hz) are explored to characterize the (N70-P100-N130) complex. Specifically, the coefficients corresponding to the location of N70, P100, and N130 peaks are investigated for their sign in order to test whether they represent a consistent (N70-P100-N130) complex in the averaged waveform. Waveforms with normal latency (N70-P100-N130) complex are observed to have positive second, negative third, and positive fourth coefficients in amplitude in their residual scale standing for the delta-theta (0-8 Hz) band activity. The method allows for the analysis of oscillatory-phase behavior of the normal and pathological PRVEP's in their delta-theta band based on a few quantitative measures consistent with the time-frequency occurrence of the major components of the evoked potential.

Alzheimer Disease↗
PubMed2002

[Detection of epileptic waves in EEG based on wavelet transform].

Detection of epileptic waves in EEG is particularly helpful in the interpretation of the underlying process in seizures. This study is aimed at providing a new method for automatic detection of epileptic waves through the wavelet analysis of EEGs. It mainly deals with the detection of spikes or spike-waves based on wavelet transform. Since spikes and spike-waves contain high frequency energy, they will be represented in a particular scale localized in a small time window. According to these feature waveforms of epileptic waves, a continuous processing system for epileptic waveforms detection is constructed. We apply discrete wavelet transform on EEGs. Because of the time-frequency domain localization of wavelet transforms, we can get the local maximal positions across several successive dyadic scales of wavelet transform. And these positions indicate the points of sharp transitions in EEGs. Then we calculate the distance between every two successive maximal positions in each scale. This distance stands for the period of subwave. Furthermore, the distribution of subwave periods of each scale can be worked out. Then, comparing the distribution of normal EEG's and epileptic EEG's. The difference between these two waveforms provides us the criteria for automatic detection and classification. In order to reduce the detection workload, we also compare the detection efficiency of each scale. The scale that provides highest accuracy is selected for our automatic detection system. The results presented in this study show that scale 3 provides the best detection accuracy. So, scale 3 is deemed to be the proper scale for automatic detection. This system has the following advantages: (1) Reduced the workload significantly by selecting proper scale(s) for automatic selection; (2) Enhanced the detection accuracy by selecting proper criteria and threshold; (3) Capable of continuous detection; (4) It is also fit for the detection of other biomedical signals. This system showed good performance, and the initial clinical results obtained are also encouraging.

Electroencephalography↗
PubMed2002

A discovery of new features of gastropod local field potentials by application of wavelet tools.

Odor input evokes characteristic, time-evolving (non-stationary) events in the spontaneously active central ganglia of the snail Helix pomatia. Assuming stationarity for the signals, one could, as the first approach, apply the Fourier-based methods, frequency amplitude characteristics (FAC) measures, for analyzing such events. We could thus for the first time describe such events in frequency and amplitude and show that the frequency, at which power increases most, is specific to the odor or its class [Comp. Biochem. Physiol. 123A (1999a) 95; Comp. Biochem. Physiol. 124A (1999b) 297]. Wavelet tools assume no record stationarity and are suitable for describing the dynamically evolving brain electrical signals precisely and quantitatively. We, therefore, tested these tools for the typical odor experiments with the procerebrum (PC), the pedal ganglion (PG) and the visceral ganglion (VG) of the Helix, which we earlier analyzed by the FAC measures and compared both results. The two basic findings of the present wavelet analysis are as follows: (i) the wavelet energy fluctuations clearly visualize dynamical interactions among the major bands (0.1-3.1 Hz), implying a possible 'mutual exclusion' between slow components < 0.8 Hz and faster ones > 0.8 Hz. (ii) Entropy behavior was characteristically different for each of the three brain regions. Only in PC the response to aversive odorants (decrease of entropy = more ordered state) is differentiated in entropy from that to attractive ones (increase of entropy=more disordered or more complexly ordered state) indicating the odor-discriminating function of this region. In VG entropy of the intrinsic activity is so high (highly disordered state) due to the strong wideband activity reaching > 50 Hz that odor stimulation results mainly in lowering of entropy (= more ordered state) regardless of the nature of the odor. In PG, however, odor presentation generally increases entropy due to the robust, wide-band activation at > 3 Hz (sensorimotor function) that is generated as a secondary, but dominant and robust, response. In respect to describing time evolution of different frequency band components the present wavelet tools can much more sensitively do so, as compared with the FAC measures. They can also characterize a change in the neuroelectrical state in terms of entropy.

Animals↗
PubMed2006

[Medical image retrieval based on wavelet transform texture analysis].

Content-based medical image retrieval has become a hot research topic due to the rapid increase of image database. In this paper, we present a method of medical images retrieval, based on texture analysis with Haar wavelet transform. Meanwhile, its effectiveness is compared with the method of retrieval based on co-occurrence matrix. A prototype system is implemented and the experiments show that the method has a much better effect.

Algorithms↗
PubMedDate not supplied

Surface myoelectric signal classification for prostheses control.

This paper represents an ongoing investigation for surface myoelectric signal segmentation and classification. The classical moving average technique augmented with principal components analysis and time-frency analysis were used for segmentation. Multiresolution wavelet analysis was adopted as an effective feature extraction technique while artificial neural networks were used for classification. Results of classifying four elbow and wrist movement signals recorded from biceps and triceps gave 5.1% classification error when two channels were used.

Adult↗
PubMed1999

Multiscale analysis of blood pressure signals.

We describe the multiresolution wavelet analysis of blood pressure waves in vasovagal syncope-affected patients compared with those in healthy people, using Haar and Gaussian bases. A comparison between scale-dependent and scale-independent measures discriminating the two classes of subjects is made. What emerges is a sort of equivalence between these two methodological approaches, that is, both methods reach the same statistical significance of separation between the two classes.

Biophysical Phenomena↗
PubMed2002

Analysis of phase-locked oscillations in multi-channel single-unit spike activity with wavelet cross-spectrum.

Electrophysiological measures of neural activity frequently display oscillatory patterns at various frequencies. Furthermore, these oscillatory patterns can become dynamically synchronized across a wide region of the brain in a task-dependent manner. In this study, phase-locked oscillations in simultaneously recorded spike trains were analyzed using the wavelet cross-spectrum. Adaptation of the existent methods of calculating wavelet cross-spectrum to spike train data was straightforward. In contrast, new methods were needed for evaluating the statistical significance of the cross-spectrum. Although a permutation test based on a large number of re-sampled cross-spectra can provide a reliable estimate of statistical significance, this was quite time-consuming. As an alternative, statistical significance was determined with a normal probability density function estimated from a small number of re-sampled cross-spectra. When applied to neuron pairs recorded in the primate supplementary motor area, the re-sampling procedure produced a reliable outcome even when it was based on as few as ten re-sampled cross-spectra. These results suggest that the wavelet analysis in combination with a re-sampling procedure provides a useful tool to examine the dynamic patterns of temporal correlation in cortical spike trains.

Action Potentials↗
PubMed2002

Choosing a wavelet for single-trial EMG.

A wavelet analysis was developed to measure the timing of multiunit bursts in surface electromyograms (EMGs) from single trials. EMG data were taken from eleven elbow and/or shoulder muscles during reaching movements in six different directions, at a range of speeds. A relatively simple wavelet (db2) was chosen, and the analysis focused on wavelet coefficients at an intermediate scale (D3), where the wavelet length approximately matched the wavelengths present in EMG bursts. Burst times were identified from the peaks of the coefficient traces and were plotted as a function of movement time. Linear regression revealed significant relations in most cases, and thus served to validate the wavelet burst identification. With a few exceptions, burst timing scaled in a manner approximately similar to the scaling of movement time. As shown previously with different analytical methods, both within and across joints, EMG bursts were not confined to distinct 'agonist' and 'antagonist' time frames, but instead showed a variety of phases relative to speed or joint torque.

Arm↗
PubMed2001

Wavelet packet fractal analysis of neuronal morphology.

An image analysis method called two-dimensional wavelet packet analysis (2D WPA) is introduced to quantify branching complexity of neurons. Both binary silhouettes and contour profiles of neurons were analyzed to determine accuracy and precision of the fractal dimension in cell classification tasks. Two-dimensional WPA plotted the slope of decay for a sorted list of discrete wavelet packet coefficients belonging to the adapted wavelet best basis to obtain the fractal dimension for test images and binary representations of neurons. Two-dimensional WPA was compared with box counting and mass-radius algorithms. The results for 2D WPA showed that it could differentiate between neural branching complexity in cells of different type in agreement with accepted methods. The importance of the 2D WPA method is that it performs multiresolution decomposition in the horizontal, vertical, and diagonal orientations.

Algorithms↗
PubMed1994

Wavelets-a new tool in sleep biosignal analysis.

The wavelet transform is a relatively new approach to data processing which has been applied in different areas such as signal, speech and image processing. In the last decade, many papers have been published on wavelet theory and its applications. The wavelet transform provides an elegant alternative to the classical Fourier or Gabor transforms unifying numerous signal processing techniques in a common framework. The purpose of the present paper is to provide an overview of the applicability of the wavelet transform to EEG signal analysis. In the first part of the paper the mathematical background is summarized. In the second part, applications to the sleep EEG field are presented and discussed. The results of these illustrations demonstrate the usefulness of the wavelet transform to solve various problems including signal parametrization, pattern recognition and biosignal representation.

Journal Article↗
PubMedDate not supplied

Seasonality of birth and conception to teenagers in Texas.

We study the births to teenagers during the years 1964-2000 and analyze separately the three main racial/ethnic groups in Texas (White, Hispanic, and African American), as well as married and unmarried teens during the years 1994-2000. By using traditional statistical methods of analysis and a filter based on the multiresolution wavelet analysis, we draw inferences about the times of the year when adolescent females of different racial/ethnic and marital groups have the highest probability for pregnancy ending in live birth. Multiple factors influencing teen pregnancy are identified and associated with temporal features of social, cultural, educational, and familial processes. In particular, we detect links between unmarried teen conception times and school terms, and weekly birth patterns associated with scheduled c-sections that differ according to racial/ethnic groups.

Adolescent↗
PubMed1997

[Localization of epileptogenic foci and visualization of propagating process in the seizure discharges using crosscorrelation analysis].

Electrocorticogram (ECoG) of intractable focal epilepsy was analyzed using AR model, wavelet analysis and crosscorrelation analysis. The sequential 3-dimensional visualization technique of phase shift maps was developed to localize the epileptic foci and to study their propagation process. The crosscorrelation of the epileptic discharges was calculated between the electrodes in every unit of time, to get the phase shift. More than two epileptogenic foci were localized and two kinds of propagating process were shown. These findings suggest that two kinds of mechanism might work in development of epileptic discharges, and our newly developed visualization technique is useful to investigate the epileptogenesis etiology.

Brain Mapping↗
PubMed2004

A Novel Approach for Predicting Human Response from ATD Tests.

A methodology was developed for predicting the human response from ATD (Anthropomorphic Test Device) tests or for improving the biofidelity of the ATD response. The ATD response and human response are considered as the output of a black box system, from which the relationship between the ATD tests and human tests was established using wavelet analysis. Based on the decompositions of both responses on a wavelet packet basis, a mapping matrix is built after executing a procedure that includes de-noising and compression, energy distribution analysis, correlation analysis and regression analysis, and spectral coherence analysis and transfer function analysis. With the mapping matrix, an ATD response is modified or reconstructed into the corresponding human response. The practical use of the methodology was illustrated in the analysis of a series of lateral impact tests conducted on a horizontal impulse accelerator with an ATD and human volunteers as the test subjects. The predictions from the ATD tests using this method have attained significant improvement in biofidelity.

Journal Article↗
PubMed1997

Time-frequency distributions of click-evoked otoacoustic emissions.

Emissions evoked by broad-band stimuli, such as clicks, show a 'frequency dispersion' reminiscent of the place-frequency distribution along the cochlea. Analysis of the time-frequency properties of transiently evoked otoacoustic emissions (TEOAEs) is therefore of considerable interest due to their close relation with cochlear mechanisms. In particular, since OAEs in response to click stimuli are expected to evoke a cumulative response from the whole cochlea, the analysis of click-evoked OAEs can yield a global view of cochlear function. Wavelet analysis is performed to obtain time-frequency distributions of click-evoked OAEs at various intensity levels from normal ears. By means of the inverse wavelet transform, the recorded responses are decomposed into elementary components representing the contribution within a narrow frequency band to the cumulative OAE. The relationship between the frequency of the elementary components, latency and level of stimulation is described.

Acoustic Stimulation↗
PubMedDate not supplied

The wavelet transform and its applications to phonocardiogram signal analysis.

The wavelet transform, which is the decomposition of a signal into a set of independent frequency channels, is shown to be a useful diagnostic tool in the analysis of heartbeat sounds. In particular, the wavelet transform enables the experimentalist to obtain qualitative and quantitative measurements of time-frequency characteristics of phonocardiogram (PCG) signals.

Normal Distribution↗
PubMed2006

Unique very low-frequency heart rate variability during deep sleep in humans.

We investigate heart rate variability (HRV) in the very low-frequency (VLF) range (0.003-0.04 Hz) during deep sleep in good sleepers. Spectral analysis of HRV during deep sleep reveals consistent peaks at <0.04 Hz. By using wavelet analysis, we find both stationary and nonstationary periodic patterns in the VLF range, the presence of which has been discussed but has not been fully established to date. Although the mechanism(s) behind the unique VLF oscillations remain to be fully explored, we conjecture that there is an endogenous rhythmic component in human HRV in the VLF range. Further, our results also suggest a need for caution in the interpretation of the VLF spectral power in HRV during deep sleep.

Adaptation, Physiological↗
PubMed2003

Wavelets and statistical analysis of functional magnetic resonance images of the human brain.

Wavelets provide an orthonormal basis for multiresolution analysis and decorrelation or 'whitening' of nonstationary time series and spatial processes. Wavelets are particularly well suited to analysis of biological signals and images, such as human brain imaging data, which often have fractal or scale-invariant properties. We briefly define some key properties of the discrete wavelet transform (DWT) and review its applications to statistical analysis of functional magnetic resonance imaging (fMRI) data. We focus on time series resampling by 'wavestrapping' of wavelet coefficients, methods for efficient linear model estimation in the wavelet domain, and wavelet-based methods for multiple hypothesis testing, all of which are somewhat simplified by the decorrelating property of the DWT.

Brain↗
PubMed2002

Discrimination of walking patterns using wavelet-based fractal analysis.

In this paper, we attempted to classify the acceleration signals for walking along a corridor and on stairs by using the wavelet-based fractal analysis method. In addition, the wavelet-based fractal analysis method was used to evaluate the gait of elderly subjects and patients with Parkinson's disease. The triaxial acceleration signals were measured close to the center of gravity of the body while the subject walked along a corridor and up and down stairs continuously. Signal measurements were recorded from 10 healthy young subjects and 11 elderly subjects. For comparison, two patients with Parkinson's disease participated in the level walking. The acceleration signal in each direction was decomposed to seven detailed signals at different wavelet scales by using the discrete wavelet transform. The variances of detailed signals at scales 7 to 1 were calculated. The fractal dimension of the acceleration signal was then estimated from the slope of the variance progression. The fractal dimensions were significantly different among the three types of walking for individual subjects (p < 0.01) and showed a high reproducibility. Our results suggest that the fractal dimensions are effective for classifying the walking types. Moreover, the fractal dimensions were significantly higher for the elderly subjects than for the young subjects (p < 0.01). For the patients with Parkinson's disease, the fractal dimensions tended to be higher than those of healthy subjects. These results suggest that the acceleration signals change into a more complex pattern with aging and with Parkinson's disease, and the fractal dimension can be used to evaluate the gait of elderly subjects and patients with Parkinson's disease.

Acceleration↗