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PubMed2006

[A method for extracting basic rhythms of EEG via wavelet analysis].

As basic electrophysiology signals of a human being, electroencephalogram (EEG) has been widely used in researches and clinics. The paper proposes a method for extracting rhythms of EEG, based on wavelet analysis. By using Daubechies mother wavelet, raw EEG is decomposed, and then we extract basic rhythms of EEG after interference is eliminated insome scales. This method not only eliminates interference well, but also extracts rhythms perfectly.

Algorithms↗
PubMed2003

Can P wave wavelet analysis predict atrial fibrillation after coronary artery bypass grafting?

The purpose of this study was the evaluation of Morlet wavelet analysis of the P wave as a means of predicting the development of atrial fibrillation (AF) in patients who undergo coronary artery bypass grafting (CABG). The P wave was analyzed using the Morlet wavelet in 50 patients who underwent successful CABG. Group A consisted of 17 patients, 12 men and 5 women, of mean age 66.9 +/- 5.9 years, who developed AF postoperatively. Group B consisted of 33 patients, 29 men and 4 women, mean age 62.4 +/- 7.8 years, who remained arrhythmid-free. Using custom-designed software, P wave duration and wavelet parameters expressing the mean and maximum energy of the P wave were calculated from 3-channel digital recordings derived from orthogonal ECG leads (X, Y, and Z), and the vector magnitude (VM) was determined in each of 3 frequency bands (200-160 Hz, 150-100 Hz and 90-50 Hz). Univariate logistic-regression analysis identified a history of hypertension, the mean and maximum energies in all frequency bands along the Z axis, the mean and maximum energies (expressed by the VM) in the 200-160 Hz frequency band, and the mean energy in the 150-100 Hz frequency band along the Y axis as predictors for post-CABG AF. Multivariate analysis identified hypertension, ejection fraction, and the maximum energies in the 90-50 Hz frequency band along the Z and composite-vector axes as independent predictors. This multivariate model had a sensitivity of 91% and a specificity of 65%. We conclude that the Morlet wavelet analysis of the P wave is a very sensitive method of identifying patients who are likely to develop AF after CABG. The occurrence of post-CABG AF can be explained by a different activation pattern along the Z axis.

Aged↗
PubMed1994

Wavelet analysis of real ear and synthesized click evoked otoacoustic emissions.

Wavelet analysis was performed to obtain time-frequency analyses of click evoked otoacoustic emissions from normal ears and one ear with a high-frequency hearing loss; mainly to introduce this relatively new method and to show its potentials for emission analysis. The same analysis was then used to obtain time-frequency decompositions of synthesized emissions. It was found that the introduction of a slightly irregular frequency to place relation for the inner ear yielded synthetic results that were remarkably similar to those obtained from real ears.

Acoustic Stimulation↗
PubMed2001

Specified-resolution wavelet analysis of activation patterns from BOLD contrast fMRI.

Functional magnetic resonance (MR) MR imaging (fMRI) with blood-oxygenation-level-dependent (BOLD) contrast localizes neuronal processing of cognitive paradigms. As magnetic resonance signal responses are small, functional mapping requires statistical analysis of temporally averaged image data. Although voxels activating at the paradigm frequency can be identified from the Fourier power spectrum, such analyses collapse the temporal information that is useful to establish consistency of responses during the paradigm. The design of a set of nonorthogonal wavelets of specified frequency resolution within the power spectrum was investigated for extracting desired frequency responses from the noisy signal intensity of individual voxels. These wavelets separate the low-frequency cognitive response to the paradigm from the respiratory and cardiac responses at higher frequencies. The retention of the temporal information, possible by wavelet analysis, allows the MR signal changes to be compared to changes in behavioral responses over the duration of an entire paradigm. The amplitude and time delay of the wavelet specified by the paradigm identify quantitatively the size of the MR signal change and the temporal delay of the hemodynamic BOLD response, respectively. This specified-resolution wavelet analysis was demonstrated for individual voxels and maps through the frontal eye fields using a visually guided saccade paradigm.

Adult↗
PubMed1999

The hydrophobic cores of proteins predicted by wavelet analysis.

MOTIVATION: In the process of protein construction, buried hydrophobic residues tend to assemble in a core of a protein. Methods used to predict these cores involve use or no use of sequential alignment. In the case of a close homology, prediction was more accurate if sequential alignment was used. If the homology was weak, predictions would be unreliable. A hydrophobicity plot involving the hydropathy index is useful for purposes of prediction, and smoothing is essential. However, the proposed methods are insufficient. We attempted to predict hydrophobic cores with a low frequency extracted from the hydrophobicity plot, using wavelet analysis. RESULTS: The cores were predicted at a rate of 68.7%, by cross-validation. Using wavelet analysis, the cores of non-homologous proteins can be predicted with close to 70% accuracy, without sequential alignment. AVAILABILITY: The program used in this study is available from Intergalactic Reality (http://www.intergalact.com). CONTACT: hirakawa@grt.kyushu-u.ac.jp, kuhara@grt.kyushu-u.ac.jp

Computer Simulation↗
PubMed1994

Detecting climate-induced patterns using wavelet analysis.

One of the difficulties encountered in the detection of ecosystem responses to climate change is distinguishing climate-induced patterns from those created by other sources. For example, changes in the trend of stream discharge records over time may reflect a composite response of changes in the climate (i.e. precipitation and temperature), land-use (e.g. timber harvesting and grazing), and local basin characteristics. Methods which quantify and relate information of temporal and spatial patterns across scales are critical to assess climatically induced changes in the forest and stream ecosystems. A methodology utilizing wavelet analysis is introduced for the purpose of identifying and isolating inferred climatic components of the hydrologic record. Trends observed in stream discharge records from eastern Oregon, USA are identified and used to illustrate the utility of a new time series technique, wavelet analysis, as a complementary approach for discerning pattern. This methodology affords an informed procedure for choosing filter dimensions for the purpose of signal decomposition. The wavelet cross-covariance is applied to precipitation and discharge records to identify the climatic component in the discharge record. Reconstruction of these dominant frequencies is effected to isolate the climatic components. The discharge pattern shows two dominant scales of pattern coincident with the precipitation record. A 3-year half-period pattern is found to be correlated with the Southern Oscillation Index at the same frequency.

Journal Article↗
PubMed2000

Singularity spectra of rough growing surfaces from wavelet analysis

We apply the wavelet transform modulus maxima method [A. Arneodo, N. Decoster, and S. G. Roux, Phys. Rev. Lett. 83, 1255 (1999)] to the analysis of simulated surfaces grown by molecular-beam epitaxy. In contrast to the structure function approach commonly used in the literature, this method permits an investigation of the complete singularity spectrum. We focus on a kinetic Monte Carlo model with Arrhenius dynamics, which in particular takes into consideration the process of thermally activated desorption of particles. We find a wide spectrum of Holder exponents, which reflects the multiaffine surface morphology. Although our choice of parameters yields small desorption rates (<3%), we observe a dramatic change in the singularity spectrum, which is shifted toward smaller Holder exponents. Our results offer a mathematical foundation of anomalous scaling: We identify the global exponent alpha(g) with the Holder exponent that maximizes the singularity spectrum.

Journal Article↗
PubMed2003

Analysis of raw microneurographic recordings based on wavelet de-noising technique and classification algorithm: wavelet analysis in microneurography.

We propose a new technique for analyzing the raw neurogram which enables the study of the discharge behavior of individual and group neurons. It utilizes an ideal bandpass filter, a modified wavelet de-noising procedure, an action potential detector, and a waveform classifier. We validated our approach with both simulated data generated from muscle sympathetic neurograms sampled at high rates in five healthy subjects and data recorded from seven healthy subjects during lower body negative pressure suction. The modified wavelet method was superior to the classical discriminator method and the regular wavelet de-noising procedure when applied to simulated neuronal signals. The detected spike rate and spike amplitude rate of the action potentials correlated strongly with number of bursts detected in the integrated neurogram (r = 0.79 and 0.89, respectively, p < 0.001). Eight major action potential waveform classes were found to describe more than 81% of all detected action potentials in all subjects. One class had characteristics similar in shape and in average discharge frequency (27.4 +/- 5.1 spikes/min during resting supine position) to those of reported single vasoconstrictor units. The newly proposed technique allows a precise estimate of sympathetic nerve activity and characterization of individual action potentials in multiunit records.

Action Potentials↗
PubMed2002

Stochastic resonance of ensemble neurons for transient spike trains: wavelet analysis.

By using the wavelet transformation (WT), I have analyzed the response of an ensemble of N (=1, 10, 100, and 500) Hodgkin-Huxley neurons to transient M-pulse spike trains (M=1 to 3) with independent Gaussian noises. The cross correlation between the input and output signals is expressed in terms of the WT expansion coefficients. The signal-to-noise ratio (SNR) is evaluated by using the denoising method within the WT, by which the noise contribution is extracted from the output signals. Although the response of a single (N=1) neuron to subthreshold transient signals with noises is quite unreliable, the transmission fidelity assessed by the cross correlation and SNR is shown to be much improved by increasing the value of N: a population of neurons plays an indispensable role in the stochastic resonance (SR) for transient spike inputs. It is also shown that in a large-scale ensemble, the transmission fidelity for suprathreshold transient spikes is not significantly degraded by a weak noise which is responsible to SR for subthreshold inputs.

Animals↗
PubMedDate not supplied

Detection of epileptic events in electroencephalograms using wavelet analysis.

This study deals with the problem of identification of epileptic events in electroencephalograms using multiresolution wavelet analysis. The following problems are analyzed: time localization and characterization of epileptiform events, and computational efficiency of the method. The algorithm presented is based on a polynomial spline wavelet transform. The multiresolution representation obtained from this wavelet transform and the corresponding digital filters derived allows time localization of epileptiform activity. The proposed detector is based on the multiresolution energy function. Electroencephalogram records from epileptic patients were analyzed, and results obtained are shown. Some comparisons with other methods are given.

Algorithms↗
PubMed1999

Automatic detection of epileptiform activity by single-level wavelet analysis.

We describe a new strategy to automatically identify epileptiform activity in EEG. Our scheme is based upon detecting epileptic spikes, via multiresolution analysis, a relatively new tool in signal processing, which allows for dramatic improvements in the efficiency of basic wavelet analysis. We perform a single-level analysis, which is fast and delivers satisfactory results, provided a wise strategy is adopted. Key points are: the identification of suitable wavelets, in order to gain high computational efficiency; the recognition of a proper resolution level; the computation of an appropriate dynamic threshold, in order to pick out the pathological events. Using a suitable wavelet as the model of a threshold-event proved to be a good choice for devising an algorithm which efficiently performs automatic analysis at high-sensitivity levels. The proposed algorithm was implemented into a C++ multiplatform code having an user-friendly interface, which runs on general-purpose PCs. Results obtained on a set of test tracings, show that the sensitivity of the automatic analysis can be as high as 96%, while less than 5% of the overall recording time is marked. The computational complexity of our algorithm is O (N). Its highly efficient implementation allows for the analysis of up to 310 s of 8 channel EEG, by spending one mere CPU second on a standard PC.

Algorithms↗
PubMed2004

Wavelet analysis for the multicomponent determination in a binary mixture of caffeine and propyphenazone in tablets.

An approach based on both discrete and continuous wavelet analysis followed by a zero-crossing technique was developed. We applied this approach to obtain a high resolution in the binary mixture of caffeine (CA) and propyphenazone (PR) in the presence of their overlapping signals in the working length. The optimization of the wavelet families was accomplished for this mixture. The de-noise procedure was carried out by using 4-level Haar discrete wavelet transform and the resulted de-noised signal was investigated by continuous Mexican (MEX) and Haar (HA) transforms. Finally, a zero-crossing technique was applied on the transformed signal and the constructed calibration was tested by analyzing the composition of the different mixture containing CA and PR. All calculations have been performed within EXCEL and Matlab 6.5 software. The obtained results indicate that our procedure is flexible and applicable for the mixture analysis.

Antipyrine↗
PubMedDate not supplied

Wavelet analysis of two-dimensional birefringence images of architectonics in biotissues for diagnosing pathological changes.

A method for polarization filtering, correlation processing, and wavelet analysis of coherent images of physiologically normal and necrotically changed (myocardium infarct) muscle tissue is presented. A technique for early optical diagnosis of the appearance of these biological tissues and the course of their degenerative-dystrophic changes is proposed.

Algorithms↗
PubMed1993

Wavelet analysis of high-resolution signal-averaged ECGs in postinfarction patients.

The authors present an original method for the discrimination of patients prone to ventricular tachycardia. The wavelet transform, which is a new time-scale technique suitable for transient signal detection, was applied to bipolar unfiltered X, Y, Z signal-averaged electrocardiograms in 20 postinfarction patients with sustained ventricular tachycardia, in 20 myocardial infarction patients without ventricular tachycardia, and in 10 healthy subjects. An improved automated algorithm for the detection and localization of sharp variations of the signal, based on coherent detection of the local maxima of the wavelet transform, was developed. A risk stratification method, based on the detection of at least one singularity at or after a point defined with reference to the QRS onset, was assessed. The optimum cutoff point, found 98 ms after the onset of QRS, provides a specificity of 90% and a sensitivity of 85%. The authors conclude that wavelet analysis makes it possible, in this group of patients, to discriminate those with ventricular tachycardia. It yields better results than those obtained from the conventional time-domain approach.

Algorithms↗
PubMed2005

Generalised wavelet analysis of cutaneous flowmotion during post-occlusive reactive hyperaemia in patients with peripheral arterial obstructive disease.

The purpose of the present study was to assess whether the generalised wavelet analysis (GWA) of the leg cutaneous laser Doppler (LD) flowmotion waves recorded during baseline (Bsl) and after skin post-occlusive hyperaemia (POH) can provide information on the leg cutaneous microcirculatory adaptation to stage II peripheral arterial obstructive disease (PAOD). With this aim the flowmotion was characterised in 20 healthy subjects (HS) and 20 stage II PAOD patients by GWA of LDF tracings during Bsl and POH test. The vascular endothelial and smooth muscle function was also evaluated exploring the arm skin vasodilatory response to iontophoretically delivered acetylcholine (Ach) and sodium nitroprusside (SNP) using LD. During Bsl there was no significant difference in leg skin perfusion between HS and PAOD patients (7.3+/-5.6 vs. 5.8+/-2.9 AU, respectively). PAOD patients revealed higher peak powers in the frequency interval of 0.007-0.02 Hz (120+/-82 vs. 85+/-62 AU(2)/Hz; P < 0.05), 0.02-0.06 Hz (116+/-128 vs. 63+/-48 AU(2)/Hz, respectively; P < 0.05) and 0.06-0.2 Hz (39+/-49 vs. 14+/-10 AU(2)/Hz; P < 0.05). These flowmotion frequencies are related to vascular endothelium activity, sympathetic activity and vessel wall myogenic activity, respectively. During POH the mean peak power of the flowmotion waves increased significantly (P < 0.05) in HS respect to Bsl with the only exception of the 0.02-0.06 Hz band. In the PAOD patients, compared to Bsl the amplitude of the flowmotion waves did not significantly change during POH. In addition, the PAOD patients presented an increased time from release to peak-flux (18.25+/-15.5 vs. 2.16+/-1.28 s, respectively; P < 0.05), an increased time from release to recovery of the basal perfusion (90.26+/-39.14 vs. 26.55+/-14.05 s, respectively; P < 0.05) and a lower slope of the POH curve (10+/-15 vs. 54+/-17 degrees , respectively; P < 0.05), compared with HS. The cutaneous arm vasodilatory response to Ach and to SNP was reduced in PAOD patients in comparison with HS (P < 0.001). In conclusion, our findings showed an increased amplitude of the frequency interval 0.007-0.02, 0.02-0.06 and 0.06-0.2 Hz during Bsl in PAOD patients which did not change during the POH test. All data suggest that in stage II PAOD patients the leg skin perfusion is not impaired during Bsl because of a compensatory mechanism related to increased endothelial, myogenic and sympathetic activities. However during reactive hyperaemia these mechanisms appear to be exhausted in accordance with the reduced vasoreactivity to Ach and SNP.

Acetylcholine↗
PubMed1997

Wavelet analysis of SAECG to identify patients with conduction defects at risk for sudden cardiac death.

The aim of this study was to determine how Wavelet transform analysis of signal-averaged ECGs can identify patients with conduction defects who are at high risk for development of ventricular tachycardia. In this study, 34 SA-ECGs and programmed electrical stimulation (PES) reports were obtained from the OSU Department of Cardiology Database (1988-1996) and divided into two groups: 17 patients that had inducible monomorphic VT by PES (VT+) and 17 that showed no arrhythmias (VT-). We used Morlet's wavelet to analyze the X, Y, Z, and RMS vector magnitudes in each group. The mean duration from the peak of the RMS vector magnitude to the QRS offset was statistically different with a T value (2-tailed distribution, unequal variance) of 0.033. We noted statistically significant (p < 0.0001) differences in Wavelet energies for 44 msec after the peak of the RMS vector magnitude largest in the Z lead, the first 22 msec, and frequency bins less than 131 Hz. Although no clinical marker could be determined using Wavelet analysis to distinguish the the VT+ from the VT- group, the results from this study show that their SA-ECGs are indeed different even though the optimal analysis has not yet been devised.

Cardiac Pacing, Artificial↗
PubMedDate not supplied

Fractional wavelet analysis for the simultaneous quantitative analysis of lacidipine and its photodegradation product by continuous wavelet transform and multilinear regression calibration.

Fractional wavelet transform (FWT) was applied to the original absorption spectra of lacidipine (LAC) and its photodegradation product (LACD), and the resulting FWT spectra were processed by continuous wavelet transform (CWT) and multilinear regression calibration (MLRC) for the simultaneous quantitative analysis of both products in their binary mixtures. These methods do not require any chemical separation step and chemical complex reaction to obtain a detectable signal for the degradation product. By using the Mexican hat function, 2 calibration functions for LAC and LACD were obtained by measuring the CWT transformed signals at 416.1 nm for LAC and 414.6 nm for LACD, after FWT processing of the original absorption spectra. The calibration graphs were linear in the concentration range of 5.08-40.64 microg/mL for LAC and 0.51-8.16 microg/mL for LACD. The limit of detection and the limit of quantitation were found to be 0.289 and 0.956 microg/mL for LAC and 0.036 and 0.118 microg/mL for LACD, respectively. For comparison, the MLRC algorithm was applied to the linear regression functions for the individual drug and its photoproduct. In this approach, a set of linear regression functions was obtained from the relationship between concentrations and FWT signals in the wavelength range 411.0-412.4 nm. Both methods were applied to the quantitative evaluation of LAC and LACD in laboratory and pharmaceutical samples, and produced very satisfactory results.

Algorithms↗