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PubMed1998

Wavelet transform system makes one-beat analysis possible in late potential evaluation.

High-frequency components of the QRS complex, including late potentials, can be analyzed by signal averaging (SA). However, this method may fail to detect transient changes as a result of cancellation. The wavelet transform, which has a superior time-frequency resolution, was used to analyze beat-to-beat changes of the QRS components in 50 normal subjects and 50 patients who showed positive late potentials. The transformed data, displayed in three dimensions and in color, were highly reproducible in each patient. Measurement of high-power duration at a frequency of 50 Hz (WD50) showed a significant correlation between WD50 and filtered QRS duration in both groups. When the mean +/- SD of WD50 in normal subjects was defined as normal, 96% of patients with late potentials were out of the normal range. The wavelet signals in patients with late potentials were more inhomogeneous than those of normal subjects. It is concluded that this newly developed color display, three-dimensional wavelet transform system showed extremely good time-frequency resolution in analyzing every beat without signal averaging.

Action Potentials↗
PubMed2001

Time-frequency analysis of arterial pressure oscillations in anesthetized dogs: effects of standardized hemorrhages.

The purpose of this preliminary study was to investigate the advantages of the time-frequency analysis through the Continuous Wavelet Transform (CWT) compared to classical Fourier analysis using the Fast Fourier Transform (FFT) in arterial pressure signals from anesthetized mongrel dogs before and during standardized hemorrhages. Systemic arterial pressure pulsations were recorded using catheter-tip manometers. CWT and FFT were applied to arterial pressure pulsations to obtain module coefficients of this transformation and its associated contours during the evolution of progressive hemorrhages, in amounts of 15, 34, and 66% of the estimated total blood volume. This mathematical analysis enabled us to identify the evolution of the frequency components of aortic valve functions, heart dynamics, respiratory influences, and vasomotor activities. Furthermore, we isolated the modulating signal of amplitude modulation phenomenon present in the arterial pressure records, as described in previous papers, being the heart rate carrier frequency. The CWT is a very sensitive and reliable procedure to analyze (time-frequency) the oscillatory phenomena in two dimensions, and to provide more information than the FFT. This new analytical procedure may provide new insights in the study of shock pathophysiology.

Animals↗
PubMed1999

Depth of anesthesia estimation and control.

A fully automated system was developed for the depth of anesthesia estimation and control with the intravenous anesthetic, Propofol. The system determines the anesthesia depth by assessing the characteristics of the mid-latency auditory evoked potentials (MLAEP). The discrete time wavelet transformation was used for compacting the MLAEP which localizes the time and the frequency of the waveform. Feature reduction utilizing step discriminant analysis selected those wavelet coefficients which best distinguish the waveforms of those responders from the nonresponders. A total of four features chosen by such analysis coupled with the Propofol effect-site concentration were used to train a four-layer artificial neural network for classifying between the responders and the nonresponders. The Propofol is delivered by a mechanical syringe infusion pump controlled by Stanpump which also estimates the Propofol effect-site and plasma concentrations using a three-compartment pharmacokinetic model with the Tackley parameter set. In the animal experiments on dogs, the system achieved a 89.2% accuracy rate for classifying anesthesia depth. This result was further improved when running in real-time with a confidence level estimator which evaluates the reliability of each neural network output. The anesthesia level is adjusted by scheduled incrementation and a fuzzy-logic based controller which assesses the mean arterial pressure and/or the heart rate for decrementation as necessary. Various safety mechanisms are implemented to safeguard the patient from erratic controller actions caused by external disturbances. This system completed with a friendly interface has shown satisfactory performance in estimating and controlling the depth of anesthesia.

Algorithms↗
PubMed2006

Wavelet transform: A better approach for the evaluation of instantaneous changes in heart rate variability.

The aim of our study was to validate the Vaidyanathan wavelet tool for HRV analysis during orthostatic testing. Two groups of normotensive male subjects were studied: 13 adolescents and 27 young adults. Both groups consisted of subjects with negative, (N-), and with positive family history for hypertension, (N+). These subjects underwent 5-minute active standing upright, preceded and followed by 5-minute periods in supine position. Continuous electrocardiogram (ECG) was recorded and HRV indices were calculated using wavelet (WT) and fast Fourier transform (FFT) simultaneously. WT and FFT data showed high level of correlation (>0.9). Due to its inherent properties, WT proved to be more informative than FFT in the analysis of the non-stationary ECG signal during orthostatic testing. WT revealed HRV dynamics more accurately since it allowed HRV evaluation for shorter intervals (60 s) than FFT (256 s). During the initial and recovery period lower parasympathetic activity (P < 0.0001; P < 0.02) and higher ratio of autonomic balance (sympathetic vs. parasympathetic) (P < 0.0001; P < 0.02) were evidenced in (N+) as compared to (N-). The upright posture was accompanied by a prompt decrease in HRV and by an elevation of the index of autonomic balance. These alterations were more pronounced in N(+). In conclusion, we believe that wavelet analysis is an appropriate approach for the estimation of HRV dynamics in non-stationary conditions. Furthermore, we demonstrate certain essential alterations in the autonomic modulation of the cardiovascular system in young normotensives with positive family history for essential hypertension.

Adolescent↗
PubMed2004

Data preprocessing by wavelets and genetic algorithms for enhanced multivariate analysis of LC peptide mapping.

Peptide mapping by means of liquid chromatography is a powerful technique used for the characterisation and analysis of the primary structure of proteins. Subtle changes in the covalent structure of the protein can be detected by means of the chromatographic profile (fingerprint). Chromatographic methods, however, display variations in the chromatographic profile even at identical instrumental settings and sample conditions. These variations may be due to changes of the chromatographic conditions, e.g. slight shifts in column temperature, and degradation or alterations of the stationary phase or small changes in the trifluoroacetic acid (TFA) concentration. Such variations may result in varying retention times and peak shapes of the analytes and differences in the chromatographic baseline, thereby having a detrimental impact on the results obtained on multivariate analysis of peptide maps. In order to reduce the non-sample-related variations and to be able to more fully extract the information in peptide mapping, approaches for achieving this objective are outlined in the present study. These methods are denoising and data compression of the chromatograms by wavelets, baseline corrections by linear interpolation, and peak shift alignments towards a target chromatogram by means of a genetic algorithm. Visual inspections of preprocessed chromatograms and principal component analysis (PCA) score plots demonstrate the efficiency of the methodology used. Furthermore, deliberately added changes, e.g. insertions of small Gaussian peaks (outliers), are more easily detected by the proposed methods than from the original chromatograms by multivariate analysis.

Algorithms↗
PubMed1999

Wavelet transform in the time-frequency analysis of patients with articulation disorders.

Spectral analysis of the human voice is a frequently used digital analysis method in the diagnosis, the planning and follow-up of the treatment of speech disorders. In the classical spectral analysis method, the principals of Joseph Fourier are used. This is called "Fourier Transform" and it accepts that all signals are formed of the synthesis of many sinusoïdal formed signals. In recent years a new transform method called "wavelet transform" accepts the complex signals formed of small signal particles called "wavelets" and it is considered that this transform will solve the documented problems of the "Fourier Transform". By using the appropriate wavelet, this transform can be used as an alternative to the Fourier transform. In this study, the patients with an articulation disorder of the "s" sound were evaluated before and after the phoniatric reeducation by using both the transform methods, and the results obtained are discussed.

Articulation Disorders↗
PubMed1999

Biorthogonal wavelet transforms for ECG parameters estimation.

The parameters of various morphologies of ECG waveform are basic in characterizing them as normal or otherwise. The use of multiscale analysis, through biorthogonal wavelets presented in this paper, appears very promising for such a characterization. This is on account of the fact that various morphologies are excited better at different scales. From these different scales, amplitudes, durations and various segments, widths can be determined more accurately. Simulation studies, with real ECG data, have shown that even when the signal-to-noise ratios are poor, the proposed technique can be used to accurately estimate the said parameters.

Biomedical Engineering↗
PubMed2006

An adaptive strategy for selecting representative calibration samples in the continuous wavelet domain for near-infrared spectral analysis.

Sample selection is often used to improve the cost-effectiveness of near-infrared (NIR) spectral analysis. When raw NIR spectra are used, however, it is not easy to select appropriate samples, because of background interference and noise. In this paper, a novel adaptive strategy based on selection of representative NIR spectra in the continuous wavelet transform (CWT) domain is described. After pretreatment with the CWT, an extension of the Kennard-Stone (EKS) algorithm was used to adaptively select the most representative NIR spectra, which were then submitted to expensive chemical measurement and multivariate calibration. With the samples selected, a PLS model was finally built for prediction. It is of great interest to find that selection of representative samples in the CWT domain, rather than raw spectra, not only effectively eliminates background interference and noise but also further reduces the number of samples required for a good calibration, resulting in a high-quality regression model that is similar to the model obtained by use of all the samples. The results indicate that the proposed method can effectively enhance the cost-effectiveness of NIR spectral analysis. The strategy proposed here can also be applied to different analytical data for multivariate calibration.

Journal Article↗
PubMed2001

[Wavelet representation of corneal topography data after nonmechanical penetrating keratoplasty--a clinical study].

BACKGROUND: Corneal surface irregularities may limit the visual outcome after penetrating keratoplasty (PK). Corneal topographers mainly render empirically derived and system-specific statistical indices for characterization of superficial inhomogeneities which may lack clinical evidence and make inter-system comparisons difficult. The purpose of this study was to detect and quantify focal surface irregularities of the cornea after nonmechanical PK by 2-dimensional wavelet decomposition based on corneal topography data. PATIENTS AND METHODS: Our study included 15 patients with keratoconus and 10 patients with Fuchs' dystrophy with all-sutures-out after penetrating keratoplasty. For trephination we used the excimer laser MEL60 (Aesculap-Meditec, Germany) (7.5/7.6 mm diameter in dystrophies, 8.0/8.1 mm in keratoconus, double-running 10-0 nylon suture). After suture removal a complete ophthalmological examination including OrbScan topography analysis (Orbtec, USA) was performed. The refraction data were extracted via "data recorder" and decomposed using 2-dimensional wavelet analysis methods (Daubechies-4-wavelets on five scales of resolution). Corneal irregularities were quantified (scale 1 = fine details to scale 5 = coarse details). RESULTS: All detail coefficients (horizontal, vertical and diagonal) correlated statistically significant with the "Irregular Astigmatism" provided by the OrbScan-system (p < 0.05). In scale 3 and 4 a relative maximum of the wavelet detail coefficients occurred, whereas the coefficients at scale 2 and 5 were significantly smaller. The horizontal and vertical detail coefficients correlated significantly inversely with the best-corrected visual acuity (p < 0.04). All detail coefficients were significantly lower in the patient group with keratoconus compared to Fuchs' dystrophy. CONCLUSIONS: Wavelet decomposition of corneal topography refraction data allows an analytical isolation and quantification of focal corneal superficial irregularities. This algorithm is independent of the currently used topography system and allows a smoothing of the raw data set adapted to scale of resolution and data compression.

Adult↗
PubMed2002

Time-varying properties of renal autoregulatory mechanisms.

In order to assess the possible time-varying properties of renal autoregulation, time-frequency and time-scaling methods were applied to renal blood flow under broad-band forced arterial blood pressure fluctuations and single-nephron renal blood flow with spontaneous oscillations obtained from normotensive (Sprague-Dawley, Wistar, and Long-Evans) rats, and spontaneously hypertensive rats. Time-frequency analyses of normotensive and hypertensive blood flow data obtained from either the whole kidney or the single-nephron show that indeed both the myogenic and tubuloglomerular feedback (TGF) mechanisms have time-varying characteristics. Furthermore, we utilized the Renyi entropy to measure the complexity of blood-flow dynamics in the time-frequency plane in an effort to discern differences between normotensive and hypertensive recordings. We found a clear difference in Renyi entropy between normotensive and hypertensive blood flow recordings at the whole kidney level for both forced (p < 0.037) and spontaneous arterial pressure fluctuations (p < 0.033), and at the single-nephron level (p < 0.008). Especially at the single-nephron level, the mean Renyi entropy is significantly larger for hypertensive than normotensive rats, suggesting more complex dynamics in the hypertensive condition. To further evaluate whether or not the separation of dynamics between normotensive and hypertensive rats is found in the prescribed frequency ranges of the myogenic and TGF mechanisms, we employed multiresolution wavelet transform. Our analysis revealed that exclusively over scale ranges corresponding to the frequency intervals of the myogenic and TGF mechanisms, the widths of the blood flow wavelet coefficients fall into disjoint sets for normotensive and hypertensive rats. The separation of the scales at the myogenic and TGF frequency ranges is distinct and obtained with 100% accuracy. However, this observation remains valid only for the whole kidney blood pressure/flow data. The results suggest that understanding of the time-varying properties of the two mechanisms is required for a complete description of renal autoregulation.

Algorithms↗
PubMed2002

Comparison of data transformation procedures to enhance topographical accuracy in time-series analysis of the human EEG.

We describe a methodology to apply current source density (CSD) and minimum norm (MN) estimation as pre-processing tools for time-series analysis of single trial EEG data. The performance of these methods is compared for the case of wavelet time-frequency analysis of simulated gamma-band activity. A reasonable comparison of CSD and MN on the single trial level requires regularization such that the corresponding transformed data sets have similar signal-to-noise ratios (SNRs). For region-of-interest approaches, it should be possible to optimize the SNR for single estimates rather than for the whole distributed solution. An effective implementation of the MN method is described. Simulated data sets were created by modulating the strengths of a radial and a tangential test dipole with wavelets in the frequency range of the gamma band, superimposed with simulated spatially uncorrelated noise. The MN and CSD transformed data sets as well as the average reference (AR) representation were subjected to wavelet frequency-domain analysis, and power spectra were mapped for relevant frequency bands. For both CSD and MN, the influence of noise can be sufficiently suppressed by regularization to yield meaningful information, but only MN represents both radial and tangential dipole sources appropriately as single peaks. Therefore, when relating wavelet power spectrum topographies to their neuronal generators, MN should be preferred.

Artifacts↗
PubMed2000

Wavelet and short-time Fourier transform analysis of electromyography for detection of back muscle fatigue.

Measurement of the time-varying characteristics of the frequency content of trunk muscle electromyography is a method to quantify the amount of fatigue endured by workers during industrial tasks, as well as a tool that may guide the training and rehabilitation of healthy and injured workers. Quantification of the change of signal power within specific frequency ranges may shed greater insight into the fatigue process. Sixteen healthy male subjects performed isometric trunk extension at 70% of their maximum voluntary contraction. Surface electromyography from medial and lateral erector spinae, and latissimus dorsi locations were processed using the short-time Fourier transform (STFT) and wavelet transform. Linear regression quantified the time rate of change of median frequency as well as frequency specific STFT filter and wavelet scale measures. The median frequency from the short-time Fourier transform declined by 22 Hz/min from an initial value of 77 Hz on average. The wavelet and STFT filter measures demonstrated this decline to be caused by a reduction in 209-349 Hz signal power in addition to an increase in 7-88 Hz signal power. A significant reduction in median frequency and significant elevation in 13-22 Hz wavelet signal component was detected in about 90% of the cases, indicating their use for detecting and quantifying fatigue.

Adult↗
PubMed2002

Wavelet de-noising of laser Doppler reactive hyperemia signals to diagnose peripheral arterial occlusive diseases.

In order to improve peripheral arterial occlusive diseases (PAOD) diagnoses, five de-noising algorithms based on a multiresolution analysis computed with wavelets are applied on reactive hyperemia signals obtained with the laser Doppler flowmetry technique. Results are presented on recordings acquired on patients suffering from PAOD and on healthy subjects.

Arterial Occlusive Diseases↗
PubMed2004

Comparison of Fourier and wavelet resampling methods.

Resampling can be used to compute the null distribution of any test statistic for the purpose of measuring the significance of the measured value. This study investigated how well the spatial and temporal correlations of simulated and experimentally observed fMRI time series were preserved under Fourier and wavelet resampling methods. The null distributions of a test statistic estimated by each resampling method were compared. In addition, both resampling methods were applied to locate activated voxels in an fMRI dataset and ROC analysis showed that wavelet resampling performed more accurately than Fourier resampling.

Brain Mapping↗
PubMed2006

MUAP extraction and classification based on wavelet transform and ICA for EMG decomposition.

We have developed an effective technique for extracting and classifying motor unit action potentials (MUAPs) for electromyography (EMG) signal decomposition. This technique is based on single-channel and short perioda9s real recordings from normal subjects and artificially generated recordings. This EMG signal decomposition technique has several distinctive characteristics compared with the former decomposition methods: (1) it bandpass filters the EMG signal through wavelet filter and utilizes threshold estimation calculated in wavelet transform for noise reduction in EMG signals to detect MUAPs before amplitude single threshold filtering; (2) it removes the power interference component from EMG recordings by combining independent component analysis (ICA) and wavelet filtering method together; (3) the similarity measure for MUAP clustering is based on the variance of the error normalized with the sum of RMS values for segments; (4) it finally uses ICA method to subtract all accurately classified MUAP spikes from original EMG signals. The technique of our EMG signal decomposition is fast and robust, which has been evaluated through synthetic EMG signals and real EMG signals.

Action Potentials↗
PubMed2004

Spatio-temporal frequency characteristics of intersensory components in audiovisually evoked potentials.

Perception of the external world is based on complex neural processes allowing for combination of sensory experiences from different modalities. Audiovisual (AV) integration is discussed in this paper on the basis of the intersensory component (IC), which is the part of the multisensory-evoked potential that is not explained by linear summation of the unisensory-evoked potentials. It was predicted that audiovisual ICs can be extracted, localized, and differentiated by means of wavelet-based frequency analysis. Healthy, right-handed subjects (n = 15) were instructed to view and listen to presented stimuli (A: auditory; V: visual; and AV: audiovisual). Electroencephalographic data was analyzed off-line by means of wavelet transformation utilizing quadratic B-spline mother wavelets. Cross-modal interaction was investigated by subtracting wavelet responses to unimodal stimuli (A, V) alone from the wavelet responses to the combined stimuli (AV; i.e., interaction = AV - (A + V)). These difference waveforms revealed the phase-locked fraction of ICs further characterized by frequency-band and location. Spatio-temporally distinct ICs were observed in all frequency bands [31-62 Hz (gamma), 16-31 Hz (beta), 8-16 Hz (alpha), 4-8 Hz (theta), 0.5-4 Hz band (delta)]. These were most pronounced and sustained in the theta frequency band with early (<100 ms) appearance in fronto-centro-parietal sites. In contrast, alpha-band ICs tended to appear later (>200 ms) in these locations. High-frequency (beta- and gamma-band) ICs were less organized in their spatial pattern with both early and late appearance. ICs may reflect sensory and cognitive/integrative processes at the cortical level. In case of intersensory processing, alpha- and theta-activity appear to be spatio-temporally distinct, and could therefore participate in different stages of perception. These findings add further support to current model views of oscillatory activity in selectively distributed networks.

Adult↗
PubMed2006

Comparative assessment of bone mass and structure using texture-based and histomorphometric analyses.

The purpose of this study was to develop a methodology for quantitatively assessing bone quantity and anisotropy based on texture analysis using Gabor wavelets. The wavelet approach has the capability to simultaneously examine the images at low and high resolutions to gain information on both global and detailed local features of the bone image. The program that implemented the texture analysis gave measures of density (M(Density)) and anisotropy (M(Anisotropy)). It also allowed us to examine the texture energy at four orientations (0 degrees , 45 degrees , 90 degrees , 135 degrees) to gain insight about the details of the anisotropy. Analysis of templates of four simulated patterns, which had same number of dots but with differing orientations, demonstrated how the texture-based analysis differentiated between these templates. The measures of M(Anisotropy) discriminated between the four simulated patterns. The M(Density) measures were similar across all patterns. These outcomes matched the design intent of the simulated patterns. We also compared the trabecular bone images obtained from a previous study, in which the right forelimbs of normal female retired breeder beagle dogs (5-7 years old) were cast for 12 months to induce bone loss, using both histomorphometry and texture analysis. Both histomorphometry and the texture analysis detected significant differences in the trabecular bone of the distal metatarsal between the control and disuse groups. Percent trabecular bone (Tb.Ar/T.Ar) and the textural density parameter (M(Density)) were highly correlated (r=0.962). M(Anisotropy) was decreased (3.9%) after the 12-month disuse protocol, but was not significantly different from normal. However, the texture energy values at all orientations (0 degrees , 45 degrees , 90 degrees and 135 degrees) were significantly decreased in the disuse group. Therefore, texture analysis was able to assess anisotropy, which could not be extracted from histomorphometric parameters. We conclude that texture analysis is an effective tool for assessing 2D bone images that yields information regarding the quantity of bone as well as the orientation of the trabecular structure that can augment our ability to discriminate between normal and pathological bone tissue.

Animals↗