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At least 649 records · Page 36Linked to original sources
PubMed1997

Continuous wavelet transform of aortic pressure oscillations in anesthetized dogs: effects of 45 degrees tilting.

The time-frequency analysis of signals by means of continuous wavelet transform (CWT) was applied to blood pressure oscillations recorded from the aorta of anesthetized dogs. This method yielded two and three-dimensional representations of either the module or phase in function of time, in contrast with the fast Fourier transform (FFT) which gives the spectrum in the frequency domain. From the CWT of arterial pressure oscillations we obtained visual information on aortic valves closure, heart rate, respiratory rate and smooth muscle contractions in arterial and arteriolar walls (very low frequency component). The objective of this study was to analyze the frequency-time behavior in two and three-dimensional cardiovascular changes during 45 degrees head-up and head-down tilts, compared with zero degree supine position. In eight pentobarbitone anesthetized dogs, the postural changes were repeated for more than ten times in each one. Heart rate variability was derived by applying a new mathematical procedure. We utilized the pronounced changes of heart rate during each respiratory cycle (inspiratory tachycardia and expiratory bradycardia) to establish a correlation with the arterial pressure fluctuations during normal and tilting conditions. Significant differences in heart rate were observed between the 45 degrees head-up and head-down tilts, compared with the supine position. The results show that anesthetized dogs might constitute an appropriate model where to study orthostatic hypotension and microgravity blood shifts.

Anesthesia↗
PubMed2001

Multivariate curve resolution of wavelet and Fourier compressed spectra.

The multivariate curve resolution method SIMPLe to use Interactive Self-Modeling Mixture Analysis (SIMPLISMA) was applied to Fourier and wavelet compressed ion-mobility spectra. The spectra obtained from the SIMPLISMA model were transformed back to their original representation, that is, uncompressed format. SIMPULSMA was able to model the same pure variables for the partial wavelet transform, although for the Fourier and complete wavelet transforms, satisfactory pure variables and models were not obtained. Data were acquired from two samples and two different ion mobility spectrometry (IMS) sensors. The first sample was thermally desorbed sodium gamma-hydroxybutyrate (GHB), and the second sample was a liquid mixture of dicyclohexylamine (DCHA) and diethylmethylphosphonate (DEMP). The spectra were compressed to 6.3% of their original size. SIMPLISMA was applied to the compressed data in the Fourier and wavelet domains. An alternative method of normalizing SIMPLISMA spectra was devised that removes variation in scale between SIMPLISMA results obtained from uncompressed and compressed data. SIMPLISMA was able to accurately extract the spectral features and concentration profiles directly from daublet compressed IMS data at a compression ratio of 93.7% with root-mean-square errors of reconstruction < 3%. The daublet wavelet filters were selected, because they worked well when compared to coiflet and symmlet. The effects of the daublet filter width and compression ratio were evaluated with respect to reconstruction errors of the data sets and SIMPLISMA spectra. For these experiments, the daublet 14 filter performed well for the two data sets.

Journal Article↗
PubMed2000

Risk evaluation of ventricular tachycardia using wavelet transform irregularity of the high-resolution electrocardiogram.

A new method for analysis of high-resolution ECG signals using a wavelet transform based on a modified Morlet function is presented. A polynomial filter is used to reduce low-frequency, high-amplitude noise components in the analysed signals. The method is tested on test ECG signals with simulated late potentials and finally verified on two post-infarction patient (PP) groups: 62 PPs with ventricular tachycardia and 44 PPs without arrhythmia. A new quantitative parameter, the irregularity factor, is proposed for discrimination between the study groups. The results show a significant difference in the parameter values for tachycardia patients compared with those for patients without arrhythmia. The sensitivity of the proposed method is 85%, and the specificity is 93%.

Adult↗
PubMed1996

Simplification of dynamic NMR spectroscopy by wavelet transforms.

Wavelet transforms considerably simplify the analysis of FIDs influenced by dynamic processes. The projection of data onto an orthonormal set of basis functions which represent well-defined points on a time-frequency plane allows a more natural description of time-dependent frequencies than Fourier transforms. The properties are illustrated by application to simulated NMR signals showing dynamics in the intermediate to slow exchange regime. Construction of highly sophisticated pulse sequences from wavelets is briefly discussed. This very general method will be useful in many areas of magnetic resonance, and theory may benefit as well as imaging.

Data Interpretation, Statistical↗
PubMed1997

Quantitative analysis of errors due to power-line interference and base-line drift in detection of onsets and offsets in ECG using wavelets.

Timing characterisation of the ECG using wavelet transforms is a new technique in which multiscale analysis reduces the influence of noise. This technique issued to investigate the effect of noise and to estimate the errors involved in the detection of onsets and offsets of ECG waves. With appropriate choice of scales of analysis, the study shows that the errors involved in the measurement of QRS width in the presence of base-line wander are negligible. The 50 Hz power-line interference introduces a maximum error of 6.25% if it is greater than 50% of the signal amplitude. The P and T complexes are not affected by power-line interference, but the base-line wander introduces a maximum error of 9.6%. In situations with the simultaneous presence of both types of noise, the use of an optimised scale restricts the errors to within clinically acceptable limits.

Electricity↗
PubMed1996

An improved computer-assisted diagnostic scheme using wavelet transform for detecting clustered microcalcifications in digital mammograms.

RATIONALE AND OBJECTIVES: We evaluated the potential usefulness of a computer-assisted diagnostic (CAD) scheme incorporating the wavelet transform for detecting clustered microcalcifications in mammograms. METHODS: A wavelet transform technique was applied to the detection of clustered microcalcifications. We examined several wavelets to study their effectiveness in detecting subtle microcalcifications. We used a database consisting of 39 mammograms containing 41 clusters of microcalcifications. The performance of the wavelet-based CAD scheme was evaluated using free-response receiver operating characteristic analysis. RESULTS: The CAD scheme with the wavelet transform was useful in detecting some of the subtle microcalcifications that were not detected by our previous scheme, which was based on the difference-image technique. When the two schemes were combined, the overall performance was improved to a sensitivity of approximately 95%, with a false-positive rate of 1.5 clusters per image. CONCLUSION: The wavelet transform approach can improve the detection of subtle clustered microcalcifications.

Calcinosis↗
PubMed1991

An analysis of the oscillatory patterns in the central nervous system with the wavelet method.

This paper discusses a simple application of the wavelet transformation to analyse nerve cell impulse patterns. The action potentials converted into delta, or Dirac, functions were convoluted in the time domain with a modified Gauss (the negative of the second derivative of Gauss) function, varying in width between 0.6 and 384 ms. The width of the Gauss function was varied in 640 steps. Some parts of the transformation were extended, analysed and averaged in the frequency domain to explore oscillatory components of the impulse pattern. The sequences of action potentials of retinal ganglion cells evoked by short flashes are taken as examples. The present analysis demonstrate some properties of mathematical "microscopic" application to transient responses of the central nervous system (CNS), whereby the degree of magnification (steps of transformation) was varied.

Action Potentials↗
PubMed1994

The wavelet transform as a tool for recognition of biosignals.

The use of the wavelet transform as a signal analysis tool has been demonstrated by its successful application to the study of various signals. The first step in addressing pattern recognition problems is to define a representation that can be used for extracting the information content of signals. The sharp variation points of a signal amplitude are among the meaningful characterizations of the signal. The wavelet transform of the signal is found to be translation variant which makes it difficult for direct application in pattern recognition. However, the zero-crossings of a wavelet transform employing a particular class of wavelets can provide the translation invariant locations of the signal variation points. A zero-crossing representation augmented by the measure of the structure between the two consecutive zero-crossings has been studied by Stephane Mallat. On the basis of this representation, we demonstrate recognition of segments of biosignals embedded in streams of signals. The feasibility of employing zero-crossings of a wavelet transform as a tool in searching for a particular pattern class in the library of biosignals is explored.

Electrocardiography↗
PubMed2005

Wavelet variance components in image space for spatiotemporal neuroimaging data.

Neuroimaging studies place great emphasis on not only the estimation but also the standard error estimates of underlying parameters derived from a temporal model. This allows inferences to be made about the signal estimates and resulting conclusions to be drawn about the underlying data. It can often be advantageous to interrogate temporal models after spatial transformation of the data into the wavelet domain. Wavelet bases provide a multiresolution decomposition of the spatial data dimension and an ensuing reduction in spatial correlation. However, widespread acceptance of these wavelet techniques has been hampered by the limited ability to reconstruct both parametric and error estimates into the image domain after analysis of temporal models in the wavelet domain. This paper introduces a derivation and a fast implementation of a method for the calculation of the variance of the parametric images obtained from wavelet filters. The technique is proposed for a class of estimators that have been shown to be useful in neuroimaging studies. The techniques are demonstrated for both functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) data sets.

Analysis of Variance↗
PubMed2000

Statistical modeling of positron emission tomography images in wavelet space.

A new method is introduced for the analysis of multiple studies measured with emission tomography. Traditional models of statistical analysis (ANOVA, ANCOVA and other linear models) are applied not directly on images but on their correspondent wavelet transforms. Maps of model effects estimated from these models are filtered using a thresholding procedure based on a simple Bonferroni correction and then reconstructed. This procedure inherently represents a complete modeling approach and therefore obtains estimates of the effects of interest (condition effect, difference between conditions, covariate of interest, and so on) under the specified statistical risk. By performing the statistical modeling step in wavelet space. the procedure allows the direct estimation of the error for each wavelet coefficient; hence, the local noise characteristics are accounted for in the subsequent filtering. The method was validated by use of a null dataset and then applied to typical examples of neuroimaging studies to highlight conceptual and practical differences from existing statistical parametric mapping approaches.

Artifacts↗
PubMed1998

Ultrasound image texture analysis for characterizing intramuscular fat content of live beef cattle.

The primary factors in determining beef quality grades are the amount and distribution of intramuscular fat percentage (IMFAT). Texture analysis was applied to ultrasound B-mode images from ribeye muscle of live beef cattle to predict its IMFAT. We used wavelet transform (WT) for multiresolutional texture analysis and second-order statistics using a gray-level co-occurrence matrix (GLCM) technique. Sets of WT- and GLCM-based texture features were calculated from ultrasonic images from 207 animals and linear regression methods were used for IMFAT prediction. WT-based features included energy ratios, central moments of wavelet-decomposed subimages and wavelet edge density. The regression model using WT features provided a root mean square error (RMSE) of 1.44 for prediction of IMFAT using validation images, while that of GLCM features provided an RMSE of 1.90. The prediction models using the WT features showed potential for objective quality evaluation in the live animals.

Adipose Tissue↗
PubMed1994

Fast wavelet transformation of EEG.

Wavelet transforms offer certain advantages over Fourier transform techniques for the analysis of EEG. Recent work has demonstrated the applicability of wavelets for both spike and seizure detection, but the computational demands have been excessive. We compare the quality of feature extraction of continuous wavelet transforms using standard numerical techniques, with more rapid algorithms utilizing both polynomial splines and multiresolution frameworks. We further contrast the difference between filtering with and without the use of surrogate data to model background noise, demonstrate the preservation of feature extraction with critical versus redundant sampling, and perform the analyses with wavelets of different shape. Comparison is made with windowed Fourier transforms, similarly filtered, at different data window lengths. We here report a dramatic reduction in computational time required to perform this analysis, without compromising the accuracy of feature extraction. It now appears technically feasible to filter and decompose EEG using wavelet transforms in real time with ordinary microprocessors.

Brain↗
PubMed2000

Frequency and temporal analysis of contralateral acoustic stimulation on evoked otoacoustic emissions in humans.

Previous studies have shown that the effect of contralateral acoustic stimulation (CAS) on ipsilateral evoked otoacoustic emissions (EOAE) depends somewhat upon the spectrum of the eliciting stimulus. The latency of the EOAE, however, is itself frequency-dependent. Consequently, two general ways of analyzing the effects of CAS may be considered: by frequency band or by temporal segment. In this study, we analyzed the effects of CAS both ways in the same subjects, essentially simultaneously. The frequency analysis of the EOAE derived from the wavelet transform (WT). The WT is known to provide a robust approach to the analysis of non-stationary signals and was anticipated to avoid possible time-frequency confounds of the cochlear mechanical system. For comparison, a more basic analysis - using a temporal moving window - was employed. The results largely support earlier findings and confirm that in humans the greatest suppression of EOAEs by CAS is obtained for lower frequency and/or longer latency EOAE components. Despite expectations for the WT analysis, the more basic, temporal, analysis tended to yield the clearer results.

Acoustic Stimulation↗
PubMed2004

Neural classification of lung sounds using wavelet coefficients.

Electronic auscultation is an efficient technique to evaluate the condition of respiratory system using lung sounds. As lung sound signals are non-stationary, the conventional method of frequency analysis is not highly successful in diagnostic classification. This paper deals with a novel method of analysis of lung sound signals using wavelet transform, and classification using artificial neural network (ANN). Lung sound signals were decomposed into the frequency subbands using wavelet transform and a set of statistical features was extracted from the subbands to represent the distribution of wavelet coefficients. An ANN based system, trained using the resilient back propagation algorithm, was implemented to classify the lung sounds to one of the six categories: normal, wheeze, crackle, squawk, stridor, or rhonchus.

Algorithms↗
PubMed2002

Wavelet-aided parametric mapping of cerebral dopamine D2 receptors using the high affinity PET radioligand [11C]FLB 457.

The study of human neuroreceptor systems by means of positron emission tomography (PET) and suitable radioligands has proven to be of great importance in research on normal brain functions and the pathophysiology and treatment of neuropsychiatric disorders. A for long identified goal is to produce detailed parametric maps of showing neuroreceptor binding parameters for the entire human brain in vivo. The application of wavelet filters has recently been proposed as a solution to handle the inherently low signal-to-noise ratio of PET images. In the present study we applied the wavelet approach to data obtained from 10 healthy subjects who were examined with [11C]FLB 457. This high affinity dopamine D2-receptor antagonist provides a signal from a range of regions with a hundredfold difference in receptor density and should thus be suitable for evaluation of the wavelet approach. For cross-validation purposes the data were analysed with four methods: a traditional region-of-interest (ROI) based analysis, a pixel-based analysis and two variants of wavelet-aided analyses. In both variants the wavelet filter was spatially applied, but a two-dimensional filter was used in one case and a three-dimensional one in the other. The same linear-graphical binding potential (BP) estimation step was used for all methods and the results of the three parametric mapping techniques were compared to the reference ROI-based method by calculating the average BP of representative ROIs. The pixel-based and the two-dimensional-wavelet-based methods yielded highly correlated but systematically lower values when compared to the reference ROI-based method. The approach utilising three-dimensional wavelet filters yielded BP maps with regional averages closely matching the values of the ROI-based method. The results show that the combination of three-dimensional spatial wavelet filtering with established parameter estimation procedures provides detailed, accurate maps of radioligand binding parameters. Such maps can be used for in inter-individual or multi-condition comparisons of binding parameters at subregional levels.

Adult↗
PubMed2002

Predicting the success of defibrillation by electrocardiographic analysis.

BACKGROUND: We investigated an electrocardiographic signal analysis technique for predicting whether an electrical shock would reverse ventricular fibrillation (VF) in an effort to minimize the damaging effects of repetitive shocks during CPR. METHODS AND RESULTS: An established model of CPR was utilized. VF was electrically induced in anesthetized 40 kg domestic pigs. Defibrillation was attempted after either 4 or 7 min of untreated VF. Failing to reverse VF, a 1 min interval of precordial compression and mechanical ventilation preceded each subsequent defibrillation attempt. The amplitude frequency spectrum of digitally filtered VF wavelets was computed with Fourier analysis during uninterrupted precordial compression from conventional right infraclavicular and left apical electrodes. Of a total of 34 electrical defibrillation attempts, 24 animals were restored to spontaneous circulation (ROSC). An amplitude spectrum analysis (AMSA) value of 21 mV Hz had a negative predictive value of 0.96 and a positive predictive value of 0.78. CONCLUSIONS: AMSA predicted when an electrical shock failed to restore spontaneous circulation during CPR with a high negative predictive value. This method potentially fulfills the need for minimizing ineffective defibrillation attempts and their attendant adverse effects on the myocardium.

Analysis of Variance↗
PubMed1996

Real-time spike detection in EEG signals using the wavelet transform and a dedicated digital signal processor card.

This paper describes a complete real-time system for EEG signal analysis. Specific software and hardware have been designed to provide biologists with an efficient tool, which allows a complete study of the different states of vigilance as well as the paroxysmal activities. The analysis method which is based on the wavelet transform is first presented and compared to the standard spectral approach. The dedicated digital signal processor card, based on the Motorola 96002 processor chip, that has been designed to support real-time acquisition and real-time processing of EEG signals is then presented. We finally illustrate the proposed method by processing real EEG signals of rats, and show that it opens up new prospects in the domain of EEG-based diagnosis. We propose a new representation, called globalization, that provides a global view and better detection of paroxysmal activities.

Algorithms↗
PubMed1997

Detailed analysis of the latencies of median nerve somatosensory evoked potential components, 2: Analysis of subcomponents of the P13/14 and N20 potentials.

Detailed analysis of P13/14 and N20 wavelets was performed for 62 normal subjects and patients with various lesions along the somatosensory pathway. A histogram of the latencies of all the identified P13/14 wavelets (measured from P13/14 onset) demonstrated three latency-groups, which were named P13, P14a and P14b subcomponents. The relationship between the three newly identified subcomponents and the conventional naming of P13 and P14 was inconstant, indicating the ambiguity of the latter. P14b was most prominent in the contralateral central region, and therefore a P15 positivity slightly after P14b was often recorded in the CPc-Fz and CPc-CPi leads (CPc and CPi are centroparietal electrodes contralateral and ipsilateral to the stimulation). P14b/P15 was lost even in patients with cortical lesions, and thalamocortical fibers were assumed for its origin. The CPc-Fz and CPi-Fz leads registered a low negativity named broad N13', suggesting frontal predominance of the overall P13/14 complex. Both P13 and P14a were identified in a patient with a pontine lesion, and a caudal brainstem origin for both was suspected due to the onset of two repetitive bursts of the ascending lemniscal volley. We refuted the presynaptic origin of the scalp P13 potential and pointed out that a prolonged and/or polyphasic P11 frequently observed in patients with high cervical lesions can be mistaken as scalp P13. A histogram of the latencies of all the identified negative wavelets of N20 in the CPc-Fz lead (measured from N20 onset) revealed five definite latency-groups, which were named N20a, N20b, N20c, N20d and N20e subcomponents. The highest peak of N20 actually corresponded to either N20b, N20c or N20d, and this uncertainty, which must be related to intracortical processes, resulted in a large instability of the N20 peak latency as well as the age and sex dependence of the N20 onset-peak interval, both of which were demonstrated by our preceding study (Sonoo, M., Kobayashi, M., Genba-Shimizu, K., Mannen, T. and Shimizu, T. Detailed analysis of the latencies of median nerve SEP components, 1: selection of the best standard parameters and the establishment of the normal values. Electroenceph. clin. Neurophysiol., 1996b, 100: 319-331). Negative subcomponents in the CPc-NC lead and positive subcomponents in the Fz-NC lead constituted mirror images of each other, which suggested that these subcomponents were generated within area 3b.

Adult↗