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PubMed1998

Time-frequency analysis of fetal heartbeat fluctuation using wavelet transform.

We examined whether the nonlinear control mechanism of the fetal autonomic nervous system would change in various fetal states. Eight thousand or more fetal heartbeats were detected from normal, hypoxemic, and acidemic fetuses. Fetal heart Doppler-signal intervals were determined in a high-precision autocorrelation method, and a time series of fetal heart rate fluctuation was obtained. The distribution of the amplitude of temporal fluctuation in the low-frequency component of fetal heart rate frequency was studied using a method of time-frequency analysis called wavelet transform. Spline 4 was used as the mother wavelet function. A gamma distribution was observed from 17 wk of gestation onward. The value of the parameter nu of this gamma distribution was approximately 1.6 and remained constant regardless of the gestational age or the time of day. The value of nu decreased significantly to 0.77 when the fetus developed acidemia and was 1.51 in hypoxemia and 1.54 in a normal condition. This study elucidates a nonlinear structure of the time series of heart rate fluctuation of the gamma distribution in the human fetus. This technique may provide a new quantitative index of fetal monitoring to diagnose fetal acidemia.

Female↗
PubMed2002

Time-frequency and principal-component methods for the analysis of EMGs recorded during a mildly fatiguing exercise on a cycle ergometer.

Electromyographic signals contain the information on muscle activity and have to be frequently averaged, compared, classified or details need to be extracted. A time-frequency analysis, based on wavelets, was previously presented. The analysis transformed an EMG signal into an EMG-intensity-pattern showing the intensities at any point in time for the frequencies filtered out by the wavelets. The purpose of the present study was:to define and apply a new EMG-pattern-space for the analysis of EMG-intensity-patterns; and to determine the variation of EMG-intensity-patterns while getting mildly fatigued by cycling on a cycle-ergometer. The coordinates spanning the pattern space were principal components of the measured EMG-intensity-patterns. A point in pattern-space thus represented an EMG-intensity-pattern. Fatigue resulted in points moving along a line in pattern space. The line was characterized by an intercept at time 0 and a slope. Thus mild fatigue caused a shift from an initial intensity-pattern representing the intercept to a final intensity-pattern adding gradually larger amounts of the pattern representing the slope. The intensity-pattern of the slope revealed the physiologically important individual strategies for coping with mild fatigue. Changes were observed at different times and at different frequencies during the cycling movement.

Adult↗
PubMed2005

Parallel Factor Analysis as an exploratory tool for wavelet transformed event-related EEG.

In the decomposition of multi-channel EEG signals, principal component analysis (PCA) and independent component analysis (ICA) have widely been used. However, as both methods are based on handling two-way data, i.e. two-dimensional matrices, multi-way methods might improve the interpretation of frequency transformed multi-channel EEG of channel x frequency x time data. The multi-way decomposition method Parallel Factor (PARAFAC), also named Canonical Decomposition (CANDECOMP), was recently used to decompose the wavelet transformed ongoing EEG of channel x frequency x time (Miwakeichi, F., Martinez-Montes, E., Valdes-Sosa, P.A., Nishiyama, N., Mizuhara, H., Yamaguchi, Y., 2004. Decomposing EEG data into space-time-frequency components using parallel factor analysis. Neuroimage 22, 1035-1045). In this article, PARAFAC is used for the first time to decompose wavelet transformed event-related EEG given by the inter-trial phase coherence (ITPC) encompassing ANOVA analysis of differences between conditions and 5-way analysis of channel x frequency x time x subject x condition. A flow chart is presented on how to perform data exploration using the PARAFAC decomposition on multi-way arrays. This includes (A) channel x frequency x time 3-way arrays of F test values from a repeated measures analysis of variance (ANOVA) between two stimulus conditions; (B) subject-specific 3-way analyses; and (C) an overall 5-way analysis of channel x frequency x time x subject x condition. The PARAFAC decompositions were able to extract the expected features of a previously reported ERP paradigm: namely, a quantitative difference of coherent occipital gamma activity between conditions of a visual paradigm. Furthermore, the method revealed a qualitative difference which has not previously been reported. The PARAFAC decomposition of the 3-way array of ANOVA F test values clearly showed the difference of regions of interest across modalities, while the 5-way analysis enabled visualization of both quantitative and qualitative differences. Consequently, PARAFAC is a promising data exploratory tool in the analysis of the wavelets transformed event-related EEG.

Adult↗
PubMed2000

[A computational spectral analysis method for multi-component drugs based on wavelet transform].

A novel computational spectral analysis method for multi-component drugs is proposed. The method applies the wavelet denoise processing technique to principle component regression(PCR). Since noise in the original spectral data is effectively filtered, the performance of PCR is obviously improved. A typical example in multi-component drug analysis has been used to verify the effectiveness of the novel algorithm. Compared with that obtained by PCR, the average of mean relative error which obtained by PCRW decreases to 0.46% from 1.48%.

Algorithms↗
PubMed2005

Analysis of week-to-week variability in skin blood flow measurements using wavelet transforms.

The study of skin blood flow responses is confounded by temporal variability in blood flow measurements. Spectral analysis has been shown useful in isolating the effects of distinct control mechanisms on various stimuli in the microcirculatory system. However, the sensitivity of spectral analysis to temporal blood blow variability has not been reported. This study was designed to assess week-to-week variability in blood flow measurements using wavelet-based spectrum analysis. Ten healthy, young subjects (mean age+/-SD, 30.0+/-3.1 years) were recruited into the study. Incremental heating (35-45 degrees C, 1 degrees step min-1) was applied on the skin over the sacrum once per week for three consecutive weeks. Wavelet analysis was used to decompose the laser Doppler blood flow signal into frequency bands determined to be associated with endothelial nitric oxide (0.008-0.02 Hz), neurogenic (0.02-0.05 Hz), myogenic (0.05-0.15 Hz), respiratory (0.15-0.4 Hz), and cardiac (0.4-2.0 Hz) control mechanisms. The results showed that coefficients of variation for the power in each frequency band at baseline are smaller than the coefficients of variation of blood flow at baseline or at maximal blood flow ratio (P<0.05). Myogenic and respiratory frequency bands showed the highest coefficients of variation among the five frequency bands. An increase in power in the endothelial nitric oxide frequency band and a decrease in power in the myogenic frequency band of the maximal blood flow response were reproduced in three consecutive weeks. Our study suggests that wavelet analysis is an effective method to overcome temporal variability in skin blood flow measurements.

Adult↗
PubMed2002

A wavelet-based heart rate variability analysis for the study of nonsustained ventricular tachycardia.

It has been reported that the sympathovagal balance (SB) can be quantified by heart rate (HR) via the low-frequency (LF) to high-frequency (HF) spectral power ratio LF/HF. In this paper, an investigation of the relationship between the autonomic nervous system (ANS) and non-sustained ventricular tachycardia (NSVT) is presented. A wavelet transform (WT)-based approach for short-time heart rate variability (HRV) assessments is proposed for this aspect of analysis. The study was conducted on an RR-interval database consisting of 87 NSVT, 61 ischemic and five normal episodes. First, instantaneous SB estimates were generated by the proposed method. Then, waveforms of the WT-based SB evolutions were quantitatively examined. Numerical results showed that while a majority of SB waveforms (about 71%) derived from the non-NSVT population (i.e., ischemic and normal) appeared to come near oscillating with certain fixed levels, approximate 75% of SB evolutions underwent significantly rapid increases prior to the onset of NSVT, suggesting that an abrupt sympathovagal imbalance might partly account for the occurrence of NSVT.

Algorithms↗
PubMed2003

[Frequency detection of the first heart sound based on wavelet transformation].

According to the valvular theory, the vibrations affected by the mitral and tricuspid valves closure in early systole produce the first heart sound (S1). S1 usually includes many frequency components. In this paper, a method using the multi-resolution analysis of wavelet transformation is recommended for detecting the frequency range of S1. First, S1 was decomposed into different levels on frequency. Then the normalized Shannon energy of the different levels was calculated. The level containing the maximum energy is the major components' level of S1. The frequency range of this level is the major frequency range of S1. The frequency range of S1 was successfully detected by the method.

Algorithms↗
PubMed2005

[Progress in automatic detection of epilepsy based on EEG analysis].

Automatic detection of epileptic events is of significance in clinical application. It is helpful to reduce the electroencephalogram analysts' workload. This paper summarizes and analyzes the detection of epileptic events by traditional and especially advanced methods, including nonlinear filtering, template matching, mimetic, wavelet transform and artificial neural network.

Electroencephalography↗
PubMed2006

Noise reduction based on ICA decomposition and wavelet transform for the extraction of motor unit action potentials.

We have studied methods for noise reduction of myoelectric signals and for extraction of motor unit action potentials from these signals. Effective MUAP peak detection is the first important step in EMG decomposition. We first combined independent component analysis and wavelet filtering to remove power line interference, and then applied a wavelet filtering method and threshold estimation calculated using wavelet transform to suppress background noise and Gaussian white noise. The technique was applied to single-channel, short-period real myoelectric signals from normal subjects and to artificially generated EMG recordings. In contrast to existing methods based on amplitude single-threshold filtering of the original myoelectric signal or a conventional digitally filtered signal, our technique is fast and robust. Moreover, the proposed algorithm is substantially automatic. The performance has been evaluated with a set of synthetic and experimentally recorded myoelectric signals. The basic tool for testing was power spectrum density (PSD) estimation by the Welch method, which allowed us to analyze the PSD of nonstationary signals.

Action Potentials↗
PubMed1996

Wavelets and molecular structure.

The wavelet method offers possibilities for display, editing, and topological comparison of proteins at a user-specified level of detail. Wavelets are a mathematical tool that first found application in signal processing. The multiresolution analysis of a signal via wavelets provides a hierarchical series of "best' lower-resolution approximations. B-spline ribbons model the protein fold, with one control point per residue. Wavelet analysis sets limits on the information required to define the winding of the backbone through space, suggesting a recognizable fold is generated from a number of points equal to 1/4 or less the number of residues. Wavelets applied to surfaces and volumes show promise in structure-based drug design.

Computer-Aided Design↗
PubMed2005

Image denoising based on wavelets and multifractals for singularity detection.

This paper presents a very efficient algorithm for image denoising based on wavelets and multifractals for singularity detection. A challenge of image denoising is how to preserve the edges of an image when reducing noise. By modeling the intensity surface of a noisy image as statistically self-similar multifractal processes and taking advantage of the multiresolution analysis with wavelet transform to exploit the local statistical self-similarity at different scales, the pointwise singularity strength value characterizing the local singularity at each scale was calculated. By thresholding the singularity strength, wavelet coefficients at each scale were classified into two categories: the edge-related and regular wavelet coefficients and the irregular coefficients. The irregular coefficients were denoised using an approximate minimum mean-squared error (MMSE) estimation method, while the edge-related and regular wavelet coefficients were smoothed using the fuzzy weighted mean (FWM) filter aiming at preserving the edges and details when reducing noise. Furthermore, to make the FWM-based filtering more efficient for noise reduction at the lowest decomposition level, the MMSE-based filtering was performed as the first pass of denoising followed by performing the FWM-based filtering. Experimental results demonstrated that this algorithm could achieve both good visual quality and high PSNR for the denoised images.

Algorithms↗
PubMedDate not supplied

Bioprocess fault detection by nonlinear multivariate analysis: application of an artificial autoassociative neural network and wavelet filter bank.

A nonlinear multivariate analysis, artificial autoassociative neural network (AANN), was applied to bioprocess fault detection. In an optimal production process of a recombinant yeast with a temperature controllable expression system, faults in test cases with faulty temperature sensors and plasmid instability of recombinant cells could be detected by the AANN. Since the raw data of measured variables included high-frequency noise, a wavelet filter bank (WFB) was applied to noise elimination before training of the AANN. The filtering performance of the WFB was compared with those of some classical first-order digital filters. The filtered signals at several resolution scales by the WFB were employed as the training data of the AANN. The computing time and summation of square of errors in training were compared, and the appropriate degree of the noise filtering and the density of the training data of the AANN were discussed. The performance of the feature capturing by the AANN was compared with that by a linear multivariate analysis, principal component analysis. A J index defined in this paper, using inputs and outputs of the AANN, was used for fault detection successfully. The output of the first unit of the trained AANN functioned effectively for the discrimination of the data in the abnormal cases from the data in the normal cases.

Algorithms↗
PubMed2003

Bubble observation and transient pressure signals in mechanical heart valve cavitation study.

BACKGROUND AND AIMS OF THE STUDY: Cavitation in the mechanical heart valve (MHV) was first detected in Edwards-Duromedics (ED) clinical explants. Early studies indicated that the pitted surface of the valve leaflet was due to cavitation phenomena occurring during valve closing. Cavitation is seen as the transient appearance of bubbles on the MHV surface on valve closure. The cavitation bubbles occur due to abrupt pressure changes in the vicinity of the valve on valve closing. Hence, analysis of the recorded field pressure can provide useful information relating to cavitation potential. In the present study, MHV cavitation potential was evaluated by counting bubble appearance probability and measuring bubble-size using an image-processing method. A simple and reliable technique using wavelet packet analysis (WPA) to evaluate cavitation potential was also investigated. METHODS: A single-valve, pneumatic-driven burst tester system with adjustable pressure control unit, was used to simulate the closing process in the heart mitral valve at three driving pressures: 200, 500 and 1,000 mmHg, using three valve models. A triggering and imaging system was developed within the burst tester system to capture images of cavitation bubbles at predetermined time delays on valve closing. Transient pressure signals were recorded on both sides of the MHV occluder, using a high-frequency piezoelectric pressure transducer and a physiological pressure transducer. The pictures recorded were analyzed using image processing software to determine bubble appearance probability and bubble size. WPA was applied to the transient closing pressure signals to evaluate cavitation potential. RESULTS: Cavitation intensity index (Cii) and bubble size-based cavitation index (BS-Ci) were measured by analyzing images captured at different time delays on valve closing at different driving pressures. WPA was used to analyze transient pressure signals at the inflow side of the MHV occluder at valve closure to calculate the WPA-based cavitation index (WPA-Ci). The three methods showed a similar trend for cavitation potential in the valves tested. CONCLUSION: In the present study, two new approaches to evaluate MHV cavitation were investigated, namely WPA and BS-Ci. The results obtained produced a similar trend to that seen with an earlier method based on counting the probability that cavitation bubbles occur. As cavitation is primarily a function of the transient pressure within the vicinity of the closing valve occluder, the WPA method can be an effective method for future investigation of cavitation potential of mechanical heart valves.

Biophysical Phenomena↗
PubMed2001

Wavelet transform for Shah convolution velocity measurements of single particles and solutes in a microfluidic chip.

Wavelet transform analysis is applied to determine the speed of fluorescent polystyrene microspheres and fluorescent solutes in a microchip. The data analysed consist of the periodical signal (Shah convolution) obtained when fluorescent particles or solute plugs move in a channel that is covered with a chromium grid pattern. This setup converts velocity into a (fluorescence emission) frequency, and previous analyses therefore used Fourier transform to extract the frequency information. In this paper it is shown that wavelet transform has some advantages over Fourier transform. With wavelet transform, time information can be obtained in addition to frequency information. Thus the speed of individual particles was determined together with their moments of appearance and disappearance in the system. With solutes small changes of velocity during the analysis were detected, and an improvement in peak frequency resolution was obtained.

Journal Article↗
PubMed2006

Impairment of instantaneous autonomic regulation relates to blood pressure fall immediately after standing in the elderly and hypertensives.

The relation between changes in blood pressure and changes in autonomic activity over a very short period of time has not been reported thus far. To examine this relation, we here introduced a new method of power spectrum analysis with wavelet transformation, which has very fine time resolution and is able to assess changes in autonomic activity quantitatively even during movement. Our subjects were 15 hypertensive and 17 normotensive subjects. A head-up tilt test was performed in all subjects, and during the test, electrocardiogram and blood pressure were recorded continuously. The power spectrums for both parameters were calculated simultaneously every 5 s using wavelet transformation. The high frequency of the RR interval of the electrocardiogram (RR-HF) and low frequency of systolic blood pressure (SBP-LF) were defined and calculated as markers of parasympathetic and alpha-1 receptor blocker, bunazosin-sensitive sympathetic activity, respectively. Focusing on the changes for 2 min immediately after head-up tilting, it was found that the changes in SBP-LF and RR-HF were significantly delayed, by at least 40 s, in hypertensives compared with normotensives and also in elderly compared with non-elderly subjects. Multiple regression analysis demonstrated that the instantaneous change in RR-HF was the most important confounding factor for a fall in blood pressure immediately after head-up tilting. In conclusion, real-time changes in autonomic activity calculated by wavelet transformation may provide sensitive and useful information about acute changes in cardiovascular regulation, such as delayed reaction of the autonomic regulation after head-up tilting, that may be major causes of the blood pressure fall in hypertensive and elderly subjects.

Adult↗
PubMed2001

Doppler ultrasound signal denoising based on wavelet frames.

A novel approach was proposed to denoise the Doppler ultrasound signal. Using this method, wavelet coefficients of the Doppler signal at multiple scales were first obtained using the discrete wavelet frame analysis. Then, a soft thresholding-based denoising algorithm was employed to deal with these coefficients to get the denoised signal. In the simulation experiments, the SNR improvements and the maximum frequency estimation precision were studied for the denoised signal. From the simulation and clinical studies, it was concluded that the performance of this discrete wavelet frame (DWF) approach is higher than that of the standard (critically sampled) wavelet transform (DWT) for the Doppler ultrasound signal denoising.

Algorithms↗
PubMed2006

The distribution and causes of meiotic recombination in the human genome.

Using the statistical analysis of genetic variation, we have developed a high-resolution genetic map of recombination hotspots and recombination rate variation across the human genome. This map, which has a resolution several orders of magnitude greater than previous studies, identifies over 25,000 recombination hotspots and gives new insights into the distribution and determination of recombination. Wavelet-based analysis demonstrates scale-specific influences of base composition, coding context and DNA repeats on recombination rates, though, in contrast with other species, no association with DNase I hypersensitivity. We have also identified specific DNA motifs that are strongly associated with recombination hotspots and whose activity is influenced by local context. Comparative analysis of recombination rates in humans and chimpanzees demonstrates very high rates of evolution of the fine-scale structure of the recombination landscape. In the light of these observations, we suggest possible resolutions of the hotspot paradox.

Animals↗
PubMed2002

Wavelet transforms for the characterization and detection of repeating motifs.

The role of repeating motifs in protein structures is thought to be as modular building blocks which allow an economic way of constructing complex proteins. In this work novel wavelet transform analysis techniques are used to detect and characterize repeating motifs in protein sequence and structure data, where the Kyte-Doolittle hydrophobicity scale (Eta Phi) and relative accessible surface area (rASA) data provide residue information about the protein sequence and structure, respectively. We analyze a variety of repeating protein motifs, TIM barrels, propellor blades, coiled coils and leucine-rich repeat structures. Detection and characterization of these motifs is performed using techniques based on the continuous wavelet transform (CWT). Results indicate that the wavelet transform techniques developed herein are a promising approach for the detection and characterization of repeating motifs for both structural and in some instances sequence data.

Algorithms↗