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At least 415 records · Page 23Linked to original sources
PubMed2005

Predicting subsequent visual field loss in glaucomatous subjects with disc hemorrhage using retinal nerve fiber layer polarimetry.

PURPOSE: To predict progression of visual field loss after an episode of disc hemorrhage in glaucoma patients on the basis of retinal nerve fiber layer (RNFL) GDx polarimetry measurements analyzed by wavelet-Fourier analysis (WFA). METHODS: Retrospective GDx data from 16 subjects (10 progressors and 6 non-progressors based on visual fields) obtained near the time of disc hemorrhage were analyzed to predict which patients would have visual field progression. Polarimetry scans throughout a follow-up period (31 months average) were also analyzed to compare field progression to RNFL thickness change after the hemorrhage. Mean RNFL thickness inferred from the polarimetry data at sixteen 22.5 degrees sectors at distances of 1.6, 1.7, and 1.8 disc diameters were used. Data were analyzed by applying to appropriate regions of disc hemorrhage patients a structural analysis (WFA) we had developed previously. A linear discriminant function (Fischer) was produced and a leave-one-out method using separate training and test data was used to assure validity of the results. RESULTS: Patients who subsequently progressed were successfully predicted with moderate success (sensitivity / specificity was 0.77 / 0.88 with ROC area = 0.858). A separate analysis comparing pre- and post-hemorrhage RNFL sector thickness revealed clear evidence of RNFL thinning at the inferior and superior sectors before progression of visual field. The thinning of RNFL thickness was not restricted to regions corresponding to the location of the hemorrhage. CONCLUSION: Wavelet-Fourier analysis can differentiate progressors from non-progressors with moderate accuracy. Comparison to a prior study of this same cohort emphasizes that relatively small regions must be considered (as opposed to larger quadrants) to see these significant changes in RNFL.

Aged↗
PubMed2006

Recovering EEG brain signals: artifact suppression with wavelet enhanced independent component analysis.

Independent component analysis (ICA) has been proven useful for suppression of artifacts in EEG recordings. It involves separation of measured signals into statistically independent components or sources, followed by rejection of those deemed artificial. We show that a "leak" of cerebral activity of interest into components marked as artificial means that one is going to lost that activity. To overcome this problem we propose a novel wavelet enhanced ICA method (wICA) that applies a wavelet thresholding not to the observed raw EEG but to the demixed independent components as an intermediate step. It allows recovering the neural activity present in "artificial" components. Employing semi-simulated and real EEG recordings we quantify the distortions of the cerebral part of EEGs introduced by the ICA and wICA artifact suppressions in the time and frequency domains. In the context of studying cortical circuitry we also evaluate spectral and partial spectral coherences over ICA/wICA-corrected EEGs. Our results suggest that ICA may lead to an underestimation of the neural power spectrum and to an overestimation of the coherence between different cortical sites. wICA artifact suppression preserves both spectral (amplitude) and coherence (phase) characteristics of the underlying neural activity.

Adult↗
PubMed2001

Comparison of Hilbert transform and wavelet methods for the analysis of neuronal synchrony.

The quantification of phase synchrony between neuronal signals is of crucial importance for the study of large-scale interactions in the brain. Two methods have been used to date in neuroscience, based on two distinct approaches which permit a direct estimation of the instantaneous phase of a signal [Phys. Rev. Lett. 81 (1998) 3291; Human Brain Mapping 8 (1999) 194]. The phase is either estimated by using the analytic concept of Hilbert transform or, alternatively, by convolution with a complex wavelet. In both methods the stability of the instantaneous phase over a window of time requires quantification by means of various statistical dependence parameters (standard deviation, Shannon entropy or mutual information). The purpose of this paper is to conduct a direct comparison between these two methods on three signal sets: (1) neural models; (2) intracranial signals from epileptic patients; and (3) scalp EEG recordings. Levels of synchrony that can be considered as reliable are estimated by using the technique of surrogate data. Our results demonstrate that the differences between the methods are minor, and we conclude that they are fundamentally equivalent for the study of neuroelectrical signals. This offers a common language and framework that can be used for future research in the area of synchronization.

Brain↗
PubMed1992

Wavelet transform as a potential tool for ECG analysis and compression.

The recently introduced wavelet transform is a member of the class of time-frequency representations which include the Gabor short-time Fourier transform and Wigner-Ville distribution. Such techniques are of significance because of their ability to display the spectral content of a signal as time elapses. The value of the wavelet transform as a signal analysis tool has been demonstrated by its successful application to the study of turbulence and processing of speech and music. Since, in common with these subjects, both the time and frequency content of physiological signals are often of interest (the ECG being an obvious example), the wavelet transform represents a particularly relevant means of analysis. Following a brief introduction to the wavelet transform and its implementation, this paper describes a preliminary investigation into its application to the study of both ECG and heart rate variability data. In addition, the wavelet transform can be used to perform multiresolution signal decomposition. Since this process can be considered as a sub-band coding technique, it offers the opportunity for data compression, which can be implemented using efficient pyramidal algorithms. Results of the compression and reconstruction of ECG data are given which suggest that the wavelet transform is well suited to this task.

Electrocardiography↗
PubMed1998

Wavelet based ST-segment analysis.

A novel algorithm for ST-segment analysis is developed using the multi-resolution wavelet approach. The system detects the QRS complexes and analyses each beat using the wavelet transform to identify the characteristic points (fiducial points). These fiducial points are, iso-electric level, the J point, and onsets and offsets of the QRS complex and T wave. The algorithm determines the T onset by looking for a point of inflection between the J point and the T peak. Furthermore, detection of characteristic points by the wavelet technique reduces the effect of noise. The results show that the proposed approach gives very accurate ST levels, as compared to the conventional (empirical) technique, at higher heart rates and with different morphologies. The algorithm detects the ST-segment length in 92.3% beats with an error of 4 ms, and in 97.3% beats the error is within 8 ms. The algorithm has been implemented on a TMS320C25 based add-on DSP card connected to a PC to provide the on-line analysis and display of ST-segment data.

Algorithms↗
PubMed2005

Long-term forecasting of internet backbone traffic.

We introduce a methodology to predict when and where link additions/upgrades have to take place in an Internet protocol (IP) backbone network. Using simple network management protocol (SNMP) statistics, collected continuously since 1999, we compute aggregate demand between any two adjacent points of presence (PoPs) and look at its evolution at time scales larger than 1 h. We show that IP backbone traffic exhibits visible long term trends, strong periodicities, and variability at multiple time scales. Our methodology relies on the wavelet multiresolution analysis (MRA) and linear time series models. Using wavelet MRA, we smooth the collected measurements until we identify the overall long-term trend. The fluctuations around the obtained trend are further analyzed at multiple time scales. We show that the largest amount of variability in the original signal is due to its fluctuations at the 12-h time scale. We model inter-PoP aggregate demand as a multiple linear regression model, consisting of the two identified components. We show that this model accounts for 98% of the total energy in the original signal, while explaining 90% of its variance. Weekly approximations of those components can be accurately modeled with low-order autoregressive integrated moving average (ARIMA) models. We show that forecasting the long term trend and the fluctuations of the traffic at the 12-h time scale yields accurate estimates for at least 6 months in the future.

Algorithms↗
PubMed2006

Multiresolution phase retrieval in the fresnel region by use of wavelet transform.

A multiresolution (multiscale) analysis based on wavelet transform is applied to the problem of optical phase retrieval from the intensity measured in the in-line geometry (lens-free). The transport-of-intensity equation and the Fresnel diffraction integral are approximated in terms of a wavelet basis. A solution to the phase retrieval problem can be efficiently found in both cases using the multiresolution concept. Due to the hierarchical nature of wavelet spaces, wavelets are well suited to multiresolution methods that contain multigrid algorithms. Appropriate wavelet bases for the best solution approximation are discussed. The proposed approach reduces the computational complexity and accelerates the convergence of the solution. It is robust and reliable, and successful on both simulated and experimental images obtained with hard x rays.

Algorithms↗
PubMed2004

Wavelet multiscale regression from the perspective of data fusion: new conceptual approaches.

Wavelet regression is a very promising technique for modern multivariate calibration and calibration transfer. Multiscale analysis of wavelet scales provides a connection between wavelet regression and data fusion. In this paper, current wavelet regression methods are reviewed from the novel perspective of data fusion. Illustrated by analysis of a public domain near-infrared dataset, the advantages and drawbacks of these methods are examined. For wavelet regression, the non-uniformity of the wavelet components, the multiscale nature of the signal, and the prevention of information leakage are crucial issues that will be addressed.

Journal Article↗
PubMedDate not supplied

Phonocardiogram signal analysis: techniques and performance comparison.

This paper presents the applications of the spectrogram, Wigner distribution and wavelet transform analysis methods to the phonocardiogram (PCG) signals. A comparison between these three methods has shown the resolution differences between them. It is found that the spectrogram short-time Fourier transform (STFT), cannot detect the four components of the first sound of the PCG signal. Also, the two components of the second sound are inaccurately detected. The Wigner distribution can provide time-frequency characteristics of the PCG signal, but with insufficient diagnostic information: the four components of the first sound, S1, are not accurately detected and the two components of the second sound, S2, seem to be one component. It is found that the wavelet transform is capable of detecting the two components, the aortic valve component A2 and pulmonary valve component P2, of the second sound S2 of a normal PCG signal. These components are not detectable using the spectrogram or the Wigner distribution. However, the standard Fourier transform can display these two components in frequency but not the time delay between them. Furthermore, the wavelet transform provides more features and characteristics of the PCG signals that will help physicians to obtain qualitative and quantitative measurements of the time-frequency characteristics.

Aortic Valve↗
PubMed2005

A wavelet-based time-frequency analysis approach for classification of motor imagery for brain-computer interface applications.

Electroencephalogram (EEG) recordings during motor imagery tasks are often used as input signals for brain-computer interfaces (BCIs). The translation of these EEG signals to control signals of a device is based on a good classification of various kinds of imagination. We have developed a wavelet-based time-frequency analysis approach for classifying motor imagery tasks. Time-frequency distributions (TFDs) were constructed based on wavelet decomposition and event-related (de)synchronization patterns were extracted from symmetric electrode pairs. The weighted energy difference of the electrode pairs was then compared to classify the imaginary movement. The present method has been tested in nine human subjects and reached an averaged classification rate of 78%. The simplicity of the present technique suggests that it may provide an alternative method for EEG-based BCI applications.

Algorithms↗
PubMed2003

Analysis of EEG records in an epileptic patient using wavelet transform.

About 1% of the people in the world suffer from epilepsy and 30% of epileptics are not helped by medication. Careful analyses of the electroencephalograph (EEG) records can provide valuable insight and improved understanding of the mechanisms causing epileptic disorders. Wavelet transform is particularly effective for representing various aspects of non-stationary signals such as trends, discontinuities, and repeated patterns where other signal processing approaches fail or are not as effective. In this research, discrete Daubechies and harmonic wavelets are investigated for analysis of epileptic EEG records. Wavelet transform is used to analyze and characterize epileptiform discharges in the form of 3-Hz spike and wave complex in patients with absence seizure. Through wavelet decomposition of the EEG records, transient features are accurately captured and localized in both time and frequency context. The capability of this mathematical microscope to analyze different scales of neural rhythms is shown to be a powerful tool for investigating small-scale oscillations of the brain signals. Wavelet analyses of EEGs obtained from a population of patients can potentially suggest the physiological processes undergoing in the brain in epilepsy onset. A better understanding of the dynamics of the human brain through EEG analysis can be obtained through further analysis of such EEG records.

Algorithms↗
PubMed2000

[Ventricular wave identification in epicardial mapping system using wavelet transform].

A novel method for the identification of ventricular wave based on wavelet transform is presented. First, "Multi-Scale Intersectional Search" is adopted for analysis of wavelet transform coefficients on some different scales, then adaptive detection is used on this basis. The results of practice show that it has a discrimination of 94.5%, and is obviously superior to any routime method.

Algorithms↗
PubMed2006

A wavelet-based data pre-processing analysis approach in mass spectrometry.

Recently, mass spectrometry analysis has a become an effective and rapid approach in detecting early-stage cancer. To identify proteomic patterns in serum to discriminate cancer patients from normal individuals, machine-learning methods, such as feature selection and classification, have already been involved in the analysis of mass spectrometry (MS) data with some success. However, the performance of existing machine learning methods for MS data analysis still needs improving. The study in this paper proposes a wavelet-based pre-processing approach to MS data analysis. The approach applies wavelet-based transforms to MS data with the aim of de-noising the data that are potentially contaminated in acquisition. The effects of the selection of wavelet function and decomposition level on the de-noising performance have also been investigated in this study. Our comparative experimental results demonstrate that the proposed de-noising pre-processing approach has potentials to remove possible noise embedded in MS data, which can lead to improved performance for existing machine learning methods in cancer detection.

Algorithms↗
PubMed1998

Ventricular late potential analysis with musical and harmonic wavelets.

Harmonic and musical wavelets were introduced by DE Newland in 1994, and have their spectrum tightly defined, therefore greatly reducing spectral leakage that may disturb signal frequency analysis. We have explored the ability of these wavelets to perform detection and quantification of ventricular late potentials (VLP) through our multiresolution time-scale method of energy comparison between the ST and TP segments of the ECG. Since reduction of spectral leakage improves the method's reliability, Newland wavelets provided better results than Daubechies wavelets in our study cases. The only drawback is the comparatively reduced time resolution of Newland wavelets. This required us to concatenate a number of ST segments to form a longer data set that is more representative of the high-resolution ECG (HR-ECG) of a patient than one individual beat. This approach may also be considered for other applications in the HR-ECG field. The spectral properties of the Newland wavelets play a major role in the improvement in our results.

Calibration↗
PubMed2006

A wavelet based method for automatic detection of slow eye movements: a pilot study.

Electro-oculographic (EOG) activity during the wake-sleep transition is characterized by the appearance of slow eye movements (SEM). The present work describes an algorithm for the automatic localisation of SEM events from EOG recordings. The algorithm is based on a wavelet multiresolution analysis of the difference between right and left EOG tracings, and includes three main steps: (i) wavelet decomposition down to 10 detail levels (i.e., 10 scales), using Daubechies order 4 wavelet; (ii) computation of energy in 0.5s time steps at any level of decomposition; (iii) construction of a non-linear discriminant function expressing the relative energy of high-scale details to both high- and low-scale details. The main assumption is that the value of the discriminant function increases above a given threshold during SEM episodes due to energy redistribution toward higher scales. Ten EOG recordings from ten male patients with obstructive sleep apnea syndrome were used. All tracings included a period from pre-sleep wakefulness to stage 2 sleep. Two experts inspected the tracings separately to score SEMs. A reference set of SEM (gold standard) were obtained by joint examination by both experts. Parameters of the discriminant function were assigned on three tracings (design set) to minimize the disagreement between the system classification and classification by the two experts; the algorithm was then tested on the remaining seven tracings (test set). Results show that the agreement between the algorithm and the gold standard was 80.44+/-4.09%, the sensitivity of the algorithm was 67.2+/-7.37% and the selectivity 83.93+/-8.65%. However, most errors were not caused by an inability of the system to detect intervals with SEM activity against NON-SEM intervals, but were due to a different localisation of the beginning and end of some SEM episodes. The proposed method may be a valuable tool for computerized EOG analysis.

Adult↗
PubMed2006

Phase coupling analysis of gastric pressure activity via wavelet packet based diagonal slice spectra.

We propose a new analysis method to detect quadratic phase coupling (QPC) behavior of human gastric interdigestive pressure activity that has been acquired by a telemetric capsule-like mini-robot. The method is referred to as diagonal slice spectra. They are the Fourier transforms of the diagonal slices of the triple correlations, and can actually detect the phase coupling and coupled components respectively by expanding the real process into the complex counterpart through Hilbert transform. In order to learn more about the QPC structure in a certain frequency band that we are mostly interested in and obtain a higher frequency resolution, the method, named the wavelet packet based diagonal slice spectrum, is introduced. It shows that the nonlinear QPC behavior occurs during gastric contractions (phase II), whereas no distinct phase coupling occurs during gastric motor quiescence (phase I). It is the nonlinear cell-to-cell coupling mechanisms, existence of fast and slow waves and their interactions that nonlinear QPC structure of the gastric pressure activity occurs.

Adult↗
PubMed2000

[Wavelet transform and its application in spectral analysis].

Recently, a new mathematical technique known as wavelet transform(WT) has become the focus of many science areas. In analytical chemistry, wavelet transform has been mainly utilized for signal smoothing, de-noising and compression. In this paper, the wavelet transform theory is introduced and its applications in spectral analysis are reviewed.

Algorithms↗
PubMed2005

[Application of wavelet transform to monthly runoff time serial analysis in Zagunao watershed, the upper Minjiang river].

Runoff is an important component of regional water resources, and its dynamics is to some extent an indicator of water resources dynamics in a region. To know the runoff dynamics and water resources in a region is essential for the sustainable utilization and planning of water resources, and for the research on hydrological response of vegetation change at watershed scale. To disclose the water resources dynamics in Upper Minjiang River, one of the large reaches of Yangze River in southwest China, this paper analyzed the runoff dynamic features of Zagunao watershed, an important watershed in Minjiang River basin. Multi-Resolution Analysis (MRA) and periodicity analysis were conducted with Dmey wavelet function on the monthly runoff data from 1962 to 2002 observed by Zagunao hydrological station, which provided a data-based approximation on the evolution of monthly runoff in Zagunao watershed. It was found that the runoff dynamics in Zagunao watershed was relatively stable during 1962 - 1978, despite that this period was just in correspondence with the term of intensive deforestation activities in Upper Minjiang River basin. It was also clear that the runoff in Zagunao watershed was increased from 1986 to 1997, which was inconsistent with the commonly accepted viewpoint that the runoff decreased with increasing vegetation cover in forest watershed. However, the increasing trend from 1986 - 1997 was consistent with the research results in Yichang by Wang Wensheng and with the global warming at global and continent scale, which meant that global climate change plays a big role in runoff dynamics in Upper Minjiang River. Periodicity analysis showed that the rich-short water periodicity at the scale of 10 years (120 months) and 5 years (60 months) was 3 and 7 times, respectively, which could provide invaluable information for the eco-hydrological function research of forest landscape in Minjiang river basin and the sustainable utilization of water resources. The results from the case study highlighted that sometimes, it was very necessary to consider the role of global climatic warming in disclosing the hydrological response of land use/cover change at watershed scale, otherwise, it would be difficult to explain some hydrological phenomena.

Algorithms↗