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PubMedDate not supplied

Spatial domain wavelet design for feature preservation in computational data sets.

High-fidelity wavelet transforms can facilitate visualization and analysis of large scientific data sets. However, it is important that salient characteristics of the original features be preserved under the transformation. We present a set of filter design axioms in the spatial domain which ensure that certain feature characteristics are preserved from scale to scale and that the resulting filters correspond to wavelet transforms admitting in-place implementation. We demonstrate how the axioms can be used to design linear feature-preserving filters that are optimal in the sense that they are closest in L2 to the ideal low pass filter. We are particularly interested in linear wavelet transforms for large data sets generated by computational fluid dynamics simulations. Our effort is different from classical filter design approaches which focus solely on performance in the frequency domain. Results are included that demonstrate the feature-preservation characteristics of our filters.

Algorithms↗

PubMed2005

Electrogram configuration and detection of supraventricular tachycardias by a morphology discrimination algorithm in single chamber ICDs.

BACKGROUND: Inappropriate ICD therapy for supraventricular tachycardia (SVT) remains a significant problem. A morphology-based algorithm (Wavelet) compares baseline and tachycardia electrograms (EGM). For this analysis different EGM sources can be programmed. This study evaluates the performance of Wavelet using two different EGM configurations (SVC-Can and RV-Can) for the detection of exercise-induced SVT. METHODS: Patients with a Medtronic model 7230 single chamber ICD and a dual coil lead were included. For each EGM source (SVC-Can or RV-Can), a baseline EGM template was acquired and the morphology similarity to this template (match percentage) was evaluated for 10-15 beats at different heart rates during exercise testing. The lower VT detection limit was programmed to 600 ms (therapies off). RESULTS: A total of 28 patients (66.9 +/- 4.7 years, 93% men) and 5,824 intracardiac QRS complexes were analyzed. With the RV-Can source, a consistently high similarity to the baseline EGM template was observed (< or =100 bpm: 90.90 +/- 0.56%; >100 bpm: 90.24 +/- 0.55%, P > 0.05). In contrast, SVC-Can was associated with a lower match percentage at baseline and a significant decrease at higher heart rates (< or =100 bpm: 77.91 +/- 2.65%; >100 bpm: 59.05 +/- 5.65%, P < 0.005). Accordingly, the specificity for appropriate detection of exercise-induced SVT was higher with RV-Can (21/21 episodes) than with SVC-Can (8/18 episodes, specificity 100% vs 44%; P < 0.0001). CONCLUSION: The RV-Can configuration appears to be superior to SVC-Can as EGM source for appropriate SVT detection with the Wavelet algorithm.

Adult↗

PubMed2003

[Nonlinear dynamic analysis of heart rate variability in patients with diabetic autonomic neuropathy].

OBJECTIVE: To explore earlier detection methods for diabetic autonomic neuropathy (DAN) by heart rate variability (HRV) analysis. METHODS: Thirty-four diabetic patients (including 22 with explicit clinical DAN symptoms) were randomly selected for this study from the in-patient department of endocrinology. On the basis of Virtual Instrumental WorkBench- LabVIEW and using several nonlinear dynamic analysis measures, including Allan factor, lyapunov exponent, approximate entropy, fractal dimension, complexity, wavelet-transform standard deviation and nonlinear energy operator, the analysis of the HRV in these diabetic patients was performed in comparison with normal subjects. RESULTS: The nonlinear indices of both DAN patients and patients without obvious DAN were significantly different from those of the normal subjects, especially in terms of Lyapunov exponent, approximate entropy, and nonlinear energy operator. CONCLUSION: Nonlinear dynamic methods of HRV analysis can provide assistance in assessing the status and impairment of the autonomic system, and can be used to efficiently detect diabetic neuropathy in early stages.

Autonomic Nervous System Diseases↗

PubMed2006

Concepts of EEG processing: from power spectrum to bispectrum, fractals, entropies and all that.

Over the past two decades, methods of processing the EEG for monitoring anaesthesia have greatly expanded. Whereas power spectral analysis was once the most important tool for extracting EEG monitoring variables, higher-order spectra, wavelet decomposition and especially methods used in the analysis of complex dynamical systems such as non-linear dissipative systems are nowadays attracting much attention. This chapter reviews some of these methods in brief. However, a comparison of some of the newer approaches with the more traditional ones with respect to clinical end-points by association measures and to the signal-to-noise ratio raises some doubt over whether the newer EEG-processing techniques really do better than the more traditional ones.

Electroencephalography↗

PubMed2006

Baseline wander correction in pulse waveforms using wavelet-based cascaded adaptive filter.

Pulse diagnosis is a convenient, inexpensive, painless, and non-invasive diagnosis method. Quantifying pulse diagnosis is to acquire and record pulse waveforms by a set of sensor firstly, and then analyze these pulse waveforms. However, respiration and artifact motion during pulse waveform acquisition can introduce baseline wander. It is necessary, therefore, to remove the pulse waveform's baseline wander in order to perform accurate pulse waveform analysis. This paper presents a wavelet-based cascaded adaptive filter (CAF) to remove the baseline wander of pulse waveform. To evaluate the level of baseline wander, we introduce a criterion: energy ratio (ER) of pulse waveform to its baseline wander. If the ER is more than a given threshold, the baseline wander can be removed only by cubic spline estimation; otherwise it must be filtered by, in sequence, discrete Meyer wavelet filter and the cubic spline estimation. Compared with traditional methods such as cubic spline estimation, morphology filter and Linear-phase finite impulse response (FIR) least-squares-error digital filter, the experimental results on 50 simulated and 500 real pulse signals demonstrate the power of CAF filter both in removing baseline wander and in preserving the diagnostic information of pulse waveforms. This CAF filter also can be used to remove the baseline wander of other physiological signals, such as ECG and so on.

Algorithms↗

PubMed2004

Sympathovagal imbalance in pediatric patients with neurocardiogenic syncope during asymptomatic time periods.

UNLABELLED: The pathophysiology of neurocardiogenic syncope (NCS) is only poorly understood. Several studies indicate that NCS is associated with an imbalance of the autonomic nervous system (ANS). We hypothesized that pediatric patients with NCS exhibit alterations of the neurovegetative status also during asymptomatic time periods. To test this hypothesis the non-invasive method of Heart Rate Variability (HRV)-Analysis was used. METHOD: Holter records (12 channel, 180 Hz; obtained during an asymptomatic 24-hour period) of 32 patients (18 male, 14 female, mean age 14.6 yrs) with a history of syncope and a positive Head-Up tilt (HUT) were compared to the recordings of 33 healthy control subjects (19 male, 14 female, mean age 13.5 yrs) with negative history of syncope and HUT. Time domain and frequency domain features were calculated. Analysis segments were divided in different signal duration groups (1, 3, 6, 24 hours). RESULTS: For all time scales the standard deviation of wavelet coefficients yields the best discrimination properties. Analysis of the total time duration (24h) shows only moderate discrimination (sensitivity 84%, specificity 45%) between patient and controls. However, analysis of 3 and 6 hour segments (starting and 6 a.m.) showed significant discrimination: sigma wave scale 13 (6-9 a.m.) sensitivity 78%, specificity 71%., sigma wave scale 11 (6-12 a.m.) sensitivity 84%, specificity 61%. The best combination of two parameters is denoted by SDNN and sigma wave scale 11 (6-12 a.m.) with sensitivity 70%, specificity 75%. CONCLUSION: The results indicate that during an asymptomatic 24-hour-period patients with NCS exhibit an imbalance of the ANS especially in the morning (6-12 a.m.). The findings suggest a time-dependent increased sympathetic or reduced vagal activity in pediatric patients with NCS. Patients could benefit from a time-adjusted medical therapy with beta-blocking agents.

Adolescent↗

PubMed1998

Time-frequency distribution methods for the analysis of click-evoked otoacoustic emissions.

Click-evoked otoacoustic emissions (CEOAEs) are time-varying signals with a clear frequency dispersion along with the time axis. Analysis of CEOAEs is of considerable interest due to their close relation with cochlear mechanisms. The particular structure of CEOAEs requires a time-frequency method with both a satisfactory time and frequency resolution. In this paper, several basic time-frequency distribution methods are considered and compared on the basis of both simulated signals and real CEOAEs. Results from simulations and real CEOAEs revealed that the wavelet approach is highly suitable for the analysis of such signals. Some examples of the application of the Wavelet Transform to CEOAEs are provided here. Applications range from the extraction of normative data from adult OAEs to the extraction of quantitative parameters for clinical purposes.

Acoustic Stimulation↗

PubMed2000

Wavelet compression of nuclear medicine images.

BACKGROUND: The wavelet transform is a new mathematical tool for the analysis of signals and images. The powerful lossy compression techniques are built on the wavelets. METHODS: The static liver scans (byte mode acquisition, 128 x 128 matrix size) were studied. Four level wavelet transform was performed for image compression by means of biorthogonal filters with linear phase characteristic. The SPIHT technique (Set Partition in Hierarchical Trees) was used to code the wavelet coefficients. RESULTS: The compression ratios 90:1, with a good quality of decompressed images, were achieved (PSNR > 32). CONCLUSIONS: The wavelet compression of scintigrams may be a useful practical tool because of substantial disk space saving and the reduction of transmission time. The decompression of images does not significantly affect their quality.

Journal Article↗

PubMed2002

Multiscale and Bayesian approaches to data analysis in genomics high-throughput screening.

Tremendous amounts of data are produced by high-throughput screening methods currently employed in drug discovery and product development. A typical cDNA microarray or oligonucleotide-based gene chip experiment easily generates over 10,000 data points for each array or chip. The challenge of inferring meaningful information is formidable given the size and number of these datasets. This paper reviews the current status of statistical tools available for gene expression analysis, with emphasis on Bayesian approaches and multiscale wavelet filtering. Fundamental concepts of Bayesian and multiscale modeling are discussed from the perspective of their potential to address important issues related to the analysis of gene expression data, such as the fact that genomic data often have non-Gaussian distributions and feature localization and multiple scales in both frequency and measurement dimension. Recent publications in these areas are reviewed. Wavelet filtering and the advantages of multiscale methods are demonstrated by application to publicly available gene expression data from the National Cancer Institute (NCI). Multiscale methods, including multiscale principal component analysis (MSPCA), are applied to extract gene subsets and to visualize data in multidimensions for comparisons. Similarity in cell lines and gene selection are effectively visualized and quantitatively compared.

Animals↗

PubMed1997

Separation of discontinuous adventitious sounds from vesicular sounds using a wavelet-based filter.

The separation of pathological discontinuous adventitious sounds (DAS) from vesicular sounds (VS) is of great importance to the analysis of lung sounds, since DAS are related to certain pulmonary pathologies. An automated way of revealing the diagnostic character of DAS by isolating them from VS, based on their nonstationarity, is presented in this paper. The proposed algorithm combines multiresolution analysis with hard thresholding in order to compose a wavelet transform-based stationary-nonstationary filter (WTST-NST). Applying the WTST-NST filter to fine/coarse crackles and squawks, selected from three lung sound databases, the coherent structure of DAS is revealed and they are separated from VS. When compared to other separation tools, the WTST-NST filter performed more accurately, objectively, and with lower computational cost. Due to its simple implementation it can easily be used in clinical medicine.

Algorithms↗

PubMed2006

High-accuracy peak picking of proteomics data using wavelet techniques.

A new peak picking algorithm for the analysis of mass spectrometric (MS) data is presented. It is independent of the underlying machine or ionization method, and is able to resolve highly convoluted and asymmetric signals. The method uses the multiscale nature of spectrometric data by first detecting the mass peaks in the wavelet-transformed signal before a given asymmetric peak function is fitted to the raw data. In an optional third stage, the resulting fit can be further improved using techniques from nonlinear optimization. In contrast to currently established techniques (e.g. SNAP, Apex) our algorithm is able to separate overlapping peaks of multiply charged peptides in ESI-MS data of low resolution. Its improved accuracy with respect to peak positions makes it a valuable preprocessing method for MS-based identification and quantification experiments. The method has been validated on a number of different annotated test cases, where it compares favorably in both runtime and accuracy with currently established techniques. An implementation of the algorithm is freely available in our open source framework OpenMS.

Algorithms↗

PubMed2001

A review of analytical techniques for gait data. Part 2: neural network and wavelet methods.

Multivariate gait data have traditionally been challenging to analyze. Part 1 of this review explored applications of fuzzy, multivariate statistical and fractal methods to gait data analysis. Part 2 extends this critical review to the applications of artificial neural networks and wavelets to gait data analysis. The review concludes with a practical guide to the selection of alternative gait data analysis methods. Neural networks are found to be the most prevalent non-traditional methodology for gait data analysis in the last 10 years. Interpretation of multiple gait signal interactions and quantitative comparisons of gait waveforms are identified as important data analysis topics in need of further research.

Biomechanical Phenomena↗

PubMed2002

[Wavelet feature extraction and classification of Doppler ultrasound blood flow signals].

The maximum frequency waveforms of Doppler ultrasound blood flow signals were analyzed using a multi-scale wavelet transform. The variation of maxima of wavelet transform modulus under various scales was extracted from the time-scale representation. This novel approach was applied to the analysis of Doppler signals from carotid blood flow. It was found that the shape of this variation from cases with normal cerebral vessels differed from those associated with abnormal cases. The curve was fitted by a polynomial, and its coefficients were put into a back-propagation (BP) neural network to make a classification. The clinical experiments showed that this approach got good performance and could be a new means in the clinical diagnosis of cerebral vascular disease.

Algorithms↗

PubMed1998

Analysis of the electroencephalographic activity during the Necker cube reversals by means of the wavelet transform.

In previous studies, a perceptual switching related potential was obtained during the observation of a multistable dynamic reversal pattern, where the averaging of the single responses was triggered by subjects pressing a button. The present methodological study aims to increase the signal quality of perceptual switching related potentials considering the dependence of the measurement method on the reaction time of the subject, which may vary significantly during a session, leading to low-amplitude waveform in the averaged event-related-potential (ERP). To overcome this problem in measuring the electrophysiological correlate of an internal event, a pattern selection method based on the wavelet transform (WT) is proposed to choose a subset of single ERPs with more homogenous latencies. Nine subjects observed a Necker cube and were instructed to press the button immediately after perceptual switching. A slow, low-amplitude positive wave with frontocentral amplitude maxima was observed around 250 ms prior to the button press. After the application of a 5 octave WT on single sweeps, the time-frequency coefficients obtained in each octave were averaged across trials. The most dominant feature representing the averaged ERP was the delta (0.5-4 Hz) coefficient occurring between 250 and 125 ms before the button press. By averaging the subset of the single sweeps containing this property, a sharpening and significant amplitude increase of the response peak was observed.

Animals↗

PubMedDate not supplied

Analysis and study of the variation of splitting in the second heartbeat sound of wavelet transform.

The second heart sound, S2, consists of two acoustic components, A2 and P2. The former is due to the closure of the aortic valve and the latter is due to the closure of the pulmonary valve. The aortic valve usually closes before the pulmonary valve, introducing a time delay known as the 'split'. A technique based on discrete wavelet transform (DWT) and continuous wavelet transform (CWT) is developed in this paper to measure the split. To quantify splitting, two components in S2 (i.e. A2 and P2) are identified, and the delay between the two components can be estimated. One normal case and three pathological cases (mitral stenosis, pulmonary stenosis and atrial septal defect) are considered in this study. The split is measured for each S2 sound of the considered signals. The split normally varies in duration over the cardiac cycle. In certain pathologies such as ASD (atrial septal defect) or PS (pulmonary stenosis), the split becomes fixed over the cardiac cycle. The main part of this paper consists of the identification and measurement of the S2 split. The study confirms the notion of 'variable splitting' for normal phonocardiogram and 'fixed splitting' for ASD and PS cases. This paper relates also to the establishment of statistical parameters to make a distinction between normal and pathological cases of phonocardiogram signals.

Aortic Valve↗

PubMed2006

Analysis and visualization of multiply oriented lattice structures by a two-dimensional continuous wavelet transform.

The phase-field-crystal model [K. R. Elder and M. Grant, Phys. Rev. E 70, 051605 (2004)] produces multigrain structures on atomistic length scale but on diffusive time scales. Since individual atoms are resolved but are treated identically it is difficult to distinguish the exact position of grain boundaries and defects within grains. In order to analyze and visualize the whole grains a two-dimensional wavelet transform has been developed, which is capable of extracting grain boundaries and the lattice orientation of a grain relative to a laboratory frame of reference. This transformation makes it possible not only to easily visualize the multigrain structure, but also to perform exact measurements on low- and high-angle boundaries, grain size distributions and boundary-angle distributions, which can then be compared to experimental data. The presented wavelet transform can also be applied to results of other atomistic simulations, e.g., molecular dynamics or granular materials.

Journal Article↗

PubMed2004

ECG analysis for sleep apnea detection.

OBJECTIVES: The objective of our study was to find out whether obstructive sleep apnea (OSA) may be detected on ECGs recorded during sleep. METHODS: We have analyzed 70 eight-hour single-channel ECG recordings taken at polysomnographia. The 70 data sets were annotated for definition of regular sleep and phases with sleep apnea. From the 70 data sets, 35 have been used as a learning set. Our analysis is based on spectral components of heart rate variability. Frequency analysis was performed using Fourier and wavelet transformation with appropriate application of the Hilbert transform. Classification is based on four frequency bands: ULF band (0-0.013 Hz), VLF band (0.013-0.0375 Hz), LF band (0.0375-0.06 Hz) and the HF band (0.17-0.28 Hz). Linear discriminant functions were applied using mainly spectral components derived from the records. Classification of cases was based on three variables. RESULTS: For the Testing Set, a sensitivity (Se) for apnea of 92.3% at a specificity (Sp) of 94.6% was achieved. For the minutes allocation on the Learning Set Se was 90.8% at Sp 92.7%. CONCLUSION: ECG analysis is useful for the detection of sleep apnea and may help to differentiate causes of cardiac arrhythmias.

Electrocardiography↗

PubMed1999

Discrete wavelet transform: a tool in smoothing kinematic data.

Motion analysis systems typically introduce noise to the displacement data recorded. Butterworth digital filters have been used to smooth the displacement data in order to obtain smoothed velocities and accelerations. However, this technique does not yield satisfactory results, especially when dealing with complex kinematic motions that occupy the low- and high-frequency bands. The use of the discrete wavelet transform, as an alternative to digital filters, is presented in this paper. The transform passes the original signal through two complementary low- and high-pass FIR filters and decomposes the signal into an approximation function and a detail function. Further decomposition of the signal results in transforming the signal into a hierarchy set of orthogonal approximation and detail functions. A reverse process is employed to perfectly reconstruct the signal (inverse transform) back from its approximation and detail functions. The discrete wavelet transform was applied to the displacement data recorded by Pezzack et al., 1977. The smoothed displacement data were twice differentiated and compared to Pezzack et al.'s acceleration data in order to choose the most appropriate filter coefficients and decomposition level on the basis of maximizing the percentage of retained energy (PRE) and minimizing the root mean square error (RMSE). Daubechies wavelet of the fourth order (Db4) at the second decomposition level showed better results than both the biorthogonal and Coiflet wavelets (PRE = 97.5%, RMSE = 4.7 rad s-2). The Db4 wavelet was then used to compress complex displacement data obtained from a noisy mathematically generated function. Results clearly indicate superiority of this new smoothing approach over traditional filters.

Algorithms↗