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PubMedDate not supplied

Frequency-specific information from click evoked otoacoustic emissions in noise-induced hearing loss.

Click evoked otoacoustic emissions (CEOAEs), pure-tone audiograms (PTAs), and Bekesy sweep frequency audiograms were recorded from 15 ears of 11 subjects with noise-induced hearing loss. For all ears, hearing threshold levels > or = 30 dB HL were found at the high frequencies. The aims of the study were to examine whether the decomposition of CEOAEs into narrow band components could identify hearing loss in a frequency-specific manner and to what extent audiometric thresholds could be predicted. CEOAEs were parcelled into 0.5-kHz-wide components by means of the wavelet transform. Reproducibility of CEOAE components was compared with audiometric threshold at corresponding frequencies. A general trend of low reproducibility for increasing audiometric thresholds was found. A reproducibility value of 60 per cent was found to best separate normal and elevated thresholds. The presence of a CEOAE component at a given frequency was always associated with audiometric thresholds < or = 20-25 dB HL. On the other hand, the absence of a component was equally associated either with normal or abnormal hearing levels. Large inter-subject variability was observed. A weak linear relationship was found between reproducibility and audiometric thresholds at corresponding frequencies, indicating that analysis of narrow band CEOAE components is valuable for separating normal from hearing-impaired ears but cannot replace the audiogram.

Acoustic Stimulation↗
PubMed1999

[The method of wavelet transformation used for extracting the characteristic of the exercise ECG].

This article introduces the basic rationale of wavelet transformation and the algorithm of typical dyadic wavelet transformation with mother wavelet of the first derivative of a Gaussian smoothing function, this wavelet transformation is used to recognize the characteristic parameters of the exercise ECG, it is concluded that this method make significant effects on restricting base line drifting and lessening noisy component in the exercise ECG, it provides a new way to do further research on characteristic recognition of the exercise ECG in the exercise test.

Algorithms↗
PubMed2006

A high resolution approach to estimating time-frequency spectra and their amplitudes.

A high resolution approach to estimating time-frequency spectra (TFS) and associated amplitudes via the use of variable frequency complex demodulation (VFCDM) is presented. This is a two-step procedure in which the previously developed time-varying optimal parameter search (TVOPS) technique is used to obtain TFS, followed by using the VFCDM to obtain even greater TFS resolution and instantaneous amplitudes associated with only the specific frequencies of interest. This combinational use of the TVOPS and the VFCDM is termed the TVOPS-VFCDM. Simulation examples are provided to demonstrate the performance of the TVOPS-VFCDM for high resolution TFS as well as instantaneous amplitude estimation. The simulation results show that the TVOPS-VFCDM approach provides the highest resolution and most accurate amplitude estimates when compared to the smoothed pseudo Wigner-Ville, continuous wavelet transform and Hilbert-Huang transform methods. Application of the TVOPS-VFCDM to renal blood flow data indicates some promise of a quantitative approach to understanding the dynamics of renal autoregulatory mechanisms as well as a possible approach to quantitatively discriminating between different strains of rats.

Algorithms↗
PubMed1998

Classification of respiratory sounds based on wavelet packet decomposition and learning vector quantization.

In this paper, a wavelet packet-based method is used for detection of abnormal respiratory sounds. The sound signal is divided into segments, and a feature vector for classification is formed using the results of the search for the best wavelet packet decomposition. The segments are classified as containing crackles, wheezes or normal lung sounds, using Learning Vector Quantization. The method is tested using a small set of real patient data which was also analysed by an expert observer. The preliminary results are promising, although not yet good enough for clinical use.

Algorithms↗
PubMed2002

Comparison of detrending methods for optimal fMRI preprocessing.

Because of the inherently low signal to noise ratio (SNR) of fMRI data, removal of low frequency signal intensity drift is an important preprocessing step, particularly in those brain regions that weakly activate. Two known sources of drift are noise from the MR scanner and aliasing of physiological pulsations. However, the amount and direction of drift is difficult to predict, even between neighboring voxels. Further, there is no concensus on an optimal baseline drift removal algorithm. In this paper, five voxel-based detrending techniques were compared to each other and an auto-detrending algorithm, which automatically selected the optimal method for a given voxel time-series. For a significance level of P < 10(-6), linear and quadratic detrending moderately increased the percentage of activated voxels. Cubic detrending decreased activation, while a wavelet approach increased or decreased activation, depending on the dataset. Spline detrending was the best single algorithm. However, auto-detrending (selecting the best algorithm or none, if detrending is not useful) appears to be the most judicious choice, particularly for analyzing fMRI data with weak activations in the presence of baseline drift.

Adult↗
PubMed2002

Sampling, noise-reduction and amplitude estimation issues in surface electromyography.

This paper reviews data acquisition and signal processing issues relative to producing an amplitude estimate of surface EMG. The paper covers two principle areas. First, methods for reducing noise, artefact and interference in recorded EMG are described. Wherever possible noise should be reduced at the source via appropriate skin preparation, and the use of well designed active electrodes and signal recording instrumentation. Despite these efforts, some noise will always accompany the desired signal, thus signal processing techniques for noise reduction (e.g. band-pass filtering, adaptive noise cancellation filters and filters based on the wavelet transform) are discussed. Second, methods for estimating the amplitude of the EMG are reviewed. Most advanced, high-fidelity methods consist of six sequential stages: noise rejection/filtering, whitening, multiple-channel combination, amplitude demodulation, smoothing and relinearization. Theoretical and experimental research related to each of the above topics is reviewed and the current recommended practices are described.

Artifacts↗
PubMed2001

An analysis of perceptual errors in reading mammograms using quasi-local spatial frequency spectra.

In this pilot study the authors examined areas on a mammogram that attracted the visual attention of experienced mammographers and mammography fellows, as well as areas that were reported to contain a malignant lesion, and, based on their spatial frequency spectrum, they characterized these areas by the type of decision outcome that they yielded: true-positives (TP), false-positives (FP), true-negatives (TN), and false-negatives (FN). Five 2-view (craniocaudal and medial-lateral oblique) mammogram cases were examined by 8 experienced observers, and the eye position of the observers was tracked. The observers were asked to report the location and nature of any malignant lesions present in the case. The authors analyzed each area in which either the observer made a decision or in which the observer had prolonged (>1,000 ms) visual dwell using wavelet packets, and characterized these areas in terms of the energy contents of each spatial frequency band. It was shown that each decision outcome is characterized by a specific profile in the spatial frequency domain, and that these profiles are significantly different from one another. As a consequence of these differences, the profiles can be used to determine which type of decision a given observer will make when examining the area. Computer-assisted perception correctly predicted up to 64% of the TPs made by the observers, 77% of the FPs, and 70% of the TNs.

Breast Neoplasms↗
PubMed2004

Rhythmic training decreases latency-jitter of omission evoked potentials (OEPs) in humans.

In this study omission evoked potentials (OEPs) were studied in rhythmic experts (n=12) and non-musicians (n=12). Trains of auditory stimuli were presented. Trials (n=90) contained five omissions and started with a random number of beats, thus making every first omission unpredictable. Participants had to tap along with the first beat after the fifth omission (n=90), thus determining timing-accuracy. Single-trial OEPs elicited by every first omission were obtained by means of wavelet denoising allowing determination of latency-jitter. Clear OEPs, consisting of a slow positive wave, maximal over Pz, were observed in response to unpredictable omissions. No group differences in OEPs amplitudes or latencies were observed. However, rhythmic experts showed less latency-jitter of both the OEPs positive wave and of behavioral responses compared with non-musicians.

Acoustic Stimulation↗
PubMed2006

Nonlinear dynamics of heart rate and oxygen uptake in exhaustive 10,000 m runs: influence of constant vs. freely paced.

We hypothesized that a freely paced 10,000 m running race would induce a smaller physiological strain (heart rate and oxygen uptake) compared with one performed at the same average speed but with an imposed constant pace. Furthermore, we analyzed the scaling properties with a wavelet transform algorithm computed log2 (wavelet transform energy) vs. log2 (scale) to get slope alpha, which is the scaling exponent, a measure of the irregularity of a time series. HR was sampled beat by beat and V2O, breath by breath. The enforced constant pace run elicited a significantly higher mean VO2 value (53 +/- 4 vs. 48 +/- 5 ml kg(-1) min(-1), P < 0.001), HR (169 +/- 13 vs. 165 +/- 14 bpm, P < 0.01), and blood lactate concentration (6.6 +/- 0.9 vs. 7.5 +/- 1 mM, P < 0.001) than the freely paced run. HR and VO2 signals showed a scaling behavior, which means that the signals have a similar irregularity (a self-similarity) whatever the scale of analysis may be, in both constant and free-paced 10,000 m runs. The scaling exponent was not significantly different according to the type of run (free vs. constant, P > 0.05) and the signal (HR vs. VO2, P > 0.05). The higher metabolic cost of constant vs. free paced run did not affect the self-similarity of HR and VO2, in either run. The HR signal only kept its scaling behavior only with a distance run, no matter the type of run (free or constant). The results suggest that the larger degree of pace variation in freely paced races may be an intentionally chosen strategy designed to minimize the physiological strain during severe exercise and to prevent a premature termination of effort, even if the variability of the heart rate and VO2, are comparable in an enforced constant vs. a freely paced run and if HR keeps the same variability until the arrival.

Adult↗
PubMed2000

Tissue segmentation on MR images of the brain by possibilistic clustering on a 3D wavelet representation.

An algorithm for the segmentation of a single sequence of three-dimensional magnetic resonance (MR) images into cerebrospinal fluid, gray matter, and white matter classes is proposed. This new method is a possibilistic clustering algorithm using the fuzzy theory as frame and the wavelet coefficients of the voxels as features to be clustered. Fuzzy logic models the uncertainty and imprecision inherent in MR images of the brain, while the wavelet representation allows for both spatial and textural information. The procedure is fast, unsupervised, and totally independent of any statistical assumptions. The method is tested on a phantom image, then applied to normal and Alzheimer's brains, and finally compared with another classic brain tissue segmentation method, affording a relevant classification of voxels into the different tissue classes.

Adult↗
PubMed2006

Regularization operators for natural images based on nonlinear perception models.

Image restoration requires some a priori knowledge of the solution. Some of the conventional regularization techniques are based on the estimation of the power spectrum density. Simple statistical models for spectral estimation just take into account second-order relations between the pixels of the image. However, natural images exhibit additional features, such as particular relationships between local Fourier or wavelet transform coefficients. Biological visual systems have evolved to capture these relations. We propose the use of this biological behavior to build regularization operators as an alternative to simple statistical models. The results suggest that if the penalty operator takes these additional features in natural images into account, it will be more robust and the choice of the regularization parameter is less critical.

Algorithms↗
PubMed1991

[Oscillatory potentials of the electroretinogram in the domains of frequency and time].

The dark and light adapted ERGs of 36 normal eyes (20 subjects) were analyzed with the Fourier frequency spectrum. The oscillatory potentials (OPs) were isolated from the ERGs with a proper bandpass. The amplitudes and implicit times of the wavelets, the accumulative amplitude and area, the dominant frequency and energy, and the total energy of OP1 to OP4 were determined. Comparison of the coefficients of variation (CV) showed that the CV of implicit times was the least, while the CV of amplitudes, dominant energy and total energy were larger. Furthermore, the CV of parameters under light adaptation were smaller than those under dark adaptation.

Adult↗
PubMed1996

Multiresolution biological transient extraction applied to respiratory crackles.

A method is proposed for the detection of transients in biological signals. The method is based on enhancing the transient-to-background ratio by a series of operations such as background whitening, wavelet-based multiresolution decomposition and application of Teager's energy operator. The transients are extracted by judiciously thresholding this processed signal. The proposed detector is applied to the discrimination of crackles in pathological respiratory sounds. It is shown that both the crackle detection performance and ability to extract the transient waveforms correctly are superior to existing detectors in the literature.

Adolescent↗
PubMed1998

Wavelet-assisted volume ray casting.

Volume rendering is an important technique for computational biology. In this paper we propose a new wavelet-assisted volume ray casting algorithm. The main idea is to use the wavelet coefficients for detecting the local frequency, and decide the appropriate sampling rate along the ray according to the maximum frequency. Our algorithm is to first apply the 3D discrete wavelet transform on the volume, then create an index volume to indicate the necessary sampling distance at each voxel. During ray casting, the original volume is traversed in the spatial domain, while the index volume is used to decide the appropriate sampling distance. We demonstrate that our algorithm provides a framework for approximating the volume rendering at different levels of quality in a rapid and controlled way.

Algorithms↗
PubMed1998

Neural cell classification by wavelets and multiscale curvature.

A new approach to automatic classification of retinal ganglion cells using multiscale techniques including the continuous wavelet transform, curvature, and standard pattern recognition techniques is described. Each neural cell is represented by its outer contour, and the wavelet transform is calculated from the complex signal defined by the aforementioned contour, leading to the so-called W-representation (Antoine et al. 1996). The normalized multiscale wavelet energy (NMWE) is used to define a set of shape measures associated with the number of details of the shape for a broad range of spatial scales. Next, the more discriminating NMWE coefficients are chosen through a feature ordering technique and fed to statistical classifiers. In addition, the normalized multiscale bending energy (NMBE) is discussed as a means of neural shape description for classification purposes based on the multiscale curvature, i.e. the curvegram, of the neural contour. It is shown that both shape descriptors are suitable for shape classification, presenting similar classification performance. In fact, NMBE has a slightly better recognition rate than NMWE in our experiments. On the other hand, NMWE is less computationally expensive than NMBE, presenting also the potentially useful property of allowing the use of more suitable different analyzing wavelets, depending on the problem under consideration. Therefore, both measures are related and provide a good framework for the design of neural cell description and classification. The methods described in this work have been successfully applied to the classification of two classes of cat retinal ganglion cells, namely alpha and beta (henceforth referred as alpha-cells and beta-cells, respectively), and three statistical classifiers were considered: minimum-distance, k-nearest neighbours and maximum likelihood. The mean recognition rates are near 90%, which is superior to the other shape measures considered. It is argued here that the proposed technique can be adopted as a new general methodology for multiscale shape analysis and recognition, being applicable also to other problems in biological shape characterization in neuroscience and general biomedical image analysis. In the context of analysis of shape complexity, the multiscale energies are coherent with subjective judgements by humans.

Animals↗
PubMed2004

Neural bases of cognitive ERPs: more than phase reset.

Up to now, two conflicting theories have tried to explain the genesis of averaged event-related potentials (ERPs): Whereas one hypothesis claims that ERPs originate from an event-related activation of neural assemblies distinct from background dynamics, the other hypothesis states that ERPs are produced by phase resetting of ongoing oscillatory activity. So far, this question has only been addressed for early ERP components. Late ERP components, however, are generally thought to represent superimposed activities of several anatomically distinct brain areas. Thus, the question of which mechanism underlies the genesis of late ERP components cannot be easily answered based on scalp recordings. In contrast, two well-investigated late ERP components recorded invasively from within the human medial temporal lobe (MTL) in epilepsy patients, the so-called MTL-P300 and the anterior MTL-N400 (AMTL-N400), are based on single source activity. Hence, we investigated whether the MTL-P300 and the AMTL-N400 are based on an event-related activity increase, a phase reset of ongoing oscillatory activity or both. ERPs were recorded from the hippocampus and rhinal cortex in subjects performing a visual oddball paradigm and a visual word recognition paradigm. With wavelet techniques, stimulus-related phase-locking and power changes were analyzed in a frequency range covering 2 to 48 Hz. We found that the MTL-P300 is accompanied by both phase reset and power increase and that both effects overlap partly in time. In contrast, the AMTL-N400 is initially associated with phase locking without power increase and only later during the course of the AMTL-N400 we observed an additional power increase. In conclusion, both aspects, event-related activation of neural assemblies and phase resetting of ongoing activity seem to be involved in the generation of late ERP components as recorded in cognitive tasks. Therefore, separate analysis of event-related power and phase-locking changes might reveal specific insights into the mechanisms underlying different cognitive functions.

Adult↗
PubMed2001

Computational and performance aspects of PCA-based face-recognition algorithms.

Algorithms based on principal component analysis (PCA) form the basis of numerous studies in the psychological and algorithmic face-recognition literature. PCA is a statistical technique and its incorporation into a face-recognition algorithm requires numerous design decisions. We explicitly state the design decisions by introducing a generic modular PCA-algorithm. This allows us to investigate these decisions, including those not documented in the literature. We experimented with different implementations of each module, and evaluated the different implementations using the September 1996 FERET evaluation protocol (the de facto standard for evaluating face-recognition algorithms). We experimented with (i) changing the illumination normalization procedure; (ii) studying effects on algorithm performance of compressing images with JPEG and wavelet compression algorithms; (iii) varying the number of eigenvectors in the representation; and (iv) changing the similarity measure in the classification process. We performed two experiments. In the first experiment, we obtained performance results on the standard September 1996 FERET large-gallery image sets. In the second experiment, we examined the variability in algorithm performance on different sets of facial images. The study was performed on 100 randomly generated image sets (galleries) of the same size. Our two most significant results are (i) changing the similarity measure produced the greatest change in performance, and (ii) that difference in performance of +/- 10% is needed to distinguish between algorithms.

Algorithms↗
PubMed2004

Polynomial wavelet regression for images with irregular boundaries.

In this paper, we focus on denoising images for which observations are equally spaced except around the boundaries which are irregular. Such images are very common in many fields, for example in geophysics. The advantages of adding a low-order polynomial term when implementing a wavelet regression for such images are presented. Besides removing the classical restriction of having a dyadic of number of observations, this strategy reduces the bias at the edges without significantly increasing the risk. In addition, this method is simple to implement, fast and efficient. Its utility is illustrated with simulation studies and a real example.

Algorithms↗