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PubMed2003

A multivariate, spatiotemporal analysis of electromagnetic time-frequency data of recognition memory.

Electromagnetic indices of "fast" (above 12 Hz) oscillating brain activity are much more likely to be considerably attenuated by time-averaging across multiple trials than "slow" (below 12 Hz) oscillating brain activity. To the extent that both types of oscillations represent the activity of temporally and topographically separable neural populations, time averaging can cause a loss of brain activity information that is important both conceptually and for multimodal integration with hemodynamic techniques. To address this issue for recognition memory, simultaneous electroencephalography (EEG) and whole-head magnetoencephalography (MEG) recordings of explicit word recognition from 11 healthy subjects were analyzed in two different ways. First, the time course of neural oscillations ranging from theta (4.5 Hz) to gamma (42 Hz) frequencies were identified using single-trial continuous wavelet transforms. Second, traditional analyses of amplitude variations of time-averaged EEG and MEG signals, event-related potentials (ERPs), and fields (ERFs) were performed and submitted to distributed source analyses. To identify data patterns that covaried with the difference between correctly recognized studied (old) words and correctly rejected nonstudied (new) words, a multivariate statistical tool, partial least squares (PLS), was applied to both types of analyses. The results show that ERPs and ERFs are mainly displaying those neural indices of recognition memory that oscillate in the theta (4.5-7.5 Hz), alpha (8-11.5), and to some extent in the beta1 (12-19.5 Hz) frequency range. The sources of the ERPs/ERFs were in good agreement with the topography of theta/alpha/beta 1 oscillations in being confined to the anterior temporal lobe at 400 ms and being distributed across temporal, parietal, and occipital areas between 500 and 700 ms. Gamma oscillations covaried either positively or negatively with theta/alpha/beta1 oscillations. A positive covariance, for instance, was detected over left anterior temporal sensors as early as 200-350 ms and is compatible with studies in rodents showing that gamma and theta oscillations emerge together out of the interaction of the hippocampus and the entorhinal and perirhinal cortices. Fast beta oscillations (20-29.5 Hz), on the other hand, did not strongly covary with slow oscillations and were likely to arise from neural populations not adequately represented in ERPs/ERFs. In summary, by providing a more comprehensive description of electromagnetic signals, time-frequency data are of potential benefit for integrating electrophysiological and hemodynamic indices of brain activity and also for integrating human and animal electrophysiology.

Adolescent↗
PubMed1998

The initiation, continuation, and termination of spontaneous episodes of circus movements in the pregnant myometrium of the rat.

OBJECTIVES: The objectives of this study were to determine the mechanisms responsible for the initiation and the termination of episodes of circus movements and to describe the characteristics of these circuits in rat myometrium. STUDY DESIGN: By means of simultaneous recordings from 240 sites in Wistar rats at 17, 19, or 21 days' gestation, a spatial and temporal analysis was performed on the electrical activation from the beginning until the end of 22 episodes of circus movements. RESULTS: The initiation of reentry was caused by (1) transient unexcitable areas at the beginning of the burst or (2) lines of conduction blocks. Circus movements were terminated by (1) reversal of recruitment (decruitment) at the end of the burst, (2) occurrence of lines of conduction blocks, or (3) interruption of the circuit. On average circuits rotated for 6 revolutions during 2 seconds. In most cases circuits remained stable; sometimes circuits drifted along the tissue and occasionally multiple wavelets were seen instead of a single circuit. CONCLUSIONS: Spatial nonhomogeneities in conduction caused by transient unexcitable areas play a major role in both the initiation and the termination of episodes of circus movements in the pregnant rat myo-metrium. In addition, most circuits remained in a single location, although sometimes meandering or even fibrillatory activity was seen.

Animals↗
PubMed2006

Use of wavelet and fast Fourier transforms in pharmacodynamics.

Progress has been made in the development and application of mechanism-based pharmacodynamic models for describing the drug-specific and physiological factors influencing the time course of responses to the diverse actions of drugs. However, the biological variability in biosignals and the complexity of pharmacological systems often complicate or preclude the direct application of traditional structural and nonstructural models. Mathematical transforms may be used to provide measures of drug effects, identify structural and temporal patterns, and visualize multidimensional data from analyses of biomedical signals and images. Fast Fourier transform (FFT) and wavelet analyses are two methodologies that have proven to be useful in this context. FFT converts a signal from the time domain to the frequency domain, whereas wavelet transforms colocalize in both domains and may be utilized effectively for nonstationary signals. Nonstationary drug effects are common but have not been well analyzed and characterized by other methods. In this review, we discuss specific applications of these transforms in pharmacodynamics and their potential role in ascertaining the dynamics of spatiotemporal properties of complex pharmacological systems.

Animals↗
PubMed2006

Evaluating different methods of microarray data normalization.

BACKGROUND: With the development of DNA hybridization microarray technologies, nowadays it is possible to simultaneously assess the expression levels of thousands to tens of thousands of genes. Quantitative comparison of microarrays uncovers distinct patterns of gene expression, which define different cellular phenotypes or cellular responses to drugs. Due to technical biases, normalization of the intensity levels is a pre-requisite to performing further statistical analyses. Therefore, choosing a suitable approach for normalization can be critical, deserving judicious consideration. RESULTS: Here, we considered three commonly used normalization approaches, namely: Loess, Splines and Wavelets, and two non-parametric regression methods, which have yet to be used for normalization, namely, the Kernel smoothing and Support Vector Regression. The results obtained were compared using artificial microarray data and benchmark studies. The results indicate that the Support Vector Regression is the most robust to outliers and that Kernel is the worst normalization technique, while no practical differences were observed between Loess, Splines and Wavelets. CONCLUSION: In face of our results, the Support Vector Regression is favored for microarray normalization due to its superiority when compared to the other methods for its robustness in estimating the normalization curve.

Animals↗
PubMed2001

[Research into estimating lumen center of dark region in colon image via cubic B-spline wavelet].

In this paper, a new method is presented to detect the edge of a colonscope image using cublic B-spline wavelet. This method can make up for the shortcoming of traditional method, resulting from blue dark area in colonscope image through estimating the circle centers. These estimated circle centers which have arc-shaped edge lines are weighted to obtained the lumen centers. This center is used to navigate colon endoscope in colonscopic operation.

Algorithms↗
PubMed2005

Nondestructive determination of the ambroxol content in tablets by Raman spectroscopy.

We describe a method for determining the ambroxol content in tablets nondestructively. To obtain a reliable quantitative calibration, we prepared 20 pellet samples (ambroxol content: 8.30-16.25 wt.%) and acquired their Raman spectra while rotating the pellets. The spectra of the rotated samples reflected the compositional variations better than those that were recorded without rotation. To reduce both the baseline variations and the spectral noise simultaneously, the spectra were pre-processed using wavelet transformation (WT). Then, we used the normalization method before partial least-squares (PLS) regression to correct Raman intensity variation from laser power fluctuation. The achieved standard error of cross validation (SECV) was 0.30%. Two different datasets where Raman intensity was artificially changed were prepared and the corresponding spectra were quantitatively analyzed. The result was reproducible even if laser intensity was fairly changed. Additionally, two different commercial tablets were analyzed and the accuracy of measurement was better for a tablet that had the similar spectral features of the standard pellet samples. The proposed method can be utilized for the analysis of commercial tablets if standard tablets of various ambroxol concentrations that have the same chemical components including additives and the same physical shape of tablets are available.

Ambroxol↗
PubMed1994

Time-frequency analysis of the surface EMG during maximum height jumps under altered-G conditions.

Surface electromyographic (SEMG) signals are often used to study motor control during human movement. Typically, the SEMG signal is used to determine when the muscle was on or off during the movement. However, determining the time-based frequency content of the SEMG signal in conjunction with the time-history of the movement may provide us with more insight into how the motion is organized. We collected SEMG from 4 muscles, (soleus, vasti, gluteus maximus, hamstrings) while subjects performed jumping and sit-to-stand tasks under altered g-environments. The experiment was performed on the Dynamic Environmental Simulator at Wright Patterson AFB, OH. The subjects performed the tasks at 1.0, 1.2, 1.4, 1.6, and 1.8 g. The SEMG signals were then analyzed using a continuous discrete wavelet transform based on a 4-coefficient Daubechie wavelet. Our initial results show that although the time-history of muscle activation patterns during movements do not always vary significantly at different g levels, the frequency content does change, with more high frequency activation at low g levels, and more low frequency activation at higher g levels. One possible explanation for the difference in frequency activation is fatigue of fast twitch fibers during the experiment. These differences were not apparent from the time-history activation patterns, and are difficult to interpret from standard FFT manipulations.

Electromyography↗
PubMed2005

Task-based model/human observer evaluation of SPIHT wavelet compression with human visual system-based quantization.

RATIONALE AND OBJECTIVE: The set partitioning in hierarchical trees (SPIHT) wavelet image compression algorithm with the human visual system (HVS) quantization matrix was investigated using x-ray coronary angiograms. We tested whether the HVS quantization matrix for the SPIHT wavelet compression improved computer model/human observer performance in a detection task with variable signals compared to performance with the default quantization matrix. We also tested the hypothesis of whether evaluating the rank order of the two quantization matrices (HVS versus default) based on performance of computer model observers in a signal known exactly but variable task (SKEV) generalized to model/human performance in the more clinically realistic signal known statistically task (SKS). MATERIALS AND METHODS: Nine hundred test images were created using real x-ray coronary angiograms as backgrounds and simulated arteries with filling defects (signals). The task for the model and human observer was to detect which one of the four computer simulated arterial segments contained the signal, four alternative-forced-choice (4 AFC). We obtained performance for four model observers (nonprewhitening matched filter with an eye filter, Hotelling, Channelized Hotelling, and Laguerre Gauss Hotelling model observers) for both the SKEV and SKS tasks with images compressed with and without the HVS quantization matrix. A psychophysical study measured performance from three human observers for the same conditions and tasks as the model observers. RESULTS: Performance for all four model observers improved with the use of the HVS quantization scheme. Improvements ranged from 5% (at compression ratio 7:1) to 50% (at compression ratio 30:1) for both the SKEV and SKS tasks. Human observer performance improvement averaged across observers ranged from 6% (at compression ratio 7:1) to 35% (at compression ratio 30:1) for the SKEV task and from 2% (at compression ratio 7:1) to 38% (at compression ratio 30:1) for the SKS task. Addition of internal noise to the model observers allowed for good prediction of human performance. CONCLUSIONS: Use of the HVS quantization scheme in the SPIHT wavelet compression led to improved model and human observer performance in clinically relevant detection tasks in x-ray coronary angiograms. Model observer performance can be reliably used to predict the human observer performance for the studied tasks as a function of SPIHT wavelet image compression. Our results further confirmed that model observer performance in the computationally more tractable SKEV task can be potentially used as a figure of merit for the more clinically realistic SKS task with real anatomic backgrounds.

Algorithms↗
PubMed2001

Image reconstruction using the wavelet transform for positron emission tomography.

We conducted positron emission tomography (PET) image reconstruction experiments using the wavelet transform. The Wavelet-Vaguelette decomposition was used as a framework from which expressions for the necessary wavelet coefficients might be derived, and then the wavelet shrinkage was applied to the wavelet coefficients for the reconstruction (WVS). The performances of WVS were evaluated and compared with those of the filtered back-projection (FBP) using software phantoms, physical phantoms, and human PET studies. The results demonstrated that WVS gave stable reconstruction over the range of shrinkage parameters and provided better noise and spatial resolution characteristics than FBP.

Algorithms↗
PubMed2006

Scale-based normalization of spectral data.

Classification of data that arise as signals or images often requires a standardization step so that information extracted from biologically equivalent signals can be quantified for comparison across classes. Differences in global trend, total energy, high-frequency noise and/or local background can arise from variabilities due to instrumentation or conditions during data collection. This article considers some common ways in which such variation is adjusted for and introduces a generalization of the popular "standard normal variate" transformation. Based on a wavelet decomposition this generalization provides increased flexibility for normalizing spectral data affected by local background noise. Examples from three types of spectroscopy data illustrate the method and its properties.

Diagnosis, Computer-Assisted↗
PubMed1999

Multiresolutional modification of speech signals for listeners with hearing impairment.

A new method, the multiresolutional modification algorithm (MMA), which modifies speech signals in frequency and time domains, is proposed for listeners with hearing impairment and early clinical results are reported. Unlike other methods, this algorithm modifies the wavelet coefficients of the speech signal in order to obtain a modified version of the original signal instead of modifying the speech waveform itself. The speech signal is first divided into subbands using an 11-level quadrature mirror filter (QMF) bank. These subbands are then modified using the modification algorithm. Finally, the inverse Wavelet transform is applied to these modified subband coefficients in order to reconstruct a modified version of the input signal. The efficacy of the MMA was evaluated using subjects with hearing impairment and subjects with no such impairment. Listening tests showed that the proposed algorithm increases the quality and intelligibility of the modified speech over the well-known modification algorithms.

Adult↗
PubMed2006

An estimate of fetal autonomic state by time-frequency analysis of fetal heart rate variability.

In this study we present a noninvasive method that enables the investigation of the fetal heart rate (FHR) fluctuations. The objective was to design a quantitative measurement to assess the fetal autonomic nervous system and to investigate its development as a function of the gestational age. Our Medical Physics group has developed a complex algorithm for online beat-to-beat detection of the fetal ECG (FECG), extracted from the maternal abdominal ECG signal. We used our previously acquired FECG data, which includes noninvasive recordings of 200 maternal abdominal ECG signals. From these, we chose 35 cases of healthy pregnancies that we divided into three groups according to gestational age: Group 1, 23 +/- 2 wk; Group 2, 32 +/- 1 wk; and Group 3, 39 +/- 1 wk. The FHR variability was analyzed by a time-frequency decomposition based on a continuous wavelet transform. We showed that, independent of the gestational age, most of the FHR power is concentrated in the very-low-frequency range (0.02-0.08 Hz) and in the low-frequency range (0.08-0.2 Hz). In addition, there is power in the high-frequency range that correlates with the frequency range of fetal respiratory motion (0.4-1.7 Hz). In the intermediate-frequency range (0.2-0.4 Hz), the power is significantly smaller. The changes in the average power spectrum in relation to gestation time were carefully and quantitatively examined. The results imply that there is a neural organization during the last trimester of the pregnancy, and the sympathovagal balance is reduced with the gestational age.

Algorithms↗
PubMed2000

Intracranial pressure processing with artificial neural networks: prediction of ICP trends.

It is well known that intracranial pressure (ICP) is influenced by an array of predictable and unpredictable factors, which gives rise to a signal heavily loaded with stochastic, i.e. random components. Hence, statistical modelling of this signal has proved to be of limited utility, in spite of the very sophisticated mathematical methods applied. In recent years, neural network algorithms (ANN), which are an alternative to statistical methods, have proved their effectiveness in the prediction of trends, as applied in a variety of medical and non-medical tasks. We therefore attempted to test the efficiency of neural models in the on-line prediction of ICP values, compare their effectiveness to statistically oriented algorithms and combine ANN methods with some newer signal processing algorithms, like wavelet decomposition. Prediction horizons of up to 5 minutes have been tested with various architectures of the neural predictor. For a 3 minute prediction horizon, a satisfactory accuracy of forecasting has been achieved with "plain" ANN, as expressed by the "average relative variance coefficient". This was measured by the ratio of the prediction error obtained, in relation to the error which would occur if a current value were taken as the forecasted one. The prediction quality with statistical autoregressive models has proved unsatisfactory, whilst the result obtained using the ANN model with the wavelet transform incorporated, performed significantly better than the ANN models alone. The prediction quality obtained with the ANN methodology seems to be satisfactory over a short time horizon, though no conclusion can be derived at this stage of the study, as to the clinical utility of this method. In particular, even with this methodology, it is not possible to forecast any sudden dehiscencies of the ICP signal with any practical reliability. From the point of view of modelling theory, such sharp deviations of the signal may be regarded as a "catastrophe". This implies the necessity for a different approach to the ICP signal analysis with the artificial intelligence methodology; one, that is more oriented towards the global properties of the signal.

Cerebral Hemorrhage↗
PubMed2005

Contextual encoding in uniform and adaptive mesh-based lossless compression of MR images.

We propose and evaluate a number of novel improvements to the mesh-based coding scheme for 3-D brain magnetic resonance images. This includes: 1) elimination of the clinically irrelevant background leading to meshing of only the brain part of the image; 2) content-based (adaptive) mesh generation using spatial edges and optical flow between two consecutive slices; 3) a simple solution for the aperture problem at the edges, where an accurate estimation of motion vectors is not possible; and 4) context-based entropy coding of the residues after motion compensation using affine transformations. We address only lossless coding of the images, and compare the performance of uniform and adaptive mesh-based schemes. The bit rates achieved (about 2 bits per voxel) by these schemes are comparable to those of the state-of-the-art three-dimensional (3-D) wavelet-based schemes. The mesh-based schemes have been shown to be effective for the compression of 3-D brain computed tomography data also. Adaptive mesh-based schemes perform marginally better than the uniform mesh-based methods, at the expense of increased complexity.

Algorithms↗
PubMed2004

waveTM: wavelet-based transmembrane segment prediction.

waveTM is a web tool for the prediction of transmembrane segments in alpha-helical membrane proteins. Prediction is performed by a dynamic programming algorithm on wavelet-denoised 'hydropathy' signals. Users submit a protein sequence and receive interactively the results. Topology prediction can also be obtained in conjunction with the algorithm OrienTM. A web server that implements the waveTM algorithm is freely available at http://bioinformatics.biol.uoa.gr/waveTM.

Algorithms↗
PubMed2005

Comparison of STFT and wavelet transform methods in determining epileptic seizure activity in EEG signals for real-time application.

Electroencephalography (EEG) is widely used in clinical settings to investigate neuropathology. Since EEG signals contain a wealth of information about brain functions, there are many approaches to analyzing EEG signals with spectral techniques. In this study, the short-time Fourier transform (STFT) and wavelet transform (WT) were applied to EEG signals obtained from a normal child and from a child having an epileptic seizure. For this purpose, we developed a program using Labview software. Labview is an application development environment that uses a graphical language G, usable with an online applicable National Instruments data acquisition card. In order to obtain clinically interpretable results, frequency band activities of delta, theta, alpha and beta signals were mapped onto frequency-time axes using the STFT, and 3D WT representations were obtained using the continuous wavelet transform (CWT). Both results were compared, and it was determined that the STFT was more applicable for real-time processing of EEG signals, due to its short process time. However, the CWT still had good resolution and performance high enough for use in clinical and research settings.

Child, Preschool↗
PubMed2005

Practical issues in the evaluation of methods for the prediction of shock outcome success in out-of-hospital cardiac arrest patients.

There is a need for robust, effective predictors of the outcome from shock for out-of-hospital cardiac arrest patients. Such technology would enable the emergency responder to provide a therapy tailored to the patient's needs. Here we report our most recent findings while dwelling intentionally on the rationale behind the decisions taken during system development. Specifically, we illustrate the need for sensible data selection, fully cross-validated results and the care necessary when evaluating system performance. We analyze 878 pre-shock ECG traces, all of at least 10 s duration from 110 patients with cardiac arrest of cardiac aetiology. The continuous wavelet transform was applied to preshock segments of ECG trace. Time-frequency markers are extracted from the transform and a linear threshold derived from a training set to provide high sensitivity prediction of successful defibrillation. These systems are then evaluated on a withheld test set. All experiments are cross-validated. When compared to popular Fourier-based techniques our wavelet transform method, COP (Cardioversion Outcome Predictor), provides a 10-20% improvement in performance with values of 66 +/- 4 specificity at 95 +/- 4 sensitivity, 61 +/- 4 specificity at 97 +/- 2 sensitivity and 56 +/- 1 specificity at 98 +/- 2 sensitivity achieved for datasets limited to 3, 6, and 9 shocks per patient, respectively. Thus, the assessment of the wavelet marker was associated with a high specificity value at or above 95% sensitivity in comparison to previously reported methods. Therefore, COP could provide an optimal index for the identification of patients for whom shocking would be futile, and for whom an alternative therapy could be considered.

Animals↗
PubMed2004

Identification of diesel front sound source based on continuous wavelet transform.

Acoustic signals from diesel engines contain useful information but also include considerable noise components. To extract information for condition monitoring purposes, continuous wavelet transform (CWT) is used for the characterization of engine acoustics. This paper first reviews CWT characteristics represented by short duration transient signals. Wavelet selection and CWT are then implemented and wavelet transform is used to analyze the major sources of the engine front's exterior radiation sound. The research provides a reliable basis for engineering practice to reduce vehicle sound level. Comparison of the identification results of the measured acoustic signals with the identification results of the measured surface vibration showed good agreement.

Algorithms↗