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PubMed2004

Multiple GABAA receptor subtypes regulate hippocampal ripple oscillations.

High-frequency oscillations (140-200 Hz) were recorded in behaving rats from the CA1 area of the hippocampus. As generation of these synchronous patterns is assumed to depend on coordinated interneuronal inhibition, we studied the interference of benzodiazepines with the fine structure and occurrence of ripple oscillations. The nonselective GABAA receptor alpha-subunit agonist, diazepam, lowered the frequency of ripple oscillations and reduced their occurrence, amplitude and duration. Zolpidem, an alpha1-subunit selective benzodiazepine elevated ripple duration but acted similar to diazepam in other respects. The nonselective alpha-subunit benzodiazepine antagonist, flumazenil, reduced ripple numbers, amplitude and duration. Wavelet based analysis of the dynamics of intraripple frequency revealed a dramatic decay within a ripple. Only diazepam (1 mg/kg) accelerated this intraripple frequency accommodation. The effects were not due to increased behavioural activity and alertness as evident from vigilance state control. The results suggest a differential role of GABAA receptor subtype specific inhibitory mechanisms in the mediation and fine-tuning of the network synchronization during approximately 200 Hz hippocampal oscillations.

Animals↗
PubMed2005

Frequency encoding in renal blood flow regulation.

With a model of renal blood flow regulation, we examined consequences of tubuloglomerular feedback (TGF) coupling to the myogenic mechanism via voltage-gated Ca channels. The model reproduces the characteristic oscillations of the two mechanisms and predicts frequency and amplitude modulation of the myogenic oscillation by TGF. Analysis by wavelet transforms of single-nephron blood flow confirms that both amplitude and frequency of the myogenic oscillation are modulated by TGF. We developed a double-wavelet transform technique to estimate modulation frequency. Median value of the ratio of modulation frequency to TGF frequency in measurements from 10 rats was 0.95 for amplitude modulation and 0.97 for frequency modulation, a result consistent with TGF as the modulating signal. The simulation predicted that the modulation was regular, while the experimental data showed much greater variability from one TGF cycle to the next. We used a blood pressure signal recorded by telemetry from a conscious rat as the input to the model. Blood pressure fluctuations induced variability in the modulation records similar to those found in the nephron blood flow results. Frequency and amplitude modulation can provide robust communication between TGF and the myogenic mechanism.

Animals↗
PubMed2006

Central neurochemical ultradian variability in depression.

UNLABELLED: Depression is characterized by blunted behavior and neuroendocrine function that generally improve with antidepressant treatment. This study examined intrinsic variability in brain neurotransmitter function, since it may be a source of blunted behavior and neuroendocrine function in depression and a marker for the illness, and has not previously been analyzed using wavelet decomposition. To measure variability in monoamine metabolites, lumbar cerebrospinal fluid (CSF) was collected in serial samples in depressed patients before and after treatment. We hypothesized that changes in variability would be observed after treatment. Mechanisms that control such variability may be critical to the pathophysiology of depression. METHOD: Time series data was obtained from serial ten-min sampling over a 24-hr period (N=144) from thirteen depressed patients, with a repeat collection after 5 weeks of antidepressant (sertraline or bupropion) treatment. Concentrations of tryptophan (TRP), the monoamine metabolites 5-HIAA (metabolite of serotonin) and HVA (metabolite of dopamine), and the HVA:5HIAA ratio were transformed to examine power in slowly (160 min/cycle) to rapidly (20 min/cycle) occurring events. Power, the sum of the squares of the coefficients in each d (detail) wavelet, reflects variability within a limited frequency bandwidth for that wavelet. Pre-treatment to post-treatment comparisons were conducted with repeated measures ANOVA. RESULTS: Antidepressant treatment was associated with increased power in the d2 wavelet from the HVA (p=0.03) and the HVA:5-HIAA ratio (p=0.03) series. The d1 and d3 wavelets showed increased power following antidepressant treatment for the ratio series (d1, p=0.01; d3, p=0.05). Significant changes in power were not observed for the 5-HIAA data series. Power differences among analytes suggest that the findings are specific to each system. CONCLUSION: The wavelet transform analysis shows changes in neurochemical signal variability following antidepressant treatment. Patterns or degrees of variability may be as important as, or possibly more important than, the mean levels of monoamine transmitters. Studies of variability observed in healthy individuals and a larger depressed sample will be needed to verify a relationship with mood and treatment response. Neurochemical measures of time-variability may be a pivotal marker in depression.

Adult↗
PubMed2004

High-frequency somatosensory thalamocortical oscillations and psychopathology in schizophrenia.

Human cortical somatosensory evoked potentials (SEPs), which are presumably generated in afferent thalamocortical and early cortical fibers, reveal a burst of superimposed early (N20) high-frequency oscillations (HFOs), around 600 Hz. There is increasing evidence of an imbalance of thalamocortical systems in schizophrenic patients. In order to assess correlations between somatosensory evoked oscillations and symptoms of schizophrenia, we investigated median nerve SEPs in 20 inpatients and their age-matched and gender-matched healthy controls using a multichannel EEG. Dipole source analysis and wavelet transformation were performed before and after application of a 450-Hz high-pass filter. In schizophrenics, the maximum HFOs occurred with a significantly prolonged latency. There was also a higher amplitude (energy) in the low-frequency range of the N20 component compared with the controls. Importantly, amplitudes (energy) of HFOs were inversely correlated with symptoms of formal thought disorder and delusions. Alterations of the thalamocortical somatosensory signal processing in schizophrenia with absence of an early HFO - assumed to be of inhibitory nature - could indicate a dysfunctional thalamic inhibition with increased amplitudes of N20, paralleled by enhanced positive schizophrenic symptoms.

Adult↗
PubMed2002

[ECG signal compression based on best basis of wavelet packets].

The paper reports a kind of new compression method of ECG signal. The method is realized by means of wavelet packets transform based on best basis performed by Shanon-Weaker entropy criterion. The result of simulation shows that this is an efficient compression method characterized by larger compression ratio and less loss, and the original signal can be recovered well.

Algorithms↗
PubMed2004

[An approach based on wavelet transform to removal of noises].

The electrocardiac signals exhibit obvious unsteadiness and contain many noises in the processing of detecting. In this paper, a wavelet transform, which can be located in the fields of time and frequency, is introduced to decompose the ECG signals on multi-scales. In characteristic scales, the electrocardiac signals can be decomposed. After removal of various noises, the electrocardiac signals are formed again perfectly. This method provides more accurate ECG data for use in medical diagnosis.

Algorithms↗
PubMed2006

New technique for visualizing cerebral vessels in MR angiographic images using three-dimensional discrete wavelet transform.

Visualization techniques for magnetic resonance (MR) angiographic images are generally based on the projection ray concept; for example, maximum intensity projection (MIP) and volume rendering (VR). A new technique based on a different concept from projection rays is explored in this study: three-dimensional (3-D) discrete wavelet transforms are used for visualizing cerebral vessels in the MR angiographic image. This technique successfully visualizes cerebral vessels and represents the spatial relationship between the cerebral vessels, as in the case of VR. The proposed technique is, thus, indicated to be promising for visualizing cerebral vessels in 3-D MR angiographic images.

Brain↗
PubMed2006

Multi-resolution Bayesian regression in PET dynamic studies using wavelets.

In the kinetic analysis of dynamic PET data, one usually posits that the variation of the data through one dimension, time, can be described by a mathematical model encapsulating the relevant physiological features of the radioactive tracer. In this work, we posit that the remaining dimension, space, can also be modeled as a physiological feature, and we introduce this concept into a new computational procedure for the production of parametric maps. An organ and, in the instance considered here, the brain presents similarities in the physiological properties of its elements across scales: computationally, this similarity can be implemented in two stages. Firstly, a multi-scale decomposition of the dynamic frames is created through the wavelet transform. Secondly, kinetic analysis is performed in wavelet space and the kinetic parameters estimated at low resolution are used as priors to inform estimates at higher resolutions. Kinetic analysis in the above scheme is achieved by extension of the Patlak analysis through Bayesian linear regression that retains the simplicity and speed of the original procedure. Application to artificial and real data (FDG and FDOPA) demonstrates the ability of the procedure to reduce remarkably the variance of parametric maps (up to 4-fold reduction) without introducing sizeable bias. Significance of the methodology and extension of the procedure to other data (fMRI) and models are discussed.

Bayes Theorem↗
PubMed1997

DSP implementation of wavelet transform for real time ECG wave forms detection and heart rate analysis.

An algorithm based on wavelet transform (WTs) suitable for real time implementation has been developed in order to detect ECG characteristics. In particular, QRS complexes, P and T waves may be distinguished from noise, baseline drift or artefacts. This algorithm is implemented in a DSP (SPROC-1400) with a 50 MHz frequency clock. The performance of this algorithm is discussed, its accuracy is evaluated and a comparison is made with a similar algorithm implemented in C language. For the standard MIT/BIH arrhythmia database, this algorithm correctly detects 99.7% of the QRS complexes.

Algorithms↗
PubMed2000

X-ray CT metal artifact reduction using wavelets: an application for imaging total hip prostheses.

Traditional computed tomography (CT) reconstructions of total joint prostheses are limited by metal artifacts from corrupted projection data. Published metal artifact reduction methods are based on the assumption that severe attenuation of X-rays by prostheses renders corresponding portions of projection data unavailable, hence the "missing" data are either avoided (in iterative reconstruction) or interpolated (in filtered backprojection with data completion; typically, with filling data "gaps" via linear functions). In this paper, we propose a wavelet-based multiresolution analysis method for metal artifact reduction, in which information is extracted from corrupted projection data. The wavelet method improves image quality by a successive interpolation in the wavelet domain. Theoretical analysis and experimental results demonstrate that the metal artifacts due to both photon starving and beam hardening can be effectively suppressed using our method. As compared to the filtered backprojection after linear interpolation, the wavelet-based reconstruction is significantly more accurate for depiction of anatomical structures, especially in the immediate neighborhood of the prostheses. This superior imaging precision is highly advantageous in geometric modeling for fitting hip prostheses.

Artifacts↗
PubMed2004

Determination of chlorogenic acid in plant samples by using near-infrared spectrum with wavelet transform preprocessing.

By theoretical analysis, it is found that wavelet transform (WT) with a wavelet function can be regarded as a smoothing and a differentiation process, and that the order of differentiation is determined by the vanishing moment, which is an important property of a wavelet function. Therefore, a method based on the continuous wavelet transform (CWT) for removing the background in the near-infrared (NIR) spectrum is proposed, and it is used in the determination of the chlorogenic acid in plant samples as a preprocessing tool for partial least square (PLS) modeling. It is shown that the benefit of the proposed method lies not only in its performance to improve the quality of PLS model and the prediction precision, but also in its simplicity and practicability. It may become a convenient and efficient tool for preprocessing NIR spectral data sets in multivariate calibration.

Algorithms↗
PubMed2005

[A new de-noising technique for spectra based on Mexican hat wavelet].

Signals in spectral analysis often have random noise, which has negative influence on the accuracy and detection limit of analysis. A new chemometrics method named Mexican Hat Wavelet De-noising Arithmetic (MWDA) is presented, which can be used to remove noise in analytical chemical signals. In this method, Mexican Hat wavelet is chosen to construct de-noising function because of its excellent properties, then the de-noising function is used to extract useful information from noisy signals. MWDA is effective for signals with either wide peaks or very sharp peaks. Many processing results of simulated and experimental signals indicate that MWDA is a simple and powerful de-noising method, even when the signal has very high noise (whose signal to noise ratio is 1). After processed, the relative errors of peak position, peak height and peak area are less than 0.2%, 3.2% and 1.1% respectively. When it is applied to experimental spectra, the results are also satisfactory. This new method can increase the accuracy of spectral analysis, and the result is credible and satisfying.

Algorithms↗
PubMed2006

Automated search for arthritic patterns in infrared spectra of synovial fluid using adaptive wavelets and fuzzy C-means analysis.

Analysis of synovial fluid by infrared (IR) clinical chemistry requires expert interpretation and is susceptible to subjective error. The application of automated pattern recognition (APR) may enhance the utility of IR analysis. Here, we describe an APR method based on the fuzzy C-means cluster adaptive wavelet (FCMC-AW) algorithm, which consists of two parts: one is a FCMC using the features from an M-band feature extractor adopting the adaptive wavelet algorithm and the second is a Bayesian classifier using the membership matrix generated by the FCMC. A FCMC-cross-validated quadratic probability measure (FCMC-CVQPM) criterion is used under the assumption that the class probability density is equal to the value of the membership matrix. Therefore, both values of posterior probabilities and selection criterion MFQ can be obtained through the membership matrix. The distinctive advantage of this method is that it provides not only the 'hard' classification of a new pattern, but also the confidence of this classification, which is reflected by the membership matrix.

Algorithms↗
PubMed2006

The application of wavelet and feature vectors to ECG signals.

The Electrocardiogram (ECG) is one of the most commonly known biological signals. Traditionally ECG recordings are analysed in the time-domain by skilled physicians. However, pathological conditions may not always be obvious in the original time-domain signal. Fourier analysis provides frequency information but has the disadvantage that time characteristics will be lost. Wavelet analysis, which provides both time and frequency information, can overcome this limitation. Here a new method, the combination of wavelet analysis and feature vectors, is applied with the intent to investigate its suitability as a diagnostic tool. ECG signals with normal and abnormal beats were examined. There were two stages in analysing ECG signals: feature extraction and feature classification. To extract features from ECG signals, wavelet decomposition was first applied and feature vectors of normalised energy and entropy were constructed. These feature vectors were used to classify signals. The results showed that normal beats and abnormal beats composed different clusters in most cases. In conclusion, the combination of wavelet transform and feature vectors has shown potential in detecting abnormalities in an ECG recording. It was also found that normalised energy and entropy are features, which are suitable for classification of ECG signals.

Algorithms↗
PubMed1998

[Analysis for the auditory brainstem responses of human with wavelet transform].

To find out the value of wavelet transform when it is applied in analysis of human's auditory brainstem responses (ABR). The subjects was divided into two groups, one group is normal hearing subjects (14 cases, 28 ears) and another is sensorial hearing loss subjects (17 cases, 27 ears). ABR of the two groups was collected and represented with wavelet transform respectively. The signals of pre- and post being transferred was contrasted and dealt with statistics. Similar to the analysis of guinea pig's ABR, the wavelet transform scale 3 is mostly adequate to the analysis of human's ABR. The time's field after transforming is not changed and the emerged rate of wave I is increased, IV-V compound is divided clearly. So the measurement of I-V range is more precise. Analysing human's ABR with wavelet transform is more precise in measurement of I-V range than with former methods.

Adult↗
PubMed1999

Enhancement of spectral analysis of myoelectric signals during static contractions using wavelet methods.

In this paper, we introduce wavelet packets as an alternative method for spectral analysis of surface myoelectric (ME) signals. Both computer synthesized and real ME signals are used to investigate the performance. Our simulation results show that wavelet packet estimate has slightly less mean square error (MSE) than Fourier method, and both methods perform similarly on the real data. Moreover, wavelet packets give us some advantages over the traditional methods such as multiresolution of frequency, as well as its potential use for effecting time-frequency decomposition of the nonstationary signals such as the ME signals during dynamic contractions. We also introduce wavelet shrinkage method for improving spectral estimates by significantly reducing the MSE's for both Fourier and wavelet packet methods.

Action Potentials↗
PubMed2001

Multivariate and multiscale monitoring of wastewater treatment operation.

In this work extensions to principal component analysis (PCA) for wastewater treatment (WWT) process monitoring are discussed. Conventional PCA has some limitations when used for WWT monitoring. Firstly, PCA assumes that data are stationary, which is normally not the case in WWT monitoring. Secondly, PCA is most suitable for monitoring data that display events in one time-scale. However, in WWT operation. disturbances and events occur in different time-scales. These two limitations make conventional PCA unsuitable for WWT monitoring. The first limitation can be overcome by use of adaptive PCA. In adaptive PCA. the PCA model is continuously updated using an exponential memory function. Variable mean, variance and co-variance are thus adapted to the changing conditions. The second problem can be solved by time-scale decomposition of data prior to analysis. The time-scale decomposition methodology involves wavelets and multiresolution analysis (MRA) in combination with PCA. MRA provides a tool for investigation and monitoring of process measurement at different time-scales by decomposing measurement data into separate frequency bands. Time-scale decomposition increases the sensitivity of the monitoring, which makes it possible to detect small but significant events in data displaying large variations. Moreover, time-scale information is sometimes important in the interpretation of a disturbance to determine its physical cause. Also, by decomposing data, the problem of changing process conditions is partly solved. All the presented methods are illustrated with examples using real WWT process data.

Principal Component Analysis↗