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PubMed1996

Selective discrete Fourier transform algorithm for time-frequency analysis: method and application on simulated and cardiovascular signals.

The Selective Discrete Fourier transform (DFT) Algorithm [SDA] method for the calculation and display of time-frequency distribution has been developed and validated. For each time and frequency, the algorithm selects the shortest required trace length and calculates the corresponding spectral component by means of DFT. This approach can be extended to any cardiovascular related signal and provides time-dependent power spectra which are intuitively easy to consider, due to their close relation to the classical spectral analysis approach. The optimal parameters of the SDA for cardiovascular-like signals were chosen. The SDA perform standard spectral analysis on stationary simulated signals as well as reliably detect abrupt changes in the frequency content of nonstationary signals. The SDA applied during a stimulated respiration experiment, accurately detected the changes in the frequency location and amplitude of the respiratory peak in the heart rate (HR) spectrum. It also detected and quantified the expected increase in vagal tone during vagal stimuli. Furthermore, the HR time-dependent power spectrum displayed the increase in sympathetic activity and the vagal withdrawal on standing. Such transient changes in HR control would have been smeared out by standard heart rate variability (HRV), which requires consideration of long trace lengths. The SDA provides a reliable tool for the evaluation and quantification of the control exerted by the Central Nervous System, during clinical and experimental procedures resulting in nonstationary signals.

Adult↗

PubMed1991

Modern spectral analysis techniques for blood flow velocity and spectral measurements with pulsed Doppler ultrasound.

Four spectral analysis techniques were applied to pulsed Doppler ultrasonic quadrature signals to compare the relative merits of each technique for estimation of flow velocity and Doppler spectra. The four techniques were 1) the fast Fourier transform method, 2) the maximum likelihood method, 3) the Burg autoregressive algorithm, and 4) the modified covariance approach to autoregressive modeling. Both simulated signals and signals obtained from an in vitro flow system were studied. Optimal parameter values (e.g., model orders) were determined for each method, and the effects of signal-to-noise ratio and signal bandwidth were investigated. The modern spectral analysis techniques were shown to be superior to Fourier techniques in most circumstances, provided the model order was chosen appropriately. Robustness considerations tended to recommend the maximum likelihood method for both velocity and spectral estimation. Despite the restrictions of steady laminar flow, the results provide important basic information concerning the applicability of modern spectral analysis techniques to Doppler ultrasonic evaluation of arterial disease.

Algorithms↗

PubMed2002

A novel algorithm for cardiac biosignal filtering based on filtered residue method.

In this paper, a new algorithm is presented for the filtering (de-noising) of cardiac bioelectrical signals. The primary target of this algorithm is the class of cardiac action potentials recorded using voltage-sensitive dyes, although the method is also applied to electrocardiographic signals. High periodicity is one of the main features of cardiac biosignals. The proposed algorithm exploits this feature in filtering signals with a minimum amount of distortion. The basic idea is to use signal averaging in time to find the stationary portion of the signal. The residue is found by subtracting the signal average from the corresponding points of the input. After passing through a low-pass filter, the filtered residue (FR) is added back to the signal average to reconstruct the output. The practical implementation of the filter residue algorithm is discussed. Stretching and shrinking operations are the basis for the conversion of quasi-periodic signals into periodic signals, which can then be subjected to the FR algorithm. Various examples are presented, and error estimation is performed to guide the selection of optimal parameters for the algorithm. The ability of the algorithm to reconstruct the variation among beats is demonstrated, and its limitations are discussed.

Action Potentials↗

PubMed2005

Identification of transient renal autoregulatory mechanisms using time-frequency spectral techniques.

Identification of the two principal mediators of renal autoregulation from time-series data is difficult, as both the tubuloglomerular feedback (TGF) and myogenic (MYO) mechanisms interact and share a common effector, the afferent arteriole. Moreover, although both mechanisms can exhibit oscillations in well-characterized frequency bands, these systems often operate in nonoscillatory states not detectable by frequency-domain analysis. To overcome these difficulties, we have developed a new approach to the characterization of the TGF and MYO systems. A laser Doppler probe is used to measure fluctuations in local cortical blood flow (CBF) in response to spontaneous changes in blood pressure (BP) and to large imposed perturbations in BP, which elicit strong, simultaneous, transient, oscillatory blood flow responses. These transient responses are identified by high-resolution time-frequency spectral analysis of the time-series data. In this report, we compare four different time-frequency spectral techniques (the short-time Fourier transform (STFT), smoothed pseudo Wigner-Ville, and two recently developed methods: the Hilbert-Huang transform and time varying optimal parameter search (TVOPS)) to determine which of these four methods is best suited for the identification of transient oscillations in renal autoregulatory mechanisms. We found that TVOPS consistently provided the best performance in both simulation examples and identification of the two autoregulatory mechanisms in actual data. While the STFT suffers in time and frequency resolution as compared to the other three methods, it was able to identify the two autoregulatory mechanisms. Taken together, our experience suggests a two level approach to the analysis of renal blood flow (RBF) data: STFT to obtain a low-resolution time-frequency spectrogram, followed by the use of a higher resolution technique, such as the TVOPS, if even higher time-frequency resolution of the transient responses is required.

Algorithms↗

PubMed2006

Learning the relationship between patient geometry and beam intensity in breast intensity-modulated radiotherapy.

Intensity modulated radiotherapy (IMRT) has become an effective tool for cancer treatment with radiation. However, even expert radiation planners still need to spend a substantial amount of time adjusting IMRT optimization parameters in order to get a clinically acceptable plan. We demonstrate that the relationship between patient geometry and radiation intensity distributions can be automatically inferred using a variety of machine learning techniques in the case of two-field breast IMRT. Our experiments show that given a small number of human-expert-generated clinically acceptable plans, the machine learning predictions produce equally acceptable plans in a matter of seconds. The machine learning approach has the potential for greater benefits in sites where the IMRT planning process is more challenging or tedious.

Artificial Intelligence↗

PubMed2006

Polyharmonic smoothing splines and the multidimensional Wiener filtering of fractal-like signals.

Motivated by the fractal-like behavior of natural images, we develop a smoothing technique that uses a regularization functional which is a fractional iterate of the Laplacian. This type of functional was initially introduced by Duchon for the approximation of nonuniformily sampled, multidimensional data. He proved that the general solution is a smoothing spline that is represented by a linear combination of radial basis functions (RBFs). Unfortunately, this is tedious to implement for images because of the poor conditioning of RBFs and their lack of decay. Here, we present a much more efficient method for the special case of a uniform grid. The key idea is to express Duchon's solution in a fractional polyharmonic B-spline basis that spans the same space as the RBFs. This allows us to derive an algorithm where the smoothing is performed by filtering in the Fourier domain. Next, we prove that the above smoothing spline can be optimally tuned to provide the MMSE estimation of a fractional Brownian field corrupted by white noise. This is a strong result that not only yields the best linear filter (Wiener solution), but also the optimal interpolation space, which is not bandlimited. It also suggests a way of using the noisy data to identify the optimal parameters (order of the spline and smoothing strength), which yields a fully automatic smoothing procedure. We evaluate the performance of our algorithm by comparing it against an oracle Wiener filter, which requires the knowledge of the true noiseless power spectrum of the signal. We find that our approach performs almost as well as the oracle solution over a wide range of conditions.

Algorithms↗

PubMed2002

Application of task-based measures of image quality to optimization and evaluation of three-dimensional reconstruction-based compensation methods in myocardial perfusion SPECT.

In this paper, we apply the channelized Hotelling observer (CHO) using a defect detection task to the optimization and evaluation of three-dimensional iterative reconstruction-based compensation methods for myocardial perfusion single-photon emission computed tomography (SPECT). We used a population of 24 mathematical cardiac-torso phantoms that realistically model the activity and attenuation distribution in three classes of patients: females, and males with flat diaphragms and raised diaphragms. Projection data were generated and subsequently reconstructed using methods based on the ordered subsets-expectation maximization (OSEM) algorithm. The methods evaluated included compensation for attenuation, detector response blurring, and scatter in various combinations. We applied the CHO to optimize the number of iterations for OSEM and the cutoff frequency and order of a three-dimensional postreconstruction Butterworth filter. Using the optimal parameters, we then compared the compensation methods. The index of comparison in these studies was the area under the receiver operating characteristics curve (AUC) for the CHO. We found that attenuation compensation with either detector response or scatter compensation gave statistically significant increases in the AUC compared to attenuation compensation alone. The greatest increase in the AUC occurred when all three compensations were applied. These results indicate that compensation for detector response and scatter, in addition to attenuation compensation, will improve defect detectability in myocardial SPECT images.

Algorithms↗

PubMed2005

Segmentation of thrombus in abdominal aortic aneurysms from CTA with nonparametric statistical grey level appearance modeling.

This paper presents a new method for deformable model-based segmentation of lumen and thrombus in abdominal aortic aneurysms from computed tomography (CT) angiography (CTA) scans. First the lumen is segmented based on two positions indicated by the user, and subsequently the resulting surface is used to initialize the automated thrombus segmentation method. For the lumen, the image-derived deformation term is based on a simple grey level model (two thresholds). For the more complex problem of thrombus segmentation, a grey level modeling approach with a nonparametric pattern classification technique is used, namely k-nearest neighbors. The intensity profile sampled along the surface normal is used as classification feature. Manual segmentations are used for training the classifier: samples are collected inside, outside, and at the given boundary positions. The deformation is steered by the most likely class corresponding to the intensity profile at each vertex on the surface. A parameter optimization study is conducted, followed by experiments to assess the overall segmentation quality and the robustness of results against variation in user input. Results obtained in a study of 17 patients show that the agreement with respect to manual segmentations is comparable to previous values reported in the literature, with considerable less user interaction.

Algorithms↗

PubMed2005

Data classification with radial basis function networks based on a novel kernel density estimation algorithm.

This paper presents a novel learning algorithm for efficient construction of the radial basis function (RBF) networks that can deliver the same level of accuracy as the support vector machines (SVMs) in data classification applications. The proposed learning algorithm works by constructing one RBF subnetwork to approximate the probability density function of each class of objects in the training data set. With respect to algorithm design, the main distinction of the proposed learning algorithm is the novel kernel density estimation algorithm that features an average time complexity of O(n log n), where n is the number of samples in the training data set. One important advantage of the proposed learning algorithm, in comparison with the SVM, is that the proposed learning algorithm generally takes far less time to construct a data classifier with an optimized parameter setting. This feature is of significance for many contemporary applications, in particular, for those applications in which new objects are continuously added into an already large database. Another desirable feature of the proposed learning algorithm is that the RBF networks constructed are capable of carrying out data classification with more than two classes of objects in one single run. In other words, unlike with the SVM, there is no need to resort to mechanisms such as one-against-one or one-against-all for handling datasets with more than two classes of objects. The comparison with SVM is of particular interest, because it has been shown in a number of recent studies that SVM generally are able to deliver higher classification accuracy than the other existing data classification algorithms. As the proposed learning algorithm is instance-based, the data reduction issue is also addressed in this paper. One interesting observation in this regard is that, for all three data sets used in data reduction experiments, the number of training samples remaining after a naive data reduction mechanism is applied is quite close to the number of support vectors identified by the SVM software. This paper also compares the performance of the RBF networks constructed with the proposed learning algorithm and those constructed with a conventional cluster-based learning algorithm. The most interesting observation learned is that, with respect to data classification, the distributions of training samples near the boundaries between different classes of objects carry more crucial information than the distributions of samples in the inner parts of the clusters.

Algorithms↗

PubMed2005

Image shadow removal using pulse coupled neural network.

This paper introduces an approach for image shadow removal by using pulse coupled neural network (PCNN), based on the phenomena of synchronous pulse bursts in the animal visual cortexes. Two shadow-removing criteria are proposed. These two criteria decide how to choose the optimal parameter (the linking strength beta). The computer simulation results of shadow removal based on PCNN show that if these two criteria are satisfied, shadows are removed completely and the shadow-removed images are almost as the same as the original nonshadowed images. The shadow removal results are independent of changes of intensities of shadows in some range and variations of the places of shadows. When the first criterion is satisfied, even if the second criterion is not satisfied, as to natural grey images that have abundant grey levels, shadows also can be removed and PCNN shadow-removed images retain the shapes of the objects in original images. These two criteria also can be used for color images by dividing a color image into three channels (R, G, B). For shadows varying drastically, such as the noisy points in images, these two criteria are still right, but difficult to satisfy. Therefore, this approach can efficiently remove shadows that do not include the random noise.

Algorithms↗

PubMed2006

Adaptive wavelet neural network control with hysteresis estimation for piezo-positioning mechanism.

An adaptive wavelet neural network (AWNN) control with hysteresis estimation is proposed in this study to improve the control performance of a piezo-positioning mechanism, which is always severely deteriorated due to hysteresis effect. First, the control system configuration of the piezo-positioning mechanism is introduced. Then, a new hysteretic model by integrating a modified hysteresis friction force function is proposed to represent the dynamics of the overall piezo-positioning mechanism. According to this developed dynamics, an AWNN controller with hysteresis estimation is proposed. In the proposed AWNN controller, a wavelet neural network (WNN) with accurate approximation capability is employed to approximate the part of the unknown function in the proposed dynamics of the piezo-positioning mechanism, and a robust compensator is proposed to confront the lumped uncertainty that comprises the inevitable approximation errors due to finite number of wavelet basis functions and disturbances, optimal parameter vectors, and higher order terms in Taylor series. Moreover, adaptive learning algorithms for the online learning of the parameters of the WNN are derived based on the Lyapunov stability theorem. Finally, the command tracking performance and the robustness to external load disturbance of the proposed AWNN control system are illustrated by some experimental results.

Algorithms↗

PubMed2005

Point processes for unsupervised line network extraction in remote sensing.

This paper addresses the problem of unsupervised extraction of line networks (for example, road or hydrographic networks) from remotely sensed images. We model the target line network by an object process, where the objects correspond to interacting line segments. The prior model, called "Quality Candy," is designed to exploit as fully as possible the topological properties of the network under consideration, while the radiometric properties of the network are modeled using a data term based on statistical tests. Two techniques are used to compute this term: one is more accurate, the other more efficient. A calibration technique is used to choose the model parameters. Optimization is done via simulated annealing using a Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm. We accelerate convergence of the algorithm by using appropriate proposal kernels. The results obtained on satellite and aerial images are quantitatively evaluated with respect to manual extractions. A comparison with the results obtained using a previous model, called the "Candy" model, shows the interest of adding quality coefficients with respect to interactions in the prior density. The relevance of using an offline computation of the data potential is shown, in particular, when a proposal kernel based on this computation is added in the RJMCMC algorithm.

Algorithms↗

PubMed2005

A fuzzy reinforcement learning approach to power control in wireless transmitters.

We address the issue of power-controlled shared channel access in wireless networks supporting packetized data traffic. We formulate this problem using the dynamic programming framework and present a new distributed fuzzy reinforcement learning algorithm (ACFRL-2) capable of adequately solving a class of problems to which the power control problem belongs. Our experimental results show that the algorithm converges almost deterministically to a neighborhood of optimal parameter values, as opposed to a very noisy stochastic convergence of earlier algorithms. The main tradeoff facing a transmitter is to balance its current power level with future backlog in the presence of stochastically changing interference. Simulation experiments demonstrate that the ACFRL-2 algorithm achieves significant performance gains over the standard power control approach used in CDMA2000. Such a large improvement is explained by the fact that ACFRL-2 allows transmitters to learn implicit coordination policies, which back off under stressful channel conditions as opposed to engaging in escalating "power wars."

Algorithms↗

PubMed2000

The effect of nucleotide bias upon the composition and prediction of transmembrane helices.

Transmembrane helices are the most readily predictable secondary structure components of proteins. They can be predicted to a high degree of accuracy in a variety of ways. Many of these methods compare new sequence data with the sequence characteristics of known transmembrane domains. However, the known transmembrane sequences are not necessarily representative of a particular organism. We attempt to demonstrate that parameters optimized for the known transmembrane domains are far from optimal when predicting transmembrane regions in a given genome. In particular, we have tested the effect of nucleotide bias upon the composition and hence the prediction characteristics of transmembrane helices. Our analysis shows that nucleotide bias of a genome has a strong and predictable influence upon the occurrences of several of the most important hydrophobic amino acids found within transmembrane helices. Thus, we show that nucleotide bias should be taken into account when determining putative transmembrane domains from sequence data.

Amino Acids↗

PubMed1991

Correlations between mean LH levels and LH pulse characteristics: differences between normal and anovulatory women.

OBJECTIVE: Since LH secretion occurs as a series of pulses, relationships between mean LH levels and LH pulse characteristics are to be expected. The aim of this study was to determine whether such relationships are similar in normal women and anovulatory patients. DESIGN: We studied the correlations between mean LH levels and the products amplitude x frequency and area x frequency of LH pulses in normal women and in patients with disorders of ovulation. Blood samples were taken from each subject every 10 minutes during 6 hours on the 8th day after the last menses. PATIENTS: The patients were divided into three groups: patients with polycystic ovary syndrome (n = 11), patients with idiopathic anovulation (n = 14) and patients with short luteal phase (n = 13). Their results were compared to those of 12 normal women. MEASUREMENTS: LH was evaluated with an immunoradiometric assay. LH data were analysed with a 3 standard deviation threshold criterion for significant peaks, and with cluster analysis algorithm using 1, 2.5 and 5% false positive error rates and 'optimal parameters' (which give less than 5% false positive and false negative error rates in LH male data). RESULTS: Highly significant correlations between amplitude x frequency, area x frequency and mean LH were found in normals and patients with short luteal phase; no significant correlation was found in patients with polycystic ovary syndrome, while significant correlations were found in patients with idiopathic anovulation only with some of our criteria for peak detection. CONCLUSION: The differences that we found between the groups suggest that when commonly used methods are employed to determine LH pulse characteristics, most of the significant LH pulses are taken into account in normals and patients with short luteal phase, but not in anovulatory patients, especially in patients with polycystic ovary syndrome. This method using two correlations appears to be a simple and useful way to show the differences in the mechanisms by which mean LH levels are achieved in normal subjects and patients.

Adult↗

PubMed2006

Evidence from Amazonian forests is consistent with isohydric control of leaf water potential.

Climate modelling studies predict that the rain forests of the Eastern Amazon basin are likely to experience reductions in rainfall of up to 50% over the next 50-100 years. Efforts to predict the effects of changing climate, especially drought stress, on forest gas exchange are currently limited by uncertainty about the mechanism that controls stomatal closure in response to low soil moisture. At a through-fall exclusion experiment in Eastern Amazonia where water was experimentally excluded from the soil, we tested the hypothesis that plants are isohydric, that is, when water is scarce, the stomata act to prevent leaf water potential from dropping below a critical threshold level. We made diurnal measurements of leaf water potential (psi 1), stomatal conductance (g(s)), sap flow and stem water potential (psi stem) in the wet and dry seasons. We compared the data with the predictions of the soil-plant-atmosphere (SPA) model, which embeds the isohydric hypothesis within its stomatal conductance algorithm. The model inputs for meteorology, leaf area index (LAI), soil water potential and soil-to-leaf hydraulic resistance (R) were altered between seasons in accordance with measured values. No optimization parameters were used to adjust the model. This 'mechanistic' model of stomatal function was able to explain the individual tree-level seasonal changes in water relations (r2 = 0.85, 0.90 and 0.58 for psi 1, sap flow and g(s), respectively). The model indicated that the measured increase in R was the dominant cause of restricted water use during the dry season, resulting in a modelled restriction of sap flow four times greater than that caused by reduced soil water potential. Higher resistance during the dry season resulted from an increase in below-ground resistance (including root and soil-to-root resistance) to water flow.

Brazil↗

PubMed2004

Multiple gene detection by in situ RT-PCR in isolated plant cells and tissues.

With the number of functional genomic approaches in plant biology increasing daily, the demand for rapid and reliable RNA localization techniques for gene characterization is being felt. We present herein a novel, liquid phase in situ RT-PCR (IS-RT-PCR) protocol using a combination of gene-specific fluorescent primers and spectral confocal microscopy to localize target RNA in epicotyl sections and xylogenic suspension cultures of Zinnia elegans. Potential sources of artefacts from fixation to gene detection were systematically eliminated using both fluorescent primers and nucleotides for 18S rRNA gene detection, resulting in a set of optimal parameters for IS-RT-PCR that may be readily adapted to any target gene. By judiciously choosing fluorescent primers with non-overlapping fluorochromes, we have shown that our technique is readily adapted to multiplex IS-RT-PCR, enabling the simultaneous localization of more than one gene within a complex tissue or heterogeneous cell population. A 6-carboxy-2',4,4',5',7,7'-hexachlorofluorescein (6-HEX)-labelled primer and a tetrachloro-6-carboxy-fluorescein (TET)-labelled primer were designed for two marker genes associated with programmed cell death in tracheary elements (TEs): an endonuclease (Zen1) and a cysteine protease (ZcP4), respectively. An additional Cyan5 (Cy5)-labelled primer was used to monitor 18SrRNA expression. As expected, the 18S signal was constitutively expressed throughout epicotyls sections and living cells in xylogenic in vitro cultures, whereas Zen1 and ZcP4 were co-localized in forming TEs both in planta and in vitro. Analogous to clustering analysis of gene expression using microarrays to elucidate common metabolic pathways and developmental processes, this novel technique is perfectly adapted to gaining a better understanding of gene function via the coordinated expression of genes in specific cell types of complex tissues and cell populations.

Artifacts↗

PubMed2006

Magnetic resonance imaging in the quantitative assessment of gouty tophi.

Measurements of tophus size can be important in monitoring the course of gout therapy, as tophus resolution is proposed as one measure of success of treatment. This multicentre study assessed the intra- and interreader reproducibility of quantitative tophus volume measurements from magnetic resonance images (MRI) in subjects with palpable gouty tophi. Subjects first underwent radiographic imaging of a selected tophus followed by MRI before and at <or=5, 10 and 20-min after gadolinium administration. After choosing optimal parameters, subjects underwent pre- and postgadolinium-enhanced MRIs of a selected tophus on two occasions separated by 5-10 days. Unenhanced spin-echo images provided satisfactory tophi images and were less subject to interfering artefacts than gadolinium-enhanced gradient-echo images. Intrareader reproducibility was excellent, with no statistically significant difference in mean tophus volume between visits (mean difference - 0.05 +/- 0.97 cm3). A small but statistically significant difference in interreader mean tophus volume was detected (mean difference 0.89 +/- 2.05 cm3; p < 0.05). MRI can quantify tophus size in gout and deserves further comparison with other techniques for tophus size monitoring in assessing effects of gout therapy.

Adolescent↗