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At least 1,387 records · Page 77Linked to original sources

PubMed2001

Three-dimensional optic nerve head algorithm for the detection of glaucomatous damage.

PURPOSE: To determine whether a three-dimensional optic disc algorithm could be useful in differentiating optic nerve heads (ONH) with a normal visual field from those with an abnormal visual field also when using a simultaneous stereoscopic computerised ONH analyser. METHODS: One eye was randomly chosen from 45 normals and 55 patients with glaucoma (mean deviation -7.7 +/- 9.0 dB, corrected pattern standard deviation 3.1 +/- 2.3 dB). All the subjects were examined with the Humphrey Perimeter (program 30-2) and with the Topcon Image-net X Rev-3.51 b. Using the topographic map of the system, the algorithm of the third moment or cup shape measure was applied to the numbers (the number of points ranged from 623 to 1,883 depending on the size of the disc area) that identify the heights of all the points of the optic disc surface. Findings were analysed by means of the Mann-Whitney U test and receiver operator characteristic curves. RESULTS: The sensitivity and specificity of this three-dimensional ONH algorithm applied to Topcon Image-net was 90.6% and 85.1% respectively. No difference was found between the groups for age, disc area and number of the analysed points. CONCLUSIONS: The algorithm of cup shape measure or third moment is a useful parameter to separate eyes with normal from those with abnormal visual field also when using a simultaneous stereoscopic system such as Image-net.

Algorithms↗

PubMed2005

A gene selection algorithm based on the gene regulation probability using maximal likelihood estimation.

A novel gene selection algorithm based on the gene regulation probability is proposed. In this algorithm, a probabilistic model is established to estimate gene regulation probabilities using the maximum likelihood estimation method and then these probabilities are used to select key genes related by class distinction. The application on the leukemia data-set suggests that the defined gene regulation probability can identify the key genes to the acute lymphoblastic leukemia (ALL)/acute myeloid leukemia (AML) class distinction and the result of our proposed algorithm is competitive to those of the previous algorithms.

Acute Disease↗

PubMed2006

A right ventricular pressure waveform based pulse contour cardiac output algorithm in canines.

Tracking changes in stroke volume or cardiac output (CO) can be useful in the diagnosis and treatment of various cardiac illnesses. Existing arterial pressure waveform based pulse contour CO algorithms perform poorly during altered systemic hemodynamics. In this study, a right ventricular pressure waveform based pulse contour CO algorithm was developed to estimate the amplitude and duration of a hypothetical triangular flow waveform in the pulmonary artery. This algorithm was tested against gold standard blood flow measurements in ten canines during acute perturbations to preload (inferior vena caval occlusion (IVCO), rapid saline infusion), afterload (descending aortic occlusion (DAO), serotonin, angiotensin II, sodium nitroprusside infusion), and cardiac contractility (dobutamine and propranolol infusion). The algorithm correctly predicted the changes in CO (r2 = 0.82) that varied from - 45 to 31% of the baseline levels. To explain this finding both the pulmonary arterial (PA) and the ascending aortic (AA) input impedances were modeled as three element windkessels. In the AA the peripheral resistance (from - 61 to 191%), characteristic impedance (from - 59 to 20%) and total arterial compliance (from - 49 to 34%) varied significantly with these perturbations. In contrast, these parameters in the PA changed little. In particular, except serotonin infusion, the characteristic impedance of the PA deviated only 6% (SD/mean) from baseline values. This suggests right ventricular pressure waveform based estimate of CO is possible during acute changes in left ventricular hemodynamics.

Algorithms↗

PubMed2006

Non-algorithmic access to calendar information in a calendar calculator with autism.

The possible use of a calendar algorithm was assessed in DBC, an autistic "savant" of normal measured intelligence. Testing of all the dates in a year revealed a random distribution of errors. Re-testing DBC on the same dates one year later shows that his errors were not stable across time. Finally, DBC was able to answer "reversed" questions that cannot be solved by a classical algorithm. These findings favor a non-algorithmic retrieval of calendar information. It is proposed that multidirectional, non-hierarchical retrieval of information, and solving problems in a non-algorithmic way, are involved in savant performances. The possible role of a functional rededication of low-level perceptual systems to the processing of symbolic information in savants is discussed.

Adolescent↗

PubMedDate not supplied

Genetic algorithms and self-organizing maps: a powerful combination for modeling complex QSAR and QSPR problems.

Modeling non-linear descriptor-target activity/property relationships with many dependent descriptors has been a long-standing challenge in the design of biologically active molecules. In an effort to address this problem, we couple the supervised self-organizing map with the genetic algorithm. Although self-organizing maps are non-linear and topology-preserving techniques that hold great potential for modeling and decoding relationships, the large number of descriptors in typical quantitative structure-activity relationship or quantitative structure-property relationship analysis may lead to spurious correlation(s) and/or difficulty in the interpretation of resulting models. To reduce the number of descriptors to a manageable size, we chose the genetic algorithm for descriptor selection because of its flexibility and efficiency in solving complex problems. Feasibility studies were conducted using six different datasets, of moderate-to-large size and moderate-to-great diversity; each with a different biological endpoint. Since favorable training set statistics do not necessarily indicate a highly predictive model, the quality of all models was confirmed by withholding a portion of each dataset for external validation. We also address the variability introduced onto modeling through dataset partitioning and through the stochastic nature of the combined genetic algorithm supervised self-organizing map method using the z-score and other tests. Experiments show that the combined method provides comparable accuracy to the supervised self-organizing map alone, but using significantly fewer descriptors in the models generated. We observed consistently better results than partial least squares models. We conclude that the combination of genetic algorithms with the supervised self-organizing map shows great potential as a quantitative structure-activity/property relationship modeling tool.

Algorithms↗

PubMed2006

Genetic algorithms as a tool for helix design--computational and experimental studies on prion protein helix 1.

Evolutionary computing is a general optimization mechanism successfully implemented for a variety of numeric problems in a variety of fields, including structural biology. We here present an evolutionary approach to optimize helix stability in peptides and proteins employing the AGADIR energy function for helix stability as scoring function. With the ability to apply masks determining positions, which are to remain constant or fixed to a certain class of amino acids, our algorithm is capable of developing stable helical scaffolds containing a wide variety of structural and functional amino acid patterns. The algorithm showed good convergence behaviour in all tested cases and can be parameterized in a wide variety of ways. We have applied our algorithm for the optimization of the stability of prion protein helix 1, a structural element of the prion protein which is thought to play a crucial role in the conformational transition from the cellular to the pathogenic form of the prion protein, and which therefore poses an interesting target for pharmacological as well as genetic engineering approaches to counter the as of yet uncurable prion diseases. NMR spectroscopic investigations of selected stabilizing and destabilizing mutations found by our algorithm could demonstrate its ability to create stabilized variants of secondary structure elements.

Algorithms↗

PubMed2005

Combining neural network and genetic algorithm for prediction of lung sounds.

Recognition of lung sounds is an important goal in pulmonary medicine. In this work, we present a study for neural networks-genetic algorithm approach intended to aid in lung sound classification. Lung sound was captured from the chest wall of The subjects with different pulmonary diseases and also from the healthy subjects. Sound intervals with duration of 15-20 s were sampled from subjects. From each interval, full breath cycles were selected. Of each selected breath cycle, a 256-point Fourier Power Spectrum Density (PSD) was calculated. Total of 129 data values calculated by the spectral analysis are selected by genetic algorithm and applied to neural network. Multilayer perceptron (MLP) neural network employing backpropagation training algorithm was used to predict the presence or absence of adventitious sounds (wheeze and crackle). We used genetic algorithms to search for optimal structure and training parameters of neural network for a better predicting of lung sounds. This application resulted in designing of optimum network structure and, hence reducing the processing load and time.

Algorithms↗

PubMed2006

An automatic algorithm for stationary segmentation of extracellular microelectrode recordings.

Extracellular microelectrode recordings (MER) often contain artifact from a variety of sources that confound traditional signal-processing techniques that require stationary signal segments. We designed an algorithm to locate the longest stationary segment of MER signals. In this paper we provide a description of the segmentation algorithm and its performance assessment. Simulation results demonstrate that the automatic segmentation algorithm we proposed is capable of accurately identifying the boundaries of the longest stationary segments in MER signals. In our simulation study the segmentation algorithm correctly identified the boundaries of the longest MER stationary segments in 99.5% of the cases.

Algorithms↗

PubMed2006

Unsupervised classification of ventricular extrasystoles using bounded clustering algorithms and morphology matching.

Ventricular extrasystoles (VE) are ectopic heartbeats involving irregularities in the heart rhythm. VEs arise in response to impulses generated in some part of the heart different from the sinoatrial node. These are caused by the premature discharge of a ventricular ectopic focus. VEs after myocardial infarction are associated with increased mortality. Screening of VEs is typically a manual and time consuming task that involves analysis of the heartbeat morphology, QRS duration, and variations of the RR intervals using long-term electrocardiograms. We describe a novel algorithm to perform automatic classification of VEs and report the results of our validation study. The proposed algorithm makes use of bounded clustering algorithms, morphology matching, and RR interval length to perform automatic VE classification without prior knowledge of the number of classes and heartbeat features. Additionally, the proposed algorithm does not need a training set.

Algorithms↗

PubMed2006

Algorithm for the classification of multi-modulating signals on the electrocardiogram.

This article discusses the algorithm to measure electrocardiogram (ECG) and respiration simultaneously and to have the diagnostic potentiality for sleep apnoea from ECG recordings. The algorithm is composed by the combination with the three particular scale transform of a(j)(t), u(j)(t), o(j)(a(j)) and the statistical Fourier transform (SFT). Time and magnitude scale transforms of a(j)(t), u(j)(t) change the source into the periodic signal and tau(j) = o(j)(a(j)) confines its harmonics into a few instantaneous components at tau(j) being a common instant on two scales between t and tau(j). As a result, the multi-modulating source is decomposed by the SFT and is reconstructed into ECG, respiration and the other signals by inverse transform. The algorithm is expected to get the partial ventilation and the heart rate variability from scale transforms among a(j)(t), a(j+1)(t) and u(j+1)(t) joining with each modulation. The algorithm has a high potentiality of the clinical checkup for the diagnosis of sleep apnoea from ECG recordings.

Algorithms↗

PubMed2003

Algorithm for the pharmacotherapy of anxiety disorders.

Since the introduction of distinct anxiety disorders in the Diagnostic and Statistical Manual of Mental Disorders, Third Edition, there has been a growing interest in these conditions, leading to a wealth of pharmacotherapy trials. Guidelines for the treatment of anxiety disorders have been developed on the basis of systematic reviews of the literature and expert consensus in areas where data are lacking. Algorithms provide scaffolding for integrating the data on pharmacotherapy and for pointing to gaps in current knowledge. Pharmacotherapy algorithms have the potential advantage of being concise, user-friendly, and evidence-based. However, such algorithms run the risk of oversimplifying complex clinical realities, and are only as good as the data on which they rest. In this article, an algorithm for the pharmacotherapy of anxiety disorders is presented.

Algorithms↗

PubMed2004

Management of infection associated with total hip arthroplasty according to a treatment algorithm.

BACKGROUND: An algorithm for the management of hip arthroplasty-associated infections was validated in a cohort study. PATIENTS: 60 patients with 63 episodes of total hip arthroplasty-associated infections observed from 1985 to 2001 were included. The treatment algorithm was based on the time of manifestation, pathogenesis, and condition of implant and soft tissue. Three treatment options were proposed, namely debridement with retention, one-stage and two-stage replacement. RESULTS: The median patients' age was 72 years, the median follow-up 28 months; 29% were early, 41% delayed, and 30% late infections, 57% of the infections were exogenously and 43% hematogenously acquired. The overall success rate for the first treatment attempt was 83% (52/63). Patients treated according to the algorithm had a better outcome than the others (44/50 = 88% vs 8/13 = 62%, Relative risk (RR) 0.31, 95% confidence interval (CI): 0.11-0.86, p < 0.03); those treated with adequate antimicrobial therapy had a better success rate (87% vs. 50%, p < 0.01). CONCLUSION: The proposed algorithm defines a rational surgical/antibiotic treatment strategy.

Aged↗

PubMed1993

An algorithm for measurement of expiratory flow rate parameters on the partial expiratory flow-volume curve.

Partial expiratory flow-volume (PEFV) curves are a useful tool in airway challenge studies, but unlike the maximal expiratory flow-volume (MEFV) curve, lung function parameters require manual calculation from the flow-volume tracing. We describe an algorithm written in QuickBASIC that analyzes a PEFV curve superimposed on a MEFV curve by (1) identifying the PEFV curve, (2) locating the maximal expiratory flow at the point on the PEFV curve that corresponds to 60% of the baseline forced vital capacity (FVC) below total lung capacity (TLC), termed MEF40%(P), and (3) identifying the size of the PEFV curve along the TLC axis. A report of these parameters is also provided. This algorithm was validated using flow-volume curves from a clinical study in which eight subjects performed two sets of MEFV and PEFV curves separated by approximately 1 hr. Paired comparison of MEF40%(P) determined by the algorithm and two independent manual calculations correlated strongly and yielded no statistically significant differences between the two methods. We conclude that this algorithm provides rapid and accurate determinations of PEFV parameters.

Algorithms↗

PubMed1994

Comparative assessment of some algorithms for differentiating noisy biomechanical data.

In this paper, a comparison is carried out between various algorithms for smoothing and differentiating noisy (non-exact) discrete time series, a problem frequently encountered in experimental movement studies. The algorithms compared are: the 'implicit procedure' of Anderssen and Bloomfield (Numer Math, 22 (1974) 157-182) with a refinement by Kosarev and Pantos (J Phys E Sci Instrum, 16 (1983) 537-543); the 'explicit procedure', a digital filter method in which the filter coefficients are computed starting from the measurements; the regularized Fourier series method (these two algorithms were also presented by Anderssen and Bloomfield); and, finally, natural B-splines regularized by the generalized cross-validation criterion. The comparison was performed mainly by analytical, noise-corrupted test sequences, whose derivatives were known a priori. The testing procedure proved capable of showing the different characteristics of the various algorithms, and providing criteria to choose that best suited to a given practical situation. In most cases, the regularized Fourier series method and the implicit procedure proved to have the best tradeoff between accuracy and speed.

Algorithms↗

PubMed1996

Comparison of different neural network algorithms in the diagnosis of acute appendicitis.

Four different neural network algorithms, binary adaptive resonance theory (ART1), self-organizing map, learning vector quantization and back-propagation, were compared in the diagnosis of acute appendicitis with different parameter groups. The results show that supervised learning algorithms learning vector quantization and back-propagation were better than unsupervised algorithms in this medical decision making problem. The best results were obtained with the learning vector quantization. The self-organizing map algorithm showed good specificity, but this was in conjunction with lower sensitivity. The best parameter group was found to be the clinical signs. It seems beneficial to design a decision support system which uses these methods in the decision making process.

Adolescent↗

PubMed1992

Validation of an adaptive software trigger and arrhythmia diagnostic algorithm.

The authors have developed an algorithm for the identification of arrhythmias using intracardiac atrial and ventricular leads. The algorithm is based on the rate of the depolarizations and a measure of the organization of electrical activity in each of the cardiac chambers. The most important requirement of the algorithm is to identify the occurrence of each cardiac event correctly. A robust amplitude-adaptive software trigger is developed, which accurately detects depolarizations in both chambers. With this reliable trigger the authors demonstrate the veracity of the arrhythmia identification algorithm.

Algorithms↗

PubMed1992

Comparison of logistic regression and Bayesian-based algorithms to estimate posttest probability in patients with suspected coronary artery disease undergoing exercise ECG.

Two multivariate methods, a logistic regression-derived algorithm and a Bayesian independence-assuming method (CADENZA), were compared concerning their abilities to estimate posttest probability of coronary disease in patients with suspected coronary disease. All patients underwent exercise testing within 3 months prior to coronary angiography. Coronary disease was defined as the presence of one or more vessels with greater than or equal to 50% luminal diameter narrowing. A group of 300 patients (disease prevalence = 37%) was used to derive the algorithm. Another group of 950 patients was used to validate the algorithm and compare it to CADENZA. Seven variables (age, sex, symptoms, diabetes, mm ST depression, ST slope, and peak heart rate) were used to generate posttest probabilities for each method. The receiver operating characteristic curve area for the logistic regression method (0.81 +/- 0.01) was significantly higher than CADENZA (0.75 +/- 0.01; p less than 0.05). There was, however, no difference in the calibration of the two methods. When given equivalent variable information, the logistic regression algorithm had better discrimination than CADENZA for estimating the probability of coronary disease following exercise electrocardiography.

Algorithms↗

PubMed1990

Speeding up the dynamic algorithm for planar RNA folding.

The simplest dynamic algorithm for planar RNA folding searches for the maximum number of base pairs. The algorithm uses O(n3) steps. The more general case, where different weights (energies) are assigned to stacked base pairs and to the various types of single-stranded region topologies, requires a considerably longer computation time because of the partial backtracking involved. Limiting the loop size reduces the running time back to O(n3). Reduction in the number of steps in the calculations of the various RNA topologies has recently been suggested, thereby improving the time behavior. Here we show how a "jumping" procedure can be used to speed up the computation, not only for the maximal number of base pairs algorithm, but for the minimal energy algorithm as well.

Algorithms↗