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At least 181 records · Page 10Linked to original sources

PubMed1995

Noninvasive blood pressure monitoring from the supraorbital artery using an artificial neural network oscillometric algorithm.

OBJECTIVE: Our objective was to overcome the limitations of linear models of oscillometric blood pressure determination by using a nonlinear technique to model the relationship between the oscillometric envelope and systolic and diastolic blood pressures, and then to use that technique for near-continuous arterial pressure monitoring at the supraorbital artery. METHODS: An adhesive pressure pad and transducer were used to collect oscillometric data from the supraorbital artery of 85 subjects. These data were then used to train an artificial neural network (ANN) to report diastolic or systolic pressure. Arterial pressure measurements defined by brachial artery auscultation were used as a reference. ANN results were compared with those obtained using a standard oscillometric algorithm that determined pressures based on fixed percentages of the maximum oscillometric amplitude. RESULTS: The ANN produced better estimates of reference blood pressures than the standard oscillometric algorithm. Mean difference between target and actual output for the ANN was 0.50 +/- 5.73 mm Hg for systolic pressures, compared to the mean difference of the standard algorithm of 2.78 +/- 19.38 mm Hg. For diastolic pressures, the ANN had a mean difference of 0.04 +/- 4.70 mm Hg, while the mean difference of the standard algorithm was -0.34 +/- 9.75 mm Hg. CONCLUSIONS: The ANN produced a better model of the relationship between the oscillometric envelope and reference systolic and diastolic pressures than did the standard oscillometric algorithm. Noninvasive blood pressure measured from the supraorbital artery agreed with pressure measured by auscultation in the brachial artery, and may sometimes be more clinically useful than an arm cuff device.

Adult↗

PubMed1989

Prototype ventilator and alarm algorithm for the NASA space station.

An alarm algorithm was developed to monitor the ventilator on the National Aeronautics and Space Administration space station. The algorithm automatically identifies and interprets critical events so that an untrained user can manage the mechanical ventilation of a critically injured crew member. The algorithm was tested in two healthy volunteers by simulating 260 critical events in each volunteer while the volunteer breathed via the ventilator. Thirteen critical events were induced eight times in random order, for the five different modes of ventilation. These events included various ventilator tubing disconnects, leaks, and occlusions, as well as power and gas supply failures. The algorithm identified the critical events and generated alarms in response to 99.2% (516 of 520, total) of the events. The alarm textual messages were correct 98% (505 of 516 messages) of the time. The alarm algorithm is an improvement over current alarms found on most ventilators because its alarm messages specifically identify failures in the patient breathing circuit or ventilator. The system may improve patient care by helping critical care personnel respond more rapidly and correctly to critical events.

Algorithms↗

PubMed1994

Spectra of data sampled at frequency-modulated rates in application to cardiovascular signals: Part 2. Evaluation of Fourier transform algorithms.

For three direct Fourier transform algorithms we quantified the influence of pulse frequency modulation (PFM) on the spectral estimation of pulse amplitude modulation (PAM). The simulation study is based on sinusoid functions sampled according to a pulse sequence which is the output of an integral pulse frequency modulator (IPFM). One algorithm exactly reproduces the theoretical spectrum derived in Part 1. The other two, including the classical FFT, scale all PFM-induced components in a different way, and in addition, generate higher modulating frequency harmonics. For a PFM depth below 30%, the sum of spurious PFM components is almost linearly dependent on this modulation depth, for all three algorithms. Dividing the effect of PFM in a 'harmonic' and 'aliasing' distortion, we found that the FFT has a relatively high harmonic distortion, compared to an algorithm that takes into account the non-uniform character of the data. In the cardiovascular (worst) case of 30% modulation in heart rate (PFM) at a frequency of 0.1 Hz, the FFT spectrum of beat-to-beat systolic blood pressure variations contains approximately 20% of spurious components caused solely by the modulation in time occurrences of the blood pressure samples. The 'non-uniform' algorithm performs twice as well in this case.

Algorithms↗

PubMed1994

The geriatric medication algorithm: a pilot study.

A geriatric medication algorithm designed to reduce inappropriate prescribing was tested in a resident outpatient clinic. The medications of patients over 65 years old taking more than three medications (n = 41) were compared pre- and post-algorithm using the paired t-test. Pre-algorithm, the average number of drugs was 5.8 per patient (SD 1.62). Fifteen medications (6.4%) were discontinued, seven were substituted for a less toxic medication, and five were added. Post-algorithm, the average number of drugs was 5.6 (SD 1.69), mean difference 0.3 (SD 0.67), p < 0.025. Drugs discontinued were more likely to be high risk compared with drugs used at baseline; drugs added were less likely to be high risk. In this pilot study, the authors conclude that the algorithm helps resident physicians reduce inappropriate prescribing.

Aged↗

PubMedDate not supplied

The early diagnosis of acute myocardial infarction. Comparison of a simple algorithm with a computer program for electrocardiogram interpretation.

The sensitivity and specificity of electrocardiographic (ECG) interpretation by a simple algorithm was compared with a computer read ECG machine. Clinical data and ECG findings on 264 consecutive patients admitted to a coronary care unit with suspected acute myocardial infarction were prospectively entered into an algorithm with 13 end-points. These end-points were compared with the interpretations of a computer read ECG machine (Marquette MAC PC). 86 patients (32.5%) had confirmed acute infarction. 85% of those with infarction had some form of ST elevation on their initial ECG. Patients with ST elevation presented earlier (4.9 +/- 4.9 versus 8.0 +/- 9.7 hours after symptom onset, p < 0.001), and were older (66.5 +/- 11.0 versus 62.0 +/- 12.5 years, p < 0.01) than those without infarction. According to the algorithm 94.2% of patients with infarction had some form of ECG abnormality, compared with 55.6% of those without infarction (p < 0.001). The area under the receiver operating characteristic (ROC) curve of the algorithm was 92.3% of the area of the graph. This was more (p < 0.01) than the area under the ROC curve of the interpretations of the computer read ECG machine (83.9%). Marked ST elevation with reciprocal changes was the most specific indicators of infarction (Likelihood ratio 51.7). The algorithm, therefore, was comparatively sensitive and specific in the early diagnosis of acute infarction.

Adult↗

PubMed1986

Parallel algorithms for the analysis of two-dimensional electrophoresis gels.

This paper describes some parallel processing algorithms for the analysis of two-dimensional electrophoresis images. The machine used for the processing was the CLIP4 Cellular Array Computer at University College, London, one of the largest processor arrays in the world. Included in this paper are an algorithm for centroid detection, Gaussian fitting algorithms, and an algorithm for the extraction of data out of the cellular array machine. It is shown that these parallel algorithms can run at a speed almost completely independent of the number of spots in the gel images.

Algorithms↗

PubMed1995

Neuromagnetic source imaging with FOCUSS: a recursive weighted minimum norm algorithm.

The paper describes a new algorithm for tomographic source reconstruction in neural electromagnetic inverse problems. Termed FOCUSS (FOCal Underdetermined System Solution), this algorithm combines the desired features of the two major approaches to electromagnetic inverse procedures. Like multiple current dipole modeling methods, FOCUSS produces high resolution solutions appropriate for the highly localized sources often encountered in electromagnetic imaging. Like linear estimation methods, FOCUSS allows current sources to assume arbitrary shapes and it preserves the generality and ease of application characteristic of this group of methods. It stands apart from standard signal processing techniques because, as an initialization-dependent algorithm, it accommodates the non-unique set of feasible solutions that arise from the neuroelectric source constraints. FOCUSS is based on recursive, weighted norm minimization. The consequence of the repeated weighting procedure is, in effect, to concentrate the solution in the minimal active regions that are essential for accurately reproducing the measurements. The FOCUSS algorithm is introduced and its properties are illustrated in the context of a number of simulations, first using exact measurements in 2- and 3-D problems, and then in the presence of noise and modeling errors. The results suggest that FOCUSS is a powerful algorithm with considerable utility for tomographic current estimation.

Algorithms↗

PubMed1995

An expectation maximization reconstruction algorithm for emission tomography with non-uniform entropy prior.

A Bayesian image reconstruction algorithm is proposed for emission tomography. It incorporates the Poisson nature of the noise in the projection data and uses a non-uniform entropy as an a priori probability distribution of the image in a maximum a posteriori (MAP) approach. The expectation maximization (EM) method was applied to find the MAP estimator. The Newton-Raphson numerical method whose convergence and positive solutions are proven, was used to solve the EM problem. The prior mean at iteration k was determined by smoothing the image obtained at iteration k-1. Comparisons between the ML and the MAP algorithm were carried out with a numerical phantom that contains a narrow valley region. The ML solution after 50 iterations was chosen as the initial solution for the MAP algorithm, since the global performance of the ML algorithm deteriorates with increasing number of iterations while its local performance in the valley region is always improving. The resulting algorithm is a compromise between ML who has the best local performance in the valley region and the MAP who has the best global performance.

Algorithms↗

PubMed1996

Algorithms for robust nonlinear regression with heteroscedastic errors.

Nonlinear regression algorithms were compared by Monte-Carlo simulations when the measurement error was dependent on the measured values (heteroscedasticity) and possibly contaminated with outliers. The tested leastsquares (LSQ) algorithms either required user-supplied weights to accommodate heteroscedasticity or the weights were estimated within the procedures. Robust versions of the LSQ algorithms, namely robust iteratively reweighted (IRR) and least absolute value (LAV) regressions, were also considered. The comparisons were based on the efficiency of the estimated parameters and their resistance to outliers. Based on these criteria, among the tested LSQ algorithms, extended least squares (ELSQ) was found to be the most reliable. The IRR versions of these algorithms were slightly more efficient than the LAV versions when there were no outliers but they provided weaker protection to outliers than the LAV variants.

Algorithms↗

PubMed1992

Mathematical characterization of Chaos Game Representation. New algorithms for nucleotide sequence analysis.

Chaos Game Representation (CGR) can recognize patterns in the nucleotide sequences, obtained from databases, of a class of genes using the techniques of fractal structures and by considering DNA sequences as strings composed of four units, G, A, T and C. Such recognition of patterns relies only on visual identification and no mathematical characterization of CGR is known. The present report describes two algorithms that can predict the presence or absence of a stretch of nucleotides in any gene family. The first algorithm can be used to generate DNA sequences represented by any point in the CGR. The second algorithm can simulate known CGR patterns for different gene families by setting the probabilities of occurrence of different di- or trinucleotides by a trial and error process using some guidelines and approximate rules-of-thumb. The validity of the second algorithm has been tested by simulating sequences that can mimic the CGRs of vertebrate non-oncogenes, proto-oncogenes and oncogenes. These algorithms can provide a mathematical basis of the CGR patterns obtained using nucleotide sequences from databases.

Algorithms↗

PubMed1995

A simulation algorithm for ultrasound liver backscattered signals.

In this study, we present a simulation algorithm for the backscattered ultrasound signal from liver tissue. The algorithm simulates backscattered signals from normal liver and three different liver abnormalities. The performance of the algorithm has been tested by statistically comparing the simulated signals with corresponding signals obtained from a previous in vivo study. To verify that the simulated signals can be classified correctly we have applied a classification technique based on an artificial neural network. The acoustic features extracted from the spectrum over a 2.5 MHz bandwidth are the attenuation coefficient and the change of speed of sound with frequency (dispersion). Our results show that the algorithm performs satisfactorily. Further testing of the algorithm is conducted by the use of a data acquisition and analysis system designed by the authors, where several simulated signals are stored in memory chips and classified according to their abnormalities.

Acoustics↗

PubMed1992

Dynamic programming algorithms for biological sequence comparison.

Efficient dynamic programming algorithms are available for a broad class of protein and DNA sequence comparison problems. These algorithms require computer time proportional to the product of the lengths of the two sequences being compared [O(N2)] but require memory space proportional only to the sum of these lengths [O(N)]. Although the requirement for O(N2) time limits use of the algorithms to the largest computers when searching protein and DNA sequence databases, many other applications of these algorithms, such as calculation of distances for evolutionary trees and comparison of a new sequence to a library of sequence profiles, are well within the capabilities of desktop computers. In particular, the results of library searches with rapid searching programs, such as FASTA or BLAST, should be confirmed by performing a rigorous optimal alignment. Whereas rapid methods do not overlook significant sequence similarities, FASTA limits the number of gaps that can be inserted into an alignment, so that a rigorous alignment may extend the alignment substantially in some cases. BLAST does not allow gaps in the local regions that it reports; a calculation that allows gaps is very likely to extend the alignment substantially. Although a Monte Carlo evaluation of the statistical significance of a similarity score with a rigorous algorithm is much slower than the heuristic approach used by the RDF2 program, the dynamic programming approach should take less than 1 hr on a 386-based PC or desktop Unix workstation. For descriptive purposes, we have limited our discussion to methods for calculating similarity scores and distances that use gap penalties of the form g = rk. Nevertheless, programs for the more general case (g = q+rk) are readily available. Versions of these programs that run either on Unix workstations, IBM-PC class computers, or the Macintosh can be obtained from either of the authors.

Algorithms↗

PubMed1990

Hypermedia and randomized algorithms for medical expert systems.

KNET is an environment for constructing probabilistic, knowledge-intensive systems within the axiomatic framework of decision theory. The KNET architecture defines a complete separation between the hypermedia user interface on the one hand, and the representation and management of expert opinion on the other. KNET offers a choice of algorithms for probabilistic inference. We and our coworkers have used KNET to build consultation systems for lymph-node pathology, bone-marrow transplantation therapy, clinical epidemiology, and alarm management in the intensive-care unit. Most important, KNET contains a randomized approximation scheme (RAS) for the difficult and almost certainly intractable problem of Bayesian inference. Our algorithm can, in many circumstances, perform efficient approximate inference in large and richly interconnected models of medical diagnosis. In this article, we describe the architecture of KNET, construct a randomized algorithm for probabilistic inference, and analyze the algorithm's performance. Finally, we characterize our algorithms' empiric behavior and explore its potential for parallel speedups. From design to implementation, then, KNET demonstrates the crucial interaction between theoretical computer science and medical informatics.

Algorithms↗

PubMed1992

BIO-SPEAD: a parallel computing environment to accelerate development of biologic signal processing algorithms.

We have created BIO-SPEAD (pronounced speed), a BIOlogical Signal Processing Environment for Algorithm Development. BIO-SPEAD is designed to accelerate development of complex algorithms which integrate information derived from single or multiple physiologic waveforms. BIO-SPEAD currently performs all of the basic analyses of several arterial blood pressure waveforms, and allows the user to utilize the results of those low-level analyses for development of more complex algorithms. We utilized a parallel programming architecture called the Process Trellis which keeps the different tasks, or processes, within BIO-SPEAD independent of each other. Additionally, we have developed a graphics interface to enable the user to visualize the waveform under analysis, the low-level system analysis, and the internal workings of the algorithm under development. The system has been used for several algorithm development projects and has demonstrated its utility.

Algorithms↗

PubMed1992

Development and validation of a logistic regression-derived algorithm for estimating the incremental probability of coronary artery disease before and after exercise testing.

OBJECTIVES: Our goals were to develop and validate a multivariate algorithm for estimating the incremental probability of the presence of coronary artery disease. BACKGROUND: Multivariate methods, including logistic regression analysis, have been extensively applied to diagnostic exercise testing. However, few previous studies have included both an incremental design and external validation. METHODS: A retrospective collection of clinical, exercise test and catheterization data was performed involving four U.S. referral medical centers. All patients had no prior history of coronary disease and had undergone coronary angiography < or = 3 months after exercise stress testing. An algorithm was developed in one center (590 patients with a 41% prevalence of coronary artery disease) with the use of logistic regression analysis and was validated in the other three centers (1,234 patients, 70% prevalence). The algorithm incorporated pretest variables (age, gender, symptoms, diabetes, cholesterol), exercise electrocardiographic (ECG) variables (mm of ST segment depression, ST slope, peak heart rate, metabolic equivalents [METs], exercise angina) and one thallium variable. Discrimination was measured with receiver operating characteristic curve analysis. Calibration (that is, reliability) was assessed from a comparison of probability estimates and the actual prevalence of disease. RESULTS: The overall incremental receiver operating characteristic curve areas for the validation group were pretest, -0.738 +/- 0.016; postexercise ECG, 0.78 (SE 0.017); and postthallium, 0.82 (SE 0.016); p < 0.01 for both increments. Within the three validation institutions, the institution with a disease prevalence closest to that of the derivation institution had the best incremental receiver operating characteristic curve areas. There was a stepwise incremental improvement in calibration especially from exercise ECG to thallium testing. CONCLUSIONS: An incremental multivariate algorithm derived in one center reliably estimated disease probability in patients from three other centers. The incremental value of testing was best demonstrated when the derivation and validation groups had a similar disease prevalence. This algorithm may be useful in decision making that relates to the diagnosis of coronary disease.

Algorithms↗

PubMed1992

Slope filtered pointwise correlation dimension algorithm and its evaluation with prefibrillation heart rate data.

Various studies have shown that a low variability in heart rate is associated with increased risk of ventricular fibrillation. Low chaotic (correlation) dimension in the heart rate also appears to predict fibrillation risk. However, these results have been based on intergroup comparisons and have not been found useful for predicting when a patient may fibrillate with any degree of sensitivity, specificity, or temporal accuracy. There are two primary limitations in using dimensional analysis to predict imminent fibrillation. The first is that the standard algorithms (for correlation dimension) assume stationarity of the system. The second limitation is that these algorithms require 10,000-50,000 data points to achieve good accuracy. Thus, even if stationarity were not an issue, there would be a lag of 2.4-12 hours to warn of impending fibrillation. An algorithm has been developed to calculate an accurate pointwise correlation dimension of heart rate data. The slope filtered pointwise correlation dimension algorithm requires as few as 1,000 points of data. Using this algorithm, it was found that the correlation dimension dropped from 2.50 +/- 0.81 to 1.07 +/- 0.18 in the minute before fibrillation in conscious pigs with an occluded coronary artery. In clinical studies, Holter tapes from patients that had suffered fatal fibrillation were also analyzed along with healthy controls and nonfibrillation ventricular patients. The fibrillation patients all had excursions of low dimension (less than 1.5), while the majority of the others did not. In the minutes before fibrillation, the correlation dimension dropped to a steady range of 0.8-1.3. Drops in the slope filtered pointwise correlation dimension appear to predict fibrillation in animals and patients.

Algorithms↗

PubMed1997

A new electrocardiographic algorithm using retrograde P waves for differentiating atrioventricular node reentrant tachycardia from atrioventricular reciprocating tachycardia mediated by concealed accessory pathway.

OBJECTIVES: The purpose of this study was to use an electrocardiographic (ECG) algorithm, derived from the results of radiofrequency ablation, to discriminate atrioventricular node reentrant tachycardia (AVNRT) from atrioventricular reciprocating tachycardia (AVRT) and to localize a concealed accessory pathway, prospectively. BACKGROUND: Information about ECG criteria for differentiating AVNRT from AVRT is limited and has not been confirmed by surgical or catheter ablation. METHODS: Four hundred six ECGs (obtained from 406 different patients) that demonstrated narrow QRS complex (< 0.12 s) supraventricular tachycardia with an RP' interval less than the P'R interval or pseudo r' wave in lead V1 or pseudo S wave in inferior leads, or both, were examined, and the results were confirmed by radiofrequency catheter ablation. The initial 226 ECGs were analyzed to develop a stepwise algorithm, and the subsequent 180 ECGs were prospectively evaluated by the new algorithm. RESULTS: The presence of a pseudo r' wave in lead V1 or a pseudo S wave in leads II, III, aVF indicated anterior-type AVNRT with an accuracy of 100%. With the difference of RP' intervals in leads V1 and III > 20 ms, posterior-type AVNRT could be differentiated from AVRT utilizing a posteroseptal pathway with a sensitivity of 71% (95% confidence interval [CI] 55% to 89%), a specificity of 87% (95% CI 67% to 97%) and a positive predictive value of 75% (95% CI 56% to 91%). According to the polarity of retrograde P waves in leads V1, II, III, aVF and I during AVRT, the concealed accessory pathway could be localized to one of the nine regions on the atrioventricular annuli with an accuracy of 75% (for a right midseptal pathway) to 93.8% (for a left posterior pathway). Overall, the new algorithm had an accuracy of 97.8% in discriminating AVNRT from AVRT and 88.1% in localizing a concealed accessory pathway, prospectively. Prediction was incorrect in only 15 patients (9.1%). CONCLUSIONS: The new ECG algorithm derived from the analysis of retrograde P waves during tachycardia could provide a criterion for differential diagnosis between AVNRT and AVRT and for predicting the location of concealed accessory pathways.

Adolescent↗

PubMedDate not supplied

A second-generation computer-based edge detection algorithm for short-axis, two-dimensional echocardiographic images: accuracy and improvement in interobserver variability.

The present study tested the hypothesis that a second-generation endocardial edge detection algorithm that used a priori endocardial and epicardial information would improve accuracy and reduce the variability of border definition. Five nonexpert observers utilized the version 2 algorithm on 20 cycles of two-dimensional short-axis images (five excellent, seven good, and eight poor quality studies stored digitally from a previously reported project). Manually defined areas by five recognized experts on these 20 cardiac cycles were considered to be "true areas." Areas defined by the experts with version 1 of the algorithm were also used for comparison. Regression of the version 2 areas with mean, manually defined excellent quality areas yielded a similar correlation (r = 0.985) to that reported between the manual and the version 1 areas (r = 0.986). For all 20 cycles in the series, however, the correlation between version 2 and the manually defined areas was lower (r = 0.952) than that of the same correlation with version 1 areas (r = 0.980). For all studies the interobserver variability (percent area difference) was +/- 14.4% for manually defined borders, +/- 11.1% for version 1-defined borders, and +/- 7.7% for version 2-defined borders. No difference in variability was observed for excellent quality studies (+/- 5.3% versus 5.2%) between version 1 and version 2 areas. However, the version 2 algorithm significantly reduced interobserver variability for good and poor quality studies (+/- 8.4% to 7.6%, p less than 0.025, and 16.3% to 9.1%, p less than 0.05, respectively). We concluded that: the version 2 algorithm provided accuracy and significantly reduced the variability of area measurement in good and poor quality studies and that epicardial information was important to the improvement by providing wall thickness information to assist in filling areas of dropout and avoidance of intracavitary structures.

Algorithms↗