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Results for “Pattern Recognition, Automated”

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At least 109 records · Page 6Linked to original sources
PubMed1979

The preparation of cervical scrape material for automated cytology using gallocyanin chrome-alum stain.

A method is described for preparing cervical scrape specimens for automated analysis on the Cerviscan prescreening system. In order to reduce the cellular clumping found in cervical scrape material, cells are collected in suspension, syringed to disaggregate the cell clumps, and then pipetted onto a glass to give a monolayer of cells. The cells are then stained with gallocyanin chrome-alum to give the required quantitation of nucleic acid content, using a rapid staining procedure. Experimental results are given which show that specimens prepared by this method are more suitable for automated analysis than the conventional Papanicolaou stained preparation.

Cell Separation↗
PubMed1995

Clinical evaluation of a method for automatic detection and removal of artifacts in auditory evoked potential monitoring.

OBJECTIVE: The objective of our study was to evaluate the method for detection and removal of artifacts in evoked potential monitoring described earlier by Cluitmans and colleagues in a clinical setting. METHODS: The method for detection and removal of artifacts by Cluitmans and colleagues is based on the assumption that a sweep of the recorded electroencephalogram (EEG) signal contains artifacts if one or more variables derived from the signal deviates strongly from the normal range of values. Once these normal ranges are defined, all future EEG recordings that are recorded under comparable circumstances can be automatically evaluated for artifacts by tracking when one or more signal variables falls outside the normal range. To assess the performance of this method in a clinical setting, recordings from a learning set were visually evaluated for artifacts. From the empirical distribution functions of the signal variables, the thresholds for automatic detection of artifacts were determined. The auditory evoked potential (AEP) waveforms resulting after automatic screening were compared with the waveforms obtained after visual evaluation of the raw signal combined with manual exclusion of signal periods containing artifacts. RESULTS: The quality of the resulting waveform was improved by our method of automatic detection and removal of artifacts in 97% of partly contaminated recordings. In only 2% of the recordings, automatic screening slightly degraded the resulting waveform. CONCLUSIONS: We conclude that the described method of automatic detection and removal of artifacts in AEP recordings effectively improves the quality of the resulting AEP waveform, without excessive rejection of artifact-free signal periods. The signal variables used in this method seem appropriate for distinguishing artifact-free signal periods from periods containing artifacts for the types of artifact that were studied.

Algorithms↗
PubMed1988

Automated sequence reading and analysis.

We report on a system developed by Bio-Rad Laboratories, Inc. which combines automated reading of DNA sequencing autoradiograms with comprehensive software for shotgun overlapping of the readings, analysis of the sequences derived and searching of databases. Reading is accomplished using a high speed optical scanner and pattern recognition software operating on a personal computer. Overlapping, analysis and database searching software each incorporate significant advances over prior systems.

Algorithms↗
PubMed1995

Automatic analysis of hand radiographs for the assessment of skeletal age: a subsymbolic approach.

The assessment of skeletal maturity is crucial for the analysis of growth disorders and plays an important role in paediatrics. For this reason, several methods have been developed for estimating skeletal maturity. Among them, the Tanner and Whitehouse method (TW2), which is based on the analysis of hand radiographs, is usually considered the most accurate and reliable. Nevertheless, TW2 is applied only in a small fraction of cases, due to its complexity and long examination times. Thus, the development of automated systems which reliably implement this method is highly desirable. However, major difficulties have been found in the development of computer-based systems for the assessment of skeletal maturity. In particular the extraction of the bones of interest has proved to be extremely challenging. In this paper, we propose a system architecture for the implementation of the TW2 method, which is based on artificial neural networks. For each bone considered, the maturation stage is determined by means of a two-step process which first locates the position of the bone in the radiograph and then analyzes the bone shape. Experimental results obtained with our implementation of the carpal version of TW2 are in good agreement with those provided by trained observers.

Adolescent↗
PubMed1995

Semi-automatic external defibrillation and implanted cardiac pacemakers: understanding the interactions during resuscitation.

Many emergency medical service (EMS) systems are currently implementing semi-automatic external defibrillation (AED) by emergency medical technicians. Surprisingly little information is available on the possible interactions between AEDs and implanted cardiac pacemakers. Therefore, at present there are no clear guidelines for the use of AEDs on patients having a cardiac pacemaker. During resuscitation, multiple interactions between pacemakers and AEDs are possible. External defibrillation can cause damage to several functions of the pacemaker. On the other hand, the presence of pacemaker spikes during cardiac arrest might prohibit recognition of the ventricular fibrillation by the AED. We report on two resuscitation attempts in which the interaction between the ventricular fibrillation, an implanted dual chamber pacemaker and the AED was decisive for the defibrillation success. A clear understanding of these possible interactions is necessary for the further refining of diagnostic algorithms and clinical strategies of prehospital defibrillation.

Adult↗
PubMed1997

Use of global symmetries in automated signal class recognition by a bayesian method

Automated or semiautomated pattern recognition in multidimensional NMR spectroscopy is strongly hampered by the large number of noise and artifact peaks occurring under practical conditions. A general Bayesian method which is able to assign probabilities that observed peaks are members of given signal classes (e.g., the class of true resonance peaks or the class of noise and artifact peaks) was proposed previously. The discriminative power of this approach is dependent on the choice of the properties characterizing the peaks. The automated class recognition is improved by the addition of a nonlocal feature, the similarities of peak shapes in symmetry-related positions. It turns out that this additional property strongly decreases the overlap of the multivariate probability distributions for true signals and noise and hence largely increases the discrimination of true resonance peaks from noise and artifacts. Copyright 1997 Academic Press. Copyright 1997Academic Press

Journal Article↗
PubMed1987

ECG waveform analysis by significant point extraction. II. Pattern matching.

From a set of significant points which characterizes the ECG waveform, the pattern matching algorithm detects and classifies QRS complexes. R waves are detected from the analysis of global curvature. Next, the morphology of the QRS complex is determined. QRS complexes with different morphologies are classified by a correlation algorithm. This method is sensitive to changes in shape, such as that of abnormal QRS complexes. The algorithm should be useful in automated analysis of waveforms, such as ECG signals recorded in clinical environments.

Algorithms↗
PubMed2000

Autoassociative MLP in sleep spindle detection.

Spindles are one of the most important short-lasting waveforms in sleep EEG. They are the hallmarks of the so-called Stage 2 sleep. Visual spindle scoring is a tedious workload, since there are often a thousand spindles in one all-night recording of some 8 hr. Automated methods for spindle detection typically use some form of fixed spindle amplitude threshold, which is poor with respect to inter-subject variability. In this work a spindle detection system allowing spindle detection without an amplitude threshold was developed. This system can be used for automatic decision making of whether or not a sleep spindle is present in the EEG at a certain point of time. An Autoassociative Multilayer Perceptron (A-MLP) network was employed for the decision making. A novel training procedure was developed to remove inconsistencies from the training data, which was found to improve the system performance significantly.

Adult↗