Biomedical subjects

Ursula Gather

Publications and source records attributed to Ursula Gather.

5 recordsLinked to original sources

PubMed2003

Dimension reduction for physiological variables using graphical modeling.

In intensive care, physiological variables of the critically ill are measured and recorded in short time intervals. The proper extraction and interpretation of the essential information contained in this flood of data can hardly be done by experience alone. Typically, decision making in intensive care is based on only a few selected variables. Alternatively, for a dimension reduction statistical latent variable techniques like principal component analysis or factor analysis can be applied. However, the interpretation of latent components extracted by these methods may be difficult. A more refined analysis is needed to provide suitable bedside decision support. Graphical models based on partial correlations provide information on the relationships among physiological variables that is helpful for variable selection and for identifying interpretable latent components. In a comparative study we investigate how much of the variability of the observed multivariate physiological time series can be explained by variable selection, by standard principal component analysis and by extracting latent compo-nents from groups of variables identified in a graphical model.

Aged↗

PubMed2003

Extraction of a local linear trend from physiological time series.

We discuss methods for robust signal extraction from noisy physiological time series as measured in intensive care. The aim is a method which allows a fast and reliable de-noising of the data and separation of artifacts from relevant changes in the patients condition. For approximating local linear trends we use robust regression estimators. We examine the performance of the L1 regression, the repeated median and the least median of squares for this task.

Hospital Information Systems↗

PubMed2002

Graphical models for multivariate time series from intensive care monitoring.

Nowadays physicians are confronted with high-dimensional data generated by clinical information systems. The proper extraction and interpretation of the information contained in such massive data sets, which are often observed with high sampling frequencies, can hardly be done by experience only. This yields new perspectives of data recording and also sets a new challenge for statistical methodology. Recently graphical models have been developed for analysing the partial correlations between the components of multivariate time series. We apply this technique to the haemodynamic system of critically ill patients monitored in intensive care. In this way we can appraise the practical value of the new procedure by re-identifying known associations within the haemodynamic system. From separate analyses for different pathophysiological states we can even conclude that distinct clinical states are characterized by distinct partial correlation structures. Hence, this technique seems useful for automatic statistical analysis of high-dimensional physiological time series and it can provide new insights into physiological mechanisms. Moreover, we can use it to achieve an adequate dimension reduction of the variables needed for online monitoring at the bedside.

Blood Pressure↗

PubMed2002

Functional cerebral asymmetries during the menstrual cycle: a cross-sectional and longitudinal analysis.

This study aims at answering two basic questions regarding the mechanisms with which hormones modulate functional cerebral asymmetries. Which steroids or gonadotropins fluctuating during the menstrual cycle affect perceptual asymmetries? Can these effects be demonstrated in a cross-sectional (follicular and midluteal cycle phases analyzed) and a longitudinal design, in which the continuous hormone and asymmetry fluctuations were measured over a time course of 6 weeks? To answer these questions, 12 spontaneously cycling right-handed women participated in an experiment in which their levels of progesterone, estradiol, testosterone, LH, and FSH were assessed every 3 days by blood-sample based radioimmunoassays (RIAs). At the same points in time their asymmetries were analyzed with visual half-field (VHF) techniques using a lexical decision, a figure recognition, and a face discrimination task. Both cross-sectional and longitudinal analyzes showed that an increase of progesterone is related to a reduction in asymmetries in a figure recognition task by increasing the performance of the left-hemisphere which is less specialized for this task. Cross-sectionally, estradiol was shown to have significant relationships to the accuracy and the response speed of both hemispheres. However, since these effects were in the same direction, asymmetry was not affected. This was not the case in the longitudinal design, where estradiol affected the asymmetry in the lexical decision and the figural comparison task. Overall, these data show that hormonal fluctuations within the menstrual cycle have important impacts on functional cerebral asymmetries. The effect of progesterone was highly reliable and could be shown in both analysis schemes. By contrast, estradiol mainly, but not exclusively, affected both hemispheres in the same direction.

Adult↗

PubMed2002

Detecting relationships between physiological variables using graphical models.

In intensive care physiological variables of the critically ill are measured and recorded in short time intervals. The proper extraction and interpretation of the information contained in this flood of information can hardly be done by experience alone. Intelligent alarm systems are needed to provide suitable bedside decision support. So far there is no commonly accepted standard for detecting the actual clinical state from the patient record. We use the statistical methodology of graphical models based on partial correlations for detecting time-varying relationships between physiological variables. Graphical models provide information on the relationships among physiological variables that is helpful e.g. for variable selection. Separate analyses for different pathophysiological states show that distinct clinical states are characterized by distinct partial correlation structures. Hence, this technique can provide new insights into physiological mechanisms.

Critical Care↗