Robust regression-based online filters for multivariate time series are proposed and their performance in real time signal extraction settings is discussed. The focus is on methods that can deal with time series exhibiting trends, level changes, outliers and a high level of noise as well as...

Data from the automatic monitoring of intensive care patients exhibits trends, outliers, and level changes as well as periods of relative constancy. All this is overlaid with a high level of noise and there are dependencies between the different items measured. Current monitoring systems tend to...

Clinical information systems can record numerous variables describing the patient’s state at high sampling frequencies. Intelligent alarm systems and suitable bedside decision support are needed to cope with this flood of information. A basic task here is the fast and correct detection of...

In critical care extremely high dimensional time series are generated by clinical information systems. This yields new perspectives of data recording and also causes a new challenge for statistical methodology. Recently graphical correlation models have been developed for analysing the partial...

Intelligent alarm systems are needed for adequate bedside decision support in critical care. Clinical information systems acquire physiological variables online in short time intervals. To identify complications as well as therapeutic effects procedures for rapid classification of the current...

In modern intensive care physiological variables of the critically ill can be reported online by clinical information systems. Intelligent alarm systems are needed for a suitable bedside decision support. The existing alarm systems based on fixed treshholds produce a great number of false...